Discrete bat algorithm for optimal problem of permutation flow shop scheduling.

Qifang Luo,Liangliang Li,Mingzhi Ma,Jian Xie,Yongquan Zhou

doi:10.1155/2014/630280

Qifang Luo, Liangliang Li + Show 3 more

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https://doi.org/10.1155/2014/630280

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Abstract

A discrete bat algorithm (DBA) is proposed for optimal permutation flow shop scheduling problem (PFSP). Firstly, the discrete bat algorithm is constructed based on the idea of basic bat algorithm, which divide whole scheduling problem into many subscheduling problems and then NEH heuristic be introduced to solve subscheduling problem. Secondly, some subsequences are operated with certain probability in the pulse emission and loudness phases. An intensive virtual population neighborhood search is integrated into the discrete bat algorithm to further improve the performance. Finally, the experimental results show the suitability and efficiency of the present discrete bat algorithm for optimal permutation flow shop scheduling problem.

Highlights

Scheduling problems are taking the very important effect in both manufacturing systems and industrial process for improving the utilization efficiency of resources [1], such as, aircraft landing scheduling problem, job shop scheduling problem, and flow shop scheduling problem
In [10], Tasgetiren et al applied the particle swarm optimization algorithm (PSO) algorithm to solve permutation flow shop scheduling problem (PFSP) for makespan and total flow time minimization by using the smallest position value rule borrowed from the random key representation of genetic algorithm (GA), and the proposed algorithm was combined with the variable neighborhood-based local search, as called PSO VNS
For each individual Compute loudness Ldi by (7); if rand > Ldi /∗ random sub-sequence inserting ∗/ Randomly select a length of sub-sequence; Randomly determine the sub-sequence with selected length in gbest x; Insert this sub-sequence into a random location in remainder sequence; else /∗ random sub-sequence inverse ∗/ Randomly select a length of sub-sequence; Randomly determine the sub-sequence with selected length in gbest x; Perform inverse operation on selected sub-sequence; Replace original sub-sequence with inverted sub-sequence end if end for Algorithm 4: The pseudocode of loudness local operation

Summary

Introduction

Scheduling problems are taking the very important effect in both manufacturing systems and industrial process for improving the utilization efficiency of resources [1], such as, aircraft landing scheduling problem, job shop scheduling problem, and flow shop scheduling problem. There are many methods that have been introduced for solving PFSP with the objective of minimizing the makespan To sum up, these methods can be classified into three categories: exact methods, constructive heuristic methods, and metaheuristic algorithms based on the constructive operation and neighborhood search. Many metaheuristic algorithms are used to solve flow shop scheduling based on the constructive operation and neighborhood search in the past few years. In [10], Tasgetiren et al applied the PSO algorithm to solve PFSP for makespan and total flow time minimization by using the smallest position value rule borrowed from the random key representation of GA, and the proposed algorithm was combined with the variable neighborhood-based local search, as called PSO VNS. The experimental results show the effectiveness of the discrete bat algorithm for PFSP

Problem Descriptions and Bat Algorithm

Discrete Bat Algorithm for PFSP

Numerical Simulation Results and Comparisons

Conclusions