Int J Adv Manuf Technol (2006) 30: 1111–1118 DOI 10.1007/s00170-005-0142-6
ORIGINA L ARTI CLE
Ajai Jain . P. K. Jain . I. P. Singh
An integrated scheme for process planning and scheduling in FMS
Received: 23 December 2004 / Accepted: 12 April 2005 / Published online: 24 November 2005 # Springer-Verlag London Limited 2005
Abstract Process planning and scheduling are the two important manufacturing functions involved in any shop floor activities. As functional integration is essential to realize the benefits of computer integrated manufacturing, a scheme for integration of process planning and scheduling has been introduced in this paper. The proposed scheme can be implemented in a company with existing process planning and scheduling departments when multiple process plans remain available on the shop floor. It consists of two main modules, introduced in detail in this paper. They are process plan selection module and scheduling module. Keywords FMS . Integration . Process planning . Scheduling
1 Introduction Process planning and scheduling are the two most important tasks in flexible manufacturing systems. These tasks strongly influence the profitability of manufacturing a product, resource utilization and product delivery time. Process planning is the systematic determination of methods by which a product is to be manufactured economically and competitively. The primary goal of process planning is to generate a sequence of operations, called a process plan while scheduling attempts to assign manufacturing A. Jain Mechanical Engineering Department, National Institute of Technology, Kurukshetra, 136 119, India e-mail:
[email protected] P. K. Jain (*) Mechanical & Industrial Engineering Department, Indian Institute of Technology, Roorkee, 247 667, India e-mail:
[email protected] I. P. Singh Seth Jai Prakash Mukund Lal Institute of Engineering and Technology, Radaur, India
resources to the operations defined in the process plan. It is bound by the process sequencing instructions that the process plan dictate and by the time-phased availability of production resources. Thus, both process planning and scheduling involve assignment of resources and are highly interrelated. However, conventionally, process planning and scheduling functions are performed separately and sequentially. This approach is based on the concept of subdividing the tasks into smaller and separated duties to satisfy the requirements of sub optimization and suitable for mass production or for a manufacturing system that is especially developed for a product [1]. However, today’s manufacturing environment is quite different from the mass production environment and characterized by profusion in product variety, decreasing lead times to delivery, exacting standards of quality and competitive cost. In such a changed manufacturing environment, conventional approach of carrying out process planning and scheduling initiate conflicts, responsibility flees, slow functioning, inability to communicate in dynamic situations (e.g., workload on machines, traffic load on material handling devices) or abnormal situations (e.g., machine breakdown and bottlenecks) and produces a schedule that lacks flexibility and adaptability [1]. These problems can be avoided by considering the integrated approach to process planning and scheduling that can respond to present manufacturing environment and facilitate flexibility, improves profitability of a product, resource utilization, product delivery time and creation of realistic process plans that can readily be executed without frequent alterations. Thus, integration of process planning and scheduling is essential to achieve eventually integrated manufacturing and to dismiss the conventional sequential manufacturing approach. The objective of the paper is to propose a scheme for integration of process planning and scheduling that can be implemented in a company with existing process planning and scheduling departments when multiple process plans (MPP) for each part type are available. The proposed scheme takes advantage of MPP while following the realtime strategy for scheduling suitable for changing workshop status. In the scheme, two modules viz., process plan
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selection module and scheduling module are suggested. The proposed scheme is implemented in an FMS.
