NDLI: Fuzzy optimization-based scheduling of identical machines with possible breakdown

Content Provider	IEEE Xplore Digital Library
Author	Yong Li Luh, P.B. Xiaohong Guan
Copyright Year	1994
Description	Author affiliation: Dept. of Electr. & Syst. Eng., Connecticut Univ., Storrs, CT, USA (Yong Li; Luh, P.B.)
Abstract	Underlying each production system are activities fraught with uncertainty; for example, uncertain future demand, machine breakdowns, and processing time estimates for one-of-a-kind parts. These uncertain events can cause any detailed schedule to become outdated, and the effects may propagate throughout the schedule, affecting product delivery dates. Scheduling algorithms considering future uncertainties could improve the quality of schedules and, as a result, smooth the production of the system. As a step towards incorporating uncertainties in the scheduling consideration, this paper presents a fuzzy optimization methodology for scheduling single operation parts on identical machines with possible breakdowns. A fuzzy optimization formulation is first developed. A Lagrangian relaxation technique is used to decompose the problem into part-level subproblems and a fuzzy membership subproblem. The Lagrange multipliers are then updated by using a subgradient method. To evaluate the performance of the resulting algorithm in a dynamic environment, fuzzy simulation is developed. Preliminary testing results show that, with possible machine breakdowns, this algorithm outperforms the deterministic one.
Starting Page	3447
Ending Page	3452
File Size	538369
Page Count	6
File Format	PDF
ISBN	0818653302
DOI	10.1109/ROBOT.1994.351040
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-05-08
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electric breakdown Job shop scheduling Uncertainty Production systems Fuzzy sets Fuzzy reasoning Fuzzy systems Scheduling algorithm Optimization methods Lagrangian functions
Content Type	Text
Resource Type	Article

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