An effective algorithm for flexible assembly job‐shop scheduling with tight job constraints

Abstract

Thus far, the available works on the flexible assembly job‐shop scheduling problem (FAJSP) consider job processing and assembly separately. However, in some real production systems, if equipment is composed of thousands of jobs and assembled in many stages, some jobs and assemblies cannot be processed simultaneously. Therefore, this work proposes an FAJSP with tight job constraints (FAJSP‐JC) in which jobs and assemblies can be processed simultaneously, and each assembly is treated as an operation. A job constraint genetic algorithm (JCGA) is presented to solve the proposed FAJSP‐JC with the goal of minimizing the makespan. In the JCGA, a novel two‐dimensional encoding method (2D‐encoding) is designed to conveniently express the operating constraints and tight job constraints, and an effective decoding method is proposed to decode the 2D‐encoded information. Furthermore, a crossover operator and a mutation operator are designed to improve the computational efficiency and expand the solution space. Ten benchmark instances of the FAJSP‐JC are constructed to test the JCGA. The Taguchi method is used to obtain the best combination of the key parameters that are used in the JCGA. Computational experiments carried out confirm that the JCGA is able to easily obtain better solutions compared to the genetic algorithm (GA) with a division encoding method and the classical GA, demonstrating its superior performance over these algorithms in terms of both solution quality and computational efficiency.

Performance Assessment of Dynamic Flexible Assembly Job Shop Control Methods

“…Particle swarm optimization algorithm is proposed by [11] to solve a medium size instance. A job constraint genetic algorithm is presented by [35] for FAJSS to minimize the makespan. [36] takes makespan, total tardiness and total workload as optimizing objectives, a distributed ant colony system is proposed to explore the pareto front of FAJSS.…”

Research of Flexible Assembly Job-Shop Batch–Scheduling Problem Based on Improved Artificial Bee Colony

“… Wu et al (2019) considered the FAJSP based on the distributed workshop architecture and proposed a model considering tardiness, production, and transportation costs and designed a genetic algorithm to solve it. Lin et al (2022) studied FAJSP with a tight job based on a genetic algorithm.…”

Mathematical modeling and two efficient branch and bound algorithms for job shop scheduling problem followed by an assembly stage

“…They introduced two hybrid metaheuristic algorithms based on particle swarm optimization and variable neighborhood search to solve the problem with practical size dimensions. Lin et al (2020) studied the flexible assembly job shop scheduling with tight job constraints to minimize makespan as the objective function. A job constraint genetic algorithm was proposed in this study and the results showed that the suggested algorithm can obtain proper solutions in various dimensions of the problem.…”