network (or arrow)diagram is constructed with each of its arcs(arrows) representing an ...... Extending MRP II hierarchical structures to PERT/CPM networks.
2014
ESSENTIALS OF OPERATIONS RESEARCH Chapter 10: Network Techniques (PERT & CPM) One of the most challenging jobs that any managers take is the management of projects that require coordinating numerous activities throughout the organization. A myriad of details must be considered in planning how to coordinate all these activities, in developing a realistic schedule, and then in monitoring the progress of the project. Now a days, two closely related operations research techniques, PERT (program evaluation and review technique) and CPM (critical path method), are available to support the project manager in carrying out these responsibilities. These techniques make heavy use of to help plan and display the coordination of all the activities. They also normally use a software package to deal with all the data needed to develop schedule information and then to monitor the progress of the project.
Prof (Dr.) Dalgobind Mahto 12/15/2014
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CHAPTER 7 NETWORK TECHNIQUES (PERT & CPM) PROGRAM EVALUATION AND REVIEW TECHNIQUE (PERT) 7.0: An Introduction A project defines a combination of interrelated activities that must be executed in a certain order before the entire task can be completed. The activities are interrelated in a logic sequences in the sense that some activities cannot start until others are completed .An activity in a project is usually viewed as a job requiring time and resources for its completion .In general, a project is a one – time effort; that is, the same sequence of activities may not be repeated in the future. In the past ,the scheduling of a project (over time) was done with little specifies the start and finish for each activity on a horizontal time scale . Its disadvantage is that the interdependency between the different activities(which mainly controls the progress of the project)cannot be determined from the bar chart .The growing complexities of today's project have demanded more systematic and more effective planning techniques with the objective of optimizing the efficiency of executing the project .Efficiency here implies effecting almost reduction in the time required to complete the project while accounting for the economic feasibility of using available resources . Project management has evolved as a new field with the development of two analytic techniques for planning, scheduling, and controlling of projects. 7.1
Background:
These are the critical path method (CPM) and the project evaluation and review technique (PERT). The two techniques were developed by two different groups almost simultaneously (1956- 1958) CPM was first developed by E.Idu Pout de Nemours and company as an application to construction projects and was later extended to a more advanced status by Mauchly associates . PERT, on the other hand was developed for the U.S. Navy by a consulting firm for scheduling the research and development activities for the Polaris missile program.
2
PERT and CPM are basically time –oriented methods in the sense that they both lead to the determination of a time schedule. Although the two methods were developed independently, they are strikingly similar. Perhaps the most important difference is that originally the time estimates for the activities were assumed deterministic in CPM and probabilistic in PERT. Today , PERT and CPM actually comprise one technique and the differences , if any, are only historical . Consequently, both techniques will be referred to as "project scheduling" techniques. Project scheduling by PERT-CPM consist of three basic phases 1. Planning 2. Scheduling and 3. Controlling. 7.2 Planning Phase The planning phase is initiated by breaking down the project into distinct activities . The time estimates for these activities are then determined and a network (or arrow)diagram is constructed with each of its arcs(arrows) representing an activity. The entire arrow diagram gives a graphic representation of the interdependencies between the activities of the project. The construction of the arrow diagram as a planning phase has the advantage of studying the different jobs in detail .perhaps suggesting improvement before the project is actually executed . More important will be its use to develop a schedule for the project. 7.3 Scheduling phase The ultimate objective of the Scheduling phase is to construct a time chart showing the start and finish times for each activity as well as its relationship to other activities in the project. In addition, the Schedule must pinpoint the critical (in view of time) activities that require special attention if the project is to be completed on time. For the non critical activities the schedule must show the amount of slack or float time that can be used advantageously when such activities are delayed or when limited resources are to be used effectively. 7.4 Controlling The final phase in project management is project control .This includes the use of the arrow diagram and the time chart for making periodic progress reports. The network may thus be updated and analyzed and, if necessary, a new schedule is determined for the remaining portion of the project.
3
7.5
Project Management
Project management is concerned with the overall planning and co-ordination of a project from conception to completion aimed at meeting the stated requirements and ensuring completion on time, within cost and to required quality standards. Project management is normally reserved for focused, nonrepetitive, time-limited activities with some degree of risk and that are beyond the usual scope of operational activities for which the organization is responsible. A project is a temporary endeavour involving a connected sequence of activities and a range of resources, which is designed to achieve a specific and unique outcome and which operates within time, cost and quality constraints and which is often used to introduce change. 7.6
Characteristic of a project
A unique, one-time operational activity or effort
Requires the completion of a large number of interrelated activities
Established to achieve specific objective
Resources, such as time and/or money, are limited
Typically has its own management structure
Need leadership
The application of a collection of tools and techniques to direct the use of diverse resources towards the accomplishment of a unique, complex, one time task within time, cost and quality constraints.
Used the techniques of operational research to plan the optimum use of resources.
One of these techniques was the use of networks to represent a system of related activities
7.7
Project Management Process
Project planning
Project scheduling
Project control
Project team -made up of individuals from various areas and departments within a
company
Matrix organization -a team structure with members from functional areas, depending on skills required
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Project Manager -most important member of project team Scope statement -a document that provides an understanding, justification, and expected result of a project
Statement of work -written description of objectives of a project
Organizational
Breakdown
Structure
-a
chart
that
shows
which
organizational units are responsible for work items Responsibility Assignment Matrix -shows who is responsible for work in a project 7.8
Work breakdown structure A method of breaking down a project into individual elements ( components, subcomponents, activities and tasks) in a hierarchical structure which can be scheduled and cost
It defines tasks that can be completed independently of other tasks, facilitating resource allocation, assignment of responsibilities and measurement and control of the project
It is foundation of project planning
It is developed before identification of dependencies and estimation of activity durations
It can be used to identity the tasks in the CPM and PERT
7.9
Project Planning •
•
Resource Availability and/or Limits –
Due date, late penalties, early completion incentives
–
Budget
Activity Information –
Identify all required activities
–
Estimate the resources required (time) to complete each activity
–
Immediate predecessor(s) to each activity needed to create
interrelationships 7.10 Project Scheduling and Control Techniques 1.Critical Path Method (CPM) 2. Program Evaluation and Review Technique (PERT) 7.11 Project Network •
Network analysis is the general name given to certain specific techniques which can be used for the planning, management and control of projects
5
•
Use of nodes and arrows
Arrows An arrow leads from tail to head directionally Indicate ACTIVITY, a time consuming effort that is required to perform a part of the work. A node is represented by a circle Indicate EVENT, a point
Nodes
in time where one or more activities start and/or finish •
•
•
•
Activity –
A task or a certain amount of work required in the project
–
Requires time to complete
–
Represented by an arrow
Dummy Activity –
Indicates only precedence relationships
–
Does not require any time of effort
Event –
Signals the beginning or ending of an activity
–
Designates a point in time
–
Represented by a circle (node)
Network –
Shows the sequential relationships among activities using nodes and arrows
Activity-on-node (AON) nodes represent activities, and arrows show precedence relationships Activity-on-arrow (AOA) arrows represent activities and nodes are events for points in time
Lay foundation
1
3 Design house and obtain financing
2
3 2
Dummy
1 Order and receive materials
Finish work
Build house
0
4 Select paint
6
3 1
1
5
Fig 7.1 : AOA Project Network for House
6
Select carpet
1
7
Lay foundations
Build house
4 3
2 2
Finish work
7 1
1 3
Start
Design house and obtain financing
3 1
6 1
5 1
Order and receive materials
Select carpet
Select paint
Fig7.2: AON Project Network for House Situations in network diagram
A
B C
Fig 7.3:
A must finish before either B or C can start
A C B Fig 7.4: both A and B must finish before C can start
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A
C
B
D
Fig 7.5: both A and C must finish before either of B or D can start
A
B Dummy
C D Fig 7.6: A must finish before B can start both A and C must finish before D can start
8
La y foundation
2
3
3
La y foundatio n
Dumm 0 y
2 1
2
Order material ( a)
4 Order material
Incorrect precedence relationship
(b)
Correct precedence relationship
Fig 7.7: Concurrent Activities
7.12
Uncertainty in project scheduling
During project execution, however, a real-life project will never execute exactly as it was planned due to uncertainty. It can be ambiguity resulting from subjective estimates that are prone to human errors or it can be variability arising from unexpected events or risks. The main reason that the Project Evaluation and Review Technique (PERT) may provide inaccurate information about the project completion time is due to this schedule uncertainty. This inaccuracy is large enough to render such estimates as not helpful. One possibility to maximize solution robustness is to include safety in the baseline schedule in order to absorb the anticipated disruptions. This is called proactive scheduling. A pure proactive scheduling is a utopia, incorporating safety in a baseline schedule that allows to cope with every possible disruption would lead to a baseline schedule with a very large make-span. A second approach, reactive scheduling, consists of defining a procedure to react to disruptions that cannot be absorbed by the baseline schedule.
