Logistics Network Design Research Articles

The design of a sustainable-resilient forward-reverse logistics network considering resource sharing and using an accelerated benders decomposition algorithm

The design of a sustainable-resilient forward-reverse logistics network considering resource sharing and using an accelerated benders decomposition algorithm

State of the art in determining the optimal reverse logistics network for your product

The primary focus of this study is to explore and answer the following research question: “In what ways can organizations enhance the efficiency and responsiveness of their reverse logistics network design?” The study presents a conceptual framework that the authors have built, incorporating several essential variables to guide the process of network design. The conceptual framework for reverse logistics networks has been developed by conducting an analysis of the fundamental factors that influence network design decisions. This paradigm facilitates the decision-making process for firms when considering the centralization or decentralization of their reverse logistics operations and the outsourcing or insourcing of specific operational aspects. Moreover, the authors have discovered and highlighted relevant literature that exhibits strong alignment with our proposed framework, hence offering useful insights for decision-making in the domain of reverse logistics network design. Within our framework, two crucial dimensions are considered at each stage: the choice between internal processing vs . outsourcing and the decision to centralize or decentralize operations. With consideration of the factors noted above, this paper proposes a mixed integer linear programming model for the design of a reverse logistics network. This model aims to minimize the total cost at each stage of reverse logistics operations.

A simulation-based genetic algorithm approach for the simultaneous consideration of reverse logistics network design and disassembly line balancing with sequencing

Reverse logistics (RL) network design and disassembly line balancing (DLB) decisions are generally considered separately. In addition, the effect of disassembly sequencing on DLB is ignored. However, companies can take better-informed decisions by simultaneously considering RL, DLB and disassembly sequencing issues. This integrated approach results in more efficient processes which reduce cost and idle time, while maximizing throughput and customer satisfaction. In this study, we propose a simulation-based genetic algorithm approach for the joint optimization of RL network design and DLB with sequencing decisions. The use of simulation modeling allows for the consideration of stochastic aspects associated with RL and DLB such as transportation and disassembly times. A numerical example was provided to present the applicability of the proposed approach. Moreover, a sensitivity analysis was carried out to study the impact of various parameters. The results indicate the superior performance of the proposed approach with respect to total cost.

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 Necessity-Based Optimization Approach for Closed-Loop Logistics Considering Carbon Emission Penalties and Rewards under Uncertainty

The recycling of waste products can bring enormous economic and environmental benefits to supply chain participants. Under the government’s reward and punishment system, the manufacturing industry is facing unfolded pressure to minimize carbon emissions. However, various factors related to the design of closed-loop logistics networks are uncertain in nature, including demand, facility capacity, transportation cost per unit of product per kilometer, landfill cost, unit carbon penalty cost, and carbon reward amount. As such, this study proposes a new fuzzy programming model for closed-loop supply chain network design which directly relies on fuzzy methods based on the necessity measure. The objective of the proposed optimization model is to minimize the total cost of the network and the sum of carbon rewards and penalties when selecting facility locations and transportation routes between network nodes. Based on the characteristics of the problem, a genetic algorithm based on variant priority encoding is proposed as a solution. This new solution encoding method can make up for the shortcomings of the four traditional encoding methods (i.e., Prüfer number-based encoding, spanning tree-based encoding, forest data structure-based encoding, and priority-based encoding) to speed up the computational time of the solution algorithm. Several alternative solution approaches were considered to evaluate the proposed algorithm including the precision optimization method (CPLEX) and priority-based encoding genetic algorithm. The results of numerous experiments indicated that even for large-scale numerical examples, the proposed algorithm can create optimal and high-quality solutions within acceptable computational time. The applicability of the model was demonstrated through a sensitivity analysis which was conducted by changing the parameters of the model and providing some important management insights. When external parameters change, the solution of the model maintains a certain level of satisfaction conservatism. At the same time, the changes in the penalty cost and reward amount per unit of carbon emissions have a significant impact on the carbon penalty revenue and total cost. The results of this study are expected to provide scientific support to relevant supply chain enterprises and stakeholders.

