A hybrid immune simulated annealing algorithm for the job shop scheduling problem

Zhang, Rui; Wu, Cheng

doi:10.1016/j.asoc.2009.06.008

Cited by 87 publications

(41 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It has been proven that, SA is capable of solving many real life combinatorial optimization problems including scheduling problems [5], facility layout planning problems [6], assembly line balancing [7], vehicle routing [8] etc. As these studies are based on implementation of SA to a specific problem, some studies that contributes to the algorithmic steps of the SA are discussed in following paragraphs.…”

Section: Literature Reviewmentioning

confidence: 99%

A New Simulated Annealing Approach for Travelling Salesman Problem

Bayram

Şahin

2013

MCA

View full text Add to dashboard Cite

Abstract-The aim of this study is to improve searching capability of simulated annealing (SA) heuristic through integration of two new neighborhood mechanisms. Due to its ease of formulation, difficulty to solve and various real life applications several Travelling Salesman Problems (TSP) were selected from the literature for the testing of the proposed methods. The proposed methods were also compared to conventional SA with swap neighborhood. The results have shown that the proposed techniques are more effective than conventional SA, both in terms of solution quality and time.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A New Simulated Annealing Approach for Travelling Salesman Problem

Bayram

Şahin

2013

MCA

View full text Add to dashboard Cite

show abstract

“…Few researches have been carried out by integrating the SA with several other procedures such as shifting bottleneck heuristic algorithm and controlled search simulated annealing algorithm [8] , integrated simulated annealing and exchange heuristic algorithm [9] , hybrid SA with random insertion perturbation scheme [10] , hybrid adaptive memory programming and simulated annealing (AMPSA) [11] in job shop scheduling (JSP) for improving the makespan. Moreover, hybrid SA has been applied in multi objective flexible job shop scheduling problems (FJSP) by Yazdani, M et al [12] , Fattahi, P [13] , Dalfard, VM and Mohammadi, G [14] and Shahsavari-Pour, N and Ghasemishabankareh, B [15] .…”

Section: Co-published By Atlantis Press and Taylor And Francis Copyrighmentioning

confidence: 99%

Hybrid Sorting Immune Simulated Annealing Algorithm For Flexible Job Shop Scheduling

Shivasankaran¹,

Kumar²,

Raja³

2015

IJCIS

View full text Add to dashboard Cite

Hybrid sorting immune simulated annealing technique (HSISAT), a Meta -heuristic is proposed for solving the multi objective flexible job-shop scheduling problem (FJSP). The major objectives are distributing the time of machines among the set of operations and scheduling them to minimize the criterion (makespan, total workload and maximum workload). The processing time is sorted for isolating the critical machines and immune simulated annealing (ISA) is applied to increase the convergence speed. Several case studies have been taken from the literature to demonstrate the convergence speed of the proposed algorithm. The computational results have proved that the proposed hybrid algorithm is an effective approach to solve the multi-objective FJSP.

show abstract

“…The methodologies include Linear Programming (Mazdeh et al [19]), dynamic programming (Lewis and Slotnick [20] [24]), Simulated Annealing (SA) Lian [17], hybrid algorithms (Zhang and Wu [25], Tavakkoli-Moghaddam et al [18], Wang et al [26]). Besides, Moradi et al [27] solved bi-objective job sequencing problem by NSGA-II (Non-dominated Sorting Genetic Algorithm-II) [28] and NRGA (Non Ranking Genetic Algorithm) algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Modified NSGA-II for a Bi-Objective Job Sequencing Problem

Bandyopadhyay¹

2012

IIM

View full text Add to dashboard Cite

This paper proposes a better modified version of a well-known Multi-Objective Evolutionary Algorithm (MOEA) known as Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed algorithm contains a new mutation algorithm and has been applied on a bi-objective job sequencing problem. The objectives are the minimization of total weighted tardiness and the minimization of the deterioration cost. The results of the proposed algorithm have been compared with those of original NSGA-II. The comparison of the results shows that the modified NSGA-II performs better than the original NSGA-II.

show abstract

A hybrid immune simulated annealing algorithm for the job shop scheduling problem

Cited by 87 publications

References 27 publications

A New Simulated Annealing Approach for Travelling Salesman Problem

A New Simulated Annealing Approach for Travelling Salesman Problem

Hybrid Sorting Immune Simulated Annealing Algorithm For Flexible Job Shop Scheduling

Modified NSGA-II for a Bi-Objective Job Sequencing Problem

Contact Info

Product

Resources

About