Machining scheme selection based on a new discrete particle swarm optimization and analytic hierarchy process

Abstract

The goal of machining scheme selection (MSS) is to select the most appropriate machining scheme for a previously designed part, for which the decision maker must take several aspects into consideration. Because many of these aspects may be conflicting, such as time, cost, quality, profit, resource utilization, and so on, the problem is rendered as a multiobjective one. Consequently, we consider a multiobjective optimization problem of MSS in this study, where production profit and machining quality are to be maximized while production cost and production time must be minimized, simultaneously. This paper presents a new discrete method for particle swarm optimization, which can be widely applied in MSS to find out the set of Pareto-optimal solutions for multiobjective optimization. To deal with multiple objectives and enable the decision maker to make decisions according to different demands on each evaluation index, an analytic hierarchy process is implemented to determine the weight value of evaluation indices. Case study is included to demonstrate the feasibility and robustness of the hybrid algorithm. It is shown from the case study that the multiobjective optimization model can simply, effectively, and objectively select the optimal machining scheme according to the different demands on evaluation indices.

Integrated optimization of mixed-model assembly sequence planning and line balancing using Multi-objective Discrete Particle Swarm Optimization

“…For instance, Hu et al . (2014) implemented a new Discrete Particle Swarm Optimization for a combinatorial problem, involving a machining scheme selection. Besides that, the researcher also introduced a probability increment-based swarm algorithm to optimize the combinatorial optimization problem in a printed circuit board assembly (Zeng et al ., 2014).…”

Material selection in engineering design based on nearest neighbor search under uncertainty: a spatial approach by harmonizing the Euclidean and Taxicab geometry

“…The basic steps in all MADM approaches are generally considered as: Decision matrix is the discrete decision space where a finite set of alternatives is expressed by its performance ratings in multiple attribute or criteria. Weightage or priority is assigned to each criterion according to the design requirements to satisfy the customer's demands and desires. Analytical hierarchy process (AHP) (Mujgan et al ., 2004; Hu et al ., 2014), fuzzy AHP (Chan et al ., 2008), digital logic (DL), modified DL (Dehghan-Manshadi et al ., 2007), and entropy are some popular methods for priority distribution (Jahan et al ., 2012b). Normalization is the process by which the performance ratings measured in the different unit are converted into a common unit.…”

“…Weightage or priority is assigned to each criterion according to the design requirements to satisfy the customer's demands and desires. Analytical hierarchy process (AHP) (Mujgan et al ., 2004; Hu et al ., 2014), fuzzy AHP (Chan et al ., 2008), digital logic (DL), modified DL (Dehghan-Manshadi et al ., 2007), and entropy are some popular methods for priority distribution (Jahan et al ., 2012b).…”

Intelligent model-based optimization of cutting parameters for high quality turning of hardened AISI D2

“…In recent years, various evolutionary algorithms have been used to optimize cutting parameters which include using the bio-inspired algorithm such as genetic algorithm (GA) (Kant and Sangwan, 2015), particle swarm optimization (PSO) (Hu et al ., 2014), artificial bee colony (ABC), and simulated annealing (SA) (Shukla and Singh, 2017). However, a remaining drawback of these prior works is that the operating parameters, which include feed rate, cutting speed, and depth of cut, are determined without considering cutting tool state (Chiang et al ., 1995).…”