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

Abstract

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.

A Novel Multi-Objective Model for the Cold Chain Logistics Considering Multiple Effects

“…In Leng et al [2], a novel bi-objective model for the LRPCCL was proposed, which handles the cargo quality decay as the second objective. Leng et al [15] developed several novel solution methods (i.e., decomposition-based hyper-heuristic approaches) to solve the bi-objective model proposed by Leng et al [2].…”

The Optimization of the Location of the Cargo in Three-Dimension Shelf: Employing the FP-Tree and the Artificial Fish Swarm Algorithms

“…Lei et al [11] suggested a two-layer genetic algorithm to resolve an optimization model providing the shortest outbound time of all outbound orders in certain historical periods. Leng et al [12] constructed a biobjective model, which helps achieve cost saving, energy saving, and emission reduction for the cold chain-based lowcarbon location-routing problem that was a simple and efficient framework combining seven well-known multiobjective evolutionary algorithms. Haoxiang et al [13] put forward an adaptive multiobjective genetic algorithm aiming at reaching the highest efficacy and shelf stability for the efficiency of warehousing.…”

Using genetic algorithm to solve multiple traveling salesman problem and considering Carbon emissions

“…Result showed that the method is quite efficient and produces higher quality solutions. (8) A novel multi-objective hybrid quantum immune algorithm was proposed based on cloud model to improve the delivery efficiency and reduce the cost of distribution requirements. Results showed that C-HQIA is an efficient algorithm for the GVROP.…”

“…Results appears that the approach performs exceptionally well to recognize customer groups and connects individual customer's benefits in VRP. (13) Youssef Harrath, A. F. (2019) proposed a two-part chromosome crossover to solve Multiple Travelling Salesman Problem. In their work a genetic algorithm was used to find the best solution.…”

“…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. (8) In order to attain efficiency in the transportation of agro products and customer fulfillment, a linear programming strategy of operations research was used to minimize the route of logistics transportation Xiao-Hong, L., Shan, M.-Y., Zhang, et al (2018) paper provides a low-carbon and environmental protection point of view in his work a hybrid genetic algorithm with heuristic rules is outlined to solve the model, and a case is utilized to confirm how effective the algorithm is. Through a practical example of the simulation outcomes shows the use of the model.…”