Abstract
For solving the job-shop scheduling problem (JSP), this paper proposes a novel two-level metaheuristic algorithm, where its upper-level algorithm controls the input parameters of its lower-level algorithm. The lower-level algorithm is a local search algorithm searching for an optimal JSP solution within a hybrid neighborhood structure. To generate each neighbor solution, the lower-level algorithm randomly uses one of two neighbor operators by a given probability. The upper-level algorithm is a population-based search algorithm developed for controlling the five input parameters of the lower-level algorithm, i.e., a perturbation operator, a scheduling direction, an ordered pair of two neighbor operators, a probability of selecting a neighbor operator, and a start solution-representing permutation. Many operators are proposed in this paper as options for the perturbation and neighbor operators. Under the control of the upper-level algorithm, the lower-level algorithm can be evolved in its input-parameter values and neighborhood structure. Moreover, with the perturbation operator and the start solution-representing permutation controlled, the two-level metaheuristic algorithm performs like a multistart iterated local search algorithm. The experiment’s results indicated that the two-level metaheuristic algorithm outperformed its previous variant and the two other high-performing algorithms in terms of solution quality.
Highlights
Production scheduling is an important tool for controlling and optimizing workloads in an industrial production system. It is a decision-making process which involves assigning jobs to machines on a timetable. e job-shop scheduling problem (JSP) is one of well-known production scheduling problems. Such a problem is defined as a much complex optimization problem both in theoretical and practical aspects. e objective of JSP is commonly to find a feasible schedule which completes all jobs by the shortest makespan
Its mechanism is that UPLA controls the input parameters of LOLA, and LOLA searches for an optimal schedule
Due to successful results of [9], this paper aims at developing an enhanced two-level metaheuristic algorithm for JSP
Summary
For solving the job-shop scheduling problem (JSP), this paper proposes a novel two-level metaheuristic algorithm, where its upper-level algorithm controls the input parameters of its lower-level algorithm. E lower-level algorithm is a local search algorithm searching for an optimal JSP solution within a hybrid neighborhood structure. E upper-level algorithm is a population-based search algorithm developed for controlling the five input parameters of the lower-level algorithm, i.e., a perturbation operator, a scheduling direction, an ordered pair of two neighbor operators, a probability of selecting a neighbor operator, and a start solution-representing permutation. Under the control of the upper-level algorithm, the lower-level algorithm can be evolved in its input-parameter values and neighborhood structure. With the perturbation operator and the start solutionrepresenting permutation controlled, the two-level metaheuristic algorithm performs like a multistart iterated local search algorithm. With the perturbation operator and the start solutionrepresenting permutation controlled, the two-level metaheuristic algorithm performs like a multistart iterated local search algorithm. e experiment’s results indicated that the two-level metaheuristic algorithm outperformed its previous variant and the two other high-performing algorithms in terms of solution quality
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