Local search algorithms for flow shop scheduling with fuzzy due-dates

Ishibuchi, Hisao; Yamamoto, Naohisa; Misaki, Shinta; Tanaka, Hideo

doi:10.1016/0925-5273(94)90118-x

Cited by 76 publications

(16 citation statements)

References 17 publications

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“…The use of possibility theory in job-shop scheduling has been further discussed by Kerr and Walker (1989) and Dubois (1989); the latter contains preliminary results that are elaborated upon in the present paper. More recently, simple forms of fuzzy flow-shop scheduling problems have been solved by Ishii et al (1992) and Ishibushi et al (1994). Besides, the link between the ideas of Bellman and Zadeh (1970) and the mainstream literature on constraint satisfaction problems has been made by Freuder and Snow (1990), but fuzzy sets are not yet widely used in CSPs to date (see Dubois et al, 1993, for a bibliography).…”

Section: The Fuzzily Constrained Scheduling Problemmentioning

confidence: 99%

Fuzzy constraints in job-shop scheduling

1995

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This paper proposes an extension of the constraint-based approach to job-shop scheduling, that accounts for the flexibility of temporal constraints and the uncertainty of operation durations. The set of solutions to a problem is viewed as a fuzzy set whose membership function reflects preference. This membership function is obtained by an egalitarist aggregation of local constraint-satisfaction levels. Uncertainty is qualitatively described in terms of possibility distributions. The paper formulates a simple mathematical model of job-shop scheduling under preference and uncertainty, relating it to the formal framework of constraint-satisfaction problems in artificial intelligence. A combinatorial search method that solves the problem is outlined, including fuzzy extensions of well-known look-ahead schemes.

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Section: The Fuzzily Constrained Scheduling Problemmentioning

confidence: 99%

Fuzzy constraints in job-shop scheduling

1995

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“…Parameters that are most often represented as fuzzy numbers are processing times and due dates (e.g. Kuroda and Wang 1996;Ishibuchi et al 1994;Ishii et al 1992), but there are models that deal with some other scheduling parameters by employing fuzzy sets (e.g. fuzzy job precedence relations, Tada 1995, or machine breakdowns, Li et al 1994).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy job shop scheduling with lot-sizing

et al. 2007

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This paper deals with a problem of determining lot-sizes of jobs in a real-world job shop-scheduling in the presence of uncertainty. The main issue discussed in this paper is lot-sizing of jobs. A fuzzy rule-based system is developed which determines the size of lots using the following premise variables: size of the job, the static slack of the job, workload on the shop floor, and the priority of the job. Both premise and conclusion variables are modelled as linguistic variables represented by using fuzzy sets (apart from the priority of the job which is a crisp value). The determined lots' sizes are input to a fuzzy multi-objective genetic algorithm for job shop scheduling. Imprecise jobs' processing times and due dates are modelled by using fuzzy sets. The objectives that are used to measure the quality of the generated schedules are average weighted tardiness of jobs, the number of tardy jobs, the total setup time, the total idle time of machines and the total flow time of jobs. The developed algorithm is analysed on real-world data obtained from a printing company

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“…Some relevant works include Ishii et al [2], Han et al [3] [4], Ishibuchi et al [4] [5], and Stanfield et al [6]. The objectives of [2][3] [4] [5] are to maximize the total degree of satisfaction with respect to the fuzzy due dates, while [6] seeks to find the optimal schedule among those that do not exceed the maximum acceptable possibility of lateness in a problem involving fuzzy due dates. All listed above consider regular performance measures, which are non-decreasing functions of completion times (see, e.g., [I]).…”

Section: Introductionmentioning

confidence: 99%

Earliness and tardiness scheduling with a fuzzy due date and job dependent weights

Lam

Cai

2000

Journal of the Chinese Institute of Industrial Engineers

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The paper considers the problem of scheduling n jobs on a single machine. Each job j is associated with a weight Wi and a processing time Pi' and the objective of the problem is to minimize the weighted earliness and tardiness of job completions from a common due date jj, where jj is a fuzzy number, governed by a triangular membership function. A fuzzy distance function is introduced first to measure the deviations of job completions from the fuzzy due date. We then show that an optimal job sequence must be V-shaped in terms of weighted processing time when the problem is agreeably weighted, in the sense that P, < Pi implies w, ~ wi. We further show that, for arbitrary weights, the optimal sequence must be W-shaped ifthe support of the due date is smaller than Pmin' the smallest processing time. Algorithms are derived which can find the best V-shaped or W-shaped sequences in pseudo-polynomial time. A polynomial algorithm is also developed for the special case with equal processing times. Numerical experiments were conducted, to examine the applicability of the algorithms proposed to general cases without any conditions.

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Local search algorithms for flow shop scheduling with fuzzy due-dates

Cited by 76 publications

References 17 publications

Fuzzy constraints in job-shop scheduling

Fuzzy constraints in job-shop scheduling

Fuzzy job shop scheduling with lot-sizing

Earliness and tardiness scheduling with a fuzzy due date and job dependent weights

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