A modified particle swarm optimisation algorithm to solve the part feeding problem at assembly lines

Abstract

The Assembly Line Part Feeding Problem (ALPFP) is a complex combinatorial optimisation problem concerned with the delivery of the required parts to the assembly workstations in the right quantities at the right time. Solving the ALPFP includes simultaneously solving two sub-problems, namely tour scheduling and tow-train loading. In this article, we first define the problem and formulate it as a multi-objective mixed-integer linear programming model. Then, we carry out a complexity analysis, proving the ALPFP to be NP-complete. A modified particle swarm optimisation (MPSO) algorithm incorporating mutation as part of the position updating scheme is subsequently proposed. The MPSO is capable of finding very good solutions with small time requirements. Computational results are reported, demonstrating the efficiency and effectiveness of the proposed MPSO.

Scheduling just-in-time part replenishment of the automobile assembly line with unrelated parallel machines

“…Fathi et al. 13–15 extended this research to three different problem scenarios. The authors designed three metaheuristics, including simulated annealing-based heuristics, novel memetic ant colony optimization, and modified particle swarm optimization.…”

Enabling Mass Customization and Manufacturing Sustainability in Industry 4.0 Context: A Novel Heuristic Algorithm for in-Plant Material Supply Optimization

“…The ultimate goal of the model was to minimize the energy consumption of the supplying strategy. Fathi et al [31] tried to solve the part delivery problem at assembly lines by suggesting a modified particle swarm optimization (PSO) algorithm. The study aimed to optimally schedule the part delivery to assembly stations via tugger trains to minimize the total number of delivery trips and the sum of inventory.…”

“…Emde et al [24] PSSP a Golz et al [15] OH b Emde and Boysen [25] DP Rao et al [26] GASA Faccio et al [27] Simulation Fathi et al [28] ACO Fathi et al [29] SA Muguerza et al [30] Fathi et al [31] PSO Emde and Schneider [32] NSA Peng and Zhou [33] HACO Zhou and Shen [9] TS PSO Zhou and Peng [34] OH & PSSP a Problem-specific solution procedure; b Other heuristics.…”

Health Condition Evaluation for a Shearer through the Integration of a Fuzzy Neural Network and Improved Particle Swarm Optimization Algorithm

“…However, PSO often suffers from the problems of slow convergence speed and premature convergence because of the quick loss of diversity in the later period [24,25]. In this paper, a premature judgment and mutation mechanism is proposed to improve the PSO, abbreviated as IPSO.…”