2 Literature review Several approaches for integration of process planning and scheduling have been discussed by researchers. One approach is to merge process planning and scheduling into a single optimization task. However, the scheduling problem alone belongs to the class of non-deterministic polynomial complete (NP-complete), the resulting problem will be even more difficult to solve [2]. Therefore, a number of approaches have been introduced to solve the problem encountered in integrating the two activities in order to achieve optimum manufacturing performance in terms of lead-time, resource utilization etc. The concept of non-linear process planning (NLPP) or flexible process planning was used by number of researchers [3–5]. In this approach, all possible plans (called multiple or flexible process plans) for each part before it enters onto the shop floor are created by considering operation flexibility (possibility of performing an operation on more than a machine), sequencing flexibility (possibility of interchanging the sequence in which required manufacturing operations are performed) and processing flexibility (possibility of producing the same manufacturing feature with alternative operations, or sequence of operations) [6]. The underlying assumption is that all problems that can be solved ahead of time should be solved before the manufacturing starts. Thus, NLPP is based on static shop floor situations. All these possible process plans are ranked according to process planning criterion (such as minimum total machining time, minimum total production time) and stored in a process planning database. The first priority plan is always ready for submission when the job is required and then scheduling makes the real decision. If the first priority plan does not fit well in the current status of the shop floor, the second priority plan will be provided for the scheduling. This procedure is repeated until a suitable plan is identified from already generated process plans. The criteria for decisions are due dates and batch size of order, the capacity of workshop and the optimization criterion for schedule (maximum throughput, minimum lead time etc.). However, when there are large numbers of parts, the number of process plans tends to increase exponentially and can cause a storage problem. Also some of the process plans created are not feasible according to real time shop status and considering all possible process alternatives for resource allocation may enormously increase the complexity of process plan representation. NLPP method has a one-way information flow, i.e., from process planning to production planning [7] and thus it may be impossible to achieve full optimal results in integrating the two activities. However, modern production systems maintain MPP [8]. Closed loop process planning (CLPP) [7, 9, 10] overcomes the shortcoming of NLPP and generates plans by means of a dynamic feedback from production scheduling and the available resources. Production scheduling tells
process planning regarding the availability of different machines on the shop floor for the coming job, so that every plan is feasible with respect to the current availability of production facilities. Every time an operation is completed on the shop floor, a feature-based work piece description is studied in order to determine the next operation and allocate the resources. This approach takes dynamic behavior of the manufacturing system into consideration. Thus, real time status is crucial for the CLPP. It is also referred to as real time process planning or dynamic process planning. The major disadvantage of this approach is that the process planning and scheduling departments in a company may have to be dismantled and reorganized to take full advantage of CLPP approach. This approach is unrealistic because the complexity of manufacturing processes might be unavoidable in achieving real-time process plan generation [11]. Distributed process planning (DPP) was also applied to integrate the two functions [1, 12–16]. DPP performs both the process planning and production scheduling simultaneously. It divides the process planning and production scheduling tasks into two phases. The first phase is preplanning. In this phase, process planning function analyzes the job based on the product data. The features and feature relationships are recognized, and corresponding manufacturing processes are determined. The required machine capabilities are also estimated. The second phase is the final planning, which matches the required job operations with the operation capabilities of the available production resource. The integration occurs at the point when resources are available and the job is required. In this approach, process planning and production scheduling are carried out simultaneously. This approach is also referred to as just-in-time process planning. The result is dynamic process planning and production scheduling constrained by real time events. DPP approach is the only one that integrates the technical and capacity-related planning tasks into a dynamic fabrication planning system [17]. However, this approach requires high capacity and capability from both hardware and software and scope of DPP are limited within some specific CAPP functions such as process and machine selection as detailed process planning tasks are shifted down to manufacturing stages for enhancing flexibility [11]. Several other researchers [11, 18–20] have addressed the issue of integration of process planning and scheduling. The foregoing review reveals that DPP is the best approach for integration of process planning and scheduling. However, this approach requires high capacity and capability from both hardware and software. Also planning and scheduling departments in a company have to be completely dismantled and reorganized in DPP and CLPP [7]. On the other hand, NLPP that is based on the static shop floor conditions is seen to be the proper means to realize the integration between process planning and scheduling [7]. In NLPP, process plans contain alternative routings, which offer high degree of flexibility to scheduling. Moreover, NLPP can be implemented in a company with existing process planning and scheduling departments and without
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any re-organization of them. However, in NLPP approach, once a process plan is selected for a part, it cannot be changed on the shop floor. Since flexibility has been recognized as a tool for improving the system performance [6, 21, 22], an integration approach that can take advantage of flexibility on the shop floor and can be implemented in a company with existing process planning and scheduling departments will be more suitable for present manufacturing systems. Thus, a need is felt to develop a methodology to integrate process planning and scheduling functions that can be implemented in a company with existing process planning and scheduling departments when MPP for each part type are available. The methodology should be able to take advantage of MPP while following a real time strategy for scheduling suitable for changing workshop status.
