Logistics Network Design Problem Research Articles

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.

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 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 .

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 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.

Fourth-party logistics network design under uncertainty environment

A novel fourth-party logistics (4PL) network design problem under uncertainty environment is studied in the current work. Demand uncertainty and two types of disruptions, facility and third-party logistics (3PL) disruptions, are simultaneously considered. To minimize the total cost, a two-stage stochastic programming model is presented, which is further approximated as a mixed-integer linear programming model using the sample average approximation (SAA) method. As a large number of disruption and demand scenarios generated in the approximation process lead to challenges in model solving, the scenario reduction (SR), SAA, dual decomposition and Lagrangian relaxation (DDLR) approaches are integrated to present an SR-DDLRSAA algorithm. The effectiveness of our model and algorithm is illustrated by adopting numerical instances and real-life cases. A comparative analysis indicates that the impact of disruptions on the 4PL network is closely related to the values of the disruption probability and the fluctuation degree of uncertain demand. Furthermore, we extend the proposed basic model by considering demand uncertainty and three types of disruptions, namely supply, facility, and 3PL disruptions, and analyze the impact of supply disruption on the 4PL network.

Benders decomposition for a reverse logistics network design problem in the dairy industry

Benders decomposition for a reverse logistics network design problem in the dairy industry

Green dynamic multimodal logistics network design problem considering financing decisions: a case study of cement logistics.

Logistics network is one of the most important parts of supply chains with significant share in achieving sustainability across them. In this paper, we investigate a new multi-objective mixed integer linear programming model for the design of multimodal logistics network. A bi-objective mathematical model is introduced and two conflicting objectives including the minimization of total cost and the total environmental impact are taken into account. Effective environmental life cycle assessment-based method is incorporated in the model to estimate the relevant environmental impacts. Due to budget constraints, financing decisions for facility construction are considered in the proposed model. To cope with the model objective functions, the augmented ε-constraint method is applied. Computational analysis is also provided by using a cement multimodal rail-road logistics network case study to present the significance of the proposed model. Results show that utilizing the proposed multi-period optimization model influences the location of multimodal terminals and their construction time. Also, the results show that the use of the proposed model enhances the efficiency of terminals. On the other hand, computational results indicate that preferences of decision-makers and the importance of environmental objective have significant impacts on the topology of transportation network.

Supply chain network design considering customer psychological behavior-a 4PL perspective

From the perspective of Fourth Party Logistics, a novel supply chain network design problem considering customer psychological behavior is proposed in this paper. First, we calculate the minimum cost of the supply chain network design in Fourth Party Logistics when the demand of customers is fully satisfied. In the shortage of investment in the process of supply chain network design, the demand of customers can be partially satisfied. Then, customer psychological behavior is considered to maximize the value function of customer satisfaction under cost constraint. After introducing the definitions of the psychological reference point and the service level for customers based on the prospect theory, we formulate a non-linear integer programming model for Fourth Party Logistics network design problem. Since the objective function of the proposed model is non-linear, an approximation linearization method is introduced to adjust the proposed model to be an equivalent linear model. Numerical experiments are designed to justify the effectiveness of the proposed method. Through a thorough analysis on customer psychological behavior, it can be seen that customers tend to be more risk-averse for gains than risk-seeking for losses in Fourth Party Logistics supply chain network design. Moreover, valuable investment insights are recapitulated. This proposed method provides an effective tool for a Fourth Party Logistics provider to give reasonable suggestions to an investor in Fourth Party Logistics supply chain network design. Furthermore, we extend the basic model of 4PL supply chain network design considering customer satisfaction with multiple commodities and provid a solution method based on greedy adding heuristic to further reduce the calculation time for large scale cases.

Benders decomposition for the distributionally robust optimization of pricing and reverse logistics network design in remanufacturing systems

The pricing and reverse logistics network design problem in remanufacturing has attracted considerable attention in recent years due to increasingly serious environmental problems. In this study, we consider a pricing and reverse logistics network design problem with price-dependent return quality uncertainty. To handle the high uncertainty in return quality, we propose a distributionally robust risk-averse model to safeguard the profits of investors in extreme situations. We propose a Benders decomposition approach to solve the proposed model. It is enhanced through valid inequalities, local branching, in-out variant methods and scenario-based aggregated cuts. Computational experiments demonstrate that the distributionally robust model can effectively hedge against high uncertainty and that the enhanced Benders decomposition methods significantly outperform their classical counterparts and the off-the-shelf solver Gurobi. Lastly, managerial insights are explored, and future research directions are outlined.

