An efficient search method for multi-objective flexible job shop scheduling problems

Xing, Lining; Chen, Yingwu; Yang, Kewei

doi:10.1007/s10845-008-0216-z

Cited by 102 publications

(43 citation statements)

References 39 publications

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“…Zhang et al [19] introduced an effective hybrid PSO algorithm combined with tabu search (TS). Xing et al [20] used ten different weight vectors to collect effective solution sets. A hybrid TS (HTS) algorithm was structured by combining adaptive rules with two neighborhoods.…”

Section: Related Workmentioning

confidence: 99%

A Novel Memetic Algorithm Based on Decomposition for Multiobjective Flexible Job Shop Scheduling Problem

Wang

2017

Mathematical Problems in Engineering

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A novel multiobjective memetic algorithm based on decomposition (MOMAD) is proposed to solve multiobjective flexible job shop scheduling problem (MOFJSP), which simultaneously minimizes makespan, total workload, and critical workload. Firstly, a population is initialized by employing an integration of different machine assignment and operation sequencing strategies. Secondly, multiobjective memetic algorithm based on decomposition is presented by introducing a local search to MOEA/D. The Tchebycheff approach of MOEA/D converts the three-objective optimization problem to several single-objective optimization subproblems, and the weight vectors are grouped by K-means clustering. Some good individuals corresponding to different weight vectors are selected by the tournament mechanism of a local search. In the experiments, the influence of three different aggregation functions is first studied. Moreover, the effect of the proposed local search is investigated. Finally, MOMAD is compared with eight state-of-the-art algorithms on a series of well-known benchmark instances and the experimental results show that the proposed algorithm outperforms or at least has comparative performance to the other algorithms.

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Section: Related Workmentioning

confidence: 99%

A Novel Memetic Algorithm Based on Decomposition for Multiobjective Flexible Job Shop Scheduling Problem

Wang

2017

Mathematical Problems in Engineering

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“…However, literature findings that focus on knowledge reuse as an enabler for improving scheduling performance are scarce. Motivated by empirical knowledge, [17] proposes an efficient search method for the multi-objective flexible job-shop scheduling in order to reach high automation levels towards generating optimal or near-optimal production schedules. Another study exploiting previous knowledge proposed a data mining technique for discovering dispatching rules that improve scheduling performance [18].…”

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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“…Otherwise, a small weight for the given objective can be defined. In this work the weight coefficients W 1 , W 2 , and W 3 for the five Kacem instances are set to 0.5, 0.3 and 0.2 according to Xing et al (2009aXing et al ( , 2009b. The advantage of utilizing the weighted summation approach is its algorithmic actualization which is effortless and the users can change the weight of different objectives for satisfying the requirements of decision makers.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Our proposed approach HDFA is compared with AL+CGA algorithm presented by Kacem et al (2002b), the PSO+SA developed by Xia and Wu, (2005), the PSO+TS introduced by Zhang et al (2009), the efficient search method (ESM) presented by Xing et al (2009b) and the artificial immune algorithm (AIA) proposed by Bagheri et al (2010). These algorithms are included in the group of approaches applied to FJSP that uses the weighted summation of objectives.…”

Section: Performance Comparisonmentioning

confidence: 99%

A hybrid discrete firefly algorithm for solving multi-objective flexible job shop scheduling problems

Karthikeyan

Asokan

Nickolas

et al. 2015

IJBIC

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Firefly Algorithm (FA) is a nature-inspired optimization algorithm that can be successfully applied to continuous optimization problems. However, lot of practical problems are formulated as discrete optimization problems. In this paper a hybrid discrete firefly algorithm (HDFA) is proposed to solve the multi-objective flexible job shop scheduling problem (FJSP). FJSP is an extension of the classical job shop scheduling problem that allows an operation to be processed by any machine from a given set along different routes. Three minimization objectives -the maximum completion time, the workload of the critical machine and the total workload of all machines are considered simultaneously. This paper also proposes firefly algorithm's discretization which consists of constructing a suitable conversion of the continuous functions as attractiveness, distance and movement, into new discrete functions. In the proposed algorithm discrete firefly algorithm (DFA) is combined with local search (LS) method to enhance the searching accuracy and information sharing among fireflies. The experimental results on the well-known benchmark instances and comparison with other recently published algorithms shows that the proposed algorithm is feasible and an effective approach for the multi-objective flexible job shop scheduling problems.

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An efficient search method for multi-objective flexible job shop scheduling problems

Cited by 102 publications

References 39 publications

A Novel Memetic Algorithm Based on Decomposition for Multiobjective Flexible Job Shop Scheduling Problem

A Novel Memetic Algorithm Based on Decomposition for Multiobjective Flexible Job Shop Scheduling Problem

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

A hybrid discrete firefly algorithm for solving multi-objective flexible job shop scheduling problems

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