A hybrid particle swarm optimization with local search for stochastic resource allocation problem

James T. Lin () and Chun-Chih Chiu
Additional contact information
James T. Lin: National Tsing-Hua University
Chun-Chih Chiu: National Tsing-Hua University

Journal of Intelligent Manufacturing, 2018, vol. 29, issue 3, No 1, 495 pages

Abstract: Abstract Discrete and stochastic resource allocation problems are difficult to solve because of the combinatorial explosion of feasible search space. Resource management is important area and a significant challenge is encountered when considering the relationship between uncertainty factors and inputs and outputs of processes in the service and manufacturing systems. These problems are unavailable in closed-form expressions for objective function. In this paper, we propose $$\hbox {PSO}_{\mathrm{OTL}}$$ PSO OTL , a new approach of the hybrid simulation optimization structure, to achieve a near optimal solution with few simulation replications. The basic search algorithm of particle swarm optimization (PSO) is applied for proper exploration and exploitation. Optimal computing budget allocation combined with PSO is used to reduce simulation replications and provide reliable evaluations and identifications for ranking particles of the PSO procedure. Two-sample t tests were used to reserve good particles and maintain the diversity of the swarm. Finally, trapping in local optimum in the design space was overcome by using the local search method to generate new diverse particles when a similar particle exists in the swarm. This study proposed intelligent manufacturing technology, called the $$\hbox {PSO}_{\mathrm{OTL}}$$ PSO OTL , and compared it with four algorithms. The results obtained demonstrate the superiority of $$\hbox {PSO}_{\mathrm{OTL}}$$ PSO OTL in terms of search quality and computational cost reduction.

Keywords: Particle swarm optimization; Optimal budget computing allocation; Local search method; Stochastic resource allocation problem; Intelligent manufacturing technology (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-015-1124-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:29:y:2018:i:3:d:10.1007_s10845-015-1124-7

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-015-1124-7

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().