The continuous pollution routing problem

In this paper, we presented an ε-accurate approach to conduct a continuous optimization on the pollution routing problem (PRP). First, we developed an ε-accurate inner polyhedral approximation method for the nonlinear relation between the travel time and travel speed. The approximation error was con...

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Bibliographic Details
Published in:Applied mathematics and computation Vol. 387; p. 125072
Main Authors: Xiao, Yiyong, Zuo, Xiaorong, Huang, Jiaoying, Konak, Abdullah, Xu, Yuchun
Format: Journal Article
Language:English
Published: Elsevier Inc 15.12.2020
Subjects:
Continuous optimization
Convex programming
Emission reduction
Vehicle routing problem
Emission reduction
Continuous optimization
Convex programming
Vehicle routing problem
ISSN:0096-3003
Online Access:Get full text
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Summary:In this paper, we presented an ε-accurate approach to conduct a continuous optimization on the pollution routing problem (PRP). First, we developed an ε-accurate inner polyhedral approximation method for the nonlinear relation between the travel time and travel speed. The approximation error was controlled within the limit of a given parameter ε, which could be as low as 0.01% in our experiments. Second, we developed two ε-accurate methods for the nonlinear fuel consumption rate (FCR) function of a fossil fuel-powered vehicle while ensuring the approximation error to be within the same parameter ε. Based on these linearization methods, we proposed an ε-accurate mathematical linear programming model for the continuous PRP (ε-CPRP for short), in which decision variables such as driving speeds, travel times, arrival/departure/waiting times, vehicle loads, and FCRs were all optimized concurrently on their continuous domains. A theoretical analysis is provided to confirm that the solutions of ε-CPRP are feasible and controlled within the predefined limit. The proposed ε-CPRP model is rigorously tested on well-known benchmark PRP instances in the literature, and has solved PRP instances optimally with up to 25 customers within reasonable CPU times. New optimal solutions of many PRP instances were reported for the first time in the experiments.
ISSN:0096-3003
DOI:10.1016/j.amc.2020.125072