Flexible Job Shop Scheduling Problem Using an Improved Ant Colony Optimization

Abstract

As an extension of the classical job shop scheduling problem, the flexible job shop scheduling problem (FJSP) plays an important role in real production systems. In FJSP, an operation is allowed to be processed on more than one alternative machine. It has been proven to be a strongly NP-hard problem. Ant colony optimization (ACO) has been proven to be an efficient approach for dealing with FJSP. However, the basic ACO has two main disadvantages including low computational efficiency and local optimum. In order to overcome these two disadvantages, an improved ant colony optimization (IACO) is proposed to optimize the makespan for FJSP. The following aspects are done on our improved ant colony optimization algorithm: select machine rule problems, initialize uniform distributed mechanism for ants, change pheromone’s guiding mechanism, select node method, and update pheromone’s mechanism. An actual production instance and two sets of well-known benchmark instances are tested and comparisons with some other approaches verify the effectiveness of the proposed IACO. The results reveal that our proposed IACO can provide better solution in a reasonable computational time.

Review of job shop scheduling research and its new perspectives under Industry 4.0

“…Neto and Godinho (2013) [12] overviewed ACO-based applications in scheduling and provided a perspective prospect of the future trend. Wang et al (2017) [90] proposed an improved ACO algorithm. The main improvements include selecting machine rules, initializing uniform distributed mechanism for ants, changing pheromones guiding mechanism, selecting node method, and updating pheromones mechanism.…”

Parallelism and Hybridization in Differential Evolution to solve the Flexible Job Shop Scheduling Problem

“…This allows us to determine the goodness of the metaheuristics considered in this work. In this comparison different population-based metaheuristics to solve FJSSP are considered: i) hGA [5]: a hybrid algorithm combining chaos particle swarm optimization with genetic algorithm ii) BEDA [6]: a bi-population based estimation of distribution algorithm iii) IACO [7]: an ant colony optimization iv) HDE [13]: a hybrid differential evolutionary algorithm Table 5 shows that the C max values obtained by DE LS are similar to the ones of remaining algorithms, for the majority of the ten instances. This observation suggests that the DE LS proposed in this work is a competitive algorithm to solve FJSSP.…”

“…The solution of FJSSP involves two decisions: to sequence the operations on the machines and to assign each operation to the appropriate set of machines to minimize the elapsed time to complete all the jobs -ORIGINAL ARTICLE -(makespan or C max ). These decisions suggest that FJSSP is a complex optimization problem (NP-hard problem [2]), consequently, the adoption of metaheuristic [3,4] has led to better results than classical dispatching or greedy heuristic algorithms [5,6,7]. Since introduced in 1997 by Storn and Price [8], the Differential Evolution (DE) metaheuristic became very popular among computer scientists and practitioners almost immediately after its original definition.…”

Ant Colony Optimization Algorithm for Maintenance, Repair and Overhaul Scheduling Optimization in the Context of Industrie 4.0

“…In addition, ACS also used local pheromone updating calculated at the end of each ant's construction step to diversify the path built by subsequent ants. ACS has been applied to job-shop scheduling problem to minimize the makespan [18][19][20] or tardiness [21][22][23]. In this paper, ACS is implemented to address MRO scheduling problem and described below.…”