Multi-echelon sustainable reverse logistics network design with incentive mechanism for eco-packages

With the pollution caused by express packaging waste, eco-packages are being commonly used to promote a green supply chain. However, extant research has yet to address the efficacious reverse logistics network design problem while considering both economic and environmental consumption in the recycling process. In this study, a multi-echelon reverse logistics network is designed to collect eco-packages from customers, instead of door-to-door recycling by couriers. This study aims to solve the capacity location-routing problem in a reverse logistics network integrated with the incentive mechanism for customers, which is regarded as NP-hard. To avoid the combinatorial explosion, a multi-objective hybrid genetic algorithm-tabu search (MOHGATS) is developed to reduce search space and improve search speed by changing search strategies. Computational experiments are conducted to verify the proposed model and algorithm based on several test instances and a real-world case study. The impacts of the quantity of eco-packages, the incentive costs for customers, and the quantity and capacity of recycling stations on the reverse logistics network are explored from both economic and environmental perspectives, providing a reference for practical operation and promotion of eco-packages on a large scale.

Designing sustainable logistics networks for classified municipal solid wastes collection and transferring with multi-compartment vehicles

The success stories of waste classification and processing systems in reducing emissions and conserving natural resources have inspired many countries to adopt similar systems. The current research on waste collection logistics network design is limited in capturing the features of waste classification systems. The environmental objectives, the use of multi-compartment vehicles to separate waste and reduce driving distance, and the joint decision-making of transfer station locations and vehicle routes are not integrated into the same optimization framework. This study aims to investigate the strategic planning of transfer stations and vehicle fleet dispatch in a city-level classified waste collection system. The problem is formulated as an integer programming problem that minimizes total travel costs while considering greenhouse gas (GHG) emissions. To solve the problem effectively, we propose a fast hybrid heuristic algorithm based on column generation (CG) and Adaptive Large Neighborhood Search (ALNS). The proposed algorithm framework is tested on various instances and applied to a real-sized logistics network in Chengdu, China. Computational results indicate that the proposed algorithm achieves high efficiency while maintaining a satisfactory gap. The results confirm that introducing the waste classification system can help reduce total travel costs and greenhouse gas emissions.

장강 내수로 항만의 물류 수요 영향요인에 관한 연구

This study examines the level of logistics demand for 22 inland ports along the Yangtze River in China and analyzes the factors determining this demand. To achieve this, a fixed effects model was used to explore the relationships between each port’s logistics demand and various variables, including economic factors, infrastructure factors, and national policies. The analysis reveals that the highest logistics demand appears in the lower Yangtze region, particularly in the eastern coastal areas where the economy and port infrastructure are well developed. Additionally, China’s Belt and Road strategy was found to significantly influence the logistics demand of inland ports. This research can provide insights into the pattern of logistics demand for China’s inland ports, assisting in the formulation of effective port and logistics infrastructure strategies. While the Korean government and industrial stakeholders are focusing on China’s coastal areas, China’s inland areas are rapidly growing, and this growth is likely to be amplified through the Yangtze River’s inland ports. The findings of this study suggest a strategic imperative for South Korea and China to prioritize the establishment of maritime and logistics infrastructure in areas of heightened economic activity, a move projected to catalyze the creation of novel business opportunities. Furthermore, it can provide essential guidelines for the efficient design and operation of logistics networks and the establishment of sustainable logistics systems.

Modelling a bi-level multi-objective post-disaster humanitarian relief logistics network design problem under uncertainty

Post-disaster humanitarian relief logistics focuses on managing the supply and shipment of relief items. Owing to the complexity arising from the hierarchical decision relationship and the uncertainties of demand and transportation, it is challenging to develop an effective humanitarian relief logistics network (HRLN). A globalized robust bi-level multi-objective HRLN model involving uncertainties in transportation time, transportation cost and demand is developed to address this problem, in which an upper-level decision-maker focuses on the allocation problem and a lower-level decision-maker considers the supply problem. For tractability, the goal programming approach is employed to trade off conflicting timeliness, satisfaction and operational cost objectives. The proposed globalized robust bi-level HRLN model is reformulated as its globalized robust counterpart formulations under two different perturbation structures. The primal–dual approach is adopted to transform the bi-level models into equivalent mixed-integer conic programs. A case study concerning an Iranian earthquake is conducted to prove the performance of the proposed model. Globalized robust models are more flexible and able to immunize against uncertainty by comparison with traditional robust optimization and deterministic solutions. The managerial insights of using the globalized robust bi-level optimization approach are reported for disaster response management. The design of an emergency aviation network to circumvent the effects of COVID-19 could be considered in future research.

