An Enhanced Two-Level Metaheuristic Algorithm with Adaptive Hybrid Neighborhood Structures for the Job-Shop Scheduling Problem

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.

A Job‐Shop Scheduling Problem with Bidirectional Circular Precedence Constraints

“…In addition, an upper-level metaheuristic can be applied to control parameters of a being-considered problem for generating its simpler subproblems [35,36]. For applying the adaptive parameter control technique, the metaheuristics usually have only two levels [4,15,16,[37][38][39]. However, for solving highly complicated problems, they may require more than two levels [35,36].…”

“…e two-level metaheuristic of [16] is a recent variant of [37]. In this variant, MUPLA and LOSAP are the upper-level and lower-level metaheuristics, respectively.…”

“…In this variant, MUPLA and LOSAP are the upper-level and lower-level metaheuristics, respectively. LOSAP [16] is a local search algorithm exploring in a probabilistic-based hybrid neighborhood structure. To generate each neighbor solution, LOSAP randomly uses one of the two predetermined neighbor operators by a preassigned probability.…”

“…e BOT combined with MID is similar to the two-level metaheuristic developed by [16]. A major difference of the MID-BOT combination from the algorithm of [16] is in the solution-decoding procedure. Once the MID-BOT combination is combined with TOP, they all together have become MUL.…”

“…Because the existing JSP-solving algorithms can be used to solve BCJSP, their results were also used in the performance comparisons. Note that the existing JSP-solving algorithms mean the algorithms developed for solving JSP in the literature, e.g., [1,2,16,20,21].…”