Lateral Transshipment Research Articles

A resilient biofuel supply chain design based on Paulownia and Jatropha under uncertainty considering the water-energy nexus

The growth in global population, pollution, and energy demand, in addition to dwindling fossil fuel reserves, are driving scientists and industries toward adopting biomass-based products over non-renewable energy sources. Moreover, the decline of water resources and the importance of conserving water have induced governments to start initiatives to control the consumption of water, especially in the agricultural and industrial sectors. Therefore, the water-energy nexus is a significant issue that has drawn the interest of researchers in recent years. A bi-objective two-stage stochastic multi-product optimization model is presented for designing a resilient second-generation biofuel supply chain under uncertainty, considering the water-energy nexus. In this supply chain, biofuel is yielded from inedible plants Paulownia and Jatropha. Various resilience strategies are adopted to cope with potential disruptions to the supply chain, including multi-sourcing, capacity expansion, imports, multiple transportation modes, and lateral transshipment. The objectives of the model are twofold: maximizing profit and minimizing water consumption. This is achieved by making some key decisions including: facility location, managing the flows of raw materials and finished products among networks, determining production volumes, setting inventory levels, allocating land for biomass crop cultivation, planning production and storage capacities, scaling capacity expansions, the number of vehicles to rent or purchase, and optimizing the amounts of raw materials to import and biomass to export. An actual case study is provided to demonstrate the real-world applicability of the approach, while multiple sensitivity analyses are performed on the problem's main parameters. The study concludes by providing valuable managerial insights that focus on the cost savings and profit increases achievable through resilience strategies. The analysis considers different scenarios and explores effective methods for handling disruptions, emphasizing water resource conservation achieved by integrating the water-energy nexus within the biofuel supply chain.

Vosviewer-Based Bibliometric Review of Transshipment Location Problem From 2000 to 2023

Objective: This study conducts a comprehensive bibliometric review of the Transshipment Location Problem (TLP) by identifying key authors, countries, journals, and research areas. It also maps the evolution of TLP and explores methodologies and tools used for optimizing logistical and distribution systems. Theoretical Framework: TLP, a variant of the classical transshipment problem, has gained attention for its role in optimizing supply chain networks. It involves both proactive transshipment (planned inventory management) and reactive transshipment (responding to unexpected events or shortages). Method: This study applies a bibliometric analysis methodology using VOSviewer to analyze publications extracted from the Scopus database. The analysis focuses on key bibliometric indicators such as co-authorship, co-citation, and keyword co-occurrence. The dataset comprises 432 selected articles published between 2000 and 2023. Results and Discussion: The review reveals that optimization tools, particularly in transportation and inventory management, are widely applied in TLP studies The United States, China, and Germany are leading contributors to TLP research. Lateral transshipment strategies are increasingly employed to enhance supply chain flexibility. A small group of influential researchers drives the field through collaborations and citations. Research Implications: The review provides valuable insights for future research, highlighting gaps in applying optimization methods in emerging fields like healthcare. It offers a clear view of the geographical and institutional landscape of TLP research and suggests areas for future study, such as sustainability. Originality/Value: This review fills a gap in the literature by systematically mapping TLP research’s evolution and its growing relevance in logistical systems optimization across various industries.

Designing a new sustainable healthcare network considering the COVID-19 pandemic: Artificial intelligence-based solutions

The COVID-19 pandemic, which is still spreading its new mutations all around the world, is considered a healthcare challenge around the world. The best approach to forestall this pandemic is to avoid exposure to the virus. Therefore, medical protective equipment is essential for fighting this pandemic. This underlines the chief role of having a sustainable supply chain network (SCN) for producing and distributing personal protective equipment to avoid shortage and augmenting costs. The reality, however, is that the COVID-19 pandemic leads to many developments in countries and this study is another step for these purposes. This research developsa multi-period, multi-objective, multi-echelon, and multi-product medical protective equipment sustainable SCN considering the production, distribution, allocation, and inventory with “risk pooling” strategy effect with the aim of filling the existing gaps in health SCN research during the COVID-19 pandemic. By applying the risk pooling strategy, lower inventory levels or higher service levels can be achieved without increasing inventory costs. This model explores the possibility of lateral transshipments between distribution centers, as a way to increase the reliability of SCN performance. We model a new production, inventory, distribution, location, and allocation problem and consider four objectives for our suggested model (i) minimizing total SCN costs, (ii) minimizing environmental effects, (iii) minimizing social impacts, and (iv) maximizing the reliability of demand delivery. The proposedmodel simultaneously examines all three dimensions of sustainability (economic, environmental, and social) as well as the reliability of demand delivery. Considering all of these decisions and assumptions brings the studied problem closer to reality. We employ various algorithms for solving our developed model with different sizes: the improved version of the augmented ε-constraint (AUGMECON2) algorithm for small and medium-sized problems and two meta-heuristic algorithms, i.e., Multi-Objective Whale Optimization Algorithm (MOWOA) and Multi-Objective Variable Neighborhood Search (MOVNS) algorithm, for large-sized ones. The Taguchi approach is used to tune the parameters of meta-heuristic algorithms, and a comparison is performed using four evaluation metrics: Mean Ideal Distance (MID), Number of Pareto Solutions (NPS), Maximum Spread (MS), and Spread of Non-Dominance Solution (SNS). Proposed solving methods for the studied problem and making comparisons between them are another innovation of this study. A couple of numerical examples are provided to illustrate the applicability of the presented solution methods. Finally, sensitivity analysis for problem parameters is performed to validate our suggested model. Our study reveals the superiority of the MOWOA over the other algorithms.

