A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints

Leng

Computational Intelligence and Neuroscience

et al. 2020

Self Cite

In response to violent market competition and demand for low-carbon economy, cold chain logistics companies have to pay attention to customer satisfaction and carbon emission for better development. In this paper, a biobjective mathematical model is established for cold chain logistics network in consideration of economic, social, and environmental benefits; in other words, the total cost and distribution period of cold chain logistics are optimized, while the total cost consists of cargo damage cost, refrigeration cost of refrigeration equipment, transportation cost, fuel consumption cost, penalty cost of time window, and operation cost of distribution centres. One multiobjective hyperheuristic optimization framework is proposed to address this multiobjective problem. In the framework, four selection strategies and four acceptance criteria for solution set are proposed to improve the performance of the multiobjective hyperheuristic framework. As known from a comparative study, the proposed algorithm had better overall performance than NSGA-II. Furthermore, instances of cold chain logistics are modelled and solved, and the resulting Pareto solution set offers diverse options for a decision maker to select an appropriate cold chain logistics distribution network in the interest of the logistics company.

Section: Discussionmentioning

confidence: 99%

Section: Location-routing Optimization Modelling For Coldmentioning

confidence: 99%

A Hyperheuristic Approach for Location-Routing Problem of Cold Chain Logistics considering Fuel Consumption

Leng

Computational Intelligence and Neuroscience

et al. 2020

Self Cite

“…worst, the reason is that the estimation of FCCE depends on plentiful parameters which may change over traveling time. For a comprehensive view of the models and factors, the reader is referred to the surveys [21] and papers [20,[28][29][30][31].…”

Section: Plos Onementioning

confidence: 99%

“…Moreover, as an extensive variant of VRP, the investigation of the green/lowcarbon LRP was not provided. Our previous work [29,30] provided a detailed assessment of the FCCE model for green/low carbon LRP, the solution approach, and the number of optimization objectives. This section provides additional papers on the recently published LCLRP, and other papers are also available in our previous paper [29,30].…”

Section: Lrp Considering Environmental Effectsmentioning

confidence: 99%

A novel bi-objective model of cold chain logistics considering location-routing decision and environmental effects

Leng

Zhang

et al. 2020

PLoS ONE

Self Cite

Economic, environmental, and social effects are the most dominating issues in cold chain logistics. The goal of this paper is to propose a cost-saving, energy-saving, and emissionreducing bi-objective model for the cold chain-based low-carbon location-routing problem. In the proposed model, the first objective (economic and environmental effects) is to minimize the total logistics costs consisting of costs of depots to open, renting vehicles, fuel consumption, and carbon emission, and the second one (social effect) is to reduce the damage of cargos, which could improve the client satisfaction. In the proposed model, a strategy is developed to meet the requirements of clients as to the demands on the types of cargos, that is, general cargos, refrigerated cargos, and frozen cargos. Since the proposed problem is NP-hard, we proposed a simple and efficient framework combining seven well-known multiobjective evolutionary algorithms (MOEAs). Furthermore, in the experiments, we first examined the effectiveness of the proposed framework by assessing the performance of seven MOEAs, and also verified the efficiency of the proposed model. Extensive experiments were carried out to investigate the effects of the proposed strategy and variants on depot capacity, hard time windows, and fleet composition on the performance indicators of Pareto fronts and cold chain logistics networks, such as fuel consumption, carbon emission, travel distance, travel time, and the total waiting time of vehicles.

“…Owing to the complexity of the CPDPTW, commercial solvers are ineffective in incorporating all the practical factors considered in the problem formulation. Compared with commercial solvers, a heuristic algorithm can offer a series of feasible solutions for practical analysis [57]. The Improved NSGA-II algorithm (INSGA-II) is developed from NSGA and is proposed by Deb et al [58] in order to complement NSGA's lack of elitism and speed [41,59,60].…”

Section: Improved Nsga-ii Algorithmmentioning

confidence: 99%

Collaborative Mechanism for Pickup and Delivery Problems with Heterogeneous Vehicles under Time Windows

et al. 2019

The sustainability and complexity of logistics networks come from the temporally and spatially uneven distributions of freight demand and supply. Operation strategies without considering the sustainability and complexity could dramatically increase the economic and environmental costs of logistics operations. This paper explores how the unevenly distributed demand and supply can be optimally matched through collaborations, and formulates and solves a Collaborative Pickup and Delivery Problem under Time Windows (CPDPTW) to optimize the structures of logistics networks and improve city sustainability and liverability. The CPDPTW is a three-stage framework. First, a multi-objective linear optimization model that minimizes the number of vehicles and the total cost of logistics operation is developed. Second, a composite algorithm consisting of improved k-means clustering, Demand-and-Time-based Dijkstra Algorithm (DTDA) and Improved Non-dominated Sorting Genetic Algorithm-II (INSGA-II) is devised to solve the optimization model. The clustering algorithm helps to identify the feasible initial solution to INSGA-II. Third, a method based on improved Shapley value model is proposed to obtain the collaborative alliance strategy that achieves the optimal profit allocation strategy. The proposed composite algorithm outperforms existing algorithms in minimizing terms of the total cost and number of electro-tricycles. An empirical case of Chongqing is employed to demonstrate the efficiency of the proposed mechanism for achieving optimality for logistics networks and realizing a win-win situation between suppliers and consumers.