Single machine scheduling problem with fuzzy precedence relation

Ishii, Hiroaki; Tada, Minoru

doi:10.1016/0377-2217(94)00162-6

Cited by 61 publications

(19 citation statements)

References 2 publications

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“…To be more specific, considering a number of man-made factors that exist in operations and planning and other arrangements is also a part of operations, fuzziness in processing times undeniably exists. Concerning due-date, we can also imagine many situations where `it is desirable in principle to strictly satisfy the due-date, a certain amount of delay may be tolerated with longer delay tends to be lower evaluations', see [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Single Machine Scheduling Problem with Rejection to Minimize the Total Earliness plus the Total Rejection Cost

Nie¹

2015

IJMLC

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Abstract-In the classical scheduling, it is always assumed that for any job, it should be processed. However, in the real world, things may be more flexible and the decision makers can make a higher-level decision, i.e., they can break the constrain by rejecting a job. It's not hard for the readers to find examples in the industrial and commercial fields to justify this breaking. To reject a job, of course, the decision makers should pay a corresponding penalty. In this paper, we considered single-machine scheduling problems under the job rejection constraint. A job was either rejected, in which case a rejection penalty had to be paid, or accepted and processed on the single machine. In this paper, we considered fuzziness in the job scheduling problem, i.e., due-dates of all jobs are fuzzy variables. The objective was to minimize the sum of the maximum earliness of the accepted jobs and the total rejection penalty of the rejected jobs. We found a polynomial-time solution for the problem through finding the Pareto optimal points. Index Terms-Rejected penalty, maximum earliness, Pareto optimal points, fuzzy due-date.

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Section: Introductionmentioning

confidence: 99%

Single Machine Scheduling Problem with Rejection to Minimize the Total Earliness plus the Total Rejection Cost

Nie¹

2015

IJMLC

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“…For reasoning that adequately reflects such a situation, several approaches have been presented recently, and their usefulness has been shown. They include the mathematical programming for the single-machine fuzzy scheduling problem considering the fuzzy precedence relations [13], the solution of the JSSP by GA considering the fuzzy processing time [14], and the solution of the JSSP by GA considering the fuzzy duetime and the fuzzy processing time [16]. In those studies of the fuzzy JSSP (FJSSP), only the single-objective case is considered.…”

Section: Introductionmentioning

confidence: 99%

Two-objective fuzzy job shop scheduling through genetic algorithm

Sakawa

Kubota

2000

Electron. Comm. Jpn. Pt. III

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This paper discusses fuzzy job shop scheduling considering the fuzzy processing time and the fuzzy duetime, and formulates the fuzzy job shop scheduling problem considering two objects, namely, minimization of the maximum fuzzy completion time and maximization of the average agreement index. For each of the functions in the formulated two‐objective fuzzy job shop scheduling problem, the fuzzy goal of the decision‐maker is specified by a linear membership function, and then an approximate solution method is proposed, where the genetic algorithm is used for the problem, which integrates the membership functions by the fuzzy decision formulated by Bellman and Zadeh. As numerical examples, 6 × 6 and 10 × 10 two‐objective fuzzy job shop scheduling problems are considered. Comparing the results to those by simulated annealing, the effectiveness of the proposed method is demonstrated. © 2000 Scripta Technica, Electron Comm Jpn Pt 3, 84(4): 60–68, 2001

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“…It has been successfully applied to model various manufacturing parameters, including for example, modelling of fuzzy customer demand (Petrovic and Petrovic 2001), fuzzy due dates and fuzzy processing times (Kuroda and Wang 1996), fuzzy production due dates (Hong and Chuang 1999) or fuzzy job precedence relations (Ishii and Tada 1995).…”

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confidence: 99%

“…Ishii and Tada (1995) considered a single machine scheduling problem with the objective to minimize the maximum lateness of jobs with fuzzy precedence relations. An algorithm for determining nondominated solutions based on a graph representation of the fuzzy precedence relations was proposed.…”

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confidence: 99%

Multi-objective genetic algorithm for single machine scheduling problem under fuzziness

Duenas

Petrov́ić

2008

Fuzzy Optim Decis Making

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This paper presents a new multi-objective approach to a single machine scheduling problem in the presence of uncertainty. The uncertain parameters under consideration are due dates of jobs. They are modelled by fuzzy sets where membership degrees represent decision maker's satisfaction grade with respect to the jobs' completion times. The two objectives defined are to minimise the maximum and the average tardiness of the jobs. Due to fuzziness in the due dates, the two objectives become fuzzy too. In order to find a job schedule that maximises the aggregated satisfaction grade of the objectives, a hybrid algorithm that combines a multi-objective genetic algorithm with local search is developed. The algorithm is applied to solve a real-life problem of a manufacturing pottery company.

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Single machine scheduling problem with fuzzy precedence relation

Cited by 61 publications

References 2 publications

Single Machine Scheduling Problem with Rejection to Minimize the Total Earliness plus the Total Rejection Cost

Single Machine Scheduling Problem with Rejection to Minimize the Total Earliness plus the Total Rejection Cost

Two-objective fuzzy job shop scheduling through genetic algorithm

Multi-objective genetic algorithm for single machine scheduling problem under fuzziness

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