Operation sequencing optimization using a particle swarm optimization approach

Abstract

Operation sequencing is one of the crucial tasks in process planning. However, it is an intractable process to identify an optimized operation sequence with minimal machining cost in a vast search space constrained by manufacturing conditions. In this paper, the complicated operation sequencing process has been modelled as a combinatorial optimization problem, and a modern evolutionary algorithm, i.e. the particle swarm optimization (PSO) algorithm, has been employed and modified to solve it effectively. Initial process plan solutions are formed and encoded into particles of the PSO algorithm. The particles ‘fly’ intelligently in the search space to achieve the best sequence according to the optimization strategies of the PSO algorithm. Meanwhile, to explore the search space comprehensively and to avoid being trapped into local optima, several new operators have been developed to improve the particles' movements, forming a modified PSO algorithm. A case study involving three prismatic parts has been used to verify the performance and efficiency of the modified PSO algorithm. A comparison has been made between the result of the modified PSO algorithm and the previous results using the genetic algorithm (GA) and the simulated annealing (SA) algorithm and the different characteristics of the three algorithms are indicated. Case studies show that the developed PSO can generate satisfactory results in optimizing the process planning problem.

A multi-dimensional tabu search algorithm for the optimization of process planning

“…The fourth part is presented by Guo et al [26] to test the computational efficiency of PSO and comparison experiments of PSO with GA and SA are also conducted. This part consists of 11 features and 14 operations.…”

“…In recent years, many metaheuristic algorithms have been applied to process planning problem due to their superiority in solving combinatorial optimization problems. These algorithms can be categorized as genetic algorithm (GA) [13][14][15][16][17][18][19][20][21], simulated annealing (SA) [22,2,23], tabu search (TS) [24,25], particle swarm optimization (PSO) [26] and ant colony optimization (ACO) [10].…”

“…In their optimization model, costs of the utilized machines and cutting tools, machine changes, tool changes, setups and departure from good manufacturing practices (penalty function) are the optimization evaluation criteria. Guo et al [26] implemented a PSO approach for optimization of operation sequencing in process planning. Initial process plans were formed and encoded into particles of the PSO algorithm which 'flew' intelligently in the search space to achieve the best solution.…”

Optimization of Operation Sequencing in CAPP for Cylindrical Part Using Super Hybrid Genetic Algorithm-Expert System (S-Genex) Approach

“…Advance it was examined by Krishna and Rao [12] utilizing Ant colony algorithm (ACA) and found that the computational time has significantly decreased. Guo et al [13] connected particle swarm optimization (PSO) for task sequencing issue, and inferred that there is as yet potential for assist change in calculation efficiency and optimality if presenting new administrators and qualities of different calculations. Besides, Salehi et al [15] again applied a genetic algorithm to create the ideal grouping of assembling tasks in fundamental and point by point planning.…”

Mathematical Modeling and a Hybrid NSGA-II Algorithm for Process Planning Problem Considering Machining Cost and Carbon Emission

“…In addition, process planning in products disassembly has also been considered. Other typical research regarding process planning can be found in [6,7].…”