Logistics Network Optimization Research Articles

Straw Logistics Network Optimization Considering Cost Importance and Carbon Emission under the Concept of Sustainable Development

As biomass power generation projects progress, the supply chain of biomass resources has become a key link for the green development of biomass power facilities. The role of biomass recycling logistics in the sustainable development of biomass resources and power generation endeavors has grown increasingly significant. To realize environmentally friendly and economical straw logistics, it is imperative to establish an efficient, economic and sustainable straw recycling logistics network. Therefore, based on the weighting theory and top-down method, this study proposes a planning model aiming at minimum total cost and minimum carbon emission to locate the logistics network. The immune algorithm is applied to solve and analyze the proposed examples. It is concluded that under the condition that the total cost is reasonable, reducing transportation cost. The verification model can enhance the environmental and economic performance of straw logistics networks. From the perspective of theory and practice, we provide ideas for the optimization of relevant biomass logistics networks and promote the realization of low-carbon sustainable development of logistics networks.

Reverse Logistics Network Design for Medical Waste Disposal under the Scenario of Uncertain Proposal Demand

With the development of the healthcare industry, the demand for medical services and protective equipment is boosted, causing the generation rate of infectious medical waste to increase rapidly. Therefore, it is of utmost importance for decision makers to effectively predict the potential risks and propose corresponding solutions. This paper investigates the reverse logistics network optimization for medical waste under the conditions of an uncertain proposal demand. Firstly, a prediction model of medical waste based on the SEIR epidemiological dynamics is constructed, in which both routine and public health emergency scenarios are simultaneously considered. Secondly, a bi-objective location-routing optimization model for a medical waste reverse logistics network is proposed, by simultaneously optimizing the total economic cost and potential risk throughout the entire logistics process. Subsequently, an NSGA-II algorithm is designed for a model solution in response to the model’s characteristics. The epidemiological dynamics-based prediction model is validated by the real case to be scientifically effective in predicting the amount of generated medical waste with a mean absolute percentage error (MAPE) of 18.08%. The constructed reverse logistics network model and the NSGA II algorithm provide a medical waste process center location, transportation routing, and vehicle selection solutions for both routine and emergency public health cases of Xi’an city with large, medium, and small scales. The above results indicate that the research scheme proposed in this paper could significantly reduce the medical waste logistics-related risks and costs and provide decision makers with more safe and reliable logistical solutions.

E-Commerce Logistics and Supply Chain Network Optimization for Cross-Border

E-Commerce Logistics and Supply Chain Network Optimization for Cross-Border

Мathematical model of the problem of optimizing logistics networks under conditions of interval determination of input data

Problem. In the conditions of rapid changes in demand and supply in transportation markets, there is a need for corresponding changes in logistics technologies and networks, which are carried out through their reengineering. Existing mathematical models for the optimization of logistics networks are designed for conditions with point-wise, clearly defined input data, which does not allow obtaining solutions resistant to changes in the external environment. This determined the relevance of the scientific and applied task of developing mathematical models of multi-criteria optimization problems of logistics networks for the conditions of interval submission of input data. Goal. The goal is to increase the efficiency of the logistics network optimization technology at the stage of re-engineering by developing a mathematical model of the multi-criteria optimization problem for the conditions of interval submission of input data. Methodology. According to the results of the decomposition of the problem of system optimization of logistics networks as the most complex defined task of their structural and topological optimization. When developing the model, the methodology of system design of territorially distributed objects, methods of multi-criteria optimization, theories of utility and decision-making were used. The apparatus of interval mathematics was used to compare solution options with vaguely specified input data. The results. The result are the formulated and completed formalization of the task of optimizing logistics networks as territorially distributed objects. The relationship for the objective function and constraints of the optimization task of topological structures of centralized three-level logistics networks is proposed. Originality. A mathematical model of the task of reengineering topological structures of centralized three-level logistics networks according to cost and efficiency indicators for interval defined input data and values of local criteria using comparison indices based on the Hukuhara difference has been developed. Practical value. The obtained results make it possible to increase the reliability of optimization results in the processes of design, development planning and reengineering of logistics networks, to obtain variants of the construction of their topological structures that are resistant to changes in external conditions.

