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

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.

Multi-population genetic algorithm with ER network for solving flexible job shop scheduling problems

“…literature rarely includes studies on how parameters N and S affect MPGA while considering a certain total individual number (TIN) [4]. Therefore, by using the example of the flexible job shop scheduling problem (FJSP) [21], this work studies how parameters N, S, and the propagation rate of advantageous genes among sub-populations affect the performance of MPGA.…”

“…To avoid premature convergence, which is the main disadvantage of standard GA, a multi-population method that is effective in improving GA is used. This results in a different algorithm: the multi-population genetic algorithm (MPGA) [4,5]. The MPGA divides the population of a standard GA into N sub-populations (the sub-population number is denoted by N) that each include the same number of individuals (the sub-population size is denoted by S).…”

Multipopulation Genetic Algorithms with Different Interaction Structures to Solve Flexible Job-Shop Scheduling Problems: A Network Science Perspective

“…Flexible Job-Shop Scheduling Problem. FJSP, which is also introduced in [24,30], can be divided into two subproblems: machine subproblem and operation subproblem, namely, selecting a specific machine for each operation and arranging a proper processing order of all operations. Usually, there exist a job set within n jobs, labeled as J � J i n i�1 , and a machine set within m machines, labeled as M � M k m k�1 .…”

“…An encoded individual must be decoded into an original solution of the FJSP to calculate the fitness. e decoding algorithm proposed by Shi et al [30] was adopted in this paper as follows:…”

“…e processing of crossing is also divided into two stages. For the machine subproblem, we implement the standard two-point crossing process [30]. In this process, we choose two individuals randomly, called parent 1 and parent 2, both of which will be divided into three parts with the same size at random common breakpoints.…”