Using dispatching rules for job shop scheduling with due date-based objectives

Chiang, Tsung-Che; Fu, Li‐Chen

doi:10.1080/00207540600786715

Cited by 75 publications

(37 citation statements)

References 22 publications

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“…This block is inspired by the remarks of Dominic et al (2004) who concluded that 9 The results of the comparative study are displayed in Table 2 for each of the five objective functions. It is worth noting the similar comparative studies has been made by other authors, such as Waikar et al (1995), Holthaus and Rajendran (1997), Rajendran and Holthaus (1999), Holthaus and Rajendran (2000), Jayamohan and Rajendran (2000), Jayamohan and Rajendran (2004), Dominic et al (2004) and Chiang and Fu (2007), but with a slightly different focus or limited scope compared to our full comparison on the five objective functions. The results can be briefly summarized per objective function along the following paragraphs.…”

Section: Comparative Resultsmentioning

confidence: 83%

“…Section A.2 list the single priority rules together with their mathematical formulation. For this overview, we based ourselves on the papers of, among others, Panwalkar and Iskander (1977), Blackstone et al (1982), Baker (1984), Russell et al (1987), Vepsalainen and Morton (1987), Anderson and Nyirenda (1990), Waikar et al (1995), Holthaus and Rajendran (1997), Rajendran and Holthaus (1999), Jayamohan and Rajendran (2000), Holthaus and Rajendran (2000), Dominic et al (2004), Mizrak and Bayhan (2006) and Chiang and Fu (2007). Finally, in Section A.3, more information is given on the combination of the single rules.…”

Section: Appendix Amentioning

confidence: 99%

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A comparison of priority rules for the job shop scheduling problem under different flow time- and tardiness-related objective functions

Sels

Gheysen

Vanhoucke

2012

International Journal of Production Research

148

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Section: Comparative Resultsmentioning

confidence: 83%

Section: Appendix Amentioning

confidence: 99%

A comparison of priority rules for the job shop scheduling problem under different flow time- and tardiness-related objective functions

Sels

Gheysen

Vanhoucke

2012

International Journal of Production Research

148

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“…The method schedules jobs on particular machines (groups of machines) based on certain priority rules, according to which subsequent operations are chosen. There are several advantages of the method: quick solution, analysis of alternative scheduling scenarios or easy implementation of the method [22,23].…”

Section: Job Scheduling Methodsmentioning

confidence: 99%

Effect of Machine Failure Prediction on Selected Parameters of Manufacturing Schedule in a Job-shop Environment

Sobaszek¹,

Gola²,

Kozłowski³

2017

Topics in Economics, Business and Management (EBM)

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Production scheduling involves a number of factors, which may disrupt or completely block the smooth operation of a production processes. This paper seeks to address one of the key factors involved, machine failure, which is capable of introducing disorganisation and nervousness into production. The relevance of machine failure to production is highlighted by the extensive research in the field of robust scheduling, as a method to absorb potential disruptions. The aim of this paper is to present technological machine failure prediction method to provide the base for a robust schedule, which is subsequently verified by the mathematical model. Furthermore, the paper elaborates on the issue of scheduling in job shop environment and the idea of robust scheduling. IntroductionJob scheduling in typical manufacturing systems is the subject of numerous analyses and research works. The research is predominantly focused on scheduling problems in job-shop environments due to the fact that this is the most popular and widely applied method of organising work in production companies [1]. A problem with much of the literature regarding job scheduling is that the work is frequently based on a set of simplifications, which determine that the character of such analyses is largely theoretical [2]. Consequently, a number of problems existing in an actual scheduling of manufacturing processes are disregarded. Practice shows that each production process involves a series of factors that are counter-productive to the realisation of the process itself [3]. One problem that should not be underestimated, as it shows great potential for disrupting production, is machine failure [4]. This paper takes a new look on robust scheduling of manufacturing jobs. The scope of the analysis included technological machine failure prediction and estimation of service times. The data for simulation was obtained from historical records from the Maintenance Department of a production company. Job-shop production systems Job shop environment characteristicsIn order to model a job-shop scheduling problem the following sets must be defined [5,6]:-M, which is a set of m machines processing the jobs: --J, which is a set of n jobs to be processed:Processing job Ji on machine Mj will be referred to as operation. Therefore what must be defined is [5,6]:

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“…There are several other rules, such as SRPT, LTWK and SPT/TWK, commented by Chiang and Fu (2006). Extensions of SPT incorporate, under combined conditions, other goals, also with good efficiency and late operations.…”

Section: The Priority Dispatching Rulesmentioning

confidence: 99%

Hybrid genetic algorithms: solutions in realistic dynamic and setup dependent job-shop scheduling problems

Branco

Coelho

Mayerle

2016

Int. J. Prod. Manag. Eng.

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This paper discusses the application of heuristic-based evolutionary technique in search for solutions concerning the dynamic job-shop scheduling problems with dependent setup times and alternate routes. With a combinatorial nature, these problems belong to an NP-hard class, with an aggravated condition when in realistic, dynamic and therefore, more complex cases than the traditional static ones. The proposed genetic algorithm executes two important functions: choose the routes using dispatching rules when forming each individual from a defined set of available machines and, also make the scheduling for each of these individuals created. The chromosome codifies a route, or the selected machines, and also an order to process the operations. In essence , each individual needs to be decoded by the scheduler to evaluate its time of completion, so the fitness function of the genetic algorithm, applying the modified Giffler and Thomson's algorithm, obtains a scheduling of the selected routes in a given planning horizon. The scheduler considers the preparation time between operations on the machines and can manage operations exchange respecting the route and the order given by the chromosome. The best results in the evolutionary process are individuals with routes and processing orders optimized for this type of problem.

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Using dispatching rules for job shop scheduling with due date-based objectives

Cited by 75 publications

References 22 publications

A comparison of priority rules for the job shop scheduling problem under different flow time- and tardiness-related objective functions

A comparison of priority rules for the job shop scheduling problem under different flow time- and tardiness-related objective functions

Effect of Machine Failure Prediction on Selected Parameters of Manufacturing Schedule in a Job-shop Environment

Hybrid genetic algorithms: solutions in realistic dynamic and setup dependent job-shop scheduling problems

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