Purpose Flexible job-shop scheduling is significant for different manufacturing industries nowadays. Moreover, consideration of transportation time during scheduling makes it more practical and useful. The purpose of this paper is to investigate multi-objective flexible job-shop scheduling problem (MOFJSP) considering transportation time. Design/methodology/approach A hybrid genetic algorithm (GA) approach is integrated with simulated annealing to solve the MOFJSP considering transportation time, and an external elitism memory library is employed as a knowledge library to direct GA search into the region of better performance. Findings The performance of the proposed algorithm is tested on different MOFJSP taken from literature. Experimental results show that proposed algorithm performs better than the original GA in terms of quality of solution and distribution of the solution, especially when the number of jobs and the flexibility of the machine increase. Originality/value Most of existing studies have not considered the transportation time during scheduling of jobs. The transportation time is significantly desired to be included in the FJSP when the time of transportation of jobs has significant impact on the completion time of jobs. Meanwhile, GA is one of primary algorithms extensively used to address MOFJSP in literature. However, to solve the MOFJSP, the original GA has a possibility to get a premature convergence and it has a slow convergence speed. To overcome these problems, a new hybrid GA is developed in this paper.

Genetic algorithm is one of primary algorithms extensively used to address the multi-objective flexible job-shop scheduling problem. However, genetic algorithm converges at a relatively slow speed. By hybridizing genetic algorithm with particle swarm optimization, this article proposes a teaching-and-learning-based hybrid genetic-particle swarm optimization algorithm to address multi-objective flexible job-shop scheduling problem. The proposed algorithm comprises three modules: genetic algorithm, bi-memory learning, and particle swarm optimization. A learning mechanism is incorporated into genetic algorithm, and therefore, during the process of evolution, the offspring in genetic algorithm can learn the characteristics of elite chromosomes from the bi-memory learning. For solving multi-objective flexible job-shop scheduling problem, this study proposes a discrete particle swarm optimization algorithm. The population is partitioned into two subpopulations for genetic algorithm module and particle swarm optimization module. These two algorithms simultaneously search for solutions in their own subpopulations and exchange the information between these two subpopulations, such that both algorithms can complement each other with advantages. The proposed algorithm is evaluated on some instances, and experimental results demonstrate that the proposed algorithm is an effective method for multiobjective flexible job-shop scheduling problem.

A new scheduling problem considering the sequence flexibility in classical job shop scheduling problem (SFJSP) is very practical in most realistic situations. SFJSP consists of two sub-problems which are determining the sequence of flexible operations of each job and sequencing all the operations on the machines. This paper proposes an improved genetic algorithm (IGA) to solve SFJSP to minimise the makespan, in which the chromosome encoding schema, crossover operator and mutation operator are redesigned. The chromosome encoding schema can express the processing sequence of flexible operations of all the jobs and the processing sequence of the operations on the machines simultaneously. The crossover and mutation operators can ensure the generation of feasible offspring for SFJSP. The simulation results on three practical instances of a bearing manufacturing corporation show that the proposed algorithm is quite efficient in solving SFJSP.

Abstract-A teaching-learning-based hybrid genetic-particle swarm optimization algorithm is proposed for multi-objective flexible job shop scheduling problem. It includes three modules: genetic algorithm (GA), bi-memory learning (BL) and particle swarm optimization (PSO). Firstly, in the BL module, a learning mechanism is introduced into GA to generate chromosomes which have a self-learning characteristic. During the process of evolution, the offspring in GA learn the characteristics of good chromosomes in the BL. Then, a discretization PSO algorithm which iterates the genetic population and particle population simultaneously is proposed. Finally, experiments are conducted to compare the rationality and validity of the proposed algorithm with others.