An adaptive multi-population genetic algorithm for job-shop scheduling problem

Abstract

Job-shop scheduling problem (JSP) is a typical NP-hard combinatorial optimization problem and has a broad background for engineering application. Nowadays, the effective approach for JSP is a hot topic in related research area of manufacturing system. However, some JSPs, even for moderate size instances, are very difficult to find an optimal solution within a reasonable time because of the process constraints and the complex large solution space. In this paper, an adaptive multi-population genetic algorithm (AMGA) has been proposed to solve this problem. Firstly, using multi-populations and adaptive crossover probability can enlarge search scope and improve search performance. Secondly, using adaptive mutation probability and elite replacing mechanism can accelerate convergence speed. The approach is tested for some classical benchmark JSPs taken from the literature and compared with some other approaches. The computational results show that the proposed AMGA can produce optimal or near-optimal values on almost all tested benchmark instances. Therefore, we can believe that AMGA can be considered as an effective method for solving JSP.

Multiobjective Optimization Design for Structural Parameters of TBM Disc Cutter Rings Based on FAHP and SAMPGA

“…Specifically, the crossover probability is set to be 0.7-0.9 and mutation probability is set 0.01-0.1 [30,44], and the mutation probability dynamically increases with the increase of computational generation.…”

A memetic algorithm for minimizing the makespan in the Job Shop Scheduling problem

“…Table 1 summarizes the results of the experiments. The columns include the name of each test benchmark problem (Problem), the size of the problem (Size), the population size (POP), the number of generations (GEN), the selection probability (SP), the mutation probability (MP), the value of the best-known solution for each problem (BKS), the value of the best solution found by using the proposed MA (MA), and the solution obtained from other evolutionary approaches made by Hasan et al [55], Gao et al [52], and Wang et al [36]. The solutions marked with an asterisk (*) are optimal.…”

“…Recent researches have mostly focused on more advanced heuristic algorithms, better known as "metaheuristics", which propose several approaches like the tabu search [16][17][18][19], simulated annealing [20][21][22], ant colony optimization [23][24][25][26], particle swarm optimization [27][28][29][30], neuronal network [31,32], and genetic algorithms (GA). In particular, the GA are based on Darwin's evolutionary theory, and they have been employed to provide successful solutions to various combinatorial problems (JSP included [33][34][35][36]) since they allow exploring in an efficient way the solution space; nevertheless, they may converge prematurely. That is why recent researches have aimed to combine the GA with other techniques that ameliorate its efficiency by developing hybrid methods as the Memetic Algorithm (MA).…”

A Fuzzy Immune GA Scheduling Framework for JSSP

“…Influenced by the factors derived from machining process or people's expectation, generally we could get the most optimistic time, the most pessimistic time, the most promising time, the most satisfied time, and the acceptable time interval of them. Therefore, to deal with the uncertainty of the problems, the processing parameters are described according to fuzzy theory [13].…”