Fig. 1 Integration Methodology for Process Planning and Scheduling
3 Framework for integrated process planning and scheduling system Figure 1 is a graphical representation of the integrated production scheduling system envisioned in this study. The system is composed of two basic modules: process plans selection module (PPSM) and scheduling module (SM). Interfacing the PPSM is the production order details, i.e., number of part types, production quantity of each part type, MPP for each part type and number of pallets available. Another source of information needed for integration is the shop status information. The shop status database provides information on the status of machines and other resources (material handling systems) as well as the parts currently on shop floor. This information includes in-process parts currently assigned to each machine, workload on each machine and the functional status (i.e., up or down) of each machine. This information is necessary for selecting the right process plan.
Number of part types
Production quantity of each part type
Multiple process plans for each part type
Production order
Number of pallets of each part type
PROCESS PLANS SELECTION MODULE - Computes total production time of multiple process plans for each part type - Selects best four process plans for each part type by ranking multiple process plans using minimum total production time as criterion - Arranges selected best four process plans in decreasing order of their priority on the basis of implemented criterion
Retrieve best process plan
Selected process plans database
SCHEDULING MODULE -Part selection at a machine using a dispatching rule -Machine selection of the selected part according to currently followed process plan
Switch over to next best process plan
-Switch to the next best process plan of a part if next required machine is not available according to currently followed process plan
Current shop floor status
Raw material
Manufacturing System
Finished part
1114 Table 1 Working of process plans selection module (for one example part) S.N.
Multiple process plans Mi (Ti)
1. 2. 3. 4. 5. 6. 7. 8.
2(58) 2(58) 3(65) 3(65) 2(58) 3(65) 2(58) 3(65)
-1(47) -1(47) -1(47) -1(47) -4(55) -4(55) -4(55) -1(47)
-2(43) -3(45) -2(43) -3(45) -2(43) -2(43) -2(43) -2(43)
-4(92) -4(92) -4(92) -4(92) -4(92) -4(92) -1(95) -1(95)
Total machining time
Total transportation time
Total production time
Ranking
240 242 247 249 248 255 251 250
23 23 25 25 27 25 21 23
263 265 272 274 275 280 272 297
1 2 3 5 6 7 4 8
Selected process plans arranged in decreasing order of their priority 2(58) 2(58) 3(65) 2(58)
-1(47) -1(47) -1(47) -4(55)
-2(43) -3(45) -2(43) -2(43)
-4(92) -4(92) -4(92) -1(95)
Mi – Machine number Ti – Operation processing time
With the database interface in place, PPSM selects the best four process plans for each part type and stores them in the database as shown in Fig. 1. SM performs part scheduling for each part for its best selected process plan. The following section describe the details of each of these modules separately.
suggests that only limited amount of flexibility should be present in the process plans [6]. However, the number and type of process plans selected for each part type may vary depending on the level of flexibility desired. Table 1 shows the working of PPSM for an example part for better understanding.
3.1 Process plans selection module
3.2 Scheduling module
After a production order is received, PPSM is invoked to rank the available MPP for each part type using minimum total production time (sum of total machining time and total transportation time) as the criterion. Total production time is taken as a criterion, as the literature review suggests that the optimal process plan which might have the shortest processing time or the least number of operations may not guarantee the best system performance [23]. Also, in a FMS, shop time of each part is composed of machining time, transportation time, and waiting time in the input and output queues of machines. While waiting times in the input and output queues are influenced by scheduling of parts, the other two categories of shop time, i.e., machining time and transportation time are influenced by the process plans selected for producing the parts. During ranking, if a tie occurs between two process plans, then one of them is selected randomly. From the ranked process plans, best four process plans for each part type are selected and arranged in decreasing order of their priority and stored in the ‘selected process plans’ database for further use. These four process plans are named as the best process plan (BPP), second best process plan (SBPP), third best process plan (TBPP), and fourth best process plan (FBPP), respectively. PPSM selects the best four process plans for each part type of the production order and these best four process plans for each part type will remain available during scheduling. Availability of MPP for each part type ensures that, as far as possible, a feasible alternative is available to take care of workshop disturbances such as non-availability of machine tool. Number of MPP available for each part type are restricted to four as by allowing availability of a large number of MPP per part type may create chaos on the shop floor. Also, literature review
This module is invoked after the execution of PPSM. Initially, from the ‘selected process plans’ database, the best process plan for each part type is passed to SM. Once a process plan for each part type and the current shop floor status is available, scheduling is carried out by following the event-driven simulation approach. Current shop floor status is the backbone of the scheduling module. This module receives the current status of various resources such as machines, robots, input and output buffers, and parts. During scheduling, part selection may be performed using a dispatching rule such as shortest processing time (SPT) and initially, machine selection for the selected part is performed according to the currently followed process plan. However, it may happen that the next destination machine according to the currently followed process plan for the selected part may not be available due to nonavailability of space in the input buffer of the machine. In such situation, the scheduler searches the ‘selected process plans’ database again to identify the next suitable process plan for the selected part depending on the current Table 2 R-1 and R-2 unit travel times [24] L/UL B-IN/ M1 M2 M3 M4 B-OUT Robot R-1 L/UL B-IN/B-OUT RobotR-2 B-IN/B-OUT M1 M2 M3 M4
0 2 -
2 0 0 3 5 5 3
3 0 3 5 7
5 3 0 3 5
5 5 3 0 3
3 7 5 3 0
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processing stage of the part and already followed route. It is important to mention that at this stage of the proposed approach, the integration between process planning and
scheduling occurs, in order to achieve a realistic production schedule. In general, by allowing MPP for each part type to be available on the shop floor, a scheduler can react fast to
Table 3 Multiple process plans of various part types Part type no.
