Solving the dual-resource constrained flexible job shop scheduling problem with learning effect by a hybrid genetic algorithm

Abstract

In this article, we investigate a novel dual-resource constrained flexible job shop scheduling problem with consideration of worker's learning ability and develop an efficient hybrid genetic algorithm to solve the problem. To begin with, a comprehensive mathematical model with the objective of minimizing the makespan is formulated. Then, a hybrid algorithm which hybridizes genetic algorithm and variable neighborhood search is developed. In the proposed algorithm, a threedimensional chromosome coding scheme is employed to represent the individuals, a mixed population initialization method is designed for yielding the initial population, and advanced crossover and mutation operators are proposed according to the problem characteristic. Moreover, variable neighborhood search is integrated to improve the local search ability. Finally, to evaluate the effectiveness of the proposed algorithm, computational experiments are performed. The results demonstrate that the proposed algorithm can solve the problem effectively and efficiently.

MILP model for the planning of a Computerized Numerical Control Lathes Machining Plant

“…It consists of three subproblems: (i) Assigning operations to machine resources (ii) Assigning operations to worker resources (iii) Sequencing the operations on machines with workers in mind to optimize performance measurement [25]. In [26], the problem is both to determine the problem is formally described using a non-linear integer model and developing a genetic algorithm to solve it. In [27] and [28], a MILP is for the job shop problem with skilled operators without considering the existence of parallel machines.…”

A simheuristic approach for the flexible job shop scheduling problem with stochastic processing times

“…Although there exists a sizable amount of literature available on the FJSSP, 6–8 the SFJSSP has not been extensively investigated. However, most of the common approaches employed to address the SFJSSP assume that the processing time follows certain probability distributions, such as uniform and normal distribution, considering values only in the positive domain.…”

Limited Search Space-Based Algorithm for Dual Resource Constrained Scheduling Problem with Multilevel Product Structure

“…In response to this trend, scheduling studies have focused on flexible manufacturing processes and have actively been conducted to solve the flexible job-shop scheduling problem (FJSP). In the FJSP research, various constraints, such as sequence dependent setup time [2][3][4], learning effect [5], and dual resource constraint [6][7][8], are considered depending on the manufacturing environment. This study deals with the dual resource constrained flexible job-shop scheduling problem (DRCFJSP) under the consideration of the worker's skill level for machines and multilevel product structures (MLPS).…”

“…The majority of the literature on scheduling considers only the machine as a constrained resource and ignores the constraints of worker availability required for the operations [12]. However, considering that the number of workers is limited in some manufacturing industries, scheduling without considering workers may be significantly different from the shop floor [6]. Studies have been conducted to solve the problem of co-considering the workers and machines as the dual resource constrained (DRC) scheduling problem.…”

“…In particular, the dual resource constraints are investigated in the job shop and flexible job shop environments. The DRCFJSP has been studied using metaheuristic algorithms, such as GA [12,13], variable neighborhood search (VNS) algorithm [6,8], artificial bee colony algorithm [14], and memetic algorithm [15], because DRCFJSP adds another subproblem of worker assignment, which makes the problem complex, in addition to the machine assignment and operation sequence determination [16]. For example, EIMaraghy et al [12] proposed a GA algorithm to solve the DRCFJSP in an environment with equal skill level of the workers.…”