CPM – CRITICAL PATH METHOD 7.13 An Introduction: In 1957, Dupont developed a project management method designed to address the challenge of shutting down chemical plants for maintenance and then restarting the plants once the maintenance had been completed. Given the complexity of the process, they developed the Critical Path Method (CPM) for managing such projects.
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CPM provides the following benefits:
Provides a graphical view of the project Predicts the time required to complete the project. Shows which activities are critical to maintaining the schedule and which are not.
CPM models the events and activities of a project as a network. Activities are depicted as nodes on the network and events that signify the beginning or ending of activities are depicted as arcs or lines between the nodes. 7.14 Work Breakdown Structures (WBS) The development of a project plan is predicated on having a clear, and detailed understanding of both the tasks involved, the estimated length of time each task will take, the dependencies between those tasks, and the sequence in which those tasks have to be performed. Additionally, resource availability must be determined in order to assign each task or group of tasks to the appropriate worker. One of the methods used to develop the list of tasks is to create what is known as a Work Breakdown Structure (WBS).
A definition
A Work Breakdown Structure (WBS) is a hierarchic decomposition or breakdown of a project or major activity into successively levels, where each level is a finer breakdown of the preceding one. In final form a WBS is very similar in structure and layout to a document outline. Each item at a specific level of a WBS is numbered consecutively (e.g. 10, 20, 30, 40, 50). Each item at the next level is numbered within the number of its parent item (e.g. 10.1, 10.2, 10,3, 10.4) The WBS may be drawn in diagrammatic form (if automated tools are available) or in a chart form resembling an outline. The WBS begins with a single overall task representing the totality of work to be performed on the project. This becomes the name of the project plan WBS Using a methodology or system life cycle steps as a guide, the project is divided into its major steps. In our case we will use the Unified Process phases. Each of
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these phases must be broken down into their next level of detail, and each of those, into still finer level of detail, until a manageable task size is arrived at. The first level of Work Breakdown Structure for the unified process is illustrated in Table 6.1 Tasks at each successively finer level of detail are numbered to reflect the task from which they were derived. Thus the first level of tasks would be numbered 1.0, 2.0, 3.0, and so forth. Each of their sub-tasks would have a two part number. The first part reflecting the parent task and the second part being the sub-task number itself e.g. 1.1, 1.2, 1.3, etc. As each of these is in turn decomposed or broken down into their component tasks, the components receive a number comprised of its parent’s number plus a unique number of its own.
Another Definition:
A manageable task is one where the expected results can be easily identified, success, failure, or completion of the task can easily be ascertained, the time to complete the task can easily be estimated, and the resource requirements of the task can easily be determined.
WBS Number 1.0 1.1 2.0 2.1 2.2 3.0 4.0
Task Description Inception Draft Project Plan Elaboration Plan User Interviews Schedule User Interviews Construction Transition
Table 7.1 First level of Work Breakdown Structure for the life cycle 7.15 Steps in CPM Project Planning: 1. 2. 3. 4.
Specify the individual activities. Determine the sequence of those activities. Draw a network diagram. Estimate the completion time for each activity.
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5. Identify the critical path (the longest path through the network) 6. Update the CPM diagram as the project progresses. 1. Specify the individual activities. From the Work Breakdown Structure, a listing can be made of all the activities in the project. This listing can be used as the basis for adding sequence and duration information in later steps. 2. Determine the sequence of those activities. Some activities are dependent upon the completion of others. A listing of the immediate predecessors of each activity is useful for constructing the CPM network diagram. 3. Draw a network diagram. Once the activities and their sequencing have been defined, the CPM diagram can be drawn. CPM originally was developed as an activity on node (AON) network, but some project planners prefer to specify the activities on the arcs. 4. Estimate the completion time for each activity. The time required to complete each activity can be estimated using past experience or the estimates of knowledgeable persons. CPM is a deterministic model that does not take into account variation in the completion time, so only one number can be used for an activity’s time estimate. 5. Identify the critical path The critical path is the longest-duration path through the network. The significance of the critical path is that the activities that lie on it cannot be delayed without delaying the project. Because of its impact on the entire project, critical path analysis is an important aspect of project planning. The critical path can be identified by determining the following four parameters for each activity:
ES – earliest start time: the earliest time at which an activity can begin given that its predecessor activities must be completed first.
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EF – earliest finish time, equal to the earliest start time for the activity plus the time required to complete the activity. LF – latest finish time: the latest time at which an activity can be completed without delaying the project. LS – latest start time, equal to the latest finish time minus the time required to complete the activity.
The slack or float time for an activity is the time between the earliest and latest start time, or between the earliest and latest finish time. Slack is the amount of time that an activity can be delayed past its earliest start or earliest finish without delaying the project. The critical path is the path through the project network in which none of the activities have slack, that is, the path for which LS=ES and LF=EF for all activities in the path. A delay in the critical path delays the project. Similarly, to accelerate the project it is necessary to reduce the total time required for the activities in the critical path. 6. Update CPM diagram As the project progresses, the actual task completion times will be known and the diagram can be updated to include this information. A new critical path may emerge, and structural changes may be made in the network if project requirements change. 7.16 CPM limitations CPM was developed for complex but fairly routine projects with minimal uncertainty in project completion times. For less routine projects there is more uncertainty in the completion times, and this uncertainty limits the usefulness of the deterministic CPM model. An alternative to CPM is the PERT (Program Evaluation and Review Technique) project planning model, which allows a range of durations to be specified for each activity. 7.17 Time Estimates Networks provide a planned approach to project management. To be effective, networks require a clear definition of all the tasks that make up the project and pertinent time estimates. If the project manager cannot clarify the necessary tasks and the resource requirements, then no matter how sophisticated the network, it will not compensate for these shortcomings. A number of claims have been made about the benefits of project management techniques but others
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have argued that in part, the benefits are due to managers having to know and clarify the tasks rather than the diagram which follows (which may by then be self-evident). The objectives of network analysis are to locate the activities that must be kept to time, manage activities to make the most effective use of resources and look for ways of reducing the total project time. For any but the smallest projects, this analysis is likely to be done using a computer package, but your understanding of the output will only develop if you have some experience of the basic steps of analysis.
ACTIVITY DURATION ESTIMATION – BETA DISTRIBUTION
ESTIMATED TIME FORMULA TE =
A + 4(B) + C 6
WHERE: A = MOST OPTIMISTIC TIME B = MOST LIKELY TIME C = MOST PESSIMISTIC TIME
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CONSTRUCTING THE CRITICAL PATH INFORMATION FOR PROJECT DELTA
Activity
Description
Predecessors
Estimated
A
Contract signing
None
5
B
Questionnaire design
A
5
C
Target market ID
A
6
D
Survey sample
B, C
13
E
Develop presentation
B
6
F
Analyze results
D
4
G
Demographic analysis
C
9
H
Presentation to client
E, F, G
2
Duration
15
Partial Project Activity Network with Task Durations
B Design 5
A Contract 5
E Dev. Present
6
D Survey 13
C Market ID 6
F Analysis 4
G Demog. 9
H Present 2
RULES WHEN USING THE FORWARD PASS 1. Add all activity times along each path as we move through the network (ES + Dur = EF), 2. Carry the EF time to the activity nodes immediately succeeding the recently completed node. That EF becomes the ES of the next node, unless the succeeding node is a merge point. 3. At a merge point, the largest preceding EF becomes the ES for that node.
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ACTIVITY NETWORK WITH FORWARD PASS
5
0
B 10 Design 5
A 5 Contract 5
10
C 11 Market ID 6
16
6
11
5
E
Dev. Present
D 24 Survey 13
24 F 28 Analysis 4
11
G 20 Demog. 9
28
H 30 Present 2
RULES FOR USING THE BACKWARD PASS 1. Subtract activity times along each path as you move through the network (LF – Dur = LS), 2. Carry back the LS time to the activity nodes immediately preceding the successor node. That LS becomes the LF of the next node, unless the preceding node is a burst point. 3. In the case of a burst point, the smallest succeeding LS becomes the LF for that node.
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ACTIVITY NETWORK WITH BACKWARD PASS
5 6
0 0
B 10 Design 5 11
A 5 Contract 5 5
10 22
11 11
5
C 11 Market ID 5 6 11
E
16
Dev. Present
6
D 24 Survey 13 24
28
24
F
28
28
Analysis
24
11
G
20
Demograph.
