A hybrid artificial bee colony algorithm for the job-shop scheduling problem with no-wait constraint

Sundar, Shyam; Suganthan, Ponnuthurai Nagaratnam; Jin, Chua Tay; Xiang, Cai; Soon, Chong Chin

doi:10.1007/s00500-015-1852-9

Cited by 83 publications

(22 citation statements)

References 29 publications

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“…Wang et al [5] present the simulation approach for process planning, operation determining and operational sequencing. Shundar et al [6] represented the use of bee colony algorithm for solving JSSP in which they use some data analyser software to compare the mathematical data received from the simulation environment to real world data. The same problem is solved by the Wisittipanich and Kachitvichyanukil [7] which are using Microsoft Visual Studio and C# programming language to solve this problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Use of Simulation Software Environments for the Purpose of Production Optimization

Ojsteršek¹,

Buchmeister²

2017

DAAAM Proceedings

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Section: Literature Reviewmentioning

confidence: 99%

Use of Simulation Software Environments for the Purpose of Production Optimization

Ojsteršek¹,

Buchmeister²

2017

DAAAM Proceedings

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“…The solutions found by the whole swarm are the new guidance for the particle movement. Sunder et al [20] introduced a hybrid artificial bee colony algorithm (ABC) for JSSP with no-wait constraint; the proposed algorithm successfully coordinates initialization, selection and determination of the ABC with the local search which brings high quality solutions. Park et al used multi-criteria optimization for the optimization of machining parameters [21].…”

Section: Literature Reviewmentioning

confidence: 99%

An Effective Use of Hybrid Metaheuristics Algorithm for Job Shop Scheduling Problem

Zhang¹,

Liu²,

Moraca³

et al. 2016

Int. j. simul. model.

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This paper presents an effective use of hybrid metaheuristics algorithm for solving Job Shop Scheduling Problem (JSSP). Integration of three metaheuristics algorithms: Shuffled Frog Leaping Algorithm (SFLA), Intelligent Water Drops algorithm (IWD) and Path Relinking (PR) algorithm were put together to solve JSSP. First, simulation model was developed and tested on the test data of Traveller Salesman Problem (TSP). Second, the model was tested on real world production line to solve the problem of Minimum Needed Workers (MNW) at the production line. The model enables individual test of three mentioned algorithms and calculation of new proposed Random MultiNeighbourhood based Shuffled Frog Leaping Algorithm with Path Relinking (RMN-SFLA-PR). Experiments were tested on two software environments MATLAB and Simio, which gives us reliable, robust and tangible results. Results show that the new proposed RMN-SFLA-PR algorithm converged to optimum almost ten times faster than individual algorithms. The most important thing is the successful rate of all independent runs of the proposed RMN-SFLA-PR is 100 % in low-dimensional cases of the 4 benchmarks (dj38) and in JSSP to solve MNW for the real world production line.

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“…Historically, several algorithms are proposed in literature to solve the job shop scheduling problem by optimizing the makespan such as: branch and bound (BB) [3], simulated annealing (SA) [4], Tabu search (TS) [5] [6], genetic algorithms (GA) [7][8] [9],neural networks (NN) [10],ant colony optimization (ACO) [11],Particle swarm optimization (PSO) [12], Bee colony optimization (BCO) [13] and firefly algorithm(FA) [14]. Additionally, some researchers have developed an hybrid optimization strategy for JSSP such as parallel GRASP with path-relinking [15] and new hybrid genetic algorithm [16].…”

Section: Introductionmentioning

confidence: 99%

Memetic chicken swarm algorithm for job shop scheduling problem

Semlali

Riffi

Chebihi

2019

IJECE

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<p>This paper presents a Memetic Chicken swarm optimization (MeCSO) to solve job shop scheduling problem (JSSP). The aim is to find a better solution which minimizes the maximum of the completion time also called Makespan. In this paper, we adapt the chicken swarm algorithm which take into consideration the hierarchical order of chicken swarm while seeking for food. Moreover, we integrate 2-opt method to improve the movement of the rooster. The new algorithm is applied on some instances of ORLibrary. The empirical results show the forcefulness of MeCSO comparing to other metaheuristics from literature in term of run time and quality of solution.</p>

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A hybrid artificial bee colony algorithm for the job-shop scheduling problem with no-wait constraint

Cited by 83 publications

References 29 publications

Use of Simulation Software Environments for the Purpose of Production Optimization

Use of Simulation Software Environments for the Purpose of Production Optimization

An Effective Use of Hybrid Metaheuristics Algorithm for Job Shop Scheduling Problem

Memetic chicken swarm algorithm for job shop scheduling problem

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