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Distributed manufacturing scheduling using intelligent agents

Weiming Shen

2002
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IEEE Intelligent Systems
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T u t o r i a l 88 1094-7167/02/$17.00 © 2002 IEEE IEEE INTELLIGENT SYSTEMS reflect the temporal relationships between manufacturing activities and the capacity limitations of a set of shared resources. The allocation also affects a schedule's optimality with respect to criteria such as cost, lateness, or throughput. The globalization of manufacturing makes such optimization increasingly important. To survive in this competitive market, manufacturing enterprises must increase their productivity
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... and profitability through greater shop floor agility. Agent-based manufacturing scheduling systems are a promising way to provide this optimization. Manufacturing scheduling's complexity Scheduling problems are not exclusive to manufacturing systems. Similar situations happen routinely in publishing houses, universities, hospitals, airports, transportation companies, and so on. Scheduling problems are typically NP-hard; that is, finding an optimal solution is impossible without using an essentially enumerative algorithm, and the computation time increases exponentially with the problem size. Manufacturing scheduling is one of the most difficult scheduling problems. A well-known manufacturing scheduling problem is classical job shop scheduling, which involves a set of jobs and a set of machines. Each machine can handle at most one job at a time. Each job consists of a chain of operations, each of which must be processed during an uninterrupted time period of given length on a given machine. The purpose is to find a best schedule-that is, an allocation of the operations to time intervals on the machines that has the minimum duration required to complete all jobs. The total possible solutions for this problem with n jobs and m machines is (n!) m . The problem becomes even more complex when it includes other variables. Additional resources In a real manufacturing enterprise, production managers or human schedulers must consider all kinds of manufacturing resources, not just jobs and machines. For example, a classical job shop scheduling problem with n jobs, m machines, and k operators could have ((n!) m ) k possible solutions. Simultaneous planning and scheduling Traditional approaches separating process planning and scheduling can obtain suboptimal solutions at two separate phases. Global optimization of a manufacturing system is only possible when process planning and scheduling are integrated. However, this makes the scheduling problem much more difficult to solve. Unforeseen dynamic situations In a job shop manufacturing environment, things rarely go as expected. Scheduled jobs get canceled and new jobs are inserted. Certain resources become unavailable and additional resources are introduced. A scheduled task takes more or less time than anticipated, and tasks arrive early or late. Other uncertainties include power system failures, machine failures, operator absence, and unavailability of tools and materials. An optimal schedule, generated after considerable effort, might become unacceptable because of unforeseen dynamic situations on the shop floor. If this happens, a new schedule must be generated to restore performance. We call such a rescheduling problem dynamic scheduling or real-time scheduling. Manufacturing scheduling solutions The scheduling problem has received considerable attention because of its highly combinatorial aspects (NPhard), dynamic nature, and practical interest for industrial applications. Consequently, researchers have proposed many different solutions. Traditional approaches Because direct methods are not available for complex scheduling problems, search is the usual strategy to solve M anufacturing scheduling is an optimization process that allocates limited manufacturing resources over time among parallel and sequential manufacturing activities. This allocation must obey a set of rules or constraints that

doi:10.1109/5254.988492
fatcat:df4gvk6tmfcnpocj6nzgqhtnsm