Optimization of Sustainable Bi-Objective Cold-Chain Logistics Route Considering Carbon Emissions and Customers’ Immediate Demands in China

Ma, Zhichao; Zhang, Jie; Wang, Huanhuan; Gao, Shaochan

doi:10.3390/su15075946

Cited by 7 publications

(9 citation statements)

References 46 publications

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“…It had functions for path-planning and position-tracking. Ma et al [9] aimed to save energy and reduce carbon emissions by calculating the cost of carbon emissions in cold chain distribution. By studying the immediate needs of customers, an optimization algorithm was constructed to optimize distribution routes.…”

Section: Related Workmentioning

confidence: 99%

“…Judge point n ode (5) , Path (6,5) forms a turnaround path, discard n ode(5) ; (6) Continue searching in the direction of R (j) , randomly select the neighboring point n ode (7) of n ode (6) , form a path, and store n ode (7) , as shown in Figure 8f. (7) Judge the points n ode(3) , n ode(4) , and n ode (5) , and whether they form a closed or turnaround path with n ode (7) , discard it; randomly select the neighboring point n ode (9) of n ode (7) , form a path, and store n ode (9) , as shown in Figure 8g; (8) Continue searching in the direction of R (j) , randomly select the neighboring point n ode (11) of n ode (9) , form a path, and store n ode (11) , as shown in Figure 8h; (9) Judge point n ode (8) , Path (11,8) forms a turnaround path, discard n ode (8) ; search for R (j) , form a path, Path (i, 1,4,3,6,7,9,11, j), and the search processing ends, as shown in Figure 8i. Output the No.…”

Section: Algorithm 3: the Sub-interval Distribution Route Model Based...mentioning

confidence: 99%

“…Delete n ode (10) from the Open list and store it in the Closed list, as shown in Figure 4e; (6) In line with Steps (1) through ( 5), determine whether the area of the closed polygon is the minimum value, and whether the polygon is convex. Finally, the nodes n ode (9) , n ode (5) , and n ode(2) meet the requirements of the cellular unit. Delete them from the Open list and store them in the Closed list to form the cellular unit.…”

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confidence: 99%

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An Intelligent Connected Vehicle Material Distribution Route Model Based on k-Center Spatial Cellular Clustering and an Improved Cockroach Optimization Algorithm

Zhou,

Wang,

Liu

et al. 2024

Symmetry

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Based on the analysis of the problems in material distribution routes, we propose the idea of integrating the intelligent connected vehicle system with material distribution, and construct an intelligent connected vehicle material distribution route model based on k-center spatial cellular clustering and an improved cockroach optimization algorithm. Firstly, we set the research scope to include the distribution center, the distribution points and the geographical environment. A cellular spatial model of distribution points is constructed to quantify and visualize the neighborhood relationship between the distribution centers and distribution points. On this basis, we construct an intelligent connected vehicle material distribution route model based on the improved cockroach optimization algorithm, and the optimal material distribution center is determined by searching for the corresponding optimal distribution route of each distribution center. In the experiment, we use the concept of symmetry to design routes that start from the initial points. The route passes through the distribution point, and finally reaches the destination. In this mode, the experiment generates symmetrically round-trip routes and generates different distribution time schedules. Case studies and comparative experiments show that the proposed algorithm has a total distance cost 1.2 km lower than the distance cost generated by the Baidu Map method and 2.7 km lower than the distance cost generated by the 360 Map method. In terms of the total time cost of the proposed algorithm, it is 0.06 h lower than the time cost generated by the Baidu Map method and 0.135 h lower than the time cost generated by the 360 Map method. Compared with the commonly used Dijkstra algorithm and the A* algorithm for route optimization, our proposed algorithm also generates a lower cost than the two other types of optimization algorithms. In the case study, the distance generated by the proposed algorithm is 1.8 km lower than that of the Dijkstra algorithm, and the total time cost is 0.09 h lower than that of the Dijkstra algorithm. The distance generated by the proposed algorithm is 1.6 km lower than that of the A* algorithm, and the total time cost is 0.08 h lower than that of the A* algorithm. Meanwhile, the proposed algorithm has a lower time complexity than the two commonly used optimization algorithms. Therefore, our proposed algorithm can find the distribution route with the lowest transportation cost. Compared to the commonly used electronic maps and the optimization algorithms for distribution route planning, our proposed algorithm can output distribution routes with lower costs under the same distribution sequence, and reduce the transportation costs for intelligent connected vehicle material distribution systems to the maximum extent.

