Logistics Network Design Research Articles

On the value of shipment consolidation and machine learning techniques for the optimal design of a multimodal logistics network

We study a multimodal logistics network for a multi-echelon supply chain (SC) with multiple products, considering economic and environmental sustainability and shipment consolidation (ShC). The SC logistics network is modelled as a Mixed Integer Linear Program (MILP) and then tested on randomly generated but realistic test instances. The effects of ShC in SC network design on economic and environmental costs are analyzed, showing that consolidation decreases the SC cost, especially when the distance between the shipper and receiver is significant. Moreover, machine learning (ML) approaches for predicting stochastic parameters using historical data are evaluated compared to the more traditional stochastic programming approaches over multiple prediction periods. The three ML models utilized; namely, Attention CNN-LSTM, Attention ConvLSTM and an ensemble of both models using Support Vector Regression, performed significantly better than the stochastic programming approaches considered (simple recourse and chance-constrained) in all scenarios. The numerical examples show that the MILP models using the predictions from the ML algorithms provide the highest value of the stochastic solution and the lowest expected value of perfect information. This study makes a case for the continued integration of ML prediction methodologies into stochastic optimization modelling in the setting of sustainable SC logistics design problems.

Last-Mile Logistics Network Design under E-Cargo Bikes

E-cargo bikes, i.e., electric bicycles equipped with baskets for transporting goods, are perceived as an advantageous solution for urban last-mile logistics. Particularly for short distances and low volumes of goods, the use of e-cargo bikes is ideal in urban areas where access of conventional cars may be prohibited. In this context, this study presents an e-cargo bike routing model for urban deliveries. The problem is formulated as a Capacitated Vehicle Routing Problem (CVRP), which takes several aspects into account, such as restrictions on bicycle capacity, road grade and workload balancing. A mathematical model is constructed to solve the problem, and a Genetic Algorithm (GA) is applied. Results on a case study in Athens, Greece are presented and analyzed, highlighting the applicability of the model in real-world operations, yielding reasonable results in short computational times.

Design of Reverse Logistics Network for Waste Tire Incineration in Cement Factories

This study focuses on recovery options for end-of-life tires (ELTs). One of the most proper recovery options for ELTs is incineration of them in cement plants. There are 49 integrated cement factories which have Environmental Permit and License Certificate in Turkey as the final processing plant. Besides benefits of recovering ELTs to environment, cost aspect of process is crucial. In this study, logistic network design of waste tires sent from collection points to these factories to minimize the costs involved is planned. There are three echelons of supply chain as waste tires collection point, contractor firms and cement factories. Model for this network considers transshipment plan of waste tires between the echelons of supply chain and routes used to gather them. Model that deal with this system are expressed as mixed integer linear programming (MILP) problem. As a case study, model verified with data’s for Ankara

The joint impact of environmental awareness and system infrastructure on e-waste collection

The prevailing literature on the design of reverse logistics networks for e-waste collection recognizes the accessibility of the collection sites as a key driver of the consumers’ return behavior. The propensity of a consumer to return an end-of-use or an end-of-life item, however, also depends on his/her sensitivity to environmental sustainability. In this paper, we study the impact of simultaneously determining the optimal density of a network of collection centers as well as the most appropriate level of investment in the public's environmental awareness. We adopt a Nerlove-Arrow-type advertising model to capture the potential influence of public awareness campaigns in increasing the consumers’ environmental sensitivity over time. The potential negative impact of the travel distance to the nearest collection site is also incorporated. Thus, we present a dynamic modeling framework for the joint design of the network infrastructure and the public campaign intensity. A case study is presented based on the operations of the Canadian E-Waste Stewardship Program in the Greater Vancouver region. We find that investments into environmental awareness can enable the take-back scheme to improve its collection rates significantly. Our findings through the case study also reveal that running an advance campaign prior to the launch of the collection network can be an effective strategy in most cases. In addition, we present an extended model, which highlights that increasing the investment in environmental awareness can be utilized as a lever to offset the impact of increased hauling costs.

