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Manufacturing Improvement: From Performance Criteria To Generalized Scheduling Approach Celia Ortiz Integrated Manufacturing Solutions Production Modeling Corporation
Edward J. Williams Advanced Manufacturing Technology Dev. Ford Motor Company
Keywords Scheduling, manufacturing improvement, performance metrics
Abstract Implementation of an agile, intelligent manufacturing system requires extensive attention to scheduling algorithms and policy. A scheduling process, in turn, both affects and depends upon many areas of a manufacturing organization, from the sales department, through the shop floor, to the shipping department. Effective scheduling policy begins with fixing performance criteria. Next, the analyst or industrial engineer must match appropriate algorithms, techniques, and scheduling rules to those performance criteria; the appropriateness of these choices is highly sensitive to the criteria. In this paper, we examine commonly used performance metrics such as total weighted completion time, maximum lateness, and total tardiness to match them with appropriate scheduling policy. Then, we present a case study which required the formulation of unique scheduling algorithms developed for special situations. We conclude by summarizing our findings and indicating promising directions for further development and practical application of scheduling algorithms.
Introduction Rapidly intensifying cost and competitive pressures in industry are increasing the importance of implementing agile, intelligent manufacturing systems. Such implementation, in turn, requires continuous support from robust scheduling policies and algorithms. These scheduling policies must be based on quantifiable metrics whose achievement optimizes the performance of the entire enterprise, not just that of manufacturing operations. The correct choice of scheduling algorithms, techniques, and rules is highly sensitive to the chosen performance criteria as well as to the particular configuration of the manufacturing system. General categories of manufacturing systems include: classic job shop, open job shop, batch shop, flow shop, manufacturing cells, assembly and transfer systems. Determination of the appropriate performance metrics as well as a correct identification of the manufacturing system configuration assists in the indication of the appropriate scheduling algorithm. Each type of manufacturing system has a unique set of decisions to be made or variables to be set in the development of a schedule. For example, determination of the appropriate lot size will be a critical issue in a batch shop facility and of no consequence in a job shop environment. This paper will classify the scheduling decisions to be made within manufacturing systems configurations, as well as match these classifications with appropriate scheduling algorithms for the selected performance criteria.
Scheduling Implementation Overview The production scheduling function within an organization is typically the result of an evolutionary process. As the manufacturing facilities grow larger and more complex, the production scheduling efforts struggle to keep pace. The usual method for dealing with the complexities of producing a manufacturing schedule is the addition of personnel. Characteristics of an scheduling function that would benefit from redesign would be increased personnel levels, considerable effort in data collection and manual data entry, and increasing lead time for schedule generation. In recent years, the focus of selecting an "ideal" software application for production scheduling has yielded results that have fallen far short of expectations. This is not the fault of the software, but rather the misalignment of the software to the production process. In general, the approach that leads to the desired result is to first reengineer the scheduling process, and then select the information systems that best support that process. The basic steps to
achieving an improved, redesigned scheduling process [Ortiz] are: (1) team building, (2) definition of performance metrics, (3) selection of a preliminary approach, (4) determination of data requirements, (5) refinement of approach, (6) selection of implementation method, (7) installation and training. Perhaps the most difficult step in the scheduling process redesign is the third, the selection of a preliminary approach. The reason for the difficulty is that there is seemingly an infinite set of production schedule generation techniques and no clearly delimited process for matching these techniques to a particular scheduling problem. There have been a few publications which focus on a specific family of scheduling problems, such as lot sizing problems [Bahl, Ritchie], and characterize solution approaches based on which features of the production system can be considered as constrained or unconstrained. This paper takes the "30,000 foot view" in that the characteristics of the production process in combination with the objective of the scheduling process can help to isolate a generalized scheduling approach. That is, based on the category of the manufacturing process and the performance metrics selection, a subset of all available schedule generation techniques can be determined.
