Logistics Network Planning Research Articles

Research on Optimization of Power Battery Recycling Logistics Network

With the popularity and development of electric vehicles, the demand for power batteries has increased significantly. Power battery recycling requires a complex and efficient logistics network to ensure that used batteries can be safely and cost-effectively transported to recycling centers and properly processed. This paper constructs a dual-objective mathematical model that minimizes the number of recycling centers and minimizes the logistics cost from the service center to the recycling center, and designs the power battery disassembly and recycling process and the recycling logistics network, and finally uses a genetic algorithm to solve it. Finally, this article takes STZF Company as an example to verify the effectiveness of this method. The verification results show that the logistics intensity of the optimized power battery recycling logistics network has been reduced by 36.2%. The method proposed in this article can provide certain reference for power battery recycling logistics network planning.

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.

A Hybrid Heuristic Algorithm for Maximizing the Resilience of Underground Logistics Network Planning

In recent times, there has been a sharp increase in the congestion of ground transportation, the scarcity of land resources, and various disasters. Hence, there is an urgent need to find an effective and sustainable approach to transportation. The construction of an underground logistics network, where transportation activities occur beneath the surface of the ground, is anticipated to emerge as a future trend. This study aims to formulate a resilient-maximizing plan for the underground logistics network, ensuring optimal meeting of transportation demands in the aftermath of ground disasters. Accordingly, a two-stage linear programming model is established to determine the layout plan for the most resilient underground logistics network. The first phase of the model is designed to generate viable layouts for the underground logistics network, while the second phase is dedicated to evaluating the resilience of the proposed layout plan. During the evaluation of network resilience, Monte Carlo simulations are used to simulate disaster scenarios. Given the inherent complexity of the model, the traditional solver cannot efficiently solve the problem. Thus, a new hybrid heuristic algorithm is designed to obtain solutions that maximize network resilience. The results show the effectiveness of the designed algorithm and the significant improvement in network resilience achieved by numerical experiments. Moreover, sensitivity analyses are conducted to reveal the relationships between resilience and budget, as well as resilience and the capacity of underground pipelines. It has a significant impact on sustainability when making decisions regarding network planning.

A location–transportation problem under demand uncertainty for a pharmaceutical network in Brazil

We study the integrated location–transportation problem under uncertain demand, addressing the case of a pharmaceutical logistics network in Brazil. We propose a mathematical model with multi-time scales to make decisions accordingly with their timing. The model considers practical features, such as fleet sizing, safety constraints in cargo transportation, and tax issues. We address uncertainty in decision-making by developing a robust counterpart and present solution methods based on Fix-and-Optimize heuristics. We perform computational experiments using real data from a partner company and evaluate the impact of uncertainty on the problem. The heuristics outperform the MIP model by reducing the average of logistics costs and taxes by 40%. Demand uncertainty and variability significantly influence the problem decisions. Also, the robust model reduces expected solution costs. Thus, these models and solution methods can support the decision-making process on location–transportation problems in Logistics Network Planning in contexts similar to the pharmaceutical industry in Brazil.

Cloud-based solution approach for a large size logistics network planning

Cloud-based solution approach for a large size logistics network planning

Retracted: Application of Data Mining Algorithm in Agricultural Products Logistics Network Planning

Retracted: Application of Data Mining Algorithm in Agricultural Products Logistics Network Planning

Integrated electric logistics vehicle recharging station location–routing problem with mixed backhauls and recharging strategies

The widespread application of electric vehicles for city last-mile logistics has been enhanced by the emerging trend of urban sustainable mobility, which intends to reduce vehicle emissions and the dependence on fossil fuels. The recharging facility location is critical for electric logistics network planning, because it significantly affects future operation costs and efficiency. This study proposes an integrated electric logistics vehicle recharging station location–routing problem with mixed backhauls and recharging strategies, which is formulated as a time-discretized multicommodity network flow optimization model based on a space–time–state–energy representation network. This study aims to optimize the selection of the recharging station location considering route planning with complicated constraints of recharging capacity, facility construction budget, vehicle loading capacity, battery remaining capacity, spatial structure of real road networks, mixed pickup and delivery requests, and service time windows. A hybrid Lagrangian relaxation and alternating direction method of multipliers (LR-ADMM) decomposition solution framework is constructed to decouple the proposed integrated problem into a recharging station location problem for strategic planning and an electric vehicle routing problem with mixed backhauls, time windows, and recharging strategies for operational decisions. These two subproblems are solved alternately by time-dependent forward dynamic programming algorithms embedded into the LR-ADMM framework. The solution quality is guaranteed by calculating the optimality gap between the best lower and upper bounds for each iteration. The experimental results based on the Sioux-Falls network and real-world West Jordan network prove the computational effectiveness and optimization quality of the proposed solution approach.