A methodology for the strategic design of city logistics networks and its application in Turkish automotive spare parts industry

City logistics approaches and modeling struggles have a significant role in urban areas in increasing the efficiency of logistics operations and reducing traffic jams and their environmental effects. By developing an effective distribution network for cities, it is possible to compete with the changing world and satisfy flexible customer requirements. In this study, as a real-world case, a city logistics model for Istanbul metropolitan area is designed using multi-objective linear programming that considers the different objectives of the stakeholders in cities by integrating the fuzzy Choquet integral technique in a multi-level distribution network for the automotive spare part industry. This paper makes decisions regarding the amount of product flowing among the echelons, the amount of stock to be kept in the warehouses, and the product delays allowed. While minimizing the transportation cost, holding cost and emission levels during these decisions, the study also aims to maximize the service quality in the warehouses. The model is applied to a logistics network of fifty demand points and thirty time periods which can be considered a middle or large-scale problem. In the model, it is also decided to transport the products with electric or fuel vehicles. In the transport sector, electric vehicles are the key to meet future needs for social, health and other human services. The results are discussed under different scenarios. This research allows the use of such a model in making strategic decisions for the distribution network design in big cities.

A Bilevel Model for Robust Network Design and Biomass Pricing Under Farmers’ Risk Attitudes and Supply Uncertainty

This paper addresses an integrated biomass pricing and logistics network design problem. A bilevel design and pricing model is proposed to capture the dynamic decision process between a biofuel producer as a Stackelberg leader and farmers as Stackelberg followers. The bilevel optimization model is transformed into a tractable single-level formulation by using optimality constraints. Other unique characteristics of our problem at hand include the incorporation of the harvesting time and frequency decisions in the biomass supply chain network design problem for the first time and consideration of the uncertainty in switchgrass yield in a robust optimization setting to take into account the risk-averse behavior of the farmers (suppliers). To efficiently solve the model, we propose a Benders decomposition algorithm enhanced by surrogate constraints, strengthened Benders cuts, and in-out cut loop stabilization. The numerical experiments show that the proposed algorithm is significantly superior to the branch-and-cut approach of CPLEX in terms of run times and gaps. We conduct a case study with data from Texas to validate the capabilities of our mathematical model and solution approach. Based on extensive experiments, the benefits of modeling are analyzed, and significant insights are explored. Funding: This research was partially supported by the National Natural Science Foundation of China [Grants 71771135, 72171129]; and the scholarship from China Scholarship Council (CSC) [Grant CSC 201906210092]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0357 .

Reverse Logistics Network Design with a 3-Phase Interactive Intuitionistic Fuzzy Goal Programming Approach: A Case Study of Covid-19 in Pathum Thani, Thailand

Reverse Logistics Network Design with a 3-Phase Interactive Intuitionistic Fuzzy Goal Programming Approach: A Case Study of Covid-19 in Pathum Thani, Thailand

