Deterministic job-shop scheduling: Past, present and future

Jain, Anant; Meeran, Sheik

doi:10.1016/s0377-2217(98)00113-1

Cited by 611 publications

(260 citation statements)

References 150 publications

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“…The problem has been known for years in the scheduling theory as a particularly hard combinatorial optimisation case. Although in the last three decades a lot of effort has been put into research in this area, good results have appeared quite recently, see state-of-the-art papers by Bl ażewicz, Domschke, and Pesch (1996), Vaessens, Aarts, and Lenstra (1996), and Jain and Meeran (1999), supplemented by a few recent studies (Jain, Rangaswamy, and Meeran, 2000;Pezella and Merelli, 2000;Nowicki and Smutnicki, 2001).…”

Section: Introductionmentioning

confidence: 99%

An Advanced Tabu Search Algorithm for the Job Shop Problem

Nowicki

Smutnicki

2005

J Sched

267

158

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The job shop scheduling problem with the makespan criterion is a certain NP-hard case from OR theory having excellent practical applications. This problem, having been examined for years, is also regarded as an indicator of the quality of advanced scheduling algorithms. In this paper we provide a new approximate algorithm that is based on the big valley phenomenon, and uses some elements of so-called path relinking technique as well as new theoretical properties of neighbourhoods. The proposed algorithm owns, unprecedented up to now, accuracy, obtainable in a quick time on a PC, which has been confirmed after wide computer tests.

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Section: Introductionmentioning

confidence: 99%

An Advanced Tabu Search Algorithm for the Job Shop Problem

Nowicki

Smutnicki

2005

J Sched

267

158

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“…However, the definition of a performing solution is quite difficult, depending on the job-shop environment, process constrains, and performance indicators [13]. Numerous approaches have been reported for the modelling and solving of the job-shop scheduling problem [14]. Wang et al [15] proposed the development of an application using a genetic algorithm including a chromosome representation in seven different machines of a job floor that enables a dynamic job-shop scheduling within complex production systems.…”

Section: State Of the Artmentioning

confidence: 99%

A mobile application for knowledge-enriched short-term scheduling of complex products

2016

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The ever-increasing product complexity, especially for the case of engineer-to-order products, highly affects the performance of manufacturing systems. Therefore, a high degree of flexibility is needed during daily decision-making activities, such as production scheduling. For addressing this challenge, this research work proposes a knowledge-enriched short-term job-shop scheduling mechanism, which is implemented into a mobile application. More precisely, it focuses on the short-term scheduling of the resources of the machine shop, through an intelligent algorithm that generates and evaluates alternative assignments of resources to tasks. Based on the requirements of a new order, a similarity mechanism retrieves successfully executed past orders together with a dataset that includes the processing times, the job and task sequence, and the suitable resources. In addition to that, the similarity mechanism is used to calculate the due-date assignments of the orders based on the knowledge stored in past cases. Afterwards, it adapts these parameters to the requirements of the new order so as to evaluate the alternative schedules and identify a good alternative in a timely manner. The deriving schedule can be presented on mobile devices, and it can be manipulated by the planner on-thefly respecting tasks precedence constraints and machine availability. A case study from the mould-making industry is used for validating the proposed method and application.

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“…Due to its extreme intractability, it has been used extensively to test the performance of a wide range of solution strategies, ranging from neural networks to mixed integer linear programming. Extensive reviews have been performed by Blazewicz et al (1996) and Jain and Meeran (1999).…”

Section: The Problem Contextmentioning

confidence: 99%

Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time

Grobler

Engelbrecht²,

Kok³

et al. 2009

Ann Oper Res

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This paper investigates the application of particle swarm optimization (PSO) to the multi-objective flexible job shop scheduling problem with sequence-dependent set-up times, auxiliary resources and machine down time. To achieve this goal, alternative particle representations and problem mapping mechanisms were implemented within the PSO paradigm. This resulted in the development of four PSO-based heuristics. Benchmarking on real customer data indicated that using the priority-based representation resulted in a significant performance improvement over the existing rule-based algorithms commonly used to solve this problem. Additional investigation into algorithm scalability led to the development of a priority-based differential evolution algorithm. Apart from the academic significance of the paper, the benefit of an improved production schedule can be generalized to include cost reduction, customer satisfaction, improved profitability, and overall competitive advantage.

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Deterministic job-shop scheduling: Past, present and future

Cited by 611 publications

References 150 publications

An Advanced Tabu Search Algorithm for the Job Shop Problem

An Advanced Tabu Search Algorithm for the Job Shop Problem

A mobile application for knowledge-enriched short-term scheduling of complex products

Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time

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