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Sustainable development is critical to cold chain logistics, including its economic, environmental, and social effects, especially in road transportation. To simultaneously address these issues, we propose a comprehensive cold-chain-based low-carbon location-routing-problem optimization model to minimize the total logistics costs and client and vehicle waiting time. The first objective comprises the fixed costs of depots to open and vehicles to rent, vehicle renting cost, driver salaries, fuel consumption cost, carbon emission costs, and damage costs of cargos that need to be refrigerated or frozen. The second objective consists of the waiting time of clients and vehicles to improve client satisfaction and the efficiency of the cold chain logistics network. In the proposed problem, we developed a strategy for improving the efficiency of the cold chain logistics network by mixing the types of cargos arranged in one vehicle. Aiming at efficiently solving the proposed model, six well-known multi-objective evolutionary algorithms (MOEAs) were used by combining an efficient framework, and first (FI) and best-improvement (BI) search mechanisms were considered. In the experiments, we examined the effectiveness of six MOEAs inserting the proposed framework and search mechanisms, and the result showed that NSGA-II/FI, SPEA2/FI, and NSGA-II/BI were the top three MOEAs. In the extensive experiments, the results showed that the delivery strategy, depot cost, depot capacity, crowding distance, and traveling speed have significant effects on the Pareto front, fuel consumption, carbon emission, vehicle and client waiting times, traveling distance, and traveling time.
Longlong Leng; Chunmiao Zhang; Yanwei Zhao; Wanliang Wang; Jingling Zhang; Gongfa Li. Biobjective low-carbon location-routing problem for cold chain logistics: Formulation and heuristic approaches. Journal of Cleaner Production 2020, 273, 122801 .
AMA StyleLonglong Leng, Chunmiao Zhang, Yanwei Zhao, Wanliang Wang, Jingling Zhang, Gongfa Li. Biobjective low-carbon location-routing problem for cold chain logistics: Formulation and heuristic approaches. Journal of Cleaner Production. 2020; 273 ():122801.
Chicago/Turabian StyleLonglong Leng; Chunmiao Zhang; Yanwei Zhao; Wanliang Wang; Jingling Zhang; Gongfa Li. 2020. "Biobjective low-carbon location-routing problem for cold chain logistics: Formulation and heuristic approaches." Journal of Cleaner Production 273, no. : 122801.
This paper proposed a novel approach for a practical version of the cold chain, namely location-routing problem-based low-carbon cold chain (LRPLCCC). In the proposed bi-objective model, the first objective is the total logistics cost, including the fixed costs of the opened depots and leased vehicles, as well as the cost of fuel consumption and carbon emissions, and the second is to minimize the amount of quality degradation that aims at improving clients’ satisfaction and maintain product freshness. The cargos of clients are classified into three types: general, refrigerated, and frozen cargos. Since the presented problem is NP-hard, a novel multi-objective hyperheuristic (MOHH) was proposed to obtain the Pareto solutions. In this framework, three selection strategies were developed to improve the performance of MOHH, that is, random simple, choice function, and FRR-MAB (fitness rate rank based multi-armed bandit), and three acceptance criteria using the decomposition approaches in MOEA/D were also developed, namely penalty-based boundary intersection, Tchebycheff, and modified Tchebycheff approaches. Extensive experiments were provided to verify the efficiency of the proposed algorithms and assessed the effects of algorithm parameters on the Pareto front. The results showed that the efficiency of the proposed algorithm outperforms several existing well-known multi-objective evolutionary algorithms (MOEA).
Longlong Leng; Jingling Zhang; Chunmiao Zhang; Yanwei Zhao; Wanliang Wang; Gongfa Li. Decomposition-based hyperheuristic approaches for the bi-objective cold chain considering environmental effects. Computers & Operations Research 2020, 123, 105043 .
AMA StyleLonglong Leng, Jingling Zhang, Chunmiao Zhang, Yanwei Zhao, Wanliang Wang, Gongfa Li. Decomposition-based hyperheuristic approaches for the bi-objective cold chain considering environmental effects. Computers & Operations Research. 2020; 123 ():105043.
Chicago/Turabian StyleLonglong Leng; Jingling Zhang; Chunmiao Zhang; Yanwei Zhao; Wanliang Wang; Gongfa Li. 2020. "Decomposition-based hyperheuristic approaches for the bi-objective cold chain considering environmental effects." Computers & Operations Research 123, no. : 105043.
The Location Routing Problem (LRP), a branch of logistics management, has been addressed in many research papers.However, there are few papers on time-dependent LRP.And only a few of them take fuel consumption into consideration.To reduce the environmental pollution from vehicle emissions and the cost pressure on logistics, a novel model named the time-dependent green location routing problem with time windows (TDGLRP) is developed. Its objective is to minimize costs including opened depot costs, enabled vehicle costs and fuel consumption costs.In TDGLRP the speed and travel times are time-dependent function. A hyper-heuristic algorithm (HH) that consists of two levels, high-level heuristics (HLHs) and low-level heuristics (LLHs), is proposed to solve the TDLGRP. The Tabu Search (TS) algorithm is taken as the high-level selection mechanism, and the Greedy algorithm is taken as the acceptance mechanism. With reference to the Solomon benchmarks, we design a series of TDGLRP instances with 100 client nodes, and analyze the impact of client distribution characteristics on the path. Based on the TDGLRP model and HH, the end of the article gives the solution results of a large-scale instances with 1000 nodes.
