A hybrid algorithm for flexible job-shop scheduling problem with setup times

Abstract

Job-shop scheduling problem is one of the most important fields in manufacturing optimization where a set of n jobs must be processed on a set of m specified machines. Each job consists of a specific set of operations, which have to be processed according to a given order. The Flexible Job Shop problem (FJSP) is a generalization of the above-mentioned problem, where each operation can be processed by a set of resources and has a processing time depending on the resource used. The FJSP problems cover two difficulties, namely, machine assignment problem and operation sequencing problem. This paper addresses the flexible job-shop scheduling problem with sequence-dependent setup times to minimize two kinds of objectives function: makespan and bi-criteria objective function. For that, we propose a hybrid algorithm based on genetic algorithm (GA) and variable neighbourhood search (VNS) to solve this problem. To evaluate the performance of our algorithm, we compare our results with other methods existing in literature. All the results show the superiority of our algorithm against the available ones in terms of solution quality.

An enhanced Genetic Algorithm with an innovative encoding strategy for flexible job-shop scheduling with operation and processing flexibility

“…Considering the problem shown in Figure 1 again, Figure 3 shows illustrative chromosomes by applying the encoding strategy of Kim et al [31]. A chromosome in P op [1] is shown in Figure 3(a), representing the process plan for job 1 (the other chromosomes in P op [2] and P op [3] are similar to that in P op [1]). Compared to the hierarchical encoding approach, the integrated encoding approach can overcome the combinational-explosive problem of generating all alternative process plans.…”

“…ECs have been successfully adopted to solve the IPPS problem, as proven by the growing number of papers on the topic. Examples include GAs [48,35,32,34,58,55,40,39,71,72,60,10,67], Symbolic Evolutionary Algorithms (SEAs) [31,30], and hybrid approaches of EC [37,1,49]. However, the current encoding strategies for the IPPS problem reported in the literature make almost all the classic genetic operators inapplicable due to the fact that a vast of offspring will be infeasible or illegal.…”

Review on flexible job shop scheduling

“…For quantifying the carbon emission of different scheduling plans, they gave three carbon efficiency indicators to estimate the carbon emission of parts and machines, and proposed a hybrid NSGA-II for solving this problem. Azzouz et al [117] proposed a hybrid algorithm based on GA and VNS to solve the FJSP considering sequence-dependent setup times with two kinds of the objective function, makespan and bi-criteria objective function.…”

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

“…Wu and Wu [ 11 ] proposed a hybrid ant colony algorithm based on the 3D disjunctive graph model by combining the elitist ant system, max-min ant system, and the staged parameter control mechanism for solving FJSP. Using the GA and variable neighborhood search (VNS), Azzouz et al [ 12 ] proposed a hybrid algorithm to solve FJSP, and the performance of the proposed algorithm was demonstrated by comparing its results with other methods. Zandieh et al [ 13 ] proposed an improved imperialist competitive algorithm that was enhanced by simulated annealing to solve FJSP.…”