중국 자동차 산업의 역물류 네트워크 최적화에 관한 연구

Reverse logistics network design is a strategic level of decision-making, and the recovery process of abandoned vehicles is inseparable from the support of a complete reverse logistics network. Against the backdrop of the green economy and the circular economy, this study designed a waste vehicle reverse logistics network under two constraints: carbon tax policy and carbon transaction market policy, respectively, and established a multi-purpose mixed integer linear plan model to minimize carbon emissions and costs. In addition, using a Chinese automobile manufacturer as an example, the results of the model of maximizing economic effects (i.e., minimizing costs) and maximizing environmental effects by conducting case verification and using lingo 11.0 are as follows. First, the cost of transportation using pure electric vehicles under the same carbon emission policy is lower than using fuel cars. Second, under the two carbon policies, carbon trading policies have a better emission reduction effect and lower network operating costs. Third, we found that transactional carbon constraints help companies reduce costs and reduce carbon emissions, and help shape long-term, even image-oriented, corporate behavior. By analyzing the optimization of China's waste vehicle reverse logistics network, this study promotes the sustainable and sound development of Chinese waste vehicle recovery companies and realizes the double benefits of the economic environment. There is a certain significance in the research and practice of developing reverse logistics activities in academia

A Novel Sustainable Reverse Logistics Network Design for Electric Vehicle Batteries Considering Multi-Kind and Multi-Technology

With the expansion of the new energy vehicle market, electric vehicle batteries (EVBs) have entered a massive retirement wave. The strategic level of facility location and configuration decisions and the tactical level of multi-product flow and multi-technology selection decisions have been integrated into a sustainable reverse logistics network (SRLN). In this paper, we considered multiple kinds of waste electric vehicle batteries (WEVBs) with multiple recycling technology and constructed a multi-level SRLN model for WEVBs with the objectives of minimum economic costs and minimum carbon emissions. To solve this model, fuzzy set theory was applied to the equivalence transformation of constraints, non-interactive and interactive methods were used to solve the multi-objective planning (MOP), and interactive fuzzy programming with priority control was proposed to find the global optimal solution for this model. Finally, numerical experiments demonstrated the feasibility and effectiveness of the proposed model and solution method. The experimental results show that the SRLN model considering carbon emissions can significantly reduce carbon emissions of the network through a slight increase in the initial network construction cost, thus effectively balancing both economic and environmental objectives. In the non-interactive solution, the Lp-metric method has a lower deviation index than the weighted sum method; in the interactive solution, the priority control method proposed in this paper outperforms the TH method in terms of the number of practical solutions and CPU time and shows strong performance in searching and finding optimal solutions. The proposed model and method can provide the theoretical basis and technical support for a WEVB SRLN under the limited information uncertainty environment.

Cold Chain Logistics Network Design for Fresh Agricultural Products with Government Subsidy

This paper investigates the cold chain logistics network design in the first mile for fresh agricultural products with government subsidy, involving the capacity, location of cold storage facilities, and transportation from production areas to cold storage facilities after harvest. A bi-level programming model is formulated considering the quality degradation of fresh agricultural products. Based on the proposition and KKT conditions, a solution method is designed for reformulating the bi-level model into a single-level programming model. Numerical experiments are conducted to verify the proposed model. Experimental results show that the solution method efficiently solves the problem of the cold chain logistics network design for fresh agricultural products with subsidy, and sensitivity analysis provides managerial insights for decision-makers.

A green routing-location problem in a cold chain logistics network design within the Balanced Score Card pillars in fuzzy environment

A green routing-location problem in a cold chain logistics network design within the Balanced Score Card pillars in fuzzy environment

A two-way logistic network design for deteriorating items considering preservation effort and cost minimisation

The deterioration of many items, such as chemicals, volatile liquids, blood banks, and medicines, among other goods, is non-negligible in supply chain management. The transportation and holding of perishable inventories are important issues that need to be addressed by supply chain managers. Considering preservation efforts, this study deals with a two-way logistic network design problem for deteriorating items. A two-way logistic network provides two-way services. First, the collecting points and hubs transport items from customers in region A to those in region B. Second, the items are transported from customers in region B to those in region A. The effort to preserve deteriorating items lowers the deterioration rate but involves preservation costs and is also considered in the model. This model aims to determine the optimal number and location of collecting points, joint replenishment cycle time, and preservation effort while minimising the network cost. A nonlinear optimisation technique is used to solve the proposed problem. Finally, we present an example to illustrate the model and solution approach.