Application of Data Mining Algorithm in Agricultural Products Logistics Network Planning

The location of agricultural products center is related to the overall operation efficiency of logistics system and has a great impact on the follow-up agricultural products distribution route planning. The distribution route planning directly determines the total transportation cost and customer service quality of the whole logistics system. In this study, the problem of location path under the background of agricultural products logistics is studied, and an algorithm of agricultural products logistics network planning based on space-time constraints is proposed. An algorithm based on K-means is used to cluster the time-space double factors of customers. Minimize the total distribution cost under the constraints of meeting customer needs and minimizing time window deviation. Experimental results show that the proposed algorithm has better performance through Taguchi analysis and master-slave two-layer model analysis, and its algorithm has better convergence and sensitivity and can better reduce the total cost of distribution and the length of transportation path.

Assessing Long-Term Impacts of Automation on Freight Transport and Logistics Networks: Large-Scale LRP Integrated in Microscopic Transport Simulation

Up to now, bulk transports have been carried out via a hub-and-spoke network in the general cargo sector. However, it is expected that the use of autonomous vehicles will enable a more flexible delivery. Such developments may, economically, make sense for shippers. From an ecological point of view, also negative effects can be expected due to enhanced transport performance. In the framework of this research, we investigate the impacts of automation on general cargo transport at the logistics network level. For assessing the impacts of autonomous vehicles on logistics network structures and on freight transport routes ex-ante, an instrument for strategic transport and logistics network planning is needed. We develop an effective heuristic to find new facilities and adjust the network, while thereby considering the routing characteristics by tackling the large-scale location routing problem (LRP). By the linked approach, we can optimize the logistics network and also measure exact transport distances, driving transport lead times and number of necessary vehicles on the infrastructure network. We operationalize this approach in the framework of a case study focusing on the food retail distribution in Germany. In fact, this research reveals that the utilization of autonomous vehicles significantly enhances transportation ranges and the number of tours, while reducing the number of operating facilities.

Robust collaborative maintenance logistics network design and planning

Maintenance service providers to advanced technical devices are confronted with uncertain demand, high cost of components, and the need for certified operators. Collaboration in terms of sharing scarce resources among different facilities in a maintenance logistics network is expected to reduce the delays in the delivery of repaired devices. This study proposes a two-stage robust optimization model for collaborative design and planning of maintenance networks under demand uncertainty. The goal of this model is to determine the optimal allocation of customers to each maintenance center along with the initial stock level of different components in each facility so as to minimize the cost of late deliveries under worst-case demand scenarios. Component and operator sharing strategies are proposed as the recourse actions in this model to hedge against the demand surge. The proposed approach is compared with a deterministic model by the aid of Monte-Carlo simulation on several test instances inspired by a real case study. Our numerical experiments demonstrate the significance of adopting the proposed collaborative mechanisms among maintenance facilities in terms of cost reduction, especially when the demand fluctuation is relatively high.

Decision onRegional Logistics Network Based on H-S and P-P Network: Taking Tobacco Transportation Logistics as anExample

Objectives: The logistics hub construction has always been the short board of logistics network planning in China. In order to improve the decision-making efficiency of logistics enterprise’s hub selection and reduce its comprehensive operation cost, this paper establishes a cost difference model for hub-and-spoke(H-S) and point-to-point(P-P) networks considering the fixed cost of hubs, transportation and route costs based on the 0-1 integer nonlinear programming. The model aims at minimizing the cost difference between the two networks, and divides the fixed cost of the hubs into three situations: full lease, lease and self-built, and fully self-built. Finally, this paper takes tobacco transportation logistics as an example, and use particle swarm algorithm to solve the model by using tobacco transportation logistics data of a logistics enterprise in Jiangsu Province. The results show that: (i) in the case of complete leasing, the total cost of the H-S network decreases with the increase of the number of hubs, and the cost change has a point of intersection with the total cost of the P-P network;(ii) when the lease and self-build are mixed, the increase is first reduced and then increased, it is U-shaped and has a minimum value, and there are two intersections with the total cost of the P-P network;(iii) the situation of completely self-built and fully leased is just the opposite. This paper takes tobacco transportation logistics as a representative, and provides a reference for logistics companies to choose the appropriate regional logistics network structure and different pivot points.

