Remanufacturing closed-loop supply chain network design based on genetic particle swarm optimization algorithm

A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks

“…The other publications of Table 1 illustrate the CLSC design and planning research that utilizes various metaheuristics such as GA ( [24], [25], [51]), GA & PSO ( [23]), tabu search ( [28]), memetic ( [27], and hybrid PSO-GA ( [52]). These studies reveal the acceptability of metaheuristic algorithms and their potential for being evolved.…”

“…In this paper, we try to elevate the trend of solution methodology evolution by proposing a new hybrid solution approach and analyze its robustness through a comprehensive evaluation study. It should be mentioned that the study of Zhou et al [52] is a single-period analysis in order to determine designing stages (and some assignments of allocation, but not flows), so their hybrid is a simple one with a small numerical example. Roughly, they just try to design binary decision variables.…”

“…In this study, on the other hand, all flow decision variables are determined in a multi-period study through a complete validation procedure. The proposed hybrid in [52] is based on PSO with GA operators, while this paper proposed a GA based metaheuristic with PSO operators.…”

Particle Swarm Optimization Algorithm based on Differential Mutation and Thermodynamic Selection

“…Zhou et al proposed a genetic PSO algorithm, and in the algorithm some mutation and crossover operators were introduced to achieve discrete optimization [6]; Wong et al introduced the mutation operation of the genetic algorithm into the PSO approach and proposed a hybrid genetic-particle swarm optimization method for the allocation of distributed generation in distribution system [7]; Liu presented the method of modification on the basic PSO algorithm by Gaussian mutation [8]; Robati et al applied balanced fuzzy sets theory to extend PSO [9]; Nakano et al applied the tabu search to improve the searching ability of the swarm and help the particles escape from the local optimal solution [10]; Kiran et al presented a method combining the PSO with ant colony optimization for forecasting energy demand [11]; Wang et al proposed the co-evolution PSO algorithm combined with simulated annealing algorithm to effectively overcome the premature convergence of PSO algorithm by utilizing global convergence of simulated annealing algorithm and cooperative search of the two algorithms [12]; Liu et al proposed a hybrid PSO with distribution estimation algorithm operator and applied the minimization-of-waiting-time local search to enhance the algorithm performance [13]. Hu et al simplified particle swarm optimization update equation and presented improved simple particle swarm optimization algorithm (SPSO), and the algorithm evolutionary process was only controlled by the position of the particle [14].…”

Literature review: Strategic network optimization models in waste reverse supply chains

“…GA zkr and Baslgl [302] 2012 exact GAMS Pishvaee and Razmi [112] 2012 exact LINDO Vahdani et al [155] 2012 exact GAMS Vahdani et al [97] 2012 exact GAMS Xu and Wei [200] 2012 heur. PSO-based heuristic Zeballos et al [158] 2012 exact GAMS + CPLEX Zhou et al [242] 2012 heur. PSO Amin and Zhang [197] 2013 exact GAMS Amin and Zhang [169] 2013 exact CPLEX Cardoso et al [227] 2013 exact GAMS + CPLEX De Rosa et al [164] 2013 exact IBM ILOG CPLEX Optimization Studio Diabat et al [235] 2013 heur.…”