Multiple priority dispatching rules for the job shop scheduling problem

Zahmani, Mohamed Habib; Atmani, Baghdad; Bekrar, Abdelghani; Aissani, Nassima

doi:10.1109/ceit.2015.7232991

Cited by 12 publications

(9 citation statements)

References 29 publications

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“…As illustrated in our previous work, using simulation, we aim to identify the best set of Dispatching Rules. 47 To do so, two groups of DR are de¯ned: \static" and \dynamic". These rules were initially proposed by Kaban et al 13 In the case of a static rule, jobs priorities remain unchanged over time.…”

Section: Experiments and Resultsmentioning

confidence: 99%

A Data Mining Based Dispatching Rules Selection System for the Job Shop Scheduling Problem

Zahmani

Atmani

2019

J. Adv. Manuf. Syst.

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Identifying the best Dispatching Rule in order to minimize makespan in a Job Shop Scheduling Problem is a complex task, since no Dispatching Rule is better than all others in different scenarios, making the selection of a most effective rule which is time-consuming and costly. In this paper, a novel approach combining Data Mining, Simulation, and Dispatching Rules is proposed. The aim is to assign in real-time a set of Dispatching Rules to the machines on the shop floor while minimizing makespan. Experiments show that the suggested approach is effective and reduces the makespan within a range of 1–44%. Furthermore, this approach also reduces the required computation time by using Data Mining to determine and assign the best Dispatching Rules to machines.

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Section: Experiments and Resultsmentioning

confidence: 99%

A Data Mining Based Dispatching Rules Selection System for the Job Shop Scheduling Problem

Zahmani

Atmani

2019

J. Adv. Manuf. Syst.

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“…(2) Combination algorithms and recent studies based on the priority dispatch rules method Ingimundardottir and Runarsson (2011) [26] introduced the learned linear priority dispatching rules for JSP in which linear classification was used for dispatching rule framework to identify good choices from inferior ones by a supervised learning approach and experimental studies showed that the output is better than that of using common priority dispatching rules. Mohamed et al (2015) [27] built a simulation model for makespan optimization, used different dispatching rules for each machine to select the best rule for every new scheduling problem and showed advantages of using multiple priority dispatching finally. Paul et al (2016) [28] adopted preference selection index method for ranking priority dispatching rules for scheduling an assembly job shop.…”

Section: Constructive Methodsmentioning

confidence: 99%

Review of job shop scheduling research and its new perspectives under Industry 4.0

et al. 2017

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Traditional job shop scheduling is concentrated on centralized scheduling or semi-distributed scheduling. Under the Industry 4.0, the scheduling should deal with a smart and distributed manufacturing system supported by novel and emerging manufacturing technologies such as mass customization, Cyber-Physics Systems (CPS), Digital Twin, and SMAC (Social, Mobile, Analytics, Cloud). The scheduling research needs to shift its focus to smart distributed scheduling modeling and optimization. In order to transferring traditional scheduling into smart distributed scheduling (SDS), we aim to answer two questions: (1) what traditional scheduling methods and techniques can be combined and reused in SDS and (2) what are new methods and techniques required for SDS. In this paper, we first review existing researches from over 120 papers and answer the first question and then we explore a future research direction in SDS and discuss the new techniques for developing future new JSP scheduling models and constructing a framework on solving the JSP problem under Industry 4.0.

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“…Moreover, a job's sequence is usually different from others except for jobs from the same family (similar products). Over the last few years, a wide range of research works has been proposed in the literature (Chiang and Torng 2016;Habib Zahmani et al 2015a;Shahzad and Mebarki 2016;Sharma and Jain 2015;Yazdani et al 2017), demonstrating the interest of manufacturing and optimization researchers to the JSSP and its ongoing challenges.…”

Section: The Job Shop Scheduling Problemmentioning

confidence: 99%

“…This knowledge could be useful for identifying the customer habits, the products they need the most, or which supplier to contact in case of a lack of a raw material. By exploiting this knowledge, companies can also enhance the manufacturing process, improve the productivity, and secure a place in a highly competitive environment (Habib Zahmani et al 2015a).…”

Section: Introductionmentioning

confidence: 99%

Multiple dispatching rules allocation in real time using data mining, genetic algorithms, and simulation

Zahmani

Atmani

2020

J Sched

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In production planning and scheduling, data mining methods can be applied to transform the scheduling data into useful knowledge that can be used to improve planning/scheduling by enabling real-time decision-making. In this paper, a novel approach combining dispatching rules, a genetic algorithm, data mining, and simulation is proposed. The genetic algorithm (i) is used to solve scheduling problems, and the obtained solutions (ii) are analyzed in order to extract knowledge, which is then used (iii) to automatically assign in real-time different dispatching rules to machines based on the jobs in their respective queues. The experiments are conducted on a job shop scheduling problem with a makespan criterion. The obtained results from the computational study show that the proposed approach is a viable and effective approach for solving the job shop scheduling problem in real time.

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Multiple priority dispatching rules for the job shop scheduling problem

Cited by 12 publications

References 29 publications

A Data Mining Based Dispatching Rules Selection System for the Job Shop Scheduling Problem

A Data Mining Based Dispatching Rules Selection System for the Job Shop Scheduling Problem

Review of job shop scheduling research and its new perspectives under Industry 4.0

Multiple dispatching rules allocation in real time using data mining, genetic algorithms, and simulation

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