Antithetic Method‐Based Particle Swarm Optimization for a Queuing Network Problem with Fuzzy Data in Concrete Transportation Systems. (6th October 2014)

• Record Type: Journal Article
• Authors: Zeng, Ziqiang; Xu, Jiuping; Wu, Shiyong; Shen, Manbin
• Is Part Of: Computer-aided civil and infrastructure engineering. Volume 29:Number 10(2014)
• Page Start: 771
• Page End: 800
• Publication Date: 2014-10-06
• Subjects: Civil engineering -- Data processing -- Periodicals; Computer-aided engineering -- Periodicals; 624.0285
• DOI: 10.1111/mice.12111
• Languages: English
• ISSNs: 1093-9687

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>The aim of this article is to develop an antithetic method‐based particle swarm optimization to solve a queuing network problem with fuzzy data for concrete transportation systems. The concrete transportation system at the Jinping‐I Hydropower Project is considered the prototype and is extended to a generalized queuing network problem. The decision maker needs to allocate a limited number of vehicles and unloading equipment in multiple stages to the different queuing network transportation paths to improve construction efficiency by minimizing both the total operational costs and the construction duration. A multiple objective decision‐making model is established which takes into account the constraints and the fuzzy data. To deal with the fuzzy variables in the model, a fuzzy expected value operator, which uses an optimistic–pessimistic index, is introduced to reflect the decision maker's attitude. The particular nature of this model requires the development of an antithetic method‐based particle swarm optimization algorithm. Instead of using a traditional updating method, an antithetic particle‐updating mechanism is designed to automatically control the particle‐updating in the feasible solution space. Results and a sensitivity analysis for the Jinping‐I Hydropower Project are presented to demonstrate the performance of our optimization method, which was proved to be very effective and efficient compared to the actual data from the project and other metaheuristic algorithms.</p> </abstract>