Hierarchical clustering framework for facility location selection with practical constraints

Facility location selection plays a critical role in the planning of logistics networks. It selects the addresses of facility nodes from a candidate set of locations to optimise multiple targets such as transportation efficiency and economic cost considering the practical constraints of the real world. Thus, it is often formulated as a combinational optimisation problem, which is solved by either mixed integer programing algorithms or heuristic methods. However, these approaches are limited by several issues such as a high computational cost and weak generalisation flexibility. In this work, a novel hierarchical clustering framework is proposed for facility location selection, which can flexibly support a wide variety of optimisation targets and the combinations of multiple practical constraints that are vital in the real logistics scenarios. Beyond the original hierarchical clustering algorithm, it incorporates a looking-forward mechanism that alleviates the ‘greedy trap’ by utilising global information. These advantages enable the proposed method to generate reliable solutions with high time efficiency. As demonstrated by the experimental results on real JD Logistics data, the proposed method outperforms the widely adopted GGA and VNS algorithms. It also has a much lower computation cost compared to the SCIP solver, while the quality of solutions are within an acceptable range.

Goal programming model applied to waste paper logistics processes

Organization and planning of reverse logistics networks make sustainable processes more efficient. Thus, an important sector for connecting the collection and waste paper recycling echelons is the intermediate center. In this study, a Mixed Integer Linear Programming model, which is multi-objective, multi-product, multi-level and multi-period, was developed to optimize the waste paper logistics processes of intermediate centers. The formulation includes the following echelons: collection, considering vehicle routing with different capacities; inventory of non-baling materials; baling sorted waste and inventory of bales; selling bales; disposal of non-recyclable waste. The aims of the model include: maximizing the collected waste; minimizing distances; maximizing both the production and sale of bales, and minimizing costs. According to research carried out in the literature, the developed model is a new proposal and to implement it, the Weighted Goal Programming and Revised Multi-Choice Goal Programming approaches were used to deal with multiple objectives and incorporate uncertainty into the quantity of waste available for collection. To analyze the proposed model, computational tests were executed with instances based on real data from a Brazilian company in the sector. For all performed tests, General Algebraic Modeling System 23.6.5 modeling language and CPLEX 12.2.0.2 solver were used for modeling and optimization. The results show that this study presents formulation and technological approaches that represent real situations and provide competitive solutions to the problem.

Logistics network planning of medical waste recycling

Medical waste has the characteristics of direct or indirect infection, toxicity and harmfulness, and is listed as "top risk" internationally, and is listed as No.1 hazardous waste in China's Hazardous Waste List. Therefore, it is particularly important to recycle and treat medical waste. The reasonable design of medical waste recycling and disposal logistics network can effectively solve this problem, among which, the location planning of related facilities is an important activity. For medical waste recycling logistics facilities (i.e., collection points) location optimization design, this paper according to the different levels of service collection point, has carried on the classification of medical units, and then according to the different collection point location method of classification is put forward. Finally using MATLAB for data experiment and analysis of the results to verify the validity of the model. The model is instructive and referential to reality.

A robust multi-objective optimization model for sustainable closed-loop supply chain network design under demand uncertainty

At this stage, the clothing logistics industry still has some shortcomings in terms of rapid response performance and warehouse integration, and the operation of reverse logistics network planning is also in the primary development stage. Therefore, on the basis of considering the forward garment logistics, the return and replacement process of garments should be taken into account to achieve a win-win situation for economic, environmental and social benefits. In addition, the impact of the existence of uncertain factors on the network planning is considered to establish a scientific and reasonable clothing closed-loop supply chain network system. Based on the distribution characteristics of clothing enterprises and supporting facilities, the location model was proposed to determine the optimal location and quantity of the corresponding facilities. At the same time, taking into account the carbon emissions in the construction, manufacturing and transportation process and the social responsibility of the enterprise, the environmental and social risk assessment targets are added on the basis of maximizing the profit of the enterprise, and a multi-objective planning model of transportation vehicle path optimization is established. According to the characteristics of the model, a two-stage algorithm is designed. The first stage obtains the optimal initial solution through the greedy algorithm, and the second stage solves the bi-level programming model by the particle swarm optimization algorithm. Finally, based on the data of a certain city, the parameter assignment of the model is carried out, and the problem is solved by CPLEX optimization software. The feasibility and correctness of the model and algorithm are verified by several numerical examples and sensitivity analysis of model parameters.

