On the capacity inequalities for the heterogeneous vehicle routing problem

IF 0.8 4区 管理学 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Operations Research Letters Pub Date : 2024-12-30 DOI:10.1016/j.orl.2024.107239
Konstantin Pavlikov
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引用次数: 0

Abstract

Fractional and Rounded capacity inequalities are two important families of valid inequalities known for the homogeneous Capacitated Vehicle Routing Problem (CVRP). Such inequalities impose the minimum number of vehicles required to service each and every subset of customers, be it a fractional or an integer value. In case of the Heterogeneous version of the routing problem (HCVRP), the minimum number of vehicles required for a subset of customers is not defined uniquely: it depends on the vehicle types and fleet composition that was engaged in serving the customers. This paper revises existing literature on the capacity-based valid inequalities for the HCVRP and presents new routines to separate them exactly using mixed integer linear programming (MILP). In addition, this paper proposes a new family of capacity-based valid inequalities for the HCVRP together with an exact routine to separate them. A computational study demonstrates applicability of considered inequalities in solving HCVRP instances using a standard MILP solver.
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来源期刊
Operations Research Letters
Operations Research Letters 管理科学-运筹学与管理科学
CiteScore
2.10
自引率
9.10%
发文量
111
审稿时长
83 days
期刊介绍: Operations Research Letters is committed to the rapid review and fast publication of short articles on all aspects of operations research and analytics. Apart from a limitation to eight journal pages, quality, originality, relevance and clarity are the only criteria for selecting the papers to be published. ORL covers the broad field of optimization, stochastic models and game theory. Specific areas of interest include networks, routing, location, queueing, scheduling, inventory, reliability, and financial engineering. We wish to explore interfaces with other fields such as life sciences and health care, artificial intelligence and machine learning, energy distribution, and computational social sciences and humanities. Our traditional strength is in methodology, including theory, modelling, algorithms and computational studies. We also welcome novel applications and concise literature reviews.
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