19
9
28
4
28
H
30
Presentation
28
2
30
COMPLETED ACTIVITY NETWORK WITH CRITICAL PATH AND ACTIVITY SLACK TIMES IDENTIFIED Critical Path is indicated in bold
5 1 6
0 0 0
B 10 Design 5 11
A 5 Contract 5 5
10 22
11 0 11
5 C 11 0 Market ID 5 6 11
E
16
12 Dev. Present
6
D 24 Survey 13 24
28
24 0
24
11
G
F
28
28
H
30
Analysis
0 Presentation
4
28
28
2
30
20
8 Demograph.
19
9
28 ES
ID
Slack
Task Name
LS
Duration
EF
LF
ACTIVITY NETWORK DEMONSTRATING LADDERING TECHNIQUE
A1 Design
A2 Design
A3 Design
A1 Coding
A2 Coding
A3 Coding
A1 Debugging
A2 Debugging
A3 Debugging
NETWORK DEMONSTRATING HAMMOCK ACTIVITY
5 13 18
0 0 0
A
5
5
5
B
9
4
22
5 9 14
12 10 22
C
12
7
21
5 D 11 0 user needs 5 6 11
21
9
31
12 9 21
11 0
11
5
G
E
25
Coding
14
A 31 Hammock 26
25
H
22
31
I
10
31
31
4
25 0
25
F
31
Debugging
6
31
35 0 35
STEPS TO REDUCE THE CRITICAL PATH 1. Eliminate Tasks On The Critical Path 2. Replan Serial Paths To Be Parallel 3. Overlap Sequential Tasks 4. Shorten The Duration On Critical Path Activities 5. Shorten Early Tasks 6. Shorten Longest Tasks 7. Shorten Easiest Tasks 8. Shorten Tasks That Cost The Least To Speed Up 7.18 Floats or Slack : Measures of float We have seen that if the difference between the maximum time available and the duration of the activity, the total float, is 0 then the activity is critical. There are two other important measures of float, free float and independent float.
Free float Free float is the time that an activity could be delayed without affecting any of the activities that follow. Free float = EST for j - EST for i - duration of activity However, free float does assume that previous activities run to time.
Independent float
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The independent float gives the time that an activity could be delayed if all the previous activities are completed as late as possible and all the following activities are to start as early as possible. Independent float = EST for j - LST for i - duration of activity The determination of total, free and independent float is illustrated in Figure 20.10.
Example:7.1 Tasks A,B,C,…..H,I constitute a project. The precedence relationship are A < D; A < E; B < F; D < F; C < G ; C < H; F < I; G < I. Draw a network to represent the project and find the minimum time of completion of the project when time , in days, of each task is as follows:
Task : A B Time : 8 10 Also identify the critical path.
C 8
Table7.2 D E 10 16
25
F 17
G 18
H 14
I 9
Sol:
Fig(7.1)
The given procedure order reveals that there are no predecessors to activities A,B and C and they all start from the initial node. Similarly, there are no successor to activities E,H and I and hence they all merge into the end node of project. The network obtained is shown in fig(7.1) The nodes of the network have been numbered by using the Fulkerson’s rule. The activity description and times are written along the activity arrows. To determine the minimum project completion time, let event 1 occur at zero time. The earliest occurrence time (E) and the latest occurrence time (L) of each event is then computed. E1 = 0, E2 = E1+ t12= 0 + 8 = 8, E3= E1+t13=0 + 8 = 8, E4 = Max.[ 0 + 10 , 8+10] = 18, E5 = Max.[ 18 + 17 , 8+18] = 35, E6 = Max.[ 8 + 16 , 35+ 9, 8+14] = 44. Similarly, L6 =E6 = 44, L5 = L6- t56 = 44 - 9 = 35, L4= L5 – t45 = 35 - 17 =18, L3 = Min.[ 44 - 14 , 35-18] = 17,
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L2 = Min.[ 44 - 16 , 18-10] = 8, L1 = Min.[ 8 - 8 , 17- 8, 18-10] = 0. The E and L values for each event have been written along the nodes in Fig(7.2)
FIG(7.2)
The critical path is now determine by any of the following method. Method 1. The network analysis table is complied as below. Table7.3 Activity
Duration
1-2 1-3 1-4 2-4 2-6 3-5 3-6 4-5 5-6
8 8 10 10 16 18 14 17 9
Start time Earliest Latest 0 0 0 9 0 8 8 8 8 28 8 17 8 30 18 18 35 35
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Finish time Earliest Latest 8 8 8 17 10 18 18 18 24 44 26 35 22 44 35 35 44 44
Total float 0 9 8 0 20 9 22 0 0
Activities1-2,2-4,4-5 and 5-6 having zero float are the critical activities and 1-2-4-5-6 is the critical path. Method 2. For identifying the critical path, the following condition are checked. If an activity satisfies all the three conditions, it is critical. (i). E=L for the tail event. (ii) E= L for the head event. (iii) Ej-Ei = Lj-Li = tij. Activities 1-2,2-4,4-5 and 5-6 satisfy these conditions. Other activities do not fulfil all the three conditions. The critical path is , therefore, 1-2-4-5-6. Method 3. The various paths and their duration are :
Table7.4 Path Duration(days) 1-2-6 24 1-2-4-5-6 44 1-4-5-6 36 1-3-5-6 35 1-3-6 22 Path1-2-4-5-6, the longest in time involving 44 days, is the critical path. It represented by bold lines in fig(7.2). E= 8 L= 8
Example 7.2: A project schedule has the following characteristics: Table7.5 Activity 1-2 1-3 2-4 3-4 3-5
Time(weeks) 4 1 1 1 6
Activity 5-6 5-7 6-8 7-8 8-10
28
Times(weeks) 4 8 1 2 5
4-9 5 (i). Construct the network. (ii). Compute E and L for each , and (iii). Find the critical path.
9-10
7
Solution: The given data result in a network shown in fig(7.5). The figures along the arrows represent the activity times. The earliest occurrence time(E) and the latest occurrence time (L) of each event are now computed by employing forward and backward pass calculations. In forward pass computations, E1=0, E2= E1+t12 = 0+ 4 = 4, E3= E1+t13 = 0+ 1 = 1, E4= Max [Ei + ti4 ] = Max.[4+1,1+1] = 5, i=2,3 E5= E3+t35 = 1+ 6 = 7, E6= E5+t56 = 7+ 4 =11, E7= E5+t57 = 7+ 8 = 15, E8 = Max [Ei + ti8 ] = Max.[11+1,15+1] = 17, i=6,7 E9= E4+t49 = 5+ 5 = 10, and E10 = Max [Ei + ti10 ] = Max.[17+5,10+7] = 22, i=8,9 E values are represented in fig 7.2. In backward pass computations, L10=E10=22, L9 = L10 - t9 ,10 = 22-7 = 15, L8 = L10 – t8 ,10 = 22-5 = 17, L7 = L8 – t78 = 17 -2 = 15, L6 = L8 – t68 = 17-1 = 16, L5 =Min [Lj - t5j] = Min[16-4 , 15-8] = 7, j=6,7 L4 = L9 – t49 = 15-5 = 10,
29
L3 =Min [Lj – t3j] = Min[10 - 4 , 7 - 6] = 1, j=4,5 L2 = L4 – t24 = 10-1 = 9, L1 =Min [Lj – t1j] = Min [9 - 4 , 1 - 1] = 0, j=2,3 L values are also represent in fig(7.2). Table 7.6 Activity Duration Start time (weeks) Earliest Latest 1-2 1-3 2-4 3-4 3-5 4-9 5-6 5-7 6-8 7-8 8-10 9-10
4 1 1 1 6 5 4 8 1 2 5 7
0 0 4 1 1 5 7 7 11 15 17 10
5 0 9 9 1 10 12 7 16 15 17 15
Finish time Earliest Latest
Total float
4 1 5 2 7 10 11 15 12 17 22 17
5 0 5 8 0 5 5 0 5 0 0 5
9 1 10 10 7 15 16 15 17 17 22 22
Path 1-3-5-7-8-10 with project duration 22 weeks is the critical path. Example7.3:The utility data for a network are given below. Determine the total, free, independent and interfering floats and identify the critical path. Table7.7 Activity 0-1 1-2 1-3 2-4 2-5 3-4 3-6 4-7 5-7 6-7 Duration 2 8 10 6 3 3 7 5 2 8 Sol: The network diagram for the given project data is shown in fig(1.3). Activity durations are written along the activity arrows. The earliest start and latest finish times of the activities are computed by employing the forward pass and backward pass calculation as explained in previous example. These times are represented in network around the respective nodes. The network analysis table is now constructed.