Multiple processplans Mi (Ti)
1.
1(60) 1(60) 2(65) 2(65) 1(60) 1(60) 2(65) 2(65)
– – – – – – – –
3 3 3 3 4 3 4 3
(75) (75) (75) (75) (85) (75) (85) (75)
– – – – – – – –
2 1 2 1 1 2 2 2
(60) (58) (60) (58) (58) (60) (60) (60)
– – – – – – – –
2.
1(60) 1(60) 3(65) 3(65) 2(70) 2(70) 2(70) 2(70)
– – – – – – – –
2 4 2 4 4 4 4 4
(82) (80) (82) (80) (80) (80) (80) (80)
– – – – – – – –
3 3 1 1 3 1 3 1
(100) – 2 (100) (100) – 2 (100) (98) – 4 (100) (98) – 4 (100) (100) – 2 (100) (98) – 2 (100) (100) – 4 (100) (98) – 4 (100)
3.
3(70) 4(82) 3(79) 1(58) 4(82) 4(82) 3(79)
– – – – – – –
2(40) 1(46) 4(37) 2(35) 1(46) 2(40) 2(40)
– – – – – – –
4(70) 2(70) 3(79) 4(59) 3(79) 3(79) 3(79)
4.
4(21) 1(48) 1(47) 2(65) 2(65) 2(65) 2(65)
– – – – – – –
3(98) 3(41) 3(70) 4(92) 4(92) 3(98) 3(98)
– – – – – – –
4 4 1 1 1 4 1
5.
4(27) 2(71) 4(35) 3(65) 3(65) 2(71) 3(65)
– – – – – – –
1(56) 3(52) 2(82) 4(38) 2(82) 3(52) 2(82)
– – – – – – –
2(25) 4(50) 3(48) 1(91) 3(48) 1(91) 1(91)
– – – – – – –
6.
2(60) 2(60) 3(65) 3(65) 2(60) 3(65)
– – – – – –
1(45) 1(45) 1(45) 1(45) 4(58) 4(58)
– – – – – –
2(40) 2(40) 3(43) 3(43) 2(40) 3(43)
– – – – – –
– – – – – – –
(70) (84) (91) (91) (74) (84) (91)
Part type no.
Multiple process plans Mi (Ti)
7.
2(60) 2(60) 3(65) 3(65) 2(60) 3(65) 3(65) 2(60)
– – – – – – – –
1 1 1 1 4 1 4 4
8.
1(65) 1(65) 2(68) 2(68) 1(65) 1(65) 2(68) 2(68)
– – – – – – – –
3(55) 3(55) 3(55) 3(55) 4(65) 4(65) 4(65) 4(65)
– – – – – – – –
2(42) 1(40) 2(42) 1(40) 1(40) 2(42) 1(40) 2(42)
– – – – – – – –
9.
2(37) 3(88) 4(81) 1(53) 3(88) 4(81) 3(88)
– – – – – – –
3(39) 4(80) 2(44) 2(40) 4(80) 2(44) 4(80)
– – – – – – –
4(52) 2(31) 3(71) 3(69) 3(69) 3(69) 3(69)
10.