Chunmiao Zhang; Yanwei Zhao; Longlong Leng. A Hyper-Heuristic Algorithm for Time-Dependent Green Location Routing Problem With Time Windows. IEEE Access 2020, 8, 83092 -83104.
AMA StyleChunmiao Zhang, Yanwei Zhao, Longlong Leng. A Hyper-Heuristic Algorithm for Time-Dependent Green Location Routing Problem With Time Windows. IEEE Access. 2020; 8 (99):83092-83104.
Chicago/Turabian StyleChunmiao Zhang; Yanwei Zhao; Longlong Leng. 2020. "A Hyper-Heuristic Algorithm for Time-Dependent Green Location Routing Problem With Time Windows." IEEE Access 8, no. 99: 83092-83104.
This paper proposes a low-carbon location routing problem (LCLRP) model with simultaneous delivery and pick up, time windows, and heterogeneous fleets to reduce the logistics cost and carbon emissions and improve customer satisfaction. The correctness of the model is tested by a simple example of CPLEX (optimization software for mathematical programming). To solve this problem, a hyper-heuristic algorithm is designed based on a secondary exponential smoothing strategy and adaptive receiving mechanism. The algorithm can achieve fast convergence and is highly robust. This case study analyzes the impact of depot distribution and cost, heterogeneous fleets (HF), and customer distribution and time windows on logistics costs, carbon emissions, and customer satisfaction. The experimental results show that the proposed model can reduce logistics costs by 1.72%, carbon emissions by 11.23%, and vehicle travel distance by 9.69%, and show that the proposed model has guiding significance for reducing logistics costs.
Chunmiao Zhang; Yanwei Zhao; Longlong Leng. A Hyper Heuristic Algorithm to Solve the Low-Carbon Location Routing Problem. Algorithms 2019, 12, 129 .
AMA StyleChunmiao Zhang, Yanwei Zhao, Longlong Leng. A Hyper Heuristic Algorithm to Solve the Low-Carbon Location Routing Problem. Algorithms. 2019; 12 (7):129.
Chicago/Turabian StyleChunmiao Zhang; Yanwei Zhao; Longlong Leng. 2019. "A Hyper Heuristic Algorithm to Solve the Low-Carbon Location Routing Problem." Algorithms 12, no. 7: 129.
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.
Longlong Leng; Yanwei Zhao; Jingling Zhang; Chunmiao Zhang. An Effective Approach for the Multiobjective Regional Low-Carbon Location-Routing Problem. International Journal of Environmental Research and Public Health 2019, 16, 2064 .
AMA StyleLonglong Leng, Yanwei Zhao, Jingling Zhang, Chunmiao Zhang. An Effective Approach for the Multiobjective Regional Low-Carbon Location-Routing Problem. International Journal of Environmental Research and Public Health. 2019; 16 (11):2064.
Chicago/Turabian StyleLonglong Leng; Yanwei Zhao; Jingling Zhang; Chunmiao Zhang. 2019. "An Effective Approach for the Multiobjective Regional Low-Carbon Location-Routing Problem." International Journal of Environmental Research and Public Health 16, no. 11: 2064.
With the aim of reducing cost, carbon emissions, and service periods and improving clients’ satisfaction with the logistics network, this paper investigates the optimization of a variant of the location-routing problem (LRP), namely the regional low-carbon LRP (RLCLRP), considering simultaneous pickup and delivery, hard time windows, and a heterogeneous fleet. In order to solve this problem, we construct a biobjective model for the RLCLRP with minimum total cost consisting of depot, vehicle rental, fuel consumption, carbon emission costs, and vehicle waiting time. This paper further proposes a novel hyper-heuristic (HH) method to tackle the biobjective model. The presented method applies a quantum-based approach as a high-level selection strategy and the great deluge, late acceptance, and environmental selection as the acceptance criteria. We examine the superior efficiency of the proposed approach and model by conducting numerical experiments using different instances. Additionally, several managerial insights are provided for logistics enterprises to plan and design a distribution network by extensively analyzing the effects of various domain parameters such as depot cost and location, client distribution, and fleet composition on key performance indicators including fuel consumption, carbon emissions, logistics costs, and travel distance and time.
Longlong Leng; Yanwei Zhao; Zheng Wang; Jingling Zhang; Wanliang Wang; Chunmiao Zhang. A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints. Sustainability 2019, 11, 1596 .
AMA StyleLonglong Leng, Yanwei Zhao, Zheng Wang, Jingling Zhang, Wanliang Wang, Chunmiao Zhang. A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints. Sustainability. 2019; 11 (6):1596.
Chicago/Turabian StyleLonglong Leng; Yanwei Zhao; Zheng Wang; Jingling Zhang; Wanliang Wang; Chunmiao Zhang. 2019. "A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints." Sustainability 11, no. 6: 1596.