A fuzzy greedy heuristic for permutation flow-shop scheduling

Sheibani, Kaveh

doi:10.1057/jors.2008.194

Cited by 20 publications

(4 citation statements)

References 26 publications

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“…In this section, a computational study is carried out to compare the HGA with three best recently developed heuristics. We mean PEM presented by Li et al (2004); H3 developed by Laha and Chakraborty (2007) and PH described by Sheibani (2010). Four methods are compared using different problem sizes (n = 10, 20, 30, 40, 50, 100 and m = 5, 10, 15, 20).…”

Section: Results Analysismentioning

confidence: 99%

A novel hybrid genetic algorithm to solve the make-to-order sequence-dependent flow-shop scheduling problem

2014

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Flow-shop scheduling problem (FSP) deals with the scheduling of a set of n jobs that visit a set of m machines in the same order. As the FSP is NP-hard, there is no efficient algorithm to reach the optimal solution of the problem. To minimize the holding, delay and setup costs of large permutation flow-shop scheduling problems with sequence-dependent setup times on each machine, this paper develops a novel hybrid genetic algorithm (HGA) with three genetic operators. Proposed HGA applies a modified approach to generate a pool of initial solutions, and also uses an improved heuristic called the iterated swap procedure to improve the initial solutions. We consider the make-to-order production approach that some sequences between jobs are assumed as tabu based on maximum allowable setup cost. In addition, the results are compared to some recently developed heuristics and computational experimental results show that the proposed HGA performs very competitively with respect to accuracy and efficiency of solution.

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Section: Results Analysismentioning

confidence: 99%

A novel hybrid genetic algorithm to solve the make-to-order sequence-dependent flow-shop scheduling problem

2014

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“…For each heuristic, the solution quality is measured by the percentage deviation of the obtained solution from the basic solution (i.e. the BLiMST solution and the BLU solution) through (6).…”

Section: Performance Evaluationmentioning

confidence: 99%

“…In this paper, we propose two new heuristics for the MEB problem based on adaptations of the Fuzzy Greedy Heuristic (FGH) for hard combinatorial optimization problems [6]. The heuristics are named the Fuzzy Greedy Heuristic Broadcast Link-based Minimum Spanning Tree (FGH_BLiMST) and the Fuzzy Greedy Heuristic Broadcast Least-Unicast-cost (FGH_BLU).…”

Section: Introductionmentioning

confidence: 99%

A Two-Phase Constructive Heuristic for Minimum Energy Broadcasting in Wireless Ad Hoc Networks

Rahmani¹,

Sheibani

2010

IJCNIS

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Abstract-Wireless ad hoc networks are usually composed of autonomous nodes, which are powered by batteries only. The energy-efficiency is perhaps one of the most important factors for each operation in terms of networks. Broadcast, for example, is one of the fundamental operations in modern telecom networks. In this paper a broadcast tree, which is rooted at a source and spans all the destination nodes, has been constructed in a way that the total transmission energy consumption is minimized. This paper describes two polynomialtime heuristics for the energy-efficient broadcasting in static ad hoc wireless networks. Both of the developed approaches are on the basis of a fuzzy greedy evaluation function, which prioritize the network nodes. According to the prioritized order of the nodes, each new node is selected for incorporation in the construction of a solution. Computational experiments indicate that our algorithms improve the well-known Broadcast Link-based Minimum Spanning Tree (BLiMST) and Broadcast Least-Unicast-cost (BLU) heuristics. It will be seen that the BLiMST and the BLU methods are a special case of our more general heuristics.

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“…In 2012 [14] H. F. Abdullah was found approximation solution for two machine flow shop scheduling problem to minimized total earliness by proposes a new algorithms. CengizKahraman a, OrhanEngin and Mustafa KerimYilmaz [15] were solved multi objective function formatted by minimized the average tardiness and the number of tardy jobs to fuzzy flow shop scheduling problem by found new artificial immune system algorithms. In 2014 [16] J.Behnamiana , S.M.T.…”

Section: Introductionmentioning

confidence: 99%

Approximation methods to solve a single machine scheduling problem with fuzzy due date to minimize multi-objective functions

Chachan¹

2021

TURCOMAT

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In this paper present three methodant colony optimization (ACO), particle swarm optimization (PSO) and bees algorithm optimization (BAO) to the solution multi-objective function of single machine problem with the fuzzy due date. The objective function to minimize total completion time and maximum lateness with a fuzzy due date. By a computer simulation used to compare the performance of each algorithm with another one from where accuracy and time.

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A fuzzy greedy heuristic for permutation flow-shop scheduling

Cited by 20 publications

References 26 publications

A novel hybrid genetic algorithm to solve the make-to-order sequence-dependent flow-shop scheduling problem

A novel hybrid genetic algorithm to solve the make-to-order sequence-dependent flow-shop scheduling problem

A Two-Phase Constructive Heuristic for Minimum Energy Broadcasting in Wireless Ad Hoc Networks

Approximation methods to solve a single machine scheduling problem with fuzzy due date to minimize multi-objective functions

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