2(49) 2(55) 2(72) 2(72) 2(72) 4(92) 4(92)
– – – – – – –
1(32) 3(37) 1(24) 3(37) 1(32) 3(37) 2(76)
– – – – – – –
3(23) 1(50) 2(59) 4(73) 4(73) 4(73) 4(73)
1(83) 3(50) 1(70) 3(50) 1(83) 3(50) 3(50)
11.
3(30) 1(23) 1(78) 2(77) 1(78) 2(77) 1(78)
– – – – – – –
1 2 2 4 4 4 4
4(90) 1(92) 4(90) 1(92) 4(90) 4(90)
12.
2(40) 2(40) 2(40) 3(70) 3(70) 3(70)
– – – – – –
1(30) 1(30) 1(30) 1(30) 4(40) 4(40)
4 4 4 4 4 1 4 1
(30) (30) (30) (30) (30) (60) (30) (60)
2(40) 4(24) 2(34) 1(78) 1(78) 1(78) 1(78)
– – – – – – –
2(25) 1(54) 3(35) 1(54) 2(89) 2(89) 2(89)
(45) (45) (45) (45) (55) (45) (55) (55)
(33) (47) (52) (94) (94) (94) (94)
– – – – – – – –
– – – – – – –
– – – – – –
2 3 2 3 2 2 2 2
3 1 3 3 3 2 1
Multiple process plans Mi (Ti)
– – – – – – – –
– – – – – – – –
– – – – – – – –
13.
1(60) 1(60) 1(60) 1(60) 2(75) 2(75) 1(60) 1(60)
4(40) 4(40) 4(40) 4(40) 4(40) 4(40) 4(40) 4(40)
14.
4(97) – 3 (54) – 2 (40) – 1 (99) 4(97) – 1 (50) – 3 (42) – 4 (100) 2(100) – 3 (54) – 2 (40) – 1 (99) 2(100) – 1 (50) – 3 (42) – 4 (100) 4(97) – 2 (65) – 3 (42) – 4 (100) 4(97) – 2 (65) – 3 (42) – 1 (99) 4(97) – 1 (50) – 4 (50) – 1 (99) 2(100) – 1 (50) – 4 (50) – 1 (99)
– – – – – – –
2(51) 1(36) 4(46) 4(99) 2(51) 4(99) 4(99)
15.
1(30) 1(30) 2(25) 3(25) 2(25) 2(25) 1(35)
– – – – – – –
2(25) 3(25) 1(30) 2(25) 1(30) 3(35) 3(35)
– – – – – – –
2(79) 4(43) 3(66) 2(93) 2(93) 2(93) 2(93)
16.
2(60) 2(60) 3(63) 3(63) 4(70) 4(70) 3(63)
– – – – – – –
1 1 4 4 1 1 4
– – – – – – –
2(43) 3(78) 4(45) 4(45) 4(45) 3(82) 3(82)
17.
3(40) 3(36) 1(66) 4(56) 1(66) 1(66) 4(56)
– – – – – – –
2(30) 4(21) 2(23) 3(69) 3(69) 3(69) 3(69)
– – – – – – –
3(64) 1(90) 1(80) 2(51) 1(90) 2(51) 1(90)
– – – – – – –
1(25) 4(20) 4(20) 3(81) 3(81) 3(81) 3(81)
4(95) 1(100) 4(95) 4(100) 4(100) 4(100)
18.
1(50) 1(50) 1(50) 1(50) 2(70) 2(70)
– – – – – –
4(20) 3(20) 2(40) 2(40) 3(20) 4(20)
– – – – – –
3(20) 4(30) 3(20) 4(30) 4(30) 3(20)
– – – – – –
2(30) 2(30) 2(30) 2(30) 1(40) 1(40)
(41) (45) (41) (45) (41) (41) (41) (41)
(66) (48) (32) (66) (66) (41) (48)
2(20) 2(20) 3(30) 2(20) 2(20) 3(30)
– – – – – – – –
Part type no.
– – – – – –
4 4 4 4 4 1 4 1
(90) (90) (90) (90) (90) (100) (90) (100)
4 3 3 4 4 3 3 4
(45) (41) (41) (45) (45) (41) (41) (45)
– – – – – – –
(100) (100) (100) (100) (100) (100) (100)
3 4 2 2 3 4 1 1
(40) (45) (45) (45) (40) (45) (55) (55)
3(25) 2(25) 3(25) 3(25) 2(30) 2(30) 2(30) – – – – – – –
2 4 2 2 4 2 2
– – – – – – –
2 2 4 1 2 2 4 2
(30) (30) (33) (32) (30) (30) (33) (30)
4(15) 4(15) 4(15) 4(15) 4(15) 4(15) 4(15)
(100) – 3 (80) (97) – 3 (80) (100) – 1 (78) (100) – 3 (80) (97) – 3 (80) (100) – 3 (80) (100) – 4 (90)
1116 Table 4 Parameters and their range considered in the present work S. No. Parameter
Range/value employed
1.
