An Adaptive Large Neighborhood Search for the Larger-Scale Instances of Green Vehicle Routing Problem with Time Windows

Abstract

Due to huge amount of greenhouse gases emission (such as CO2), freight has been adversely affecting the global environment in facilitating the global economy. Therefore, green vehicle routing problem (GVRP), aiming to minimize the total carbon emissions in the transportation, has become a hot issue. In this paper, an adaptive large neighborhood search (ALNS) algorithm is proposed to solve large-scale instances of GVRP. The core of ALNS algorithm is destroy operators and repair operators. In the destroy operators, a new removal heuristic applying to the characteristics of GVRP is proposed. The heuristic can quickly remove customers who bring a large amount of carbon emissions with pertinence, and these customers may be arranged more properly in future repair operators. In the repair operators, a fast insertion method is developed. In the fast insertion method, the feasibility of a new route is judged by checking the constraints of partial customers after the inserted customer, instead of checking the constraints of all customers. Thus, the computational time of the ALNS algorithm is greatly saved. Computational experiments were performed on Solomon benchmark with 100 customers and Homberger benchmark instances with up to 1000 customers. Given the same computational time, the proposed ALNS improves the average accuracy by 8.49% compared with the classic ALNS. In the optimal situation, the improvement can achieve 33.61%.

Robust Electric Vehicle Routing Problem with Time Windows under Demand Uncertainty and Weight-Related Energy Consumption

“…Computational experiments are carried out to verify the effectiveness of the ALNS and explore the law of robust model. The instances for experiments are θ q adapted from the PRP instances [37] . First of all, in order to explore the effectiveness of the ALNS, we compare the deterministic solution of the EVRPTW with the deterministic solution result of the EVRPTWFM given by Goeke and Schneider [15] .…”

“…It was first proposed by Ropke and Pisinger [34] as an extension of large neighbourhood search (LNS). Subsequently, Pisinger and Ropke [35] improved the ALNS algorithm and proposed a general framework, which obtains very competitive results for multiple types of VRP problems [20,36,37] . Especially, Yu et al [38] designed the ALNS to solve large-scale instances of GVRP, where effective removal and insertion operators and a fast solution evaluation are proposed to improve the computation efficiency.…”

“…The instances for experiments are adapted from the pollution routing problem (PRP) instances [37] , which contain 9 subsets of various scales, and each subset includes 20 instances of the same size. Each subset size ranges from 10 to 200.…”

A Review of Heuristics and Hybrid Methods for Green Vehicle Routing Problems considering Emissions

“…+ere are other works with applications of heuristics to solve GVRPs, such as ALNS for multicompartment vehicles for city logistics and G-VRPTW [107,108], GA for the effect of governmental time window policy on the routing planning decisions of cold chain distribution companies [109], fuzzy hierarchical clustering method and GA for the customer-oriented routing problem with consideration of the environment [110], and a TS and VNS for VRP in the home health care sector called VRPTW with synchronization, precedence, and fuel consumption constraints (VRPTW-SPFC) [111].…”

“…In addition, there are three types of customers for each form depending on the number and frequency of visits required. Referring to the instances generated for MDVRP, the most frequently used one is that which is cited by Cordeau et al [144] named as • E-PCVRP, [125]; HeVRPMD, Eskandarpour et al [65]; WCP-MDP, Wei et al [120]; BSS-EV-LRP, Yang and Sun [67] Chao et al [146] • Bi-PVRP-SC, López-Sánchez et al [50] Taillard [147] • • Eco-WCVRP, Molina et al [123]; BSS-EV-LRP, Yang and Sun [67] Solomon [73] • 2E-VRPSyn, Anderluh et al [46]; mixed-fleet logistics distribution problem under CO 2 emission cap, Islam and Gajpal [101]; HHC with synchronized visits and carbon emissions, Luo et al [102]; MTHVRP-PCIC, Lyu and He [41]; B-MFGVRPTW, Ren et al [104]; G-VRPTW, Yu et al [108]; cold chain logistics path optimization, Zhao et al [100]; G-VRPTW, Sanchez et al [103]; PRP, FCVRP, EMVRP, Kramer et al [53]; G-VRPTW, Küçükoglu et al [117]; PRP, Demir et al [51] • Bi-PVRP-SC, López-Sánchez et al [50] Christofides and Eilon [150] • GVRP, Dewi and Utama [22]; 2E-CVRPSC, Mühlbauer and Fontaine [47]; E-PCVRP, Trachanatzi et al [125]; WCP-MDP, Wei et al [120]; GVRP, de Oliveira da Costa et al [18] Gaskell [151] • GVRP, Dewi and Utama [22] Cordeau instances. +e authors presented a set of 23 instances based the sets from Christofides et al [148] (instances 1-7), Gillett and Johnson [152] (instances 8-11), and Chao et al [146] (12)(13)…”