A reliable emergency logistics network for COVID-19 considering the uncertain time-varying demands

The evolving COVID-19 epidemic pose significant threats and challenges to emergency response operations. This paper focuses on designing an emergency logistic network, including the deployment of emergency facilities and the allocation of supplies to satisfy the time-varying demands. A Demand prediction-Network optimization-Decision adjustment framework is proposed for the emergency logistic network design. We first present an improved short-term epidemic model to predict the evolutionary trajectory of the epidemic. Then, considering the uncertainty of the estimated demands, we construct a capacitated multi-period, multi-echelon facility deployment and resource allocation robust optimization model to improve the reliability of the decisions. To address the conservativeness of robust solutions during the evolution of the epidemic, an uncertainty budget adjustment strategy is proposed and integrated into the rolling horizon optimization approach. The results of the case study show that (i) the short-term prediction method has higher accuracy and the accuracy increases with the amount of observed data; (ii) considering the demand uncertainty, the proposed robust optimization model combined with uncertainty budget adjustment strategy can improve the performance of the emergency logistic network; (iii) the proposed solution method is more efficient than its benchmark, especially for large-scale cases. Moreover, some managerial insights related to the emergency logistics network design problem are presented.

A robust, sustainable, resilient, and responsive model for forward/reverse logistics network design with a new approach based on horizontal collaboration.

Optimizing the strategic and tactical decisions regarding sustainability and resilience is important because they affect the performance of the supply chain over a long period. This study proposes a new multi-objective model for resilient, sustainable, and responsive forward/reverse logistics network design considering cost, social responsibility, CO2 emission, water consumption, responsiveness time, and collaboration risk. The most significant impacts on costs are identified using the design of experiments, and a robust optimization method is applied to deal with operational risk. In addition, horizontal collaboration for the first time as a quantitative model is suggested to deal with the disruption risk. Also, a multi-objective solving algorithm with a new aggregation model is developed, and the efficiency of the proposed solution method is shown. The analysis of results based on the iron and steel industries shows the efficiency of resilience strategies and the improvement of costs, sustainability and responsiveness indicators. Also, the findings demonstrate that collaboration has a higher performance than fortification. Finally, managers and supply chain decision-makers are provided with managerial implications to make more effective decisions about the supply chain network design, technology selection, supply chain sustainability, and collaborators selection, using the presented results.

A hierarchical hub location model for the integrated design of urban and rural logistics networks under demand uncertainty.

This paper contributes to the integrated design issue of urban and rural logistics networks under demand uncertainty. A hierarchical hub location model is proposed, which minimizes the expected total system cost by optimizing the locations, number and capacities of "urban-town‒village" hierarchical logistics hubs. The interactions among the logistics hubs and among the hub‒and‒spoke connections, as well as the hub capacity constraints are explicitly considered in the presence of logistics demand uncertainty. A demand scenario‒based branch‒and‒Benders‒cut algorithm is developed to solve the proposed model. A case study of Jiangling urban‒rural region in Hubei province of China is conducted for the illustration of the model and solution algorithm. The results generated by the proposed algorithm are benchmarked against those obtained by GUROBI solver and the practical scheme being currently implemented in the region. The results showed that the proposed methodology can greatly improve the efficiency of the urban‒rural logistics system in terms of expected total system cost. It is important to explicitly model the demand uncertainty, otherwise a significant decision bias may emerge. The proposed algorithm outperforms the GUROBI solver in terms of problem size solved and computational time.

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.

A novel bi-objective optimization model for an eco-efficient reverse logistics network design configuration

Ecological sustainability has become a top priority for governments and business practitioners because of the massive surge in electronic waste (e-waste). This ecological context has compelled electronic firms to adopt reverse logistics (RL) and to invest in advanced technologies with a focus on sustainable recovery practices and on reducing excessive carbon emissions (CEs). This study proposes a multi-objective mixed integer programming (MOMIP) model for configuring a RL network design; it incorporates multiple products, multiple recovery facilities, multiple processing technologies, and a selection of vehicle types. The novelty of this study lies in its consideration of four green technologies (inspection, dismantling, repair/refurbishing, and recycling facilities), and its efforts to maximize return yield potential in the form of product, components, and material recovery. The main objective of the model is to minimize overall RL network cost and environmental impact due to processing and transportation. Further, a weighted goal programming technique is used to determine efficient solutions and furnish a trade-off among the conflicting objectives. The mathematical model is validated utilizing a real life case study of an electronics manufacturing firm in India. The total RL network cost and emissions are both reduced, and different technologies are selected automatically for the RL processing facilities. The results demonstrate that the return yield increases significantly with the greener technology selections. The study also draws significant implications, specifically that carbon tax regulatory policies aid in significantly reducing carbon emissions to a large extent along with increasing product return yield. Hence, the results will help industrial managers in their strategic and tactical decision making. Their evaluations of recovery options, technology selection, and vehicle selection with regard to economic and ecological impact will permit decision makers to gain valuable managerial insights.