The Scheduling Problem The scheduling problem can be viewed simplistically as the assignment of jobs to resources in a manner that will attain a desired objective. In manufacturing systems, this objective is to improve profitability, usually by increasing throughput or minimizing production costs. At the detailed level, the number of decision variables required to maximize a profit objective function usually makes the problem solution intractable. In order to isolate a solution approach, it is helpful to view the problem at the macro level to determine the types of decisions to be made in determining a production schedule. The decision variables can then be categorized to include: 1) Sequence of release to the system: This decision involves determining the order in which the jobs will be made available to include in the production schedule. Constraining factors such as material or design specifications often remove this decision variable from the scheduling problem. 2) Resource or routing selection: This scheduling decision includes determining on which particular machine or sequence of machines a job will be processed. 3) Lot sizing: This decision involves determining how many items of an order will be processed on a particular machine before a setup or change over is performed to process a different item. 4) Sequence at a resource: This determines the order in which jobs will be processed at a particular resource. 5) Available capacity: For those systems where additional capacity can be obtained, usually by the addition of overtime, this is the determination of how much overtime is to be added. The decisions listed above must be made in consideration of the constraints of the production process. Usually, the macro level view of the constraints isolates them into two main types, resource capacity constraints and precedence relationships. The precedence relationship constraint is a direct result of the mechanical characteristics of the production process. For example, raw material stock must be cut at the saw before rough machining can be performed at a lathe. Some organizations will also view due dates as a constraint by having a policy in which no orders will be allowed to be late, i.e., be shipped after their due date. Care must be taken when using this type of policy in that an infeasible solution can result if there in no flexibility in available capacity. The desired outcomes of a solution to the scheduling problem will be an assignment of jobs to resources in such a way that enhances the value of the performance metric. Given a finite capacity of the resources, the assignment of jobs will result in scheduled start and finish times for each manufacturing operation.
Manufacturing Process Categories And Decisions Categorization of the manufacturing process itself is a key step in determining a generalized solution approach. Basic categories are described in [Morton; Pinedo] and include the classic job shop, open job shop, batch shop, flow shop, manufacturing cell, assembly line, and transfer line. Job shops are typically identified in that they are “build to order,” have small or single lot sizes, no work in process inventories, high product variety and a fixed, unidirectional process routing. Open Job Shops differ from Classic Job Shops only in that there may be some small amount of build to stock, which will yield a small amount of work in process. Examples of Classic Job Shops include the building of stamping dies or rapid prototypes. Open Job Shop
process examples include mainframe computers and modular homes. The scheduling decisions to be made in a job shop environment are sequence of release to the system, lot sizing and sequence at a resource. The precedence relationships of manufacturing operations are typically severe, with many operations that must be performed in a specific order. Characteristics not seen in job shops include banks of parallel, identical machines, automated material handling and sequence dependent setup times. The Batch Shop is categorized by high product variety, large order lot quantities, a small number of possible routings and work-in-process inventories. Examples of parts produced by a Batch Shop include automotive components (stamped body parts, turned powertrain parts) and appliances. The scheduling decisions to be made in a Batch Shop are sequence of release to the system, resource or routing selection, lot sizing and sequencing at a resource. Characteristics not seen in a Batch Shop include dedicated (to a part or part family) equipment and a large number of operations steps (5 or more). Flow Shops are categorized by high product variety, high variability in order lot quantities, complex routing with many alternatives and rerouting, banks of parallel identical machines and work-in-process inventories. Examples of parts produced in a Flow Shop configuration are valves and plumbing fixtures, hand tools, farm implements and some manual assembly processes. Flow Shops often exhibit a near even mix of build to order and build to stock orders. The scheduling decisions to be made are sequence of release to the system, resource or routing selection, lot sizing and sequence at a resource. Sequence dependent setup times are often a major factor. Characteristics not seen in a flow shop are dedicated equipment and a high degree of automated material handling. Flow Shops generally represent the most challenging scheduling problem. The Manufacturing Cell manufacturing process category is a more recent design developed to circumvent the problems and complexities introduced by the Flow Shop configuration. Manufacturing Cell processes group equipment into cells dedicated to processing a single part type or family. Manufacturing Cells are