Abstract
Uncertainty is prevalent and unavoidable in business operations. This paper presents a mathematical model for the optimal routing of shipping raw materials to customers that requires simultaneous pickup and delivery with soft time windows for travel time in a fuzzy random environment. It minimizes total traveling time while maximizing customer satisfaction and meeting constraints characterized by fuzziness and randomness in pickup and travel time. The model is strong NP-hard. Through embedding of customer satisfaction as a constraint and converting fuzzy random variables into deterministic ones using expected values, a viable algorithm is developed using the Global-Local-Neighbor Particle Swarm Optimization (GLNPSO) technique, and tested by solving a real routing problem faced by a large construction project in China. Results are encouraging, both in solution quality and potential savings, to justify the solution method and model formulation.
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