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Section: Related Workmentioning

confidence: 99%

Section: Algorithm 3: the Sub-interval Distribution Route Model Based...mentioning

confidence: 99%

mentioning

confidence: 99%

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An Intelligent Connected Vehicle Material Distribution Route Model Based on k-Center Spatial Cellular Clustering and an Improved Cockroach Optimization Algorithm

Zhou,

Wang,

Liu

et al. 2024

Symmetry

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“…A hybrid particle swarm optimization (HPSO) algorithm was designed to solve the problem of minimum freshness requirements for different types of cold chain distribution. Ma et al [22] optimized local distribution paths for immediate demand, balancing enterprise economic efficiency and customer satisfaction while reducing the environmental pollution. To minimize distribution costs and maximize customer satisfaction, we designed an improved ant colony algorithm to solve the initial distribution path with an insertion method for immediate customer demand.…”

Section: Introduction 1importance and Motivationmentioning

confidence: 99%

Research on Cold Chain Logistics Transportation Scheme under Complex Conditional Constraints

Xu,

Sun,

Zhang

et al. 2023

Sustainability

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A mathematical model is proposed to minimize the sum of vehicle fixed cost, fuel cost, carbon-emission cost, cooling cost, time-penalty cost and split-compensation cost, on the basis of considering the three-level cold-chain-logistics network of manufacturer, distribution center, and seller. The model is constructed based on the constraints of customer time window, vehicle load, demand-splitable, and semi-open driving of multiple distribution centers. We to divide the customer areas according to geographical locations and to carry out the transportation processes in stages. The target solution, which includes vehicle routing, service time and type, cargo details, etc., has been formulated. A two-stage hybrid-heuristic-path-scheme solution algorithm that combines a taboo table, a genetic algorithm, an optimal-path-generation algorithm, a load-capacity-constraint algorithm, and a time-window-constraint algorithm is designed in view of the complexity of the model and the uniqueness of the solution scheme. This paper aims to reasonably plan the resource allocation of cold chain logistics enterprises, reduce the comprehensive cost of cold chain transportation, improve customer satisfaction, and respond to the green logistics policy advocated by the state by reducing vehicle transit time and fuel consumption, and promote energy conservation and emission reduction.

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“…MA aims to minimize distribution cost and maximize customer satisfaction, he design an improved ant colony algorithm to solve the initial distribution path and use the insertion method to solve the immediate customer demand. [5]. Lisa et al mainly studied the construction of cold chain distribution models, combined with the distribution data of cold chain companies, and used Matlab programming tools and commonly used hybrid mutation probabilities to obtain optimization results [6].In recent years, research on path planning and algorithms in China has also been greatly strengthened.…”

Section: Introductionmentioning

confidence: 99%

Research on Optimization of Urban Logistics Delivery Path Under Fuzzy Demand

Qin,

Chen

2023

Fuzzy Systems and Data Mining IX

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Optimization of delivery routes is a key method to reduce delivery costs. If scientific and reasonable methods are adopted to arrange delivery routes, it can reduce delivery costs, increase enterprise profits, and increase vehicle loading, achieving the goal of stable growth for enterprises. In this paper, the logistics distribution in the same city under the condition of fuzzy demand is taken as the research object, and the vehicle load, customer demand, customer service time and other constraints are taken as the constraints. The Simulated annealing algorithm is used to solve the model, and a reasonable and satisfactory optimization scheme is obtained. By comparing the solutions before and after optimization, it is confirmed that the optimization model can effectively solve the logistics distribution problem under fuzzy demand.

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Optimization of Sustainable Bi-Objective Cold-Chain Logistics Route Considering Carbon Emissions and Customers’ Immediate Demands in China

Cited by 7 publications

References 46 publications

An Intelligent Connected Vehicle Material Distribution Route Model Based on k-Center Spatial Cellular Clustering and an Improved Cockroach Optimization Algorithm

An Intelligent Connected Vehicle Material Distribution Route Model Based on k-Center Spatial Cellular Clustering and an Improved Cockroach Optimization Algorithm

Research on Cold Chain Logistics Transportation Scheme under Complex Conditional Constraints

Research on Optimization of Urban Logistics Delivery Path Under Fuzzy Demand

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