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Fig(7.3) Table 7.8 Activity
1 0-1 1-2 1-3 2-4 2-5 3-4 3-6 4-7 5-7 6-7
Duration
2 2 8 10 6 3 3 7 5 2 8
Start Time Earliest Latest 3 0 2 2 10 10 12 12 16 13 19
4 0 8 2 16 22 19 12 22 25 19
Finish time Earliest Latest 5 6 2 2 10 16 12 12 16 22 13 25 15 22 19 19 21 27 15 27 27 27
Total Free Interfering 7 8 0 0 6 0 0 0 6 0 12 0 7 1 0 0 6 6 12 12 0 0
Float___________________ Independent 9 0 0 0 -60 -60 1 0 0 0 0
Total float is the positive difference between latest and earliest finish times or latest and earliest start time. For activity 1-2 , Total float (T.F)=16-10 = 8-2 = 6. Similarly , for activity , say 2-5 , Total float = 25 -13=22-10 =12 and so on. Total float calculations are depicted in column 7 of table 1.28. Free float activity i-j = T.F .- head event slack
31
10 0 6 0 6 12 6 0 0 0 0
= T .F. – (L - E) of event j. Thus free float of activity 0-1 = 0-( L – E ) of event 1, = 0 – ( 2 - 2)= 0, Free float of activity 1-2 = 6-(16-10) = 6-6 = 0, etc. Free float of various activities are calculated in column 8 of the network analysis table. Independent float of activity i-j= F.F – tail event slack = F.F – (L-E) of event i. Thus independent float of activity 0-1 = 0 – (0-0)=0, Independent float of activity 1-2 = 0 – ( 2 -2) = 0, Independent float of activity 2-4 = 0 – (16 -10)= -6 0 and so on. Independent float of various activities are calculated in column 9 of the table .If independent float of an activity is negative , it is taken a zero. Interfering float of activity i-j = Max[L.F . time of i-j – E.S. time of j-k, 0] Thus interfering float of activity 0-1 = Max. [L.F. time of 0-1 – E.S. time of 1-2 or 1-3 , 0 ] = Max. [2 -2 , 0]= 0, Interfering float of activity 1-2 = Max. [L.F. time of 1-2 – E.S. time of 2-4 or 2-5 , 0] = Max. [16 -10,0] = 6 , etc More conveniently , interfering float of an activity = T.F. – F.F. Thus , interfering float of activity 2-5 = 12-0=12 , etc. Alternating , interfering float of an activity 2-5 = 25 -13=12, etc. Interfering floats of various activities are calculated in column 10 of table 7.8. Example7.4: A PERT network is shown if fig 1.4. The activity times in days are given along the arrows. The scheduled times for some important events are given along the nodes. Determine the critical path and probabilities of meeting the scheduled dates for the specified events. Tabulate the result and determine slack for each event.
Fig(7.4)
32
Solution: Activity 1-2 1-3 2-1 3-4 3-5 3-6 3-7 4-8 5-2 6-9 7-10 8-11 9-12 10-12 11-12
Table 7.9 2
t0
tm
tp
te
2 2 1 1 1 2 2 5 3 4 4 2 1 1 3
4 4 1 3 4 5 3 6 6 6 5 4 2 4 4
12 25 5 6 9 12 5 14 9 10 7 6 4 8 11
5.00 7.17 84.83 3.17 4.33 5.67 3.17 7.17 6.00 6.33 5.17 4.00 2.17 4.17 5.00
2.78 14.69 1.36 0.69 1.78 2.78 0.25 2.25 1.00 1.00 0.25 0.44 0.25 1.36 1.78
The arrow diagram for the given data is shown in fig 7.4. The expected activity times are shown along the arrows. The earliest and latest occurrence times as well as the slack of the event are also written along the nodes.
Fig(7.5)
33
Table 7.10 represent the network analysis . Floats for the activities in question are calculated in the last column. Critical path is 1-3-4-8-11-12 and the project completion is 26.51 days: Table 7.10 Activity
Duration
Start Time Earliest Latest
Finish time Earliest Latest
1-2 1-3 2-11 3-4 3-5 3-6 3-7 4-8 5-12 6-9 7-10 8-11 9-12 10-12 11-12
5.00 7.17 4.83 3.17 4.33 5.67 3.17 7.17 6.00 6.33 5.17 4.00 2.17 4.17 5.00
0 0 5.00 7.17 7.17 7.17 7.17 10.34 11.50 12.84 10.34 17.51 19.17 15.51 21.51
5.00 7.17 9.83 10.34 11.50 12.84 10.34 17.51 17.50 19.17 15.51 21.51 21.34 19.68 26.51
11.68 0 16.68 7.17 16.18 12.33 14.00 10.34 20.51 18.01 17.17 17.51 24.34 22.34 21.51
16.68 7.17 21.51 10.34 20.51 18.00 17.17 17.51 26.51 24.34 22.34 21.51 26.51 26.51 26.51
Total float 11.68 0 11.68 0 9.01 5.16 6.83 0 9.01 5.17 6.83 0 5.17 6.83 0
Probability of completing the project in the scheduled completion time of 24 days (since Ts(12)=24): Z = ______________24-26.51______________ = -2.55_ = -0.5634.
p( Ts ≤ 24) = 1- value of probability for Z = 0.5634=1- 0.7146 = 29.54 %. Probability that event 3 will occur on the scheduled date: Ts (3) = 4, E = L= 7.17
Z= 4 -7.17 = -0.8271
p = 1- value of probability for Z = 0.8271 = 1- 0.7956 = 20.44% Probability of meeting scheduled date for event 5: The earliest occurrence time of event 5 is 11.50, while the scheduled time is 12 .Event 5 is not on critical path and hence its occurrence can be delayed by 9 days. Variance of path 1-3-5=14.69 + 1.78 = 16.47.
34
Ts = 12, E=11.50 Z = 12 – 11.50 = 0.123
Probability = 54.89%
Ts =12 L= 20.51. Z = 12 – 20.51 = -2.1
Probability = 1 – 0.982 = 0.018 = 1.8 %
Example7.5: In the PERT network shown in fig 7.6 , the activity time estimates(in weeks) are given along the arrows. If the scheduled completion time is 23 weeks, calculate the latest possible occurrence times of the events. Calculate the slack for each event and identify the critical path. What is the probability that project will be completed on the scheduled date ?.
Fig(7.6) Sol: The expected time of the activities and their variances are computed below. 2
Activity
t0
tm
tp
te
1-2 2-3 2-4 3-5 4-6 5-6 5-7 6-7
3 3 2 4 4 0 3 2
3 6 4 6 6 0 4 5
3 9 6 8 8 0 5 8
3 6 4 6 6 0 4 5
0 1 4/9 4/9 4/9 0 1/9 1
Table7.11 The earliest occurrence times of the events have been computed on the network of fig
7.6 , taking the earliest time of event 1 as zero. The earliest occurrence time of event 7 is 20. But the scheduled completion time of the project is 23 weeks and hence the latest occurrence times of the events have been computed taking L(7)=23. Slacks for the event have been shown along the nodes. Path 1-2-3-5-6-7 is the critical path.
35
Fig(7.7)
Probability of completing the project on schedule date : T=23 weeks, E = 20 weeks. Varience of the critical path = 0=1+4/9 = 0+1=22/9=2.444 Z=1.92. Probability = 97.26% Example 7.6: The following table gives data on normal time and cost and crash time and cost for a project. Activity Normal Crash Time (days) Time(Days) Cost Cost(Rs.) (Rs.) 1-2 6 60 4 100 1-3 4 60 2 200 2-4 5 50 3 150 2-5 3 45 1 65 3-4 6 90 4 200 4-6 8 80 4 300 5-6 4 40 2 100 6-7 3 45 2 80 470
36
The indirect cost per day is Rs .10. (i). Draw the network for project. (ii). Find the critical path. (iii). Determine minimum total time and corresponding cost. Solution: First the cost slope for each activity and normal direct cost of the project is calculated. This is shown in the table below. Activity Cost slope (Rs./day)
1-2 20
1-3 70
2-4 50
2-5 10
3-4 55
4-6 55
5-6 30
6-7 35
(i). Next, the network is draw and critical path is determined. This is shown in fig 1.8.
Fig(7.8) (ii). The critical path is 1-2-4-6-7. (iii). Normal duration = 22 days. Normal cost = Rs.(470+22*10)= Rs.690. Now represent the network on time-scaled diagram. This is shown in fig (7.8). E=22 L=22
37
References: [1].
[2].
[3].
[4]. [5].
[6].
[7].
[8].
[9].
[10].
[11].
[12].
[13].
[14]. [15].