Design of a Support System for Complicated Logistics Location Integrating Big Data

Logistics location is an important component of logistics planning that affects traffic pressure and vehicle emissions. To date, there has not been an adequate study of the integration of big data into the location for a complicated logistics system. This study developed a decision support system that can address location problems for complicated logistics systems, e.g., a multilevel urban underground logistics system (ULS), using logistics big data. First, information needed in the logistics location, such as the traffic performance index (TPI) and the origin/destination (OD) matrix, was collected and calculated using a big data platform, and this information was digitized and represented based on a geographic information system (GIS) tool. Second, a two‐stage location model for a ULS was designed to balance the construction costs and traffic congestion. The first stage is establishing a set‐covering model to identify optimum locations for secondary hubs based on the ant colony optimization algorithm, and the second stage is clustering of the secondary hubs to determine locations for primary hubs using the iterative self‐organizing data analysis technique algorithm (ISODATA). Finally, the Xianlin district of Nanjing, China, was chosen as a case study to validate the effectiveness of the proposed system. The system can be used to facilitate logistics network planning and to promote the application of big data in logistics.

Node Location Study of an Urban Underground Container Logistics Network

Underground logistics systems are becoming a new way to solve the problems of urban logistics delivery and surface traffic congestion. Among them, intelligent containers, as a means of transport in the underground logistics trunk network, will allow for significantly less volumes for the logistics OD (starting point to the destination of the flow of goods), where container logistics network node site selection has been a key issue of the city’s underground logistics network planning. Based on an analysis of the characteristics of urban logistics OD, this paper builds a model that targets and optimizes parameters, such as infrastructure cost and operating cost, from the perspective of the system builder. The cplex algorithm was used to solve the model. Finally, the validity and logic of the model and algorithm are verified via case study analysis.

Regional Logistics Network Design in Mitigating Truck Flow-Caused Congestion Problems

Truck flow plays a vital role in urban traffic congestion and has a significant influence on cities. In this study, we develop a novel model for solving regional logistics network (RLN) design problems considering the traffic status of the background transportation network. The models determine not only the facility location, initial distribution planning, roadway construction, and expansion decisions but also offer an optimal solution to the logistics network service level and truck-type selections. We first analyze the relationship between the urban transportation network and the RLN design problem using real truck data and traffic flow status in a typical city. Then, we develop the uncover degree function (UDF), which reflects the service degree of the RLN and formulates based on an impedance function. Subsequently, the integrated logistics network design models are proposed. We model the RLN design problem as a minimal cost problem and design double-layer Lagrangian relaxation heuristics algorithms to solve the model problems. Through experiments with data from the six-node problem and Sioux-Falls network, the effectiveness of the models and algorithms is verified. This study contributes to the planning of regional logistics networks while mitigating traffic congestion caused by truck flow.

Hybrid optimization procedures applying for two-echelon urban underground logistics network planning: A case study of Beijing

Motivated by urban land scarcity and surging demand, the underground space has become a frontier domain for future city logistics industry. The Underground Logistics System (ULS) is a sustainable option to overcome traffic congestion while improving freight transport efficiency. This study presents a comprehensive layout design and facility planning method for the two-echelon urban ULS network with hub-and-spoke structure. The underground network is organized by the primary and secondary ULS nodes and tunnels which have strong alignment with existing logistics nodes such as distribution centers and major demand terminals. A bi-objective mixed-integer linear programming model considering minimal costs and maximal system utilization was established to formulate the location-allocation of ULS nodes, tunnel layout and flow routing in the network. Given the inherent problem complexity, a series of simplification techniques and hybrid optimization procedures integrating both exact and meta-heuristic algorithms were developed to obtain high quality solutions. Finally, a real-world simulation was carried out in Beijing, China to demonstrate the applicability of proposed method. Results show that our procedures can effectively obtain the best configurations of ULS network under various scenarios and has better convergence efficiency and optimality performance. Moreover, a group of horizontal tests and sensitivity analysis reveals further insights regarding ULS network decision-making and layout strategies.

Assessing uncertainty and risk in an expeditionary military logistics network

Uncertainty is rampant in military expeditionary operations spanning high-intensity combat to humanitarian operations. These missions require rapid planning and decision-support tools to address the logistical challenges involved in providing support in often austere environments. The US Army’s adoption of an enterprise resource planning system provides an opportunity to develop automated decision-support tools and other analytical models designed to take advantage of newly available logistical data. This research presents a tool that runs in near-real time to assess risk while conducting capacity planning and performance analysis designed for inclusion in a suite of applications dubbed the Military Logistics Network Planning System, which previously only evaluated the mean sample path. Logistical data from combat operations during Operation Iraqi Freedom drive supply requisition forecasts for a contingency scenario in a similar geographic environment. A nonstationary queueing network model is linked with a heuristic logistics scheduling methodology to provide a stochastic framework to account for uncertainty and assess risk.