Optimisation of multi-level logistics network for agricultural products under community group buying model

This paper introduces a rapidly developing new online retail model, community group buying, and proposes a three-level agricultural logistics network optimisation model. Under the community group buysing model, it is necessary to use different types of vehicles for transportation of agricultural products with different temperature control requirements. The study establishes a multi-objective mixed integer programming model with the objectives of shortest transportation time and minimum total cost, taking into account the freshness penalty cost incurred during transport. The multi-objective problem is transformed into a single objective by normalisation and weighting methods. According to the calculation for the actual case, this paper solves the problems of community group buying grid warehouse location, multiple vehicles use strategy, loading capacity, transportation path optimisation and self-pickup station demand allocation. In addition, through sensitivity analysis, the management insights of community group buying enterprises are obtained: (1) The community group buying enterprises should use brokers of the community group buying model to enhance customer stickiness; (2) The enterprises should focus on developing business in less developed regions; (3) The enterprises need to adjust the proportion of time efficiency and logistics costs according to the real situation.

Retracted: Cross-Border E-Commerce Platform Logistics and Supply Chain Network Optimization Based on Deep Learning

Retracted: Cross-Border E-Commerce Platform Logistics and Supply Chain Network Optimization Based on Deep Learning

Research on Optimization of Medical Waste Emergency Disposal Transportation Network for Public Health Emergencies in the Context of Intelligent Transportation

In order to build a more comprehensive emergency disposal and transportation network system for medical waste, it is necessary to consider various uncertain factors and data characteristics. Therefore, in the context of intelligent transportation, this article considers the uncertainty of the quantity and regional population density of infectious medical waste generation as well as the emergency disposal of infectious medical waste under multi-cycle and multi-objective conditions, and it constructs a multi-cycle emergency disposal logistics network optimization model for infectious medical waste under uncertain conditions. Through deterministic transformation of the model and data mining of the medical waste disposal logistics network in Wuhan, China, the multi-objective model under uncertain conditions was also solved and sensitivity analyzed using the MOPSO-NSGA2 intelligent algorithm, verifying the effectiveness and superiority of the algorithm.

Optimization of multipath cold-chain logistics network

Optimization of multipath cold-chain logistics network

Research On Emergency Parcel Transfer and Structure Optimization of E-Commerce Logistics Network Based on Multi-Objective Planning

This paper addresses the problem of urgent forwarding and structural optimization of parcels in e-commerce logistics networks. Firstly, based on the historical data of past freight volumes of different routes, this paper predicts the freight volume forecast data of each route by establishing an ARIMA time series forecast model; secondly, a multi-objective optimization model based on genetic algorithm is established to derive the optimal freight volume allocation scheme after the disappearance of route DC5; again, a multi-objective optimization model based on bat algorithm is established to derive the DC9 route Finally, a multi-objective optimization model based on the bat algorithm was developed to derive the route adjustment and cargo allocation scheme after the disappearance of route DC9. This paper concludes that after the cancellation of the DC5 route, the number of routes that are not in normal operation is 23 and the number of logistics paths that are allocated to new cargo is 56 in order to achieve the most balanced route load possible; after the closure of the DC9 route, 21 new roads are opened and 15 roads are closed, which are in a normal flow state and the transhipment is balanced. This paper has more far-reaching applications for multi-objective large-scale NP problems by establishing a multi-objective optimization model based on a heuristic algorithm, which can reduce the solution time of the calculation.

Optimization of reverse logistics network for medical waste recycling

Optimization of reverse logistics network for medical waste recycling

Multi-objective combinatorial optimization analysis of the recycling of retired new energy electric vehicle power batteries in a sustainable dynamic reverse logistics network.