Trietsch, D., & Baker, K. R. (2012). PERT 21: Fitting PERT/CPM for use in the 21st century. International Journal of Project Management, 30(4), 490–502. http://doi.org/10.1016/j.ijproman.2011.09.004 Moder, J. J., Phillips, C. R., & Davis, E. W. (1983). Project Management with CPM, PERT and Precedence Diagramming. New York. http://doi.org/10.1016/00160032(65)90247-4 Lu, M., & AbouRizk, S. M. (2000). Simplified CPM/PERT simulation model. Journal of Construction Engineering and Management, 126(3), 219–226. http://doi.org/10.1061/(ASCE)0733-9364(2000)126:3(219) Swanson, H. S., & Woolsey, R. E. D. (1974). A PERT-CPM tutorial. ACM SIGMAP Bulletin. http://doi.org/10.1145/1217031.1217043 Omar, A. (2009). Uncertainty in Project Scheduling--Its Use in PERT/CPM Conventional Techniques. Cost Engineering, 51(7), 30–34. Retrieved from http://search.ebscohost.com.ezproxy.liv.ac.uk/login.aspx?direct=true&db=bth&AN=4445 1682&site=eds-live&scope=site Mazlum, M., & Güneri, A. F. (2015). CPM, PERT and Project Management with Fuzzy Logic Technique and Implementation on a Business. Procedia - Social and Behavioral Sciences, 210, 348–357. http://doi.org/10.1016/j.sbspro.2015.11.378 Quezado, P. C. A. M., Cardoso, C. R. de O., & Tubino, D. F. (1999). Programação E Controle Da Produção Sob Encomenda Utilizando Pert / Cpm E Heurísticas. Enegep, 1999, 1–20. Retrieved from http://www.abepro.org.br/biblioteca/ENEGEP1999_A0381.PDF Trietsch, D. (2005). Why a Critical Path By Any Other Namewould Smell Less Sweet? Towards a Holistic Approach to PERT/CPM. Project Management Journal, 36(1), 27– 36. Assis Mota, A., Mota, L. T. M., & Morelato, A. (2007). Visualization of power system restoration plans using CPM/PERT graphs. IEEE Transactions on Power Systems, 22(3), 1322–1329. http://doi.org/10.1109/TPWRS.2007.901118 Zhu, Z., & Heady, R. B. (1994). A Simplified Method of Evaluating PERT/CPM Network Parameters. IEEE Transactions on Engineering Management, 41(4), 426–430. http://doi.org/10.1109/17.364568 Novais, I. F., Jorge, E. M. de F., Junior, C. P. C., & Souza, D. T. (2011). Gerenciamento De Projeto Otimista (Gpo): Um Método Que Integra Pert/Cpm À Ccpm. Revista de Gestão E Projetos, 2(2), 150–165. http://doi.org/10.5585/gep.v2i2.25 Piñeiro Fernández, S. (1995). PERT y CPM: Programación y control de proyectos. Cuadernos de Estudios Empresariales, (5), 271–292. Retrieved from http://dialnet.unirioja.es/servlet/articulo?codigo=164224&info=resumen&idioma=SPA Simmons, L. F. (2002). Project management-critical path method (CPM) and PERT simulated with ProcessModel. Proceedings of the Winter Simulation Conference, 2, 1786–1788. http://doi.org/10.1109/WSC.2002.1166468 Haga, W. A., & Marold, K. A. (2004). A Simulation Approach to the PERT/CPM TimeCost Trade-Off Problem. Project Management Journal, 35(1), 31–37. NAZARETH, M. M., MELLO, L. C. B. D. B., & CHAKOUR, P. R. (2015). ESTUDO COMPARATIVO ENTRE PERT/CPM E CORRENTE CRÍTICA. XXXV Encontro
38
[16].
[17].
[18]. [19]. [20].
[21].
[22].
[23]. [24].
[25].
[26].
[27].
[28].
Nacional De Egenharia De Produção, 17. Retrieved from http://www.abepro.org.br/publicacoes/index.asp?ano=2015&area=&pchave=ESTUDO+ COMPARATIVO+ENTRE+PERT%2FCPM+E+CORRENTE+CR%CDTICA&autor= Rodrigues, A. G. (1999). SYDPIM integration of SD and PERT/CPM tools: Assessing Fagan Analysis in a large-scale software project. The 17th International Conference of the System Dynamics Society, (Forrester 1961), 1–13. http://doi.org/10.1016/j.dss.2012.05.061 Davis, G. B. (1963). The Application of Network Techniques (PERT/CPM) to the Planning and Control of an Audit. Journal of Accounting Research, 1(1), 96–101. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=6700824&site=ehostlive&scope=site Nazrul, M., Sharif, M., Draghichi Eugen, . (2012). Project Completion Probability After Crashing Pert / Cpm Network. Management Of Sustainable Development, 4, 59–63. Vanhoucke, M. (2012). The PERT/CPM Technique. Project Management with Dynamic Scheduling, 11–35. http://doi.org/10.1007/978-3-642-25175-7 Dane, C. W., Gray, C. F., & Woodworth, B. M. (1979). FACTORS AFFECTING THE SUCCESSFUL APPLICATION OF PERT/CPM SYSTEMS IN A GOVERNMENT ORGANIZATION. Interfaces, 9(5), 94–98. http://doi.org/10.1287/inte.9.5.94 Schonberger, R. J. (1981). Why Projects Are “Always” Late: A Rationale Based on Manual Simulation of a PERT/CPM Network. Interfaces, 11(5), 66–70. http://doi.org/10.1287/inte.11.5.66 Luttman, R. J., Laffel, G. L., & Pearson, S. D. (1995). Using PERT/CPM (Program Evaluation and Review Technique/Critical Path Method) to design and improve clinical processes. Quality Management in Health Care, 3(2), 1–13. Retrieved from http://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference&D=med3&NEWS=N&AN =10141769 Omar, A. (2009). Uncertainty in Project Scheduling — Its Use in PERT / CPM Conventional Techniques. Cost Engineering, 51(7), 30–35. Marasović, J., & Marasović, T. (2006). CPM/ PERT project planning methods as Elearning optional support. In SoftCOM 2006 - International Conference on Software, Telecommunications andComputer Networks (pp. 352–356). http://doi.org/10.1109/SOFTCOM.2006.329776 Koehler, A. B., & McClure, R. H. (1979). the Use of Arcs and Nodes for the Determination of Critical Paths in Pert/Cpm Networks. Decision Sciences, 10(2), 329– 333. http://doi.org/10.1111/j.1540-5915.1979.tb00027.x Bendicho Joven, J. (1991). Optimización de la productividad y minimización de holguras en el PERT/CPM. Tierra Y Tecnología: Revista de Información Geológica. Retrieved from http://dialnet.unirioja.es/servlet/articulo?codigo=4040641&info=resumen&idioma=SPA Araújo, T. R., Tarrento, G. E., Joaquim Junior, C. F., & Pierre, F. C. (2012). Utilização Das Técnicas Pert-Cpm Para Redução Do Prazo De Entrega : Estudo De Caso Em Uma Indústria Automobilística. Tekhne E Logos, 3(14), 13. Esen, ., ndo du, . B., & irtilo lu, . S. (200 ). Plann ng a tour sm in ormat on system by cpm-pert method and sampl ng or mun c pal t es. In 7th International Scientific Conference on Modern Management of Mine Producing, Geology and
39
[29].
[30].
[31].
[32]. [33].
[34].
[35].
[36].
[37].
[38]. [39].
[40].
[41].
Environmental Protection, SGEM 2007. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.084890291516&partnerID=tZOtx3y1 Woxvold, E. R. A. (1992). Extending MRP II hierarchical structures to PERT/CPM networks. In Annual International Conference Proceedings - American Production and Inventory Control Society (pp. 447–451). Retrieved from https://www.scopus.com/inward/record.url?eid=2-s2.00027012590&partnerID=40&md5=b3d2a29ef6ca4c9eff8b51e4f3a6b0f0 Rand, G. (2000). Critical chain: the theory of constraints applied to project management. International Journal of Project Management, 18(3), 173–177. http://doi.org/http://dx.doi.org/10.1016/S0263-7863(99)00019-8 Campbell, S. M. (1982). Critical Path Method. Appraisal Journal. Retrieved from http://search.ebscohost.com.libaccess.hud.ac.uk/login.aspx?direct=true&db=bth&AN=53 63622&site=ehost-live 张光发. (2011). 基于PERT与CPM的船舶分段建造计划协调与优化. 大 连 理 工 大 学 学 报. Shtub, A. (1997). Project segmentation—a tool for project management. International Journal of Project Management, 15(1), 15–19. http://doi.org/10.1016/S02637863(96)00017-8 Abdallah, H., Emara, H. M., Dorrah, H. T., & Bahgat, A. (2009). Using Ant Colony Optimization algorithm for solving project management problems. Expert Systems with Applications, 36(6), 10004–10015. http://doi.org/10.1016/j.eswa.2008.12.064 Gregoriou, G., Kirytopoulos, K., & Kiriklidis, C. (2013). Project management educational software (ProMES). Computer Applications in Engineering Education, 21(1), 46–59. http://doi.org/10.1002/cae.20450 Walker II, E. D. (2004). Introducing Project Management Concepts Using a Jewelry Store Robbery. Decision Sciences Journal of Innovative Education, 2(1), 65–69. http://doi.org/10.1111/j.0011-7315.2004.00020.x Maria, C., & Miranda, G. De. (2003). Sistema de apoio a decisão para seleção de atividades críticas no gerenciamento de projetos com avaliação multicritério. Review Literature And Arts Of The Americas, 1–8. Ragsdale, C. (1989). The current state of network simulation in project management theory and practice. Omega, 17(1), 21–25. http://doi.org/10.1016/0305-0483(89)90016-9 Lewis, J. P. (2007). CHAPTER 6: Scheduling Project Work. In Fundamentals of Project Management : Fundamentals of Project Management (pp. 69–80). Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=32725622&site=ehostlive Darmody, P. B. (2007). Henry L. Gantt and Frederick Taylor: The Pioneers of Scientific Management. AACE International Transactions, 15.1–15.3. Retrieved from http://ezproxy.lib.bbk.ac.uk/login?url=http://content.ebscohost.com/ContentServer.asp?T =P&P=AN&K=25912748&S=R&D=buh&EbscoContent=dGJyMNLe80SeprQ4zdnyOL Cmr0mep65SsKa4SLeWxWXS&ContentCustomer=dGJyMPGvrkiwqLFKuePfgeyxw2 DNydIA\nhttp://search.ebscohost.com/log Steyn, H. (2001). Comparisons between and combinations of different approaches to accelerate engineering projects. South African Journal of Industrial Engineering, 14(2), 63–74.
