An Effective Approach for the Multiobjective Regional Low-Carbon Location-Routing Problem

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%

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

Leng

Computational Intelligence and Neuroscience

et al. 2020

Self Cite

“…However, services also should be rejected when it arrived out of the outer time window [35]. Leng et al applied a multi-objective hyper-heuristic approach (MOHH) to obtain Pareto-optimal solutions of a multi-objective regional low-carbon location routing problem (MORLCLRP) with three practical constraints: simultaneous pickup and delivery, heterogeneous fleet, and hard time windows [36]. Du et al investigated a real-time vehicle-dispatching system with mixed time window for consolidating milk runs [35].…”

Section: Vehicle Routing Problem With Time Window Constraintsmentioning

confidence: 99%

Optimization of a Low-Carbon Two-Echelon Heterogeneous-Fleet Vehicle Routing for Cold Chain Logistics under Mixed Time Window

Wen

2020

Sustainability

Due to the rise of social and environmental concerns on global climate change, developing the low-carbon economy is a necessary strategic step to respond to greenhouse effect and incorporate sustainability. As such, there is a new trend for the cold chain industry to establish the low-carbon vehicle routing optimization model which takes costs and carbon emissions as the measurements of performance. This paper studies a low-carbon vehicle routing problem (LC-VRP) derived from a real cold chain logistics network with several practical constraints, which also takes customer satisfaction into account. A low-carbon two-echelon heterogeneous-fleet vehicle routing problem (LC-2EHVRP) model for cold chain third-party logistics servers (3PL) with mixed time window under a carbon trading policy is constructed in this paper and aims at minimizing costs, carbon emissions and maximizing total customer satisfaction simultaneously. To find the optimal solution of such a nondeterministic polynomial (NP) hard problem, we proposed an adaptive genetic algorithm (AGA) approach validated by a numerical benchmark test. Furthermore, a real cold chain case study is presented to demonstrate the influence of the mixed time window’s changing which affect customers’ final satisfaction and the carbon trading settings on LC-2EHVRP model. Experiment of LC-2EHVRP model without customer satisfaction consideration is also designed as a control group. Results show that customer satisfaction is a critical influencer for companies to plan multi-echelon vehicle routing strategy, and current modest carbon price and trading quota settings in China have only a minimal effect on emissions’ control. Several managerial suggestions are given to cold chain logistics enterprises, governments, and even consumers to help improve the development of cold chain logistics.

“…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%

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.