Flexible job shop scheduling with sequence-dependent setup and transportation times by ant colony with reinforced pheromone relationships

Rossi, Andrea

doi:10.1016/j.ijpe.2014.03.006

Cited by 89 publications

(32 citation statements)

References 30 publications

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“…In some works, the features incorporated into the LSR-FJS definition are taken into account: lot streaming [2][3][4][5], limited machines availability [6][7][8][9][10][11], setup times [3,5,[12][13][14][15][16][17], and transport times [3,13,17]. However, these extensions are most often considered separately.…”

Section: Related Workmentioning

confidence: 99%

Off-Line and Dynamic Production Scheduling – A Comparative Case Study

Bożek¹,

Wysocki²

2016

Management and Production Engineering Review

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Accepted: 16 February 2016 A comprehensive case study of manufacturing scheduling solutions development is given. It includes highly generalized scheduling problem as well as a few scheduling modes, methods and problem models. The considered problem combines flexible job shop structure, lot streaming with variable sublots, transport times, setup times, and machine calendars. Tabu search metaheuristic and constraint programming methods have been used for the off-line scheduling. Two dynamic scheduling methods have also been implemented, i.e., dispatching rules for the completely reactive scheduling and a multi-agent system for the predictivereactive scheduling. In these implementations three distinct models of the problem have been used, based on: graph representation, optimal constraint satisfaction, and Petri net formalism. Each of these solutions has been verified in computational experiments. The results are compared and some findings about advantages, disadvantages, and suggestions on using the solutions are formulated.

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Section: Related Workmentioning

confidence: 99%

Off-Line and Dynamic Production Scheduling – A Comparative Case Study

Bożek¹,

Wysocki²

2016

Management and Production Engineering Review

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“…In order to evaluate their model, they used the same benchmark as in Oddi et al (2011) and prove that the memetic algorithm has obtained a better result than the IFS. Recently, Rossi (2014) investigate the SDST-FJSP with transportation times using ant-colony algorithm with reinforced pheromone. The most recent comprehensive survey of scheduling problem with setup times is given by Allahverdi (2015).…”

Section: Introductionmentioning

confidence: 99%

A hybrid algorithm for flexible job-shop scheduling problem with setup times

Azzouz

Ennigrou

Saïd

2017

Int. J. Prod. Manag. Eng.

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Job-shop scheduling problem is one of the most important fields in manufacturing optimization where a set of n jobs must be processed on a set of m specified machines. Each job consists of a specific set of operations, which have to be processed according to a given order. The Flexible Job Shop problem (FJSP) is a generalization of the above-mentioned problem, where each operation can be processed by a set of resources and has a processing time depending on the resource used. The FJSP problems cover two difficulties, namely, machine assignment problem and operation sequencing problem. This paper addresses the flexible job-shop scheduling problem with sequence-dependent setup times to minimize two kinds of objectives function: makespan and bi-criteria objective function. For that, we propose a hybrid algorithm based on genetic algorithm (GA) and variable neighbourhood search (VNS) to solve this problem. To evaluate the performance of our algorithm, we compare our results with other methods existing in literature. All the results show the superiority of our algorithm against the available ones in terms of solution quality.

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“…Hence, it cannot be solved by some exact approaches [5] because of the increased computational time in an exponential manner. In recent years, numerous heuristics approaches are proposed to solve FJSP, and these methods include simulated annealing (SA) [6], tabu search (TS) [7], ant colony optimization (ACO) [8], particle swarm optimization (PSO) [9], artificial bee colony (ABC) [10], GA [11], evolutionary algorithm (EA) [12], and improved or hybrid algorithms [13,14].…”

Section: Introductionmentioning

confidence: 99%

A Variable Interval Rescheduling Strategy for Dynamic Flexible Job Shop Scheduling Problem by Improved Genetic Algorithm

Wang

Luo

Cai

2017

Journal of Advanced Transportation

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In real-world manufacturing systems, production scheduling systems are often implemented under random or dynamic events like machine failure, unexpected processing times, stochastic arrival of the urgent orders, cancellation of the orders, and so on. These dynamic events will lead the initial scheduling scheme to be nonoptimal and/or infeasible. Hence, appropriate dynamic rescheduling approaches are needed to overcome the dynamic events. In this paper, we propose a dynamic rescheduling method based on variable interval rescheduling strategy (VIRS) to deal with the dynamic flexible job shop scheduling problem considering machine failure, urgent job arrival, and job damage as disruptions. On the other hand, an improved genetic algorithm (GA) is proposed for minimizing makespan. In our improved GA, a mix of random initialization population by combining initialization machine and initialization operation with random initialization is designed for generating high-quality initial population. In addition, the elitist strategy (ES) and improved population diversity strategy (IPDS) are used to avoid falling into the local optimal solution. Experimental results for static and several dynamic events in the FJSP show that our method is feasible and effective.

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Flexible job shop scheduling with sequence-dependent setup and transportation times by ant colony with reinforced pheromone relationships

Cited by 89 publications

References 30 publications

Off-Line and Dynamic Production Scheduling – A Comparative Case Study

Off-Line and Dynamic Production Scheduling – A Comparative Case Study

A hybrid algorithm for flexible job-shop scheduling problem with setup times

A Variable Interval Rescheduling Strategy for Dynamic Flexible Job Shop Scheduling Problem by Improved Genetic Algorithm

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