40
[42].
[43].
[44].
[45].
[46].
[47].
[48].
[49].
[50].
[51].
[52].
[53].
[54].
idd, J. B. (1991). Do today’s projects need power ul network planning tools? International Journal of Production Research, 29(10), 1969. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=5782667&site=ehostlive Kuchta, D. (2001). Use of fuzzy numbers in project risk (criticality) assessment. International Journal of Project Management, 19(5), 305–310. http://doi.org/10.1016/S0263-7863(00)00022-3 Moder, J. J., & Rodgers, E. G. (1968). Judgment Estimates of the Moments of Pert Type Distributions. Management Science, 15(2), B–76–B–83. http://doi.org/10.1287/mnsc.15.2.B76 Alcaide, D., Chu, C., Kats, V., Levner, E., & Sierksma, G. (2007). Cyclic multiple-robot scheduling with time-window constraints using a critical path approach. European Journal of Operational Research, 177(1), 147–162. http://doi.org/10.1016/j.ejor.2005.11.019 Riggs, L. S. (1989). Risk Management in CPM Networks. Computer-Aided Civil and Infrastructure Engineering, 4(3), 229–235. http://doi.org/10.1111/j.14678667.1989.tb00022.x John, B., Vera, A., Matessa, M., Freed, M., & Remington, R. (2002). Automating CPMGOMS. In CHI ’02: Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 147–154). http://doi.org/http://doi.acm.org/10.1145/503376.503404 Kastor, A., & Sirakoulis, K. (2009). The effectiveness of resource levelling tools for Resource Constraint Project Scheduling Problem. International Journal of Project Management, 27(5), 493–500. http://doi.org/10.1016/j.ijproman.2008.08.006 Lei, X. (2011). Assumption analysis and duration simulation of three-point estimate in PERT technique. In 2011 International Conference on Computer and Management, CAMAN 2011. http://doi.org/10.1109/CAMAN.2011.5778896 Galloway, P. D. (2006). Comparative study of university courses on critical-path method scheduling. Journal of Construction Engineering and Management, 132(7), 712–722. http://doi.org/10.1061/(ASCE)0733-9364(2006)132:7(712) Trietsch, D. (2005). WHY A CRITICAL PATH BY ANY OTHER NAME WOULD SMELL LESS SWEET? Project Management Journal, 36(1), 27–36. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=16605721&site=ehostlive Yang, J.-B. (2003). Applying the theory of constraints to construction scheduling. PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON STRUCTURAL AND CONSTRUCTION ENGINEERING. Trietsch, D., Mazmanyan, L., Gevorgyan, L., & Baker, K. R. (2012). Modeling activity times by the Parkinson distribution with a lognormal core: Theory and validation. European Journal of Operational Research, 216(2), 386–396. http://doi.org/10.1016/j.ejor.2011.07.054 Levy, F. L., Thompson, G. L., & Weist, J. D. (1963). The ABCs of the CRITICAL PATH Method. Harvard Business Review, 41(5), 98–108. Retrieved from http://liverpool.idm.oclc.org/login?url=http://search.ebscohost.com/login.aspx?direct=tru e&db=bth&AN=6770388&site=eds-live&scope=site
41
[55].
[56].
[57].
[58].
[59].
[60].
[61].
[62]. [63].
[64]. [65].
[66]. [67].
[68].
[69].
Elmaghraby, S. E. (1990). Project bidding under deterministic and probabilistic activity durations. European Journal of Operational Research, 49(1), 14–34. http://doi.org/10.1016/0377-2217(90)90117-T Al Samman, T. A. S., & Al Brahemi, R. M. R. (2014). Fuzzy PERT for Project Management. International Journal of Advances in Engineering and Technology, 7(4), 1150–1160. Retrieved from http://www.e-ijaet.org/media/4I22IJAET0722555_v7_iss4_1150-1160.pdf Ahuja, V., & Thiruvengadam, V. (2004). Project scheduling and monitoring: current research status. Construction Innovation, 4, 19–31. http://doi.org/10.1191/1471417504ci064oa Mehrotra, K., Chai, J., & Pillutla, S. (1996). A study of approximating the moments of the job completion time in PERT networks. Journal of Operations Management, 14(3), 277–289. http://doi.org/10.1016/0272-6963(96)00002-2 Doskočil, R., & Doubravský, K. (2012). Implementation of the PERT Method in MS Excel. In The 18th International Business Information Management Association Conference (pp. 1262–1271). Davis, E. W. (1975). PROJECT NETWORK SUMMARY MEASURES CONSTRAINED-RESOURCE SCHEDULING. AIIE Trans. http://doi.org/10.1080/05695557508974995 Schrage, L. (1972). Solving Resource-Constrained Network Problems by Implicit Enumeration-Nonpreemptive Case. Operations Research, 20(3), 668–677. http://doi.org/10.1287/opre.20.3.668 Pohl, J., & Chapman, A. (1987). Probabilistic project management. Building and Environment, 22(3), 209–214. http://doi.org/10.1016/0360-1323(87)90009-6 utierrez, . J., & ouvelis, P. (1991). Parkinson’s Law and ts mplications or Project Management. Management Science, 37(8), 990–1001. http://doi.org/10.1287/mnsc.37.8.990 Blair, H., Marek, V., & Remmel, J. (2001). Spatial logic programming. Proceedings SCI. Retrieved from http://www3.cs.uky.edu/~marek/papers.dir/02.dir/spatial1.pdf Budd, C. S., & Cooper, M. (2004). A Project Management Approach to Increasing Agency Margins. Journal of Promotion Management, 11(1), 29–49. http://doi.org/10.1300/J057v11n01ñ03 Liu, J. (2012). Schedule Uncertainty Control: A Literature Review. Physics Procedia, 33, 1842–1848. http://doi.org/10.1016/j.phpro.2012.05.293 Monhor, D. (2011). A new probabilistic approach to the path criticality in stochastic PERT. Central European Journal of Operations Research, 19(4), 615–633. http://doi.org/10.1007/s10100-010-0151-x Tache, F., & spăşoiu, C.-E. (2013). The Dynamic of Project Monitoring and Evaluation Mechanisms within Modern Organizations. Review of International Comparative Management, 14(4), 628–637. Elmaghraby, S. E. E., Herroelen, W. S., & Leus, R. (2003). Note on the paper “resourceconstrained project management using enhanced theory o constraint” by Wei et al. International Journal of Project Management, 21(4), 301–305. http://doi.org/10.1016/S0263-7863(02)00085-6
42
[70].
[71].
[72].
[73].
[74].
[75]. [76].
[77].
[78].
[79].
[80].
[81].
[82].
[83].
Regnier, E. (2005). Hidden Assumptions in Project Management Tools. DRMI Newsletter, (11), 1–4. Retrieved from http://www.nps.edu/Academics/Centers/DRMI/docs/1jan05-newsletter.pdf PhD, C. S. B., & PhD, M. C. (2008). A Project Management Approach to Increasing Agency Margins. Journal of Promotion Management. Retrieved from http://www.tandfonline.com/doi/abs/10.1300/J057v11n01_03 Bhaskaran, P. (1988). PROGRESS MONITORING FOR A HIGHWAY PROJECT WITH CPM NETWORK PLAN. Indian Highways, 16(4), 43–52. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.00023993605&partnerID=40&md5=e00344ddaa94fb7e66eaa725222101c7 Yang, J. (2007). How the Critical Chain Scheduling Method working for construction projects. Cost Engineering, 49(4), 25–33. Retrieved from http://140.126.6.125/new/Portals/6/Faculty/Jyh-Bin_Yang/PDF/CE-2007-1.pdf Burgber, P. H. (1964). PERT AND THE AUDITOR. Accounting Review, 39(1), 103. Retrieved from http://w3.bgu.ac.il/lib/customproxy.php?url=http://search.ebscohost.com/login.aspx?direc t=true&db=bth&AN=7106861&site=edslive&authtype=ip,uid&custid=s4309548&groupid=main&profile=eds Simmons, L. F. (2002). PROJECT MANAGEMENT – CRITICAL PATH METHOD ( CPM ). Winter Simulation Conference, 1786–1788. Fanli, M., Dalong, T., & Yuechao, W. (2006). Modeling of common organizational structure for reconfigurable assembly system. In Proceedings of the World Congress on Intelligent Control and Automation (WCICA) (Vol. 2, pp. 6738–6743). http://doi.org/10.1109/WCICA.2006.1714388 Sueyoshi, T. (2000). Stochastic DEA for restructure strategy: an application to a Japanese petroleum company. Omega, 28(4), 385–398. http://doi.org/10.1016/S03050483(99)00069-9 Khamooshi, H., & Cioffi, D. F. (2013). Uncertainty in Task Duration and Cost Estimates: Fusion of Probabilistic Forecasts and Deterministic Scheduling. Journal of Construction Engineering and Management, 139(5), 488–497. http://doi.org/10.1061/(ASCE)CO.1943-7862.0000616 A.a, S., & R.F.b, S. (2015). Developing a novel data envelopment analysis model to determine prospective benchmarks of green supply chain in the presence of dual-role factor. Benchmarking, 22(4), 711–730. http://doi.org/10.1108/BIJ-12-2012-0087 Shabani, A., & Farzipoor Saen, R. (2015). Developing a novel data envelopment analysis model to determine prospective benchmarks of green supply chain in the presence of dual-role factor. Benchmarking: An International Journal, 22(4), 711–730. http://doi.org/10.1108/BIJ-12-2012-0087 Shabani, A., & Saen, R. F. (2015). Developing a novel data envelopment analysis model to determine prospective benchmarks of green supply chain in the presence of dual-role factor. Benchmarking, 22(4), 711–730. http://doi.org/10.1108/BIJ-12-2012-0087 Weiss, G. (1984). Stochastic bounds on distributions of optimal value functions with applications to pert, network flows and reliability. Annals of Operations Research, 1(1), 59–65. http://doi.org/10.1007/BF01874452 Rhee, S.-H. (2004). A Dispatching Rule for Efficient Workflow. Concurrent Engineering, 12(4), 305–318. http://doi.org/10.1177/1063293X04042471
43
[84].
