Topic

# Job shop scheduling

About: Job shop scheduling is a(n) research topic. Over the lifetime, 35258 publication(s) have been published within this topic receiving 658347 citation(s).

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Abstract: The theory of deterministic sequencing and scheduling has expanded rapidly during the past years. In this paper we survey the state of the art with respect to optimization and approximation algorithms and interpret these in terms of computational complexity theory. Special cases considered are single machine scheduling, identical, uniform and unrelated parallel machine scheduling, and open shop, flow shop and job shop scheduling. We indicate some problems for future research and include a selective bibliography.

4,625 citations

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TL;DR: This paper proposes 260 randomly generated scheduling problems whose size is greater than that of the rare examples published, and the objective is the minimization of the makespan.

Abstract: In this paper, we propose 260 randomly generated scheduling problems whose size is greater than that of the rare examples published. Such sizes correspond to real dimensions of industrial problems. The types of problems that we propose are: the permutation flow shop, the job shop and the open shop scheduling problems. We restrict ourselves to basic problems: the processing times are fixed, there are neither set-up times nor due dates nor release dates, etc. Then, the objective is the minimization of the makespan.

1,945 citations

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TL;DR: An approximation method for solving the minimum makespan problem of job shop scheduling by sequences the machines one by one, successively, taking each time the machine identified as a bottleneck among the machines not yet sequenced.

Abstract: We describe an approximation method for solving the minimum makespan problem of job shop scheduling. It sequences the machines one by one, successively, taking each time the machine identified as a bottleneck among the machines not yet sequenced. Every time after a new machine is sequenced, all previously established sequences are locally reoptimized. Both the bottleneck identification and the local reoptimization procedures are based on repeatedly solving certain one-machine scheduling problems. Besides this straight version of the Shifting Bottleneck Procedure, we have also implemented a version that applies the procedure to the nodes of a partial search tree. Computational testing shows that our approach yields consistently better results than other procedures discussed in the literature. A high point of our computational testing occurred when the enumerative version of the Shifting Bottleneck Procedure found in a little over five minutes an optimal schedule to a notorious ten machines/ten jobs problem on which many algorithms have been run for hours without finding an optimal solution.

1,507 citations

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TL;DR: A classification scheme is provided, i.e. a description of the resource environment, the activity characteristics, and the objective function, respectively, which is compatible with machine scheduling and which allows to classify the most important models dealt with so far, and a unifying notation is proposed.

Abstract: Project scheduling is concerned with single-item or small batch production where scarce resources have to be allocated to dependent activities over time. Applications can be found in diverse industries such as construction engineering, software development, etc. Also, project scheduling is increasingly important for make-to-order companies where the capacities have been cut down in order to meet lean management concepts. Likewise, project scheduling is very attractive for researchers, because the models in this area are rich and, hence, difficult to solve. For instance, the resource-constrained project scheduling problem contains the job shop scheduling problem as a special case. So far, no classification scheme exists which is compatible with what is commonly accepted in machine scheduling. Also, a variety of symbols are used by project scheduling researchers in order to denote one and the same subject. Hence, there is a gap between machine scheduling on the one hand and project scheduling on the other with respect to both, viz. a common notation and a classification scheme. As a matter of fact, in project scheduling, an ever growing number of papers is going to be published and it becomes more and more difficult for the scientific community to keep track of what is really new and relevant. One purpose of our paper is to close this gap. That is, we provide a classification scheme, i.e. a description of the resource environment, the activity characteristics, and the objective function, respectively, which is compatible with machine scheduling and which allows to classify the most important models dealt with so far. Also, we propose a unifying notation. The second purpose of this paper is to review some of the recent developments. More specifically, we review exact and heuristic algorithms for the single-mode and the multi-mode case, for the time–cost tradeoff problem, for problems with minimum and maximum time lags, for problems with other objectives than makespan minimization and, last but not least, for problems with stochastic activity durations.

1,414 citations

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TL;DR: This work considers algorithmic problems in a distributed setting where the participants cannot be assumed to follow the algorithm but rather their own self-interest, and suggests a framework for studying such algorithms.

Abstract: We consider algorithmic problems in a distributed setting where the participants cannot be assumed to follow the algorithm but rather their own self-interest. As such participants, termed agents, are capable of manipulating the algorithm, the algorithm designer should ensure in advance that the agents' interests are best served by behaving correctly. Following notions from the field of mechanism design, we suggest a framework for studying such algorithms. Our main technical contribution concerns the study of a representative task scheduling problem for which the standard mechanism design tools do not suffice. Journal of Economic Literature Classification Numbers: C60, C72, D61, D70, D80.

1,261 citations