Optimal Design of Reverse Logistics Recycling Network for Express Packaging Considering Carbon Emissions

With the development of China’s express delivery industry, the number of express packaging has proliferated, leading to many problems such as environmental pollution and resource waste. In this paper, the process of reverse logistics network design for express packaging recycling is given as an example in the M region, and a four-level network containing primary recycling nodes, recycling centers, processing centers, and terminals is established. A candidate node selection model based on the K-means algorithm is constructed to cluster by distance from 535 courier outlets to select 15 candidate nodes of recycling centers and processing centers. A node selection model based on the NSGA-II algorithm is constructed to identify recycling centers and processing centers from 15 candidate nodes with minimizing total cost and carbon emission as the objective function, and a set of Pareto solution sets containing 43 solutions is obtained. According to the distribution of the solution set, the 43 solutions are classified into I, II, and III categories. The results indicate that the solutions corresponding to Class I and Class II solutions can be selected when the recycling system gives priority to cost, Class II and Class III solutions can be selected when the recycling system gives priority to environmental benefits, and Class III solutions can be selected when the society-wide recycling system has developed to a certain extent. In addition, this paper also randomly selects a sample solution from each of the three types of solution sets, conducts coding interpretation for site selection, vehicle selection, and treatment technology selection, and gives an example design scheme.

Vehicle Routing Optimization with Cross-Docking Based on an Artificial Immune System in Logistics Management

Background: Manufacturing companies optimize logistics network routing to reduce transportation costs and operational costs in order to make profits in an extremely competitive environment. Therefore, the efficiency of logistics management in the supply chain and the quick response to customers’ demands are treated as an additional source of profit. One of the warehouse operations for intelligent logistics network design, called cross-docking (CD) operations, is used to reduce inventory levels and improve responsiveness to meet customers’ requirements. Accordingly, the optimization of a vehicle dispatch schedule is imperative in order to produce a routing plan with the minimum transport cost while meeting demand allocation. Methods: This paper developed a two-phase algorithm, called sAIS, to solve the vehicle routing problem (VRP) with the CD facilities and systems in the logistics operations. The sAIS algorithm is based on a clustering-first and routing-later approach. The sweep method is used to cluster trucks as the initial solution for the second phase: optimizing routing by the Artificial Immune System. Results: In order to examine the performance of the proposed sAIS approach, we compared the proposed model with the Genetic Algorithm (GA) on the VRP with pickup and delivery benchmark problems, showing average improvements of 7.26%. Conclusions: In this study, we proposed a novel sAIS algorithm for solving VRP with CD problems by simulating human body immune reactions. The experimental results showed that the proposed sAIS algorithm is robustly competitive with the GA on the criterion of average solution quality as measured by the two-sample t-test.

A Study on Collaborative Logistics Network Design with Truck Sharing under Demand Uncertainty

The sharing economy is a new innovative business model for more flexible and sustainable logistics. This paper proposes a stochastic programming model for supply chain network design with truck sharing. The model considers demand uncertainty and optimizes allocation and operation of trucks. A case study with realistic data is presented by applying the model to a supply chain with different collaboration level. A sensitivity analysis on cost parameter for sharing service is also conducted to evaluate the impact on business sustainability. The results show that higher collaboration level successfully copes with demand uncertainty, and improves revenues in the entire supply chain.

Forward and Reverse Logistics Network Design with Sustainability for New and Refurbished Products in E-commerce

Forward and Reverse Logistics Network Design with Sustainability for New and Refurbished Products in E-commerce

Reverse Logistics Network Design under Disruption Risk for Third-Party Logistics Providers

Reverse logistics is attracting attention due to the increasing concerns over environmental issues and the important economic impacts. The design of a reverse logistics network is a major strategic problem in the field of reverse logistics. As cost pressures in product returns continue to mount, a growing number of manufacturers have begun to outsource reverse logistics operations to third-party logistics (3PL) providers. On the other hand, considering disruption risks caused by natural or man-made factors in the reverse logistics network design is inevitable. This paper studies third-party reverse logistics network designs under uncertain disruptions. The problem is formulated as a risk-averse two-stage stochastic programming model with a mean risk objective. Two types of risk measures, value at risk (VaR) and conditional value at risk (CVaR), were examined, respectively. Finally, the sensitivity analysis of the model was carried out. The validity of the mean risk criteria is proved by comparison with risk-neutral approach. Moreover, the performance of the proposed model was examined by stochastic measures.