[85].
[86].
[87].
[88]. [89].
[90].
[91].
[92].
[93].
[94].
[95]. [96].
[97].
[98].
Mota, A. A., Mota, L. T. M., & Morelato, A. (2004). Dynamic evaluation of reenergization times during power systems restoration. In Transmission and Distribution Conference and Exposition: Latin America, 2004 IEEE/PES (pp. 161–166). http://doi.org/10.1109/TDC.2004.1432370 Elmaghraby, S. E. (1964). An Algebra for the Analysis of Generalized Activity Networks. Management Science, 10 (3)(November 2015), 494–514. Retrieved from http://www.jstor.org/stable/2627427 Lee, D., Asce, a M., Arditi, D., & Asce, M. (2006). in Stochastic Project Scheduling Simulation. Journal of Construction Engineering and Management, 132(3), 268. Retrieved from http://link.aip.org/link/?JCEMD4/132/268/1 Alzraiee, H., Zayed, T., & Moselhi, O. (2015). Dynamic planning of construction activities using hybrid simulation. Automation in Construction, 49, 176–192. http://doi.org/10.1016/j.autcon.2014.08.011 Arsham, H. (1993). Managing project activity-duration uncertainties. Omega, 21(1), 111– 122. http://doi.org/10.1016/0305-0483(93)90043-K Yakhchali, S. H. (2011). A simple approach to project scheduling in networks with stochastic durations. Proceedings of the World Congress on Engineering 2011, WCE 2011, 1, 560–565. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.080755123057&partnerID=tZOtx3y1 Lee, D., & Arditi, D. (2006). Automated statistical analysis in Stochastic Project Scheduling Simulation. Journal of Construction Engineering and Management, 132(3), 268–277. http://doi.org/10.1061/(ASCE)0733-9364(2006)132:3(268) Mota, C. M. de M., de Almeida, A. T., & Alencar, L. H. (2009). A multiple criteria decision model for assigning priorities to activities in project management. International Journal of Project Management, 27(2), 175–181. http://doi.org/10.1016/j.ijproman.2008.08.005 Maravas, A., & Pantouvakis, J. P. (2012). Project cash flow analysis in the presence of uncertainty in activity duration and cost. International Journal of Project Management, 30(3), 374–384. http://doi.org/10.1016/j.ijproman.2011.08.005 Walter, J. G., Christine, S., & Martin, T. (2000). Crashing of stochastic processes by sampling and optimisation. Business Process Management Journal, 6(1), 65. http://doi.org/10.1108/14637150010313357 Ock, J. H., & Han, S. H. (2010). Measuring Risk-associated Activity’s Duration: A Fuzzy Set Theory Application. KSCE Journal of Civil Engineering, 14(5), 663–671. http://doi.org/10.1007/s12205-010-1003-x Weaver, P. (2008). A Brief History of Scheduling - Back to the Future –. PM World Today, X(II), 1–18. Gong, D., & Rowings, J. E. (1995). Calculation of safe float use in risk-analysis-oriented network scheduling. International Journal of Project Management, 13(3), 187–194. http://doi.org/10.1016/0263-7863(94)00004-V Chanas, S., & Zieliński, P. (2001). Critical path analysis in the network with uzzy activity times. Fuzzy Sets and Systems, 122(03), 195–204. http://doi.org/10.1016/S01650114(00)00076-2 Sinason, D. H., McEldowney, J. E., & Pinello, A. S. (2002). Improving audit planning and control with project management techniques. Internal Auditing, 17(6), 12–16. Retrieved from
44
[99]. [100].
[101].
[102].
[103]. [104]. [105].
[106]. [107].
[108].
[109].
[110]. [111].
[112].
[113].
http://pl8cg5fc8w.search.serialssolutions.com.ezproxy1.lib.asu.edu/?ctx_ver=Z39.882004&ctx_enc=info:ofi/enc:UTF8&rfr_id=info:sid/ProQ%253Aabiglobal&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft. genre=article&rft.jtitle=Internal+Auditing&rft.atitle=Improv Bako, A. (1976). All paths in an activity network. Mathematische Operationsforschung Und Statistik, 7, 851–858. Birrell, G. S. (1980). Construction Planning\= Beyond the Critical Path. Journal Of The Construction Division Asce, 106(3), 389–407. Retrieved from http://cedb.asce.org/cgi/WWWdisplay.cgi?5015684 Vazsonyi, A. (19 0). L’Histoire de randeur et de la Décadence de la Méthode PERT. Management Science, 16(8), B–449 – B–455. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=7111513&site=ehostlive Vanhoucke, M. (2012). Project Management with Dynamic Scheduling. Project Management with Dynamic Scheduling, 11–36. http://doi.org/10.1007/978-3-642-251757 Morris, P. W. G. (2013). Systems Project Management. Reconstructing Project Management, 27–51. http://doi.org/10.1002/9781118536698.ch3 André, P., Vanhoucke, M., & Salvaterra, F. (201 ). introdução duração agregada. Mundo Project Management, 62, 56–65. Mahdi, I. M. (2004). A new LSM approach for planning repetitive housing projects. International Journal of Project Management, 22(4), 339–346. http://doi.org/10.1016/S0263-7863(03)00071-1 Ragsdale, C. (1989). The current state of network simulation in project management theory and practice. International Journal of Management Science, 17(1), 21–25. Wei, C.-C., Liu, P.-H., & Tsai, Y.-C. (2002). Resource-constrained project management using enhanced theory of constraint. International Journal of Project Management, 20(7), 561–567. http://doi.org/10.1016/S0263-7863(01)00063-1 Benitez Codas, M. M. (1987). Development of project management in Brazil - a historical overview. International Journal of Project Management, 5(3), 144–148. http://doi.org/10.1016/0263-7863(87)90018-4 Yassine, A. (2004). An Introduction to Modeling and Analyzing Complex Product Development Processes Using the Design Structure Matrix ( DSM ) Method. Urbana, 1– 17. Retrieved from http://ie406.cankaya.edu.tr/uploads/files/Modeling and Analyzing Complex Product Development Processes Using the Design Structure Matrix.pdf Taylor, J. (2006). CHAPTER 6: Network Analysis. Survival Guide for Project Managers, 91–113. Tax??n, L., & Lilliesk??ld, J. (2008). Images as action instruments in complex projects. International Journal of Project Management, 26(5), 527–536. http://doi.org/10.1016/j.ijproman.2008.05.009 Bonett, D. G., & Deckro, R. F. (1993). A multinomial project evaluation and review technique for information systems analysis and design. Information & Management, 25(1), 51–55. http://doi.org/10.1016/0378-7206(93)90025-O Birrell, G. S. (1980). Construction Planning—Beyond the Critical Path. Journal of the Construction Division, 106(3), 389–407. Retrieved from http://cedb.asce.org/cgi/WWWdisplay.cgi?9725
45
[114]. Elazouni, A. M., & Gab-Allah, A. a. (2004). Finance-Based Scheduling of Construction Projects Using Integer Programming. Journal of Construction Engineering and Management, 130(1), 15–24. http://doi.org/10.1061/(ASCE)0733-9364(2004)130:1(15) [115]. Vanhoucke, M., & Demeulemeester, E. (2003). The application of project scheduling techniques in a real-life environment. Project Management Journal, 34, 30–42. [116]. Shou, Y., & Yeo, K. T. (2000). Estimation of project buffers in critical chain project management. In Proceedings of the 2000 IEEE International Conference on Management of Innovation and Technology: (Vol. 1, pp. 162–167). http://doi.org/10.1109/ICMIT.2000.917313 [117]. Basu, A., & Blanning, R. W. (2001). Workflow Analysis Using Attributed Metagraphs. Proceedings of the 34th Hawaii International Conference on System Sciences, 00(c), 1–9. http://doi.org/10.1109/HICSS.2001.927235 [118]. Weaver, P. (2006). A brief history of scheduling. myPrimavera Conference. Retrieved from http://www.mosaicprojects.com.au/Planning.html [119]. Bowman, R. A. (1994). Stochastic gradient-based time-cost tradeoffs in PERT networks using simulation. Annals of Operations Research, 53(1), 533–551. http://doi.org/10.1007/BF02136842 [120]. Tan, W., Jiang, C., Li, L., & Lv, Z. (2008). Role-oriented process-driven enterprise