20–50
2. 3. 4. 5. 6. 7.
Production quantity of each part type in a production order Number of part types in a production order Number of operations per job Transportation time Operation time Number of pallets released Dispatching rule
3–4 4 2–7 units 20–100 units 8–16 i.e. 8, 10, 12, 14, 16 Shortest processing time
unforeseen workshop disturbances such as non-availability of a machine tool. The scheduler is capable enough to select an alternative path, if possible, at any time and at any processing stage of a part to overcome the disturbances occurring on the shop floor. As MPP are allowed, it may happen; that various parts of the same part type may follow different process plans owing to shop floor disturbances at different stages of their completion. The final outcome will be a process plan that a part will follow in the shop.
4 Implementation of proposed scheme of integration An example FMS consisting of four CNC machines (M1, M2, M3, and M4), each with an individual input and output buffer of capacity three and two respectively is considered for implementation of the proposed integration scheme [24]. A giant robot R-2 is available to serve all four machines. There is a load and unload (L/UL) station of infinite capacity to load/unload the parts on/from the pallets. The system input (B-IN) and output buffer (B-OUT) have a capacity of ten and eight respectively. There is another robot R-1 in the system, which operates between L/ UL station and B-IN/B-OUT for part transfer. Travel times of R-1 and R-2 while serving other system resources are shown in Table 2. The part flow into the FMS is as follows: A piece of raw material is first loaded onto a pallet at load station by manual operation. The palletized part is then transported to B-IN by R-1. R-2 then moves parts from B-IN to various machines according to their process plans. When MPP are available, a part can switch over to an alternate process plan to overcome the non-availability of machine due to limited buffer space. The sequence of part flow at a machine is: machine input buffer → machine table → machine output buffer. When all the machining operations on a part are completed, R-2 transports it back to B-OUT. R-1 again transports finished parts from B-OUT to unload station, where part is unloaded from the pallet manually. This empty pallet is again loaded with raw part of same part type, if available, and sent into the system.
Table 5 Process plan type followed for production orders Type of process plan
% Age of total parts following shown type of process plan for Case study 1
Number of pallets released
8
10
12
14
16
BPP SBPP TBPP FBPP BPP SBPP TBPP FBPP BPP SBPP TBPP FBPP BPP SBPP TBPP FBPP BPP SBPP TBPP FBPP
83.87 5.16 9.03 1.94 67.47 12.62 15.84 4.07 67.01 7.47 17.24 8.28 58.17 10.79 20.56 10.48 58.60 14.48 16.31 10.61
Case study 2 97.76 0.87 1.37 92.52 2.33 3.01 2.14 80.26 11.36 1.55 6.83 75.91 13.81 1.55 8.73 59.96 23.62 2.23 14.19
Case study 3 88.98 7.91 3.11 73.68 20.62 4.85 0.85 69.39 23.35 4.85 1.69 62.57 19.16 11.67 5.60 56.12 32.30 8.05 3.53
Case study 4 76.37 11.23 1.62 10.78 71.70 12.85 2.25 13.20 70.44 7.55 0.36 21.65 65.14 9.34 3.41 22.11 62.03 6.29 1.62 30.06
Case study 5 78.75 18.81 2.00 0.44 62.43 18.81 6.94 11.82 56.94 24.30 5.48 13.28 52.95 20.31 10.93 15.81 33.77 24.86 18.81 22.56
Case study 6 75.79 8.32 13.81 2.88 65.98 16.82 10.58 6.62 66.76 17.50 7.57 8.17 41.90 23.62 17.38 17.10 41.68 19.83 13.81 24.68
- - No part follows the process plan; BPP -Best Process Plan; SBPP - Second Best Process Plan; TBPP -Third Best Process Plan; FBPP Fourth Best Process Plan
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Experiments are designed and conducted for the production orders consisting of various part types whose MPP are shown in Table 3 where Mi represents the machine number and Ti represents the operation time. The process plans shown in bold for each part type in Table 3 are the outcome of PPSM and represent the best four process plans that will remain available during part manufacturing on shop floor. SM performs part scheduling on real time basis by simulating the Petri net model of the FMS. Several assumptions that are in line with previous studies such as (1) operations times are deterministic and known in advance, (2) setup times are included as part of operation times, (3) all parts are available for processing at the start, although part entry into the system is dependent on the pallet availability, (4) each machine is continuously available for processing jobs, and there are no interruptions owing to breakdowns, maintenance, or other such causes, (5) part preemption is not allowed, (6) pallet availability is limited and a pallet with fixture can only load one part at the most, and (7) number of