categorized by medium product variety, high variability in order lot quantities, a fixed, single resource routing, minimal work in process inventories and small to medium lot sizes. Examples of parts produced in the Manufacturing Cell configuration include heating, ventilation and air conditioning (HVAC) components and electronics. Orders processed in a manufacturing cell may be build to order or build to stock, but build to order generally comprises a high percentage of the orders. The scheduling decision to be made in a manufacturing cell is simply the sequence of release to the system due to the single resource, single step operation. That is, typically the cell is scheduled as a single resource with a given capacity, setups of machines within the cell are a minor issue, and capacity is usually flexible (addition of personnel and/or overtime will increase the capacity of the cell). Assembly Line and Transfer Line manufacturing categories are characterized by large order and processing lot quantities on dedicated equipment. There is minimal or no work-in-process other than what is loaded on the equipment. There are many manufacturing operations; however, alternative routings are generally unavailable. Examples of Assembly Line manufacturing include automobile and appliance assembly. Transfer Lines are used to produce light bulbs, paint, and hardware. Scheduling decisions are limited to sequence of release to the system and some lot sizing. Many similar models may be produced on the Assembly Line with little or no changeover necessary. At the macro level view, the major difference between an Assembly Line and a Manufacturing Cell is the original capital investment to provide the manufacturing resource. The Assembly Line generally represents a much higher equipment cost and, in order to provide a reasonable return on investment, is intended to operate 24 hours a day, allowing no flexibility in available capacity. On the other hand, Manufacturing Cells have a much lower capital equipment cost and can be designed to provide flexible capacity with an adequate return on investment. The decision to add capacity with overtime exists for all systems that operate less than 24 hours a day, 7 days a week, but is typically a more relevant issue in Job Shops, Manufacturing Cells, and some Batch Shops. Table 1. Scheduling decisions by manufacturing process category
Classic Job Shop Open Job Shop Batch Shop Flow Shop Manufacturing Cell Assembly Line Transfer Line
Release Sequence X X X X X X X
Common Performance Metrics
Resource Selection
Lot Sizing
X X
X X X X
Resource Sequence X X X X
Performance metrics are the values which represent the outcome of the production schedule's objective function. Maximizing profitability is invariably the objective of a production schedule. However, other objective functions may be substituted that will have the same effect while requiring fewer variables to be introduced. For example, a system which has sufficient orders to consume nearly all available capacity may focus on minimizing late deliveries of those orders. In a system where the amount of capacity can be considered as flexible (by adding overtime), the objective may be to satisfy orders in a timely fashion while minimizing overtime costs. Due dates are a common driver in the determination of a production schedule, but frequently little effort is given to reducing the cost of missed due dates. In some systems, some amount of lateness is acceptable as long as it does not exceed a specified amount. In other systems, such as production of components for a Just-In-Time assembly process, the cost of late delivery can be considered prohibitive because it would cause a shutdown of the assembly facility. Some common performance metrics include lateness (maximum or average), total cost, machine utilization, setup time or cost (total or average), inventory costs, and contribution to profit. In order to use a performance metric to drive schedule generation, the performance metric must be quantifiable and traceable. The units could be time units such as days or weeks to measure lateness, dollars to measure costs or contribution to profit, or a ratio to measure machine utilization. In order for a performance metric to be traceable, it must achieve two criteria. First, the metric must be able to be calculated for a production schedule. It is one thing to specify "contribution to profit" as a performance metric, and quite another to develop a scheduling system that will calculate this value for a given production schedule. Secondly, the performance metric must be able to be tracked in the actual production system. A production schedule is a plan for the future, that is, a plan that allocates jobs to resources over some future time period. In order to continue to generate schedules that are feasible and actually produce the desired results, a continuous comparison must be made of the planned operations versus the actual production history. By far the most commonly used "informal" performance metric is lateness. In many scheduling efforts, much time is spend juggling orders so that all are completed on time. Frequently, little regard is given to inventory and other "less visible" costs. Careful consideration must be given to the origin and meaning of due dates when calculating lateness. If a job's due date is on Monday, and the job is loaded on the truck at 11:59 p.m. Monday night, will it be late? How are due dates negotiated with the customer; is there any flexibility? Is there a penalty or reward for completing a job early or late? The answers to these questions are unique to a particular manufacturing process and organization but regardless, they must be answered and the terms clearly defined.
