Abstract
With the aim of reducing cost, carbon emissions, and service periods and improving clients’ satisfaction with the logistics network, this paper investigates the optimization of a variant of the location-routing problem (LRP), namely the regional low-carbon LRP (RLCLRP), considering simultaneous pickup and delivery, hard time windows, and a heterogeneous fleet. In order to solve this problem, we construct a biobjective model for the RLCLRP with minimum total cost consisting of depot, vehicle rental, fuel consumption, carbon emission costs, and vehicle waiting time. This paper further proposes a novel hyper-heuristic (HH) method to tackle the biobjective model. The presented method applies a quantum-based approach as a high-level selection strategy and the great deluge, late acceptance, and environmental selection as the acceptance criteria. We examine the superior efficiency of the proposed approach and model by conducting numerical experiments using different instances. Additionally, several managerial insights are provided for logistics enterprises to plan and design a distribution network by extensively analyzing the effects of various domain parameters such as depot cost and location, client distribution, and fleet composition on key performance indicators including fuel consumption, carbon emissions, logistics costs, and travel distance and time.
Highlights
City logistics, in the context of freight transportation, exert pressure on the economy, society, environment, and citizens [1]
Among the literatures about multiobjective hyper-heuristic (MOHH), two categories can be discerned: multiobjective evolutionary algorithm-based HH (MOEAs-based HH) and MOHH-II. The former investigates the selection of the level heuristic (LLHs), which consists of local search, crossovers, or mutation operators implemented in the frameworks of MOEAs; the latter utilizes the metaheuristics as LLHs, such as strengthen Pareto evolutionary algorithm2 (SPEA2), nondominated sorting genetic algorithm-II (NSGA-II), and multiobjective genetic algorithm (MOGA)
The following assumptions are made: (1) each client must be satisfied only once; (2) each vehicle must return to the departure depot; (3) the load of each vehicle in each edge must not exceed its capacity; (4) the load of each depot must be less than its capacity; (5) the number of each type of vehicle is unlimited; (6) the maximum load of a route is the principle for selecting the type of vehicle; (7) the vehicle must arrive at client’s nodes before the closing time window; (8) if a vehicle arrives before the opening time window, it waits until the opening time windows
Summary
In the context of freight transportation, exert pressure on the economy, society, environment, and citizens [1]. Among several tools of logistic network design, the traveling salesman problem (TSP) [2], the vehicle-routing problem (VRP) [3], and the location-routing problem (LRP) [4] are the most important and widely studied combinatorial optimization problems, especially the LRP These basic tools are incapable of addressing sustainable development for the economy, society, and the environment simultaneously. Premutation and coordination of the joint bond of economic, social, and environmental benefits have emerged as one of the most addressed problems This inspires us to model the LRP by considering the environmental effect, which aims to reduce cost, shorten the service period, and reduce greenhouse gases (GHG) emissions, especially CO2.
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