cooperative work using the combined rule scheduling strategies. Information Systems Frontiers, 10(5), 519–529. http://doi.org/10.1007/s10796-008-9107-2 [121]. Esogbue, A. O., & Marks, B. R. (1977). DYNAMIC PROGRAMMING MODELS OF THE NONSERIAL CRITICAL PATH-COST PROBLEM. Management Science, 24(2), 200–209. http://doi.org/10.1287/mnsc.24.2.200 [122]. Yakhchali, S. H., & Ghodsypour, S. H. (2010). Computing latest starting times of activities in interval-valued networks with minimal time lags. European Journal of Operational Research, 200(3), 874–880. http://doi.org/10.1016/j.ejor.2009.01.051 [123]. Gras, N. S. B. (1917). Work, wages, and profits. Journal of Applied Psychology (Vol. 1). http://doi.org/10.1037/h0064346 [124]. MacLeod, K. R., & Petersen, P. F. (1996). Estimating the tradeoff between resource allocation and probability of on-time completion in project management. Project Management Journal, 27(1), 26. Retrieved from http://search.proquest.com/docview/218776691?accountid=10297\nhttp://sfx.cranfield.ac .uk/cranfield?url_ver=Z39.882004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&genre=article&sid=ProQ:ProQ%3Aabi global&atitle=Estimating+the+tradeoff+between+resource+allocati [125]. Senthilkumar, V., Varghese, K., & Chandran, A. (2010). A web-based system for design interface management of construction projects. Automation in Construction, 19(2), 197– 212. http://doi.org/10.1016/j.autcon.2009.10.007 [126]. Kassem, M., Dawood, N., & Chavada, R. (2015). Construction workspace management within an Industry Foundation Class-Compliant 4D tool. Automation in Construction, 52, 42–58. http://doi.org/10.1016/j.autcon.2015.02.008 [127]. Demeulemeester, E., & Herroelen, W. (1992). A Branch-and-Bound Procedure for the Multiple Resource-Constrained Project Scheduling Problem. Management Science, 38(12), 1803–1818. http://doi.org/doi:10.1287/mnsc.38.12.1803 [128]. Brown, K. A., & Lee Hyer, N. (2002). Whole-brain thinking for project management. Business Horizons, 45(3), 47–57. http://doi.org/10.1016/S0007-6813(02)00202-1
46
[129]. Matthews, M. D. (1986). Networking and information management: Its use by the project planning function. Information and Management, 10(1), 1–9. http://doi.org/10.1016/0378-7206(86)90056-X [130]. Wu, Y., Zhuang, X. C., Song, G. H., Xu, X. D., & Li, C. X. (2009). Solving resourceconstrained multiple project scheduling problem using timed colored Petri nets. Journal of Shanghai Jiaotong University (Science), 14(6), 713–719. http://doi.org/10.1007/s12204-009-0713-z [131]. Martinez, J. C., & Knoke, J. R. (1996). Using CPM-chart animation to illustrate the evolution of schedules. Computing in Civil Engineering (New York), 627–633. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.00029716777&partnerID=40&md5=b5a938f69ae1d9193da1cf4645b32cd7 [132]. Shi, J. J., & Deng, Z. (2000). Object-oriented resource-based planning method (ORPM) for construction. International Journal of Project Management, 18(3), 179–188. http://doi.org/10.1016/S0263-7863(99)00013-7 [133]. Chang, T. C., & William Ibbs, C. (1990). Priority ranking-a fuzzy expert system for priority decision making in building construction resource scheduling. Building and Environment, 25(3), 253–267. http://doi.org/10.1016/0360-1323(90)90051-R [134]. Isha Sharma, & Suri, D. P. K. (2011). Schedule Risk Analysis Simulator using Beta Distribution. International Journal on Computer Science and Engineering, 3(6), 2408– 2414. [135]. Sánchez-Algarra, P., & Anguera-Argilaga, M. T. (2005). Time management in the cost evaluation of limited resource programs. Quality and Quantity. http://doi.org/10.1007/s11135-004-2979-4 [136]. Min, Y., & Shou-Rong, L. (2013). Influence of behavioral factors on project schedule management: A Monte Carlo method. In 2013 25th Chinese Control and Decision Conference, CCDC 2013 (pp. 4831–4835). http://doi.org/10.1109/CCDC.2013.6561809 [137]. Dobrilovic, D., Jevtic, V., & Stojanov, J. (2011). Application of modified shortest path algorithm for project duration assessment. In SACI 2011 - 6th IEEE International Symposium on Applied Computational Intelligence and Informatics, Proceedings (pp. 495–498). http://doi.org/10.1109/SACI.2011.5873054 [138]. Ash, R. C. (1999). Activity scheduling in the dynamic, multi-project setting: choosing\nheuristics through deterministic simulation. WSC’99. 1999 Winter Simulation Conference Proceedings. “Simulation - A Bridge to the Future” (Cat. No.99CH37038), 2(Id), 937–941. http://doi.org/10.1109/WSC.1999.816802 [139]. Frizelle, G. D. M. (1993). Model for maximizing the return on capital projects under timing uncertainty. International Journal of Project Management, 11(1), 39–47. http://doi.org/10.1016/0263-7863(93)90008-B [140]. SUBULAN, K., SALTABAS, A., TASAN, A. S., & GIRGIN, S. C. (2011). MODELING AND ANALYZING OF A CONSTRUCTION PROJECT CONSIDERING RESOURCE ALLOCATION THROUGH A HYBRID METHODOLOGY: PETRI NETS AND FUZZY RULE BASED SYSTEMS. In Proceedings of the 41st International Conference on Computers & Industrial Engineering (pp. 1045–1050). Retrieved from http://www.usc.edu/dept/ise/caie/Checked\nPapers\n[ruhi\n12th\nsept]/word\nformat\npa pers/REGISTRATION\nPAID\nPAPERS\nFOR\nPROCEEDINGS/pdf/335\n17\nMOD ELING\nAND\nANALYZING\nOF\nA\nCONSTRUCTION.pdf
47
[141]. Lee, D.-E., Bae, T.-H., & Arditi, D. (2011). Advanced Stochastic Schedule Simulation System. Civil Engineering and Environmental Systems, 29(1), 1–18. http://doi.org/10.1080/10286608.2011.637623 [142]. Siu, M. F. F., Lu, M., & Abourizk, S. (2015). Bi-level project simulation methodology to integrate superintendent and project manager in decision making: Shutdown/turnaround applications. In Proceedings - Winter Simulation Conference (Vol. 2015-January, pp. 3353–3364). http://doi.org/10.1109/WSC.2014.7020169 [143]. Bonett, D. G., & Deckro, R. F. (1993). A MULTINOMIAL PROJECT EVALUATION AND REVIEW TECHNIQUE FOR INFORMATION-SYSTEMS ANALYSIS AND DESIGN. Information & Management, 25(1), 51–55. Retrieved from ://A1993LK92000005 [144]. Simpson, W. P., & Patterson, J. H. (1996). A multiple-tree search procedure for the resource-constrained project scheduling problem. European Journal of Operational Research, 89(3), 525–542. http://doi.org/10.1016/0377-2217(94)00247-9 [145]. Samanta, D., & Tripathy, D. P. (2004). Project planning and scheduling using Pronet. Journal of Mines, Metals and Fuels, 52(5-6), 73–77. [146]. Bonett, D. G., & Deckro, R. F. (1993). A multinomial project evaluation and review technique for information systems analysis and design. Information and Management, 25(1), 51–55. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.050749132201&partnerID=40&md5=7bacdc34e6f2645b4e5f48b49ea58917 [147]. Vanhoucke, M. (2007). Work continuity optimization for the Westerscheldetunnel project in the Netherlands. Tijdschrift Voor Economie En Management, 52, 435–449. Retrieved from http://hdl.handle.net/1854/LU-419379 [148]. Yassine, A. a. (2004). An Introduction to Modeling and Analyzing Complex Product Development Processes Using the Design Structure Matrix ( DSM ) Method. Quaderni Di Management (Italian Management Review), (9), 1–17. [149]. Taylor III, B. W., & Moore, L. J. (1980). R & D PROJECT PLANNING WITH Q-GERT NETWORK MODELING AND SIMULATION. Management Science, 26(1), 44–59. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=7357823&site=ehostlive [150]. Corder, S., & Ruby Jr., R. (1993). Microcomputer-based software alternatives for project management. Journal of Education for Business, 69(1), 56. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=a9h&AN=9310297632&site=eh ost-live
48