Emergency logistics network design based on space–time resource configuration

The occurrence of natural disasters or accidents causes the obstruction or interruption of road traffic connectivity and affects the transportation of essential materials, especially for cross-regional delivery under emergency situations. Affected by COVID-19, government administrators establish cross-regional quarantine roadblocks to reduce the risk of virus transmission caused by cross-regional transportation. In this study, we propose an emergency logistics network design problem with resource sharing under collaborative alliances. We construct a state–space–time network-based bi-objective mixed integer programming model to optimize the vehicle routes in order to meet customer demands for essential materials with the lowest cost and highest emergency response speed under limited transportation resources. A two-stage hybrid heuristic algorithm is then proposed to find good-quality solutions for the problem. Clustering results are obtained using a 3D k-means clustering algorithm with the consideration of time and space indices. The optimization of the initial population generated by the improved Clarke and Wright saving method and improved nondominated sorting genetic algorithm-II with elite retention strategy provides stable and excellent performance for the searching of Pareto frontier. The cost difference of the entire emergency logistics network before and after collaboration, i.e., the profit, is fairly allocated to the participants (i.e., logistics service providers) through the Shapley value method. A real-world case in Chongqing City, China is used to validate the effectiveness of the proposed model and algorithm. This study contributes to smart transportation and logistics system in emergency planning and has particular implications for the optimal response of existing logistics system to the current COVID-19 pandemic.

Application of Integrated Multiple Criteria Data Envelopment Analysis to Humanitarian Logistics Network Design

This paper considers a humanitarian logistics network (HTLN) design problem, including the emergency relief facilities (ERFs) location-allocation decision for the efficient distribution of emergency supplies from the ERFs to the affected areas. A goal programming (GP) approach is applied to consider the multiple objectives simultaneously. Solving the GP model with a given weight assigned to each goal yields a single HTLN scheme, so there will be various schemes available by solving the GP with multiple values of the weights. For evaluating these schemes and identifying the most efficient one, we apply the data envelopment analysis (DEA) methods considering each scheme as a decision-making unit (DMU). Since the classical DEA (C-DEA) intrinsically aims to identify efficient DMUs and the efficient frontier, the use of C-DEA may not lead to a full ranking in many situations. There are several independent evaluation approaches to increasing discriminating power. Among them, this study integrates the multiple criteria DEA (MC-DEA) with the following three DEA methods, (i) stratification DEA (S-DEA), (ii) cross-efficiency DEA (CE-DEA), and (iii) super-efficiency DEA (SE-DEA), to make the most use of each method’s strengths. Through a case study of designing the HTLN system for South Carolina, the procedure of implementing the integrated multiple criteria DEA (IMC-DEA) method is demonstrated. It is observed that the IMC-DEA method performs well in terms of designing the HTLN system and would help the decision-makers consider more efficient options and make a final decision.

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.

Robust facility location with structural complexity and demand uncertainty

Building robust logistics networks against facility disruptions has been gaining more attention from both researchers and practitioners. However, in pursuit of high built-in network robustness, one usually has to increase the network redundancy and complexity, which adds to the difficulty of management and induces additional operating costs. In this paper, we aim to balance the trade-off between robustness and complexity in the logistics network design problem and propose a model that explicitly considers the demand uncertainty. Due to the non-convexity of our model, we present a linear reformulation method that transforms the model into a MILP. From our numerical studies, in some cases, we can simultaneously enhance robustness and reduce complexity. When the demand uncertainty increases, the network will become less robust. It will require more backup links to guarantee a given robustness requirement. Consequently, the network can exhibit higher complexity and incur additional costs. We also observe it might be possible to dramatically reduce the complexity at a reasonable cost. One can achieve a relatively simple and robust network by only maintaining backup links for customer zones with large demand volumes. Finally, we provide discussions on managerial implications.

A two-stage stochastic programming model for multi-period reverse logistics network design with lot-sizing

This paper proposes an integrated model for a multi-period reverse logistics (RL) network design problem under return and demand uncertainty. The reverse logistics network is modeled as a two-stage stochastic programming model to make strategic and tactical decisions. The strategic decisions are the first stage decisions in establishing network’s facilities and tactical decisions are the second stage decisions on material flow, inventory, backorder, shortage, and outsourcing. The uncertainties considered in this study are the primary market return and secondary market demand. The model aims to determine optimal numbers of sorting centers and warehouses, optimal lot sizes, and transportation plan that minimize the expected total system cost over the planning horizon. A case study was conducted to validate the proposed model. Numerical results indicate that the stochastic model solution outperforms result of expected value solution.

