An Improved Particle Swarm Optimization Approach for Solving Machine Loading Problem in Flexible Manufacturing System

Abstract

Machine loading problem in flexible manufacturing system is considered as a vital prerelease decision. Loading problem is concerned with assignment of necessary operations of the selected jobs to various machines in an optimal manner to minimize system unbalance under technological constraints of limited tool slots and operation time. Such a problem is combinatorial in nature and found to be NP-hard; thus, finding the exact solutions is computationally intractable and becomes impractical as the problem size increases. To alleviate above limitations, a meta-heuristic approach based on particle swarm optimization (PSO) has been proposed in this paper to solve the machine loading problem. Mutation, a commonly used operator in genetic algorithm, has been introduced in PSO so that trapping of solutions at local minima or premature convergence can be avoided. Logistic mapping is used to generate chaotic numbers in this paper. Use of chaotic numbers makes the algorithm converge fast toward global optimum and hence reduce computational effort further. Twenty benchmark problems available in open literature have been solved using the proposed heuristic. Comparison between the results obtained by the proposed heuristic and the existing methods show that the results obtained are encouraging at significantly less computational effort.

Smart Cyber-Physical Manufacturing: Extended and Real-Time Optimization of Logistics Resources in Matrix Production

“…Heuristic optimization methods are used in the case of NP-hard optimization problems: genetic algorithm was used to increase machine utilization, reduce throughput time and delivery delays [36,37], while discrete particle swarm optimization (PSO) was applied to solve the dynamic travelling salesman problem in chip manufacturing, where machine failure can force changes to the problem specification [38]. A typical application field of PSO is flow shop and job shop manufacturing [39] or the machine loading problem in flexible manufacturing systems, where the feeding process is generally robotized or automatized [40]. Heuristic methods can be used not only for the optimization of processes but also for the allocation of IT structure in the manufacturing process: a fruit fly algorithm was used to find the optimal location of the wireless sensor network in the intelligent workshop [41].…”

Particle swarm optimisation algorithm and multi-start simulated annealing algorithm for scheduling batches of parts in multi-cell flexible manufacturing system

“…Chen et al (2013) proposed discrete PSO algorithm for permutation flow shop scheduling problem. Santuka et al (2015) investigated an improved PSO Approach for Solving Machine Loading Problem in FMS. Marichelvam and Prabaharan (2015) proposed multi-objective improved hybrid PSO algorithm for solving realistic industrial scheduling problems.…”

“…This problem has enormous practical significance in both FMS and other types of manufacturing applications -e.g., scheduling circuit probing machines for integrated circuit testing (Liu and Chang, 2000), press shop in automobile industry (Salmasi et al, 2010), painting automobiles with different colours (Salmasi et al, 2011) and industrial scheduling (Marichelvam and Prabaharan, 2015). In recent years, particle swarm optimisation 'PSO' algorithm and multi-start simulated annealing (MSA) algorithm has been increasingly applied to various research and optimisation problems (Chen et al, 2013;Santuka et al, 2015;Marichelvam and Prabaharan, 2015;Lin, 2013;Rodrigues et al, 2014). Therefore, this paper proposes PSO and MSA algorithms to provide an optimal or near-optimal solution to the MCFMS problem.…”

Solving machine loading problem of flexible manufacturing systems using a modified discrete firefly algorithm

“…In recent years among metaheuristics, particularly interesting are the results obtained using the swarmbased approaches (Pandey, 2011;Santuka et al, 2015). In summary it emerges that the ML problem in FMSs is a topic deeply analysed nevertheless currently under investigation.…”