Scheduling Policies Many types of schedule generation techniques are available, and in order to isolate a detailed scheduling policy which enhances an objective function, a general approach must first be selected. Scheduling problems have many solution techniques applied to them. Some of the possible approaches are sequencing and dispatching rules, critical path methods, mathematical optimization, and heuristics. Sequencing and dispatching rules are the most common schedule generation technique. Sequencing and dispatching rules are used to determine the sort order of jobs queued up to the system or a particular resource, as well as in the assignment of jobs to queues or resources. Sequencing and dispatching rules may be further classified as either static or dynamic [Pinedo]. Static rules depend entirely upon the characteristics of the job and/or resource and are not a function of time. Dynamic rules are time-dependent and may be affected by the state of the entire system under consideration. Examples of static rules are: Service In Random Order (SIRO), Earliest Due Date (EDD), Shortest Processing Time (SPT), Longest Processing Time (LPT), First In First Out (FIFO), Last In First Out (LIFO), and Shortest Setup Time (SST). Dynamic rule examples are Minimum Slack (MS), Shortest Queue (SQ), Shortest Queue at Next Operation (SQNO), and Shortest Expected Remaining Processing Time (SERPT). Hundreds or possibly thousands of sequencing and dispatching rules can be developed and have been the target of many research efforts [Abbott, Brown]. The impact of these rules upon performance metrics is highly dependent upon the manufacturing process specifics and detailed determination of which rules to use and where to apply them is left to step 5 in the scheduling implementation methodology outlined previously. Static sequencing rules are the easiest
scheduling policy to apply. Dynamic rules are frequently implemented with discrete event scheduling systems and commonly used to route jobs based on queue length and inventory levels. Critical path methods are commonly seen project planning where precedence relationship constraints are a major factor. Critical path methods rely upon determining those manufacturing operations that have the least or no slack time available. One definition of the "critical path" is that set of operations which determines the longest sum of processing times. When used in conjunction with resource constraints, the techniques of "smoothing" or "leveling" is applied to match the operations to the available resource capacity. Smoothing is a technique that will delay the scheduling of an operation in order to find free capacity no longer than the available slack time. Leveling will delay the scheduling or an operation as long as necessary to find available capacity on a resource. Smoothing may result in an infeasible schedule, that is, a schedule where resources are allocated more jobs than their capacity can complete. Leveling will always produce a feasible schedule, but will delay the finish time of the job if necessary. Critical path methods provide the basis for many scheduling policies in use where there are complex precedence relationships. Mathematical optimization is a classification of many techniques including those that will guarantee an optimal solution and more recently, "quasi-optimization" which will produce a greatly improved solution. These techniques directly incorporate the objective function and performance metrics to determine the optimal schedule. These methods include linear and dynamic programming, simulated annealing, and other multi-variable search techniques. Mathematical optimization and quasi-optimization are usually only seen in custom developed scheduling systems. Heuristics in the sense of scheduling policies is a catch-all intended to represent custom logic that is specifically developed for a particular application. Many heuristics have been developed that use combinations of the other methods listed above. For a listing and details of many heuristic scheduling system, see [Morton]. The appropriate scheduling policy to use is a function of the manufacturing process category and the selected performance metrics, as shown in the following table. Table 2. Application of scheduling policy by manufacturing process category Sequencing Rules Classic Job Shop Open Job Shop Batch Shop Flow Shop Manufacturing Cell Assembly Line Transfer Line
Critical Path XXXXX XXXXX
XXXXX XXXXX XXXXX X X
Optimization
Heuristics XX XX
X X X X
Case Study - Cellular Manufacturing
Conclusions And Directions For Future Work
References Abbot, R. A. and T. J. Green. 1982. Determination of Appropriate Dynamic Slack Sequencing Rules for an Industrial Flow Shop via Discrete Simulation, Proceedings of the 1982 Winter Simulation Conference. Bahl, Harish C., Ritzman, Larry P., and Jatinder, N. D. Gupta. 1987. Determining Lot Sizes and Resource Requirements, A Review. Operations Research 35(3):329-345. Brown, R. G. 1968. Simulation to Explore Alternative Sequencing Rules. Naval Research Logistics Quarterly 15:281-286.
Morton, Thomas E. and David W. Pentico. 1993. Heuristic Scheduling Systems with Applications to Production Systems and Project Management. New York, New York: John Wiley & Sons, Incorporated. Pinedo, Michael. 1995. Scheduling: Theory, Algorithms, and Systems. Englewood Cliffs, New Jersey: PrenticeHall, Incorporated. Ritchie, E., and Tsado, A. K. 1986. A Review of Lot-Sizing Techniques for Deterministic Time Varying Demand. Production and Inventory Management, Third Quarter:64-79.
