Abstract
In this paper, we consider a variant of the location-routing problem (LRP), namely the the multiobjective regional low-carbon LRP (MORLCLRP). The MORLCLRP seeks to minimize service duration, client waiting time, and total costs, which includes carbon emission costs and total depot, vehicle, and travelling costs with respect to fuel consumption, and considers three practical constraints: simultaneous pickup and delivery, heterogeneous fleet, and hard time windows. We formulated a multiobjective mixed integer programming formulations for the problem under study. Due to the complexity of the proposed problem, a general framework, named the multiobjective hyper-heuristic approach (MOHH), was applied for obtaining Pareto-optimal solutions. Aiming at improving the performance of the proposed approach, four selection strategies and three acceptance criteria were developed as the high-level heuristic (HLH), and three multiobjective evolutionary algorithms (MOEAs) were designed as the low-level heuristics (LLHs). The performance of the proposed approach was tested for a set of different instances and comparative analyses were also conducted against eight domain-tailored MOEAs. The results showed that the proposed algorithm produced a high-quality Pareto set for most instances. Additionally, extensive analyses were also carried out to empirically assess the effects of domain-specific parameters (i.e., fleet composition, client and depot distribution, and zones area) on key performance indicators (i.e., hypervolume, inverted generated distance, and ratio of nondominated individuals). Several management insights are provided by analyzing the Pareto solutions.
Highlights
City logistics poses challenges to governments, businesses, carries, and citizens, in the context of freight transport [1] in terms of three effects: economy, society, and the environment.it requires new business operating models for addressing the above triple effects
The paper is structured as follows: Section 2 provides a review of related literature; Section 3 describes the MORLCLRP with simultaneous pickup and delivery, heterogeneous fleet, and hard time windows; Section 4 gives a brief description of the hyper-heuristic framework together with a general multiobjective evolutionary algorithms (MOEAs) structure for the MORLCLRP; Section 5 describes the computational experiments and simulated results; and, Section 6 outlines the study conclusions
We presented a multiobjective hyper-heuristic approach (MOHH) algorithm to solve a MORLCLRP considering simultaneous pickup and delivery, hard time windows, and heterogeneous fleets
Summary
City logistics poses challenges to governments, businesses, carries, and citizens, in the context of freight transport [1] in terms of three effects: economy, society, and the environment. The basic LRP model is only concerned with logistics costs and neglects the environmental and social effects of transportation. Fewer studies have incorporated fuel consumption and carbon emission (FCCE) into basic LRP models, such as Pourhejazy et al [5] and see Section 2.3. In the real-world logistics network, cost, service duration, and client satisfaction were considered the most significant performance indicators, and traveling cost incorporated FCCE cost. For HLHs, we provided four selection strategies and three acceptance criteria to improve the performance of the MOHH framework and developed three MOEAs as the pool of LLHs. We conducted extensive computational experiments to assess the efficiency of the proposed algorithms and developed managerial implications by assessing problem parameters, such as client and depot locations, speed zone areas, and fleet composition. The paper is structured as follows: Section 2 provides a review of related literature; Section 3 describes the MORLCLRP with simultaneous pickup and delivery, heterogeneous fleet, and hard time windows; Section 4 gives a brief description of the hyper-heuristic framework together with a general MOEA structure for the MORLCLRP; Section 5 describes the computational experiments and simulated results; and, Section 6 outlines the study conclusions
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