Design a Robust Logistics Network with an Artificial Physarum Swarm Algorithm

The robust optimization of logistics networks can improve the ability to provide sustainable service and business sustainability after uncertain disruptions. The existing works on the robust design of logistics networks insisted that it is very difficult to build a robust network topology, and this kind of optimization problem is an NP-hard problem that cannot be easily solved. In nature, Physarum often needs to build a robust and efficient topological network to complete the foraging process. Recently, some researchers used Physarum to build a robust transportation network in professional biological laboratories and received a good performance. Inspired by the foraging behavior of natural Physarum, we proposed a novel artificial Physarum swarm system to optimize the logistics network robustness just on a personal computer. In our study, first, the robustness optimization problem of a logistics network is described as a topology optimization model based on graph theory, and four robustness indicators are proposed to build a multi-objective robustness function of logistics network topology, including the relative robustness, the betweenness robustness, the edge robustness and the closeness robustness. Second, an artificial Physarum swarm system is developed to simulate the foraging behavior of a natural Physarum swarm to solve this kind of complex robust optimization problem. The proposed artificial Physarum swarm system can search for optimal solutions by expansion and contraction operations and the exchange of information with each other through a self-learning experience and neighbor-learning experiences. The plasmodium of Physarum forms the edges, and the external food sources simulate the logistics nodes. Third, an experimental example is designed on the basis of Mexico City to verify the proposed method, and the results reveal that the artificial Physarum swarm system can help us effectively improve the logistics network robustness under disruptions and receive a better performance than natural Physarum. The article may be helpful for both theory and practice to explore the robust optimization in logistics operation and provide engineers with an opportunity to resist logistics disruptions and risk loss by a novel artificial intelligence tool.

Transborder logistics network design for agricultural product transportation in the Greater Mekong Subregion

This research aimed to design a transportation model for the international trade of agricultural products between Thailand, Laos, and Cambodia, known as the Great Mekong Subregion (GMS). Agricultural products are transported from a farmers’ cooperative to a foreign end market. The mathematical models GA, DE, and VaNSAS were developed to determine the optimal mode of transportation. The objectives of the proposed methods are to (1) maximize the total profit for the entire agricultural chain and (2) minimize the makespan or the arrival time of the containers to the end market in order to maintain the freshness of the agricultural product. The computational result shows that VaNSAS produces a 100 % optimal solution for small-size problem instances, while DE and GA produce a 63.63 % and 72.72 % optimal solution, respectively. For large-size problem instances, VaNSAS shows a profit that is higher than that of DE and GA by 10.53 % and 8.96 %, respectively, while it shows a makespan that is lower by 9.57 % and 7.20 %, respectively.

Sustainable forward-reverse logistics for multi-product delivery and pickup in B2C E-commerce towards the circular economy

The circular economy is an important part of sustainable business around the world. Accordingly, this study focuses on developing a sustainable framework for multi-echelon multiple electronics products' closed-loop supply chain in the e-commerce industry toward the circular economy. The proposed framework captures the complexities associated with forward and reverse logistics. The order is picked up from the supplier and delivered to the customer in the forward flow. Return products are picked up from customer locations and delivered to the inspection centre in the reverse flow. During the inspection process, products are classified into three categories: Reselling, Refurbishing, and Recycling (3R). The products that can be resold, refurbished, and recycled are then delivered to their respective final destinations, i.e., the supplier's warehouse, the refurbishing centre, and the recycling centre, respectively. A mixed-integer nonlinear programming (MINLP) model is developed to reduce total cost and maximize revenue associated with the forward and reverse flow of goods in a closed-loop supply chain while prioritising sustainability. To achieve this, a global solver in the LINGO 19 package software is used to solve and generate the exact solutions of the model. We have conducted 15 computational experiments for small-to-medium-to-large-sized problems to test the model. A sensitivity analysis is also performed to analyze the effect on total expected revenue from sustainable Closed-loop Supply Chain (CLSC) of variations in the major parameters of the model. Policymakers can use the obtained results and perform sensitivity analysis to make effective and efficient strategies favouring consumers and companies that could help enhance the country's economy. • Developed a sustainable multi-echelon forward and reverse logistics network design for an e-commerce platform. • Considered fresh product, returned resellable, refurbishable, and recyclable products for study. • Proposed MINLP model helps to maximize the total revenue with minimizing the transportation cost with carbon emission tax. • Sensitivity analysis is performed to see the effects in outcome with variations in main parameters of the model.

