Modular design of a hybrid genetic algorithm for a flexible job–shop scheduling problem

Gutiérrez, Celia; García‐Magariño, Iván

doi:10.1016/j.knosys.2010.07.010

Cited by 35 publications

(18 citation statements)

References 19 publications

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“…Gutierrez and Garcia-Magarino [6] set makespan as an objective, proposing a hybrid genetic algorithm based on genetic algorithm (GA) and repair heuristics to solve FJSP. Li et al [7] set makespan as an objective, proposing a hybrid bee colony algorithm based on a simulated annealing algorithm (SAA) and an artificial bee colony (ABC) for solving FJSP, with the algorithm being proved through some benchmarks.…”

Section: Flexible Job-shop Scheduling Problem (Fjsp)mentioning

confidence: 99%

Modelling and Simulation of a Multi-Resource Flexible Job-Shop Scheduling

Ba¹,

Li²,

Yang³

2016

Int. j. simul. model.

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Flexible job-shop scheduling problem (FJSP) in the field of production scheduling presents a quite difficult combinatorial optimization problem. Machines are mostly considered to be the only resource in many research projects dealing with FJSP. In actual production, there are many other factors which influence production scheduling, such as transportation, storage and detection. If machines are considered to be the only resource, the problem may not be in accord with the actual production. Thus, in order to make FJSP more in line with the real production situation, machines, warehouses, vehicles and detection equipment are all considered to be the scheduled resources simultaneously due to the shortage of flexible job shop scheduling problem in resources. A new mathematical model for a multiresource flexible job-shop scheduling problem (MRFJSP) is proposed. The constraints of the model are presented. The makespan is the main target which will be minimized. A genetic algorithm which includes elitist strategy is proposed to solve the MRFJSP. Due to the complexity of MRFJSP, each key module of the genetic algorithm is redesigned. Finally, the model and algorithm are proved through an application case.

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Section: Flexible Job-shop Scheduling Problem (Fjsp)mentioning

confidence: 99%

Modelling and Simulation of a Multi-Resource Flexible Job-Shop Scheduling

Ba¹,

Li²,

Yang³

2016

Int. j. simul. model.

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“…the FJSP, where GA is combined with repair heuristics. For stochastic job shop scheduling problem, Zhang [4] developed a hybrid particle swarm optimization algorithm to minimize the expected total weighted tardiness as the objective function. Some researchers focused on FJSP with practical constraints in different industries.…”

Section: Introductionmentioning

confidence: 99%

Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion

Gao

Suganthan

Pan

et al. 2016

Knowledge-Based Systems

124

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“…If not, the probability will be big. Gutiérrez and García-Magariño (2011) proposed a hybrid genetic algorithm which combines genetic algorithm with repair heuristics for flexible job-shop scheduling problems. Hou et al (2014) proposed an improved genetic algorithm for balancing product family assembly line which is a mixed assembly line for a family of similar products.…”

Section: Introductionmentioning

confidence: 99%

A modified ant colony optimization algorithm for multi-objective assembly line balancing

Zhong

2016

Soft Comput

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In this paper, a novel ant colony optimization algorithm called modified ant colony optimization algorithm (MACO) is proposed for multi-objective single-model assembly line balancing problem (SALBP). The proposed MACO presents a novel heuristic information combined with subsequent task number and deviation time that can guide ants to find better solutions for SALBP. The proposed MACO also adopts three assignment methods (i.e., forward, backward and local rebalancing assignment methods) and stratified sequential algorithm combined with Pareto-optimal front as multi-objective decision. The objectives of SALBP are to minimize the number of workstations, maximize assembly line efficiency and minimize workload variation among workstations. In the latter part of the paper, the proposed MACO has been applied to solve Scholl benchmark problems which include both small-size and large-size problems. The performance of the proposed MACO has been compared with the multi-objective genetic algorithm and the multiple assignment genetic algorithm and has obtained improved results in many test problems.

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Modular design of a hybrid genetic algorithm for a flexible job–shop scheduling problem

Cited by 35 publications

References 19 publications

Modelling and Simulation of a Multi-Resource Flexible Job-Shop Scheduling

Modelling and Simulation of a Multi-Resource Flexible Job-Shop Scheduling

Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion

A modified ant colony optimization algorithm for multi-objective assembly line balancing

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