Platform Strategy for Product Family Design Using Particle Swarm Optimization

2011 ◽  
Author(s):  
Seung Ki Moon ◽  
KyoungJong Park ◽  
Timothy W. Simpson
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Product Family ◽  
Design Strategy ◽  
General Aviation ◽  
Multidisciplinary Optimization ◽  
Product Family Design ◽  
Swarm Optimization ◽  
Platform Design ◽  
Multi Objective

Product family design allows innovative companies to create customized product roadmaps, to manage designers and component partners, and to develop the next generation of products based on platform strategies. In product family design, problems for determining a design strategy or the degree of commonality for a platform can be considered as a multidisciplinary optimization problem with respect to design variables, production cost, company’s revenue, and customers’ satisfaction. In this paper, we investigate strategic module-based platform design to identify an optimal platform strategy in a product family. The objective of this paper is to introduce a multi-objective particle swarm optimization (MOPSO) approach to select the best platform design strategy from a set of Pareto-optimal solutions based on commonality and design variation within the product family. We describe modifications to apply the proposed MOPSO to the multi-objective problem of product family design and allow designers to evaluate varying levels of platform strategies. To demonstrate the effectiveness of the proposed approach, we use a case study involving a family of General Aviation Aircraft. The limitations of the approach and future work are also discussed.

scholarly journals An Enhanced Multi-Objective Particle Swarm Optimization in Water Distribution Systems Design

Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1334
Author(s):  
Mohamed R. Torkomany ◽  
Hassan Shokry Hassan ◽  
Amin Shoukry ◽  
Ahmed M. Abdelrazek ◽  
Mohamed Elkholy
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Particle Swarm ◽  
Large Network ◽  
Swarm Optimization ◽  
Original Algorithm ◽  
Multi Objective

The scarcity of water resources nowadays lays stress on researchers to develop strategies aiming at making the best benefit of the currently available resources. One of these strategies is ensuring that reliable and near-optimum designs of water distribution systems (WDSs) are achieved. Designing WDSs is a discrete combinatorial NP-hard optimization problem, and its complexity increases when more objectives are added. Among the many existing evolutionary algorithms, a new hybrid fast-convergent multi-objective particle swarm optimization (MOPSO) algorithm is developed to increase the convergence and diversity rates of the resulted non-dominated solutions in terms of network capital cost and reliability using a minimized computational budget. Several strategies are introduced to the developed algorithm, which are self-adaptive PSO parameters, regeneration-on-collision, adaptive population size, and using hypervolume quality for selecting repository members. A local search method is also coupled to both the original MOPSO algorithm and the newly developed one. Both algorithms are applied to medium and large benchmark problems. The results of the new algorithm coupled with the local search are superior to that of the original algorithm in terms of different performance metrics in the medium-sized network. In contrast, the new algorithm without the local search performed better in the large network.

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