pallets available for each part type is in the same proportion of the total pallets available as the proportion of their required production quantity with respect to the total production requirement are made in the present work. Petri net model of the FMS and various decisions required during smooth flow of parts on shop floor during scheduling are not included here for want of space and readers can refer to Jain [24]. The various variables with their range/values considered in the present work are summarized in Table 4. Number of pallets released to the system (np) is variable and varies from eight (40% of work-in-process) to sixteen (80% of work-inprocess). Six case studies are taken into consideration to assess the performance of the proposed integration methodology and results are shown in Table 5. Table 5 clearly reveals that as np is varied from eight to 16, all four process plans are followed by various parts on shop floor to overcome the uncertainty of non-availability of machines due to limited buffer space. Thus proposed integration methodology is effective enough to make use of MPP in case of workshop disruptions. Since, the present study considers availability of four MPP per part type during FMS scheduling, so it serves as a pillar for FMS scheduling with MPP in which either less or more than four MPP per part type are available. It is also observed that when the number of pallets released into the system are eight, the swapping between the process plans is less and as the number of pallets released to the system is increased to sixteen, swapping between the process plans increases. This is due to the fact that with the increase in the number of pallets, the system becomes more congested and thus parts have to swap their currently followed process plan more frequently. The effectiveness of MPP over single process plan (SPP) is assessed through makespan and mean flow time performance measures. The best process plan of each part type is taken as the available process plan in SPP environment. Simulation runs are performed for three case studies and
Table 6 %Age change in performance measure In MPP over SPP Performance measures
%Age change in performance measure for Case study 7
Case study 8
Case study 9
Number 8 Makespan +13.57 of pallets Mean flow time +6.57 released 10 Makespan +23.70 Mean flow time +20.41 12 Makespan +24.68 Mean flow time +23.97 14 Makespan +29.38 Mean flow time +24.83 16 Makespan +31.34 Mean flow time +17.10
+19.20 +12.20 +25.08 +16.51 +20.55 +20.28 +25.84 +24.32 +26.96 +30.07
+13.96 +6.17 +15.94 +14.81 +13.70 +2.59 +21.00 +19.91 +25.62 +20.23
+ -Improvement - -Detoriation
results are depicted in Table 6. If the value of a performance measure in SPP and MPP environments are T1 and T2 respectively then % change in the performance measure is defined as given below. % change in performance measure ¼
T1 T2 100 T1
(1)
Table 6 clearly reveals that for the taken case studies, % age change in makespan and mean flow time is always positive, i.e., availability of MPP during FMS scheduling improves makespan and mean flow time. Thus, the proposed integrated scheme is effective enough to make use of MPP during FMS scheduling and results in improved system performance.
5 Conclusion Integration of process planning and scheduling has been recognized as playing an important role to form integrated manufacturing. The lack of integrated methodologies in the literature, in connection with MPP, is the main driving force behind this paper. In this paper, we systematically discussed the issue of integration of process planning and scheduling. The various approaches used by the researcher community have been discussed with their advantages and disadvantages. On the basis of discussion, a framework for integration of process planning and scheduling that can quickly integrate both functions and can be implemented in a company without dismantling and reorganizing existing process planning and scheduling departments is proposed. The proposed methodology consists of two main modules viz., process plans selection module and scheduling module. The proposed scheme of integration is implemented in an FMS. The taken case studies show that the proposed
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methodology is effective enough to make use of the MPP on the shop floor. The pattern of alternative process plans followed is dependent on the number of pallets released to the system. With the increase in the number of pallets released into the system, all of the available process plans are followed in a uniform way. Availability of MPP during scheduling results in improved system performance as measured by makespan and mean flow time.
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