Different Performances of Different Intelligent Algorithms for Solving FJSP: A Perspective of Structure.

Xiao-Qiu Shi,Yong-Lai Wei,Yan-Yan Li,Ding-Shan Deng,Wei Long

doi:10.1155/2018/4617816

Xiao-Qiu Shi, Yong-Lai Wei + Show 3 more

Open Access

https://doi.org/10.1155/2018/4617816

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Abstract

There are several intelligent algorithms that are continually being improved for better performance when solving the flexible job-shop scheduling problem (FJSP); hence, there are many improvement strategies in the literature. To know how to properly choose an improvement strategy, how different improvement strategies affect different algorithms and how different algorithms respond to the same strategy are critical questions that have not yet been addressed. To address them, improvement strategies are first classified into five basic improvement strategies (five structures) used to improve invasive weed optimization (IWO) and genetic algorithm (GA) and then seven algorithms (S1–S7) used to solve five FJSP instances are proposed. For the purpose of comparing these algorithms fairly, we consider the total individual number (TIN) of an algorithm and propose several evaluation indexes based on TIN. In the process of decoding, a novel decoding algorithm is also proposed. The simulation results show that different structures significantly affect the performances of different algorithms and different algorithms respond to the same structure differently. The results of this paper may shed light on how to properly choose an improvement strategy to improve an algorithm for solving the FJSP.

Highlights

Brucker and Schlie proposed the flexible job-shop scheduling problem (FJSP) [1] for the first time in 1990, in which every operation can be processed on more than one machine. erefore, FJSP is more difficult than the classical job-shop scheduling problem (JSP), which is a NP-hard problem [2] in which every operation can be processed on just one machine
For the purpose of addressing how structures affect different algorithms and how different algorithms respond to the same structure, we use the seven algorithms to solve the five FJSP instances proposed by Kaceam [23]
To evaluate S1–S7 fairly, we introduce four evaluation indexes based on total individual number (TIN) as follows: optimal value based on TIN (OVTIN), average value based on TIN (AVTIN), population diversity based on TIN (PDTIN), and premature convergence rate based on TIN

Summary

Introduction

Brucker and Schlie proposed the flexible job-shop scheduling problem (FJSP) [1] for the first time in 1990, in which every operation can be processed on more than one machine. erefore, FJSP is more difficult than the classical job-shop scheduling problem (JSP), which is a NP-hard problem [2] in which every operation can be processed on just one machine. We use the proposed seven algorithms to solve the five FJSP instances proposed in Reference [23], and the performance of these algorithms is illustrated to answer the two questions mentioned above. To compare these seven algorithms fairly, we consider the total individual number (TIN) in this paper. For IWO, if the number of iterations is 100, the minimal population size is 10, the maximal population size is 100, the minimal seed size is 1, the maximal seed size is 5, and the TIN is 30,000 From this perspective, it is not fair if we just use optimal value and/or efficiency to evaluate the different intelligent algorithms. The results show that different structures significantly affect different algorithms, and those different algorithms have different responses to the same structure

FJSP and Its Mathematical Model

Proposed Seven Algorithms

The Seven Algorithms for FJSP

Numerical Simulations

Conclusions