Reliable design of urban surface-underground integrated logistics system network with stochastic demand and social-environmental concern

Transforming urban goods movement from roads to underground is an emerging concept to improve supply chain performance and alleviate overcrowded environments. Existing literature merely discussed the underground logistics system (ULS) network design problem within a simplified boundary, whereas several critical system features (e.g., stochastic freight demand and co-modality) were omitted. To bridge the knowledge gaps, this paper proposes a reliable modeling approach to solve an urban surface-underground integrated logistics network (USUIL) design problem with a holistic consideration of cost-benefit objectives, road-ULS modal split, and demand uncertainties. To begin with, we introduce a city-wide hub-and-spoke USUIL network structure with a two-tier parallel distribution process. Five major external benefits of underground goods movement are formulated using real-world data. Next, a deterministic model is developed to optimize the network decisions including hub-satellite location-allocation, deployment of tunnels and pipelines, logistics service assignment, and flow routing. Then, to deal with the influences of case-wise demand uncertainties, the model is reformulated into a reliable one by incorporating the ideas of solution robustness and stochastic equivalents. A two-stage heuristic-based optimization tool that hybridizes the adaptive immune genetic algorithm, plant growth simulation algorithm, and Clarke-Wright saving algorithm was proposed to solve the problem. Finally, numerical experiments are conducted on the Beijing city case to validate the proposed method, and the best network schemes obtained from different models and scenarios are compared. Results show that a greater robust control factor in our model helps the network to lower the integrated freight transport cost while improving the social-environmental benefit at the expense of deploying more facilities. The reliable model performs well in balancing the conservativeness (i.e., less capacity insufficiency risk) and optimality (i.e., less cost due to redundant facilities) of the USUIL network design when faced with diverse demand cases.

A two-stage MCDM model for reverse logistics network design of waste batteries in Turkey

Inadequate environmental resources and overpopulation reveal the need to protect and recover natural resources attentively. In this sense, the reverse logistics concept emerged as a key solution since it deals with product flow from the final user to the origin. There are various items that need to be considered in well-planned reverse logistics network designs and one of these items is batteries which include hazardous and precious materials in it. Hence, waste management of batteries via recycling becomes a very significant issue from both economic and environmental benefits. Accordingly, depending on the importance of the topic, a two-stage methodology is proposed in this study for providing a network design under multiple objectives. Within the first stage, the importance weights of objectives are obtained via Spherical Fuzzy Analytical Hierarchy Process (SF-AHP) and they are found as 0.248 for cost minimization, 0.3 for carbon emission minimization, 0.256 for employment rate maximization and 0.196 for development rate maximization. Afterward, in the second stage, a Multi-Objective Mixed Integer Linear Programming Model (MO-MILP) is developed to design the reverse logistics network and an application is performed in Turkey for validation. The model is solved for various scenarios including different quantities to be collected. Hence, it is obtained that the satisfaction degrees for employment and development objectives are 100% in all of the scenarios. However, the satisfaction degree of carbon emission minimization is around 96% and the less satisfied objective is the cost minimization having a satisfaction degree of 75% on average.

Column-and-constraint-generation-based approach to a robust reverse logistic network design for bike sharing

Dockless bike-sharing systems have grown rapidly in China in recent years. A large number of shared bikes have been put into use, which results in a serious problem of uncollected faulty bikes. However, since the exact number of faulty shared bikes cannot be known a priori, a reverse logistic network must be designed strategically to reduce the total costs of the collection process. This paper studies a robust reverse logistic network design problem of shared bikes that integrates the locations of recycling centres and the planning of collection routes in uncertain collection demand situations. A two-stage robust location routing model is presented to capture the uncertainty of collection demands, and then a set-partition reformulation is presented to address the computationally intractable model. We develop a column-and-constraint-generation-based algorithm combining a tailored column generation algorithm to efficiently solve the proposed model. Numerical experiments and parameter analyses, which are based on real data from bike-sharing companies, are conducted to evaluate the performance of the proposed algorithm. A case study is presented to further analyse the spatial features of the reverse logistic network of shared bikes. Benefits of applying robust optimization are demonstrated experimentally in terms of total cost under uncertain collection demand.