This paper investigates the combined impact of depot location, fleet composition and routing decisions on vehicle emissions in city logistics. We consider a city in which goods need to be delivered from a depot to customers located in nested zones characterized by different speed limits. The objective is to minimize the total depot, vehicle and routing cost, where the latter can be defined with respect to the cost of fuel consumption and CO2 emissions. A new powerful adaptive large neighborhood search metaheuristic is developed and successfully applied to a large pool of new benchmark instances. Extensive analyses are performed to empirically assess the effect of various problem parameters, such as depot cost and location, customer distribution and heterogeneous vehicles on key performance indicators, including fuel consumption, emissions and operational costs. Several managerial insights are presented.
It has been around thirty years since the heterogeneous vehicle routing problem was introduced, and significant progress has since been made on this problem and its variants. The aim of this survey paper is to classify and review the literature on heterogeneous vehicle routing problems. The paper also presents a comparative analysis of the metaheuristic algorithms that have been proposed for these problems.
This paper introduces the fleet size and mix location-routing problem with time windows (FSML-RPTW) which extends the location-routing problem by considering a heterogeneous fleet and time windows. The main objective is to minimize the sum of vehicle fixed cost, depot cost and routing cost. We present mixed integer programming formulations, a family of valid inequalities and we develop a powerful hybrid evolutionary search algorithm (HESA) to solve the problem. The HESA successfully combines several metaheuristics and offers a number of new advanced efficient procedures tailored to handle heterogeneous fleet dimensioning and location decisions. We evaluate the strengths of the proposed formulations with respect to their ability to find optimal solutions. We also investigate the performance of the HESA. Extensive computational experiments on new benchmark instances have shown that the HESA is highly effective on the FSMLRPTW.
We introduce the electric vehicle routing problem with shared charging stations (E-VRP-SCS). The E-VRP-SCS extends the electric vehicle routing problem with nonlinear charging function (E-VRP-NL) by considering several companies that jointly invest in charging stations (CSs). The objective is to minimize the sum of the fixed opening cost of CSs and the drivers cost. The problem consists of deciding the location and technology of the CSs and building the routes for each company. It is solved by means of a multistart heuristic that performs an adaptive large neighborhood search coupled with the solution of mixed integer linear programs. It also contains a number of advanced efficient procedures tailored to handle specific components of the E-VRP-SCS. We perform extensive computational experiments on benchmark instances. We assess the competitiveness of the heuristic on the E-VRP-NL and derive 38 new best known solutions. New benchmark results on the E-VRP-SCS are presented, solved, and analyzed.
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