Collaborative reverse logistics network for electric vehicle batteries management from sustainable perspective

With the proposal of carbon neutralization, countries pay much more attention to environmental protection. As waste electric vehicle battery (WEVB) has an important impact on the environment, its reverse logistics process has been a vital issue, in which an excellent reverse logistics network (RLN) becomes a prerequisite for waste recycling, cost reduction, profit increasement and efficiency improvement. However, reverse logistics network design (RLND) for WEVBs is still a significant challenge. In general, the amount and quality of the returned product in a RLN are highly uncertain, and the WEVB market in China is no exception to this. From a circular economic and environmental perspective, this paper is devoted to designing a multi-participants-based RLN for WEVBs, considering recycling and remanufacturing processes. Then, a fuzzy optimization model is proposed to determine the number and location of facilities in this RLN, promote sustainable development, reduce environmental pollution, and consider carbon emissions and two types of WEVBs. Finally, the results from a case study in Chongqing show good performance in cost reduction, profit increasement and efficiency improvement, which could significantly support the decision-making of WEVBs recycling.

A Multiobjective Fuzzy Optimized Model for a Sustainable Facility Location of Life Vehicle Reverse Logistic Considering the Recycling

Today, reverse logistics networks are increasingly expanding in various industries due to considering parameters such as environmental conditions, customer awareness, economic savings, social responsibilities, and gaining points such as customer loyalty. In the design of the reverse logistics network, the options of taking parts, repair, recycling, reproduction, and disposal can be considered. In this research, an integrated forward/reverse logistics network is modeled and an attempt is made to provide a general model and by adding the recycling option to the previous models, to a more general optimization model that includes more recovery options. It is achieved and the network should be optimized in the forward and reverse mode at the same time, taking into account the production of various products, first with the assumption of known demand and return, and then by fuzzing the parameters with uncertainty, as well as the limited capacities of the network centers. To defuzzification of this model, a method based on alpha cut is used for cost coefficients of decision variables and the center of mass method for demand and fuzzy return in the objective function. In fuzzy limits, a method based on the comparison of fuzzy numbers is used. In order to show the application of the Mixed Integer Linear Programming results, several examples have been simulated and coded in GAMS software to obtain accurate answers. In order to solve the model in large dimensions of the problem, a multi-objective genetic algorithm has been developed and coded by MATLAB software. The results of solving the examples in both cases, the exact and near-optimal solutions obtained from the genetic algorithm, have been compared and at the end, the sensitivity analysis of the network has been done. The obtained results show that considering imprecise parameters in a fuzzy manner, firstly, a more realistic model is obtained. Secondly, in examining the answer, due to their connection, the decision maker has unlimited options for the correct decision according to the existing conditions.

Smart and sustainable city logistics: Design, consolidation, and regulation

Growing e-commerce delivery increases traffic congestion and demands for smart last-mile city distribution systems. In this context, municipalities and logistics service providers aim towards sustainable and profitable city logistics, which are often conflicting goals. Whether to use single-echelon or two-echelon systems with micro-depots and city freighters as well as quantifying the benefits of shipment consolidation between independent providers under different system designs are unanswered questions in academia and practice.We provide a general methodological framework based on continuous approximation theory to study the design of city logistics networks with multiple logistics service providers that choose transportation strategies, each defined by a network structure and specific transportation modes. We provide analytical results on the comparison of different transportation strategies. By providing thresholds on the benefit of consolidated systems, we analytically quantify shipment consolidation effects in city centers. We then embed this continuous approximation into a mixed-integer non-linear program and study parameter sensitivities for a real-world case study.Our results show that higher customer density and longer distances between depots and distribution areas promote 2-tier transportation strategies. Consolidation between logistics service providers allows for emission reductions independent of the transportation strategy and correlate with cost reductions up to 50% on the lower echelon. Outsourcing promotes 2-tier last-mile deliveries with city freighters and in a city with heterogeneous districts, it is beneficial to use a hybrid network that includes both single- and two-echelon strategies using multiple transportation modes for different demand classes.