Integrated location and inventory planning in service parts logistics with customer-based service levels

We analyze the integrated logistics network design and inventory stocking problem in service parts logistics, where each customer requires a certain response time-based service level. We introduce a non-linear mixed integer optimization model that explicitly captures the interdependency between network design (locating facilities, and allocating customers’ demands to facilities) and inventory stocking decisions (stock levels and their corresponding stochastic fill rates). We then provide two different linearized mixed integer formulations for this problem that can solve small and medium size problems. We show that it is still a challenging problem in general, for which we have a Lagrangian-relaxation based approach that provides extremely tight lower and upper bounds. Our extensive computational study shows the effectiveness of the proposed approach for a variety of problem instances, producing provably near-optimal integrated solutions.

Global logistics network design problem with rules of origin

Purpose: This paper aims to incorporate rules of origin into international taxation into firms' global operations. The purposes of this research are to develop a new model for the global logistics network design of an assembly system with rules of origin and to analyze how the changes of international relationships will affect logistics network design and evaluation functions.Design/methodology/approach: In this study, we develop a global logistics network design model prescribing allocating amounts for production, assembly, and distribution. A path-based programming model is presented that represents the complexities associated with international and interenterprise issues. The objective is to minimize total costs, which are costs of production, assembly, transportation, and customs duties. In addition to customs duties, rules of origin, which were, not addressed in previous studies, are considered. Several computational experiments with industrial examples are presented to validate the model.Findings: The results indicate that the tax planning and alignment with the FTA might have a significant impact on the decision-making in the global supply chain for multi-national operating companies. Therefore, the multi-national operating companies need to be aware of importance of what-if simulation before or after FTA changes.Originality/value: While designing global supply chain, the tax effects and use of free-trade agreements (FTAs), were generally omitted. In this paper, we proposed a global logistics network design model with rules of origin. We show the effectiveness and necessity to consider rules of origin. This model can be used in making an optimal global logistics network for global companies. The results of this model are used as a target for a real optimal logistics network. Companies can make a better logistics network by using this model.

Regional multimodal logistics network design considering demand uncertainty and CO2 emission reduction target: A system-optimization approach

This paper investigates an interesting regional multimodal logistics network design problem with CO2 emission reduction target and uncertain demands in the context of urban cluster development. From the perspective of system optimization, a regional logistics network involves a centralized logistics authority and a centralized carrier, where the logistics authority concerns the regional logistics network configuration in terms of the number, location and scale of logistics parks and the subsidies of rail transportation links, and the carrier’s decisions include the choice of transportation route for each logistics demand. Given a determined logistics demand pattern, this multi-stakeholder decision making problem is first formulated as a bi-level programming model followed by its equivalent mathematical programming with equilibrium constraints (MPEC) to depict the leader-follower behaviors. To capture the risk aversion level of the logistics authority in uncertain demand environment, an improved adjustable robust optimization method is proposed, including an individual control parameter and providing an exact expression of the maximum satisfaction probability. Computational results and related impact analysis demonstrate that the proposed models and solution methods are effective, which can provide the beneficial theory basis and practice guide for the green and sustainable development of regional logistics.

Low-carbon Marine Logistics Network Design under Double Uncertainty of Market Demand and Carbon Trading Price

Wang, W.; Ren, Y.; Bian, W., and Jia, X., 2019. Low-carbon marine logistics network design under double uncertainty of market demand and carbon trading price. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 30–39. Coconut Creek (Florida), ISSN 0749-0208.This paper discusses the low-carbon marine logistics network design problem under the uncertainty of market demand and carbon emission price, and aims at the minimum cost of shipping logistics network operation (including carbon emission cost), and constructs a three-level marine logistics network site-distribution model based on scenario. The problem is transformed into an unconstrained optimization problem by the penalty function method, and the artificial bee colony algorithm (ABC) is used to solve the model. The study found that the moderate fluctuation of demand is conducive to the integration effect of the marine logistics network, which reduces the total cost of the logistics network. Finally, the validity of the model is verified by an example analysis of a domestic industrial product manufacturer.

Multimodal Green Logistics Network Design of Urban Agglomeration with Stochastic Demand

This study investigates a multimodal green logistics network design problem of urban agglomeration with stochastic demand, in which different logistics authorities among the different cities jointly optimize the logistics node configurations and uniform carbon taxes over logistics transport modes to maximize the total social welfare of urban agglomeration and consider logistics users’ choice behaviors. The users’ choice behaviors are captured by a logit-based stochastic equilibrium model. To describe the game behaviors of logistics authorities in urban agglomeration, the problem is formulated as two nonlinear bilevel programming models, namely, independent and centralized decision models. Next, a quantum-behaved particle swarm optimization (QPSO) embedded with a Method of Successive Averages (MSA) is presented to solve the proposed models. Simulation results show that to achieve the overall optimization layout of the green logistics network in urban agglomeration the logistics authorities should adopt centralized decisions, construct a multimode logistics network, and make a reasonable carbon tax.