The recycling of retired new energy vehicle power batteries produces economic benefits and promotes the sustainable development of environment and society. However, few attentions have been paid to the design and optimization of sustainable reverse logistics network for the recycling of retired power batteries. To this end, we develop a six-level sustainable dynamic reverse logistics network model from the perspectives of economy, environment, and society. We solve the multi-objective combinatorial optimization model to explore the layout of the sustainable reverse logistics network for retired new energy vehicle power batteries recycling. A case study is implemented to verify the effectiveness of the proposed model. The results show that (a) the facility nodes near the front of the network fluctuate more by opening and closing; (b) the dynamic reverse logistics network is superior to its static counterpart; and (c) cooperation cost changes affect the transaction volume between third-party and cooperative enterprises and total network cost.

Emergency Parcel Dispatching and Structure Optimization of E-Commerce Logistics Network Based on CCNSGA-II

Emergency Parcel Dispatching and Structure Optimization of E-Commerce Logistics Network Based on CCNSGA-II

Emergency Routing and Structural Optimization of E-commerce Logistics Network for Parcel Transportation Based on Multiple Models

Emergency Routing and Structural Optimization of E-commerce Logistics Network for Parcel Transportation Based on Multiple Models

Research on layout optimization of logistics network of metropolitan area based on hub-and-spoke theory

In order to give full play to the role of regional logistics in the regional economic development of the metropolitan area, a kind of hub-and-spoke regional logistics network based on hub-and-spoke model was established, which has the characteristics of hub-and-spoke network scale economy, scope economy, density economy and structure economy. Seventeen technical and economic indicators are sorted out from regional economy, industrial and commercial development, transportation infrastructure development and logistics industry development, and 35 counties and districts in the Capital metropolitan area are used as the research objects in 2020. The model is used to determine the network layout among node cities and to optimize the logistics network in the Capital metropolitan area.

Optimization of sustainable reverse logistics network with multi-objectives under uncertainty

Optimization of sustainable reverse logistics network with multi-objectives under uncertainty

Emergency logistics network optimization with time window assignment

During natural disasters or accidents, an emergency logistics network aims to ensure the distribution of relief supplies to victims in time and efficiently. When the coronavirus disease 2019 (COVID-19) emerged, the government closed the outbreak areas to control the risk of transmission. The closed areas were divided into high-risk and middle-/low-risk areas, and travel restrictions were enforced in the different risk areas. The distribution of daily essential supplies to residents in the closed areas became a major challenge for the government. This study introduces a new variant of the vehicle routing problem with travel restrictions in closed areas called the two-echelon emergency vehicle routing problem with time window assignment (2E-EVRPTWA). 2E-EVRPTWA involves transporting goods from distribution centers (DCs) to satellites in high-risk areas in the first echelon and delivering goods from DCs or satellites to customers in the second echelon. Vehicle sharing and time window assignment (TWA) strategies are applied to optimize the transportation resource configuration and improve the operational efficiency of the emergency logistics network. A tri-objective mathematical model for 2E-EVRPTWA is also constructed to minimize the total operating cost, total delivery time, and number of vehicles. A multi-objective adaptive large neighborhood search with split algorithm (MOALNS-SA) is proposed to obtain the Pareto optimal solution for 2E-EVRPTWA. The split algorithm (SA) calculates the objective values associated with each solution and assigns multiple trips to shared vehicles. A non-dominated sorting strategy is used to retain the optimal labels obtained with the SA algorithm and evaluate the quality of the multi-objective solution. The TWA strategy embedded in MOALNS-SA assigns appropriate candidate time windows to customers. The proposed MOALNS-SA produces results that are comparable with the CPLEX solver and those of the self-learning non-dominated sorting genetic algorithm-II, multi-objective ant colony algorithm, and multi-objective particle swarm optimization algorithm for 2E-EVRPTWA. A real-world COVID-19 case study from Chongqing City, China, is performed to test the performance of the proposed model and algorithm. This study helps the government and logistics enterprises design an efficient, collaborative, emergency logistics network, and promote the healthy and sustainable development of cities.

