Reprocessing Operations Scheduling Using Fuzzy Logic and Fuzzy MAX-MIN Ant Systems

Reprocessing Operations Scheduling Using Fuzzy Logic and Fuzzy MAX-MIN Ant Systems

Copyright: © 2014 |Pages: 20
DOI: 10.4018/978-1-4666-4908-8.ch009
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This chapter discusses the scheduling of the reusable components’ reprocessing operations after the used products are disassembled and classified. The chapter starts with an introduction about the issue of scheduling disassembly operations and the scheduling in remanufacturing cells encountered at the used products post-disassembly stage. Then, related studies dealing with similar problems are discussed in the background section. Next, the focal problem of this chapter is stated in the problem statement section. A detailed description about the approaches (i.e., the fuzzy logic and the fuzzy MAX-MIN ant systems) can be found in the proposed methodology section. Right after this, an illustrative example is explained in the experimental study section. The potential research directions regarding the main problem considered in this chapter are highlighted in the future trends section. Finally, the conclusion drawn in the last section closes this chapter.
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Generally speaking, scheduling consists of planning and prioritizing activities that need to be performed in an orderly sequence of operations. The objectives of scheduling can be classified into two broad categories (Shanker & Modi, 1999): (1) job related objective – tardiness, makespan, waiting time, flow time, work-in-process and costs; (2) resource related objectives – utilization, idle time, and costs.

Typically, in a scheduling problem, the things to be accomplished are called “jobs”, and each job may require one or more segments called “operations”. Each of these operations requires some resources such as machines and tools for a certain amount of time. When several jobs are to be executed together, the composition of their resource requirements implies additional ordering constraints that prohibit simultaneous demands on non sharable resources. Consequently, a scheduler satisfies both the explicit ordering constraints imposed by the plans and the implicit ordering constraints derived from the availabilities of the resources. Giffler and Thomson (1960) developed an enumerative procedure to generate all active schedules for the general “n” job “m” machine problem. In a similar vein, in (Toker, Kondakci, & Erkip, 1994), the authors proposed an approximation algorithm for the “n” job “m” machine resource constraint job shop problem; while Stafford and Tseng (2002) developed two general models for a family of “m” machines, “n” jobs flowshop sequencing problems.

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