Sustainable Logistics Network Optimization From Dry Ports to Seaport: A Case Study From Pakistan

Several considerations are explored in each mode of transportation for the safe, quick, and environmentally friendly movement of goods. To obtain the advantages in all transportation modes, it is critical to develop appropriate models and algorithms. By expanding our understanding of the planning of bi-objective optimization problems, this study contributes to the field of real-time green logistics analysis. The paper provides case studies based on road and road-rail transportation from dry ports to a seaport in Pakistan. Analysis of the cases shapes the economic and environmental planning of transport. Because there is little research on this topic in Pakistan, the innovative aspect is to reduce the cost and greenhouse gas (GHG) emissions of road and intermodal freight transportation between dry ports and seaport. A mixed-integer linear programming model is used for the formulation of the bi-objective problem. Pareto front solutions are provided to trade-off the cost and GHG emissions in delivering goods from dry ports to seaport in Pakistan. The study uses real-life data from one of the largest logistics service providers in Pakistan. Results show that intermodal transportation is 82.6% more economical and 78% greener compared with road freight transportation. Further, this study could be extended by including other factors such as noise, air pollution, and traffic accidents.

B2C e-commerce logistics network optimization model

In this paper, considering the characteristics of e-commerce logistics and the problems arising from the construction of logistics network, based on the full investigation and research of the B2C electricity supplier operation mode, we have studied and summarized the research results of scholars at home and abroad aiming at the related problems, and summarized and summarized the methods and steps of logistics network optimization in the whole country. First, we established the logistics network structure.The definition of network level, node function and connection between nodes is carried out. Then, through the improved urban agglomeration order gravity model, the scope of self logistics city is screened. Finally, the link of logistics network structure is scheduled for completion with the goal of cost optimization.

Designing a Reverse Logistics Network Model for Waste Batteries

The logistics industry plays an important role in circular economy. Therefore, not only economic benefits, but also environmental protection factors have to be considered in reverse logistics. This paper uses the multi-objective 0-1 mixed integer programming to establish a reverse logistics network optimization model for waste batteries. The objective function is to minimize both, logistics costs and carbon dioxide emissions. The model considers the basic settings of reverse logistics (including recycling nodes, manufacturing, and processing nodes) and the material flow between different settings. In solving the model, Lingo 14.0 is used in this paper. An actual case of a waste battery reverse logistics enterprise verifies the effectiveness of the model in this paper. The results show that the application of this model can effectively improve the operating efficiency of waste battery reverse logistics enterprises.

Cross-Border E-Commerce Platform Logistics and Supply Chain Network Optimization Based on Deep Learning

E-commerce and logistics are symbioses with each other, but cross-border e-commerce (CBEC) still cannot break away from cross-border logistics. With the progress of economic internationalization, economic and trade ties around the world have become closer and closer, and the level of international business exchanges has been improved. The rise of multinational e-commerce has also caused unprecedented difficulties to multinational logistics and supply chain management. The application of deep neural networks in various fields provides opportunities for cross-border e-commerce platforms to solve these problems. The existing logistics distribution model cannot keep up with the development of CBEC and has become a constraint and bottleneck for the development of CBEC. Therefore, this article introduces deep learning neural network to cross-border logistics and supply chain based on the analysis of the existing cross-border logistics model and supply chain model and the status quo of e-commerce development. It optimizes the existing cross-border logistics and supply chain network in order to break through the current bottleneck in the development of CBEC. This paper shows through research that introducing deep learning neural networks into CBEC logistics and supply chain can improve the efficiency of logistics and supply chain. Compared with the previous efficiency, the efficiency of network optimization can be increased to about 50%, reducing the cost of cross-border logistics and supply chain. The research in this article has great theoretical and guiding significance for the development of CBEC.