A two-stage stochastic programming framework for blood product inventory management with ABO substitution and lateral transshipment

This work proposes a two-stage stochastic optimization model for managing the inventory of a two-echelon blood supply chain comprised of a blood center and a network of hospitals and transfusion points. The proposed model identifies optimal distribution quantities across the supply chain, considering demand stochasticity and product perishability. Furthermore, the model utilizes redistribution policies, such as lateral transshipment and product substitution based on ABO compatibility, as corrective actions to minimize outdates (i.e., expired product) and shortages across the network. Using a computational experiment that includes a network with one regional blood center and twenty hospitals, we show that inventory management policies that include both ABO substitution and transshipment can significantly decrease the expected total network costs. Similarly, we see corresponding reductions in the expected number of outdated units and emergency requests to external networks for additional supply to satisfy unmet demand.

Analyzing economic effect on mRNA vaccine inventory management with redistribution policy

This study focused on the challenges posed by the fluctuating demand for COVID-19 vaccines, considering factors such as side effects, religious objections, and absenteeism, which result in the accumulation of excess vaccines. Recognizing the resulting social, economic, and environmental issues, this study investigated the application of a lateral transshipment policy for the management of the inventory of short-term vaccines, considering related unpredictabilities. A discrete event simulation built on foundational principles derived from a mixed-integer linear programming model was employed to explore the dynamics of mRNA-based vaccine distribution among two hospitals based on lateral transshipment and reordering policies. Through the simulation of various scenarios over periods of 1-30 days, transshipment based on the availability policy is employed to determine the quantity of vaccines to be transshipped, constrained to vial amounts, and the (s, S) inventory system for reordering. The results of this study underscore the efficacy of lateral transshipment, particularly in situations where demand discrepancies exist between hospitals, thereby revealing its superiority over non-transshipment strategies within 7 days.

A hybrid deep reinforcement learning approach for a proactive transshipment of fresh food in the online–offline channel system

To reduce the waste of fresh foods, one of the e-commerce companies in South Korea utilizes lateral transshipment in the network of online platforms and offline shops, which is called the online–offline channel system (OOCS). Even though the OOCS has achieved success in real practice, there is room for further study on this system with regard to deriving a transshipment policy. For this reason, this study aims to develop a solution approach that could derive a promising policy and analyze the impacts of transshipment in the OOCS. The main contributions are summarized as follows. First, we propose a model to deal with the proactive transshipment of perishable products in the OOCS. In particular, this is the first study that introduces the concept of the heterogeneous shelf life considering different properties of online and offline channels. Second, we develop the hybrid deep reinforcement learning (DRL) approach by combining the soft actor–critic algorithm with two novel acceleration methods. The developed method could obtain a promising policy without assumptions about demand distribution and mitigate computational burdens by reducing action spaces. On a set of experiments carried out on real-world demand data, the transshipment policy derived from the hybrid DRL approach could obtain the best profit compared to existing algorithms. Third, we examine the impacts of transshipment by differing types of demand and varying the unit transshipment cost parameter. We find that transshipment substantially reduces the outdating cost by allowing the offline channel to make good use of the old products that will be discarded in the online channel, which is new to the literature.

A Three-Stage Stochastic Model to Improve Resilience with Lateral Transshipment in Multi-Period Emergency Logistics

Driven by the growing threat of natural disasters caused by climate change, there is an urgent need to strengthen the emergency rescue logistics network. However, insufficient research has been conducted on optimizing both pre-disaster preparation and post-disaster response, resulting in lower resilience and inefficiency of emergency logistics management. To this end, this study explores the optimization of emergency rescue resource allocation and transportation network design, considering the uncertainty and multi-period nature of natural disaster rescue. By employing a lateral transshipment strategy, a three-stage stochastic programming model is established, which aims to balance economic benefits with the need for devastations, thereby enhancing the resilience of the logistics network. Numerical experiments verify the effectiveness of the proposed model with different instances and the performance of the lateral transshipment strategy by comparing it with a two-stage stochastic programming model. Sensitivity analysis is performed on the costs of constructing a depot and the penalties for unmet needs. The analysis yielded valuable insights that can be used to enhance emergency rescue operations, supply chain network design, and logistics network design. The research outcome can benefit emergency responders and logistics professionals in optimizing their operations.

A multi-period multi-objective closed-loop blood supply chain configuration and optimisation under disruption

The COVID-19 pandemic has highlighted the stability of the blood supply chain in case of interruption and other emergencies. Blood is a precious and perishable substance, and any disruption in its supply can cause serious consequences. Current research on the blood supply chain mostly focuses on a multi-objective perspective, and rarely involves both multi-objective and multi-period methods simultaneously. Therefore, we develop a multi-period multi-objective closed-loop blood supply chain model that considers disruptions and uncertainties of demand and recovery, aiming at achieving a balance among various objectives, and coordination of various periods. Fuzzy chance-constraint, meta-heuristic approaches and exact solution (CLPEX) are adopted to mitigate the impact of uncertainties and calculate the optimal solution of a calculating case. The calculation outcomes indicate that: With regard to economic costs and social benefit, the multi-period system is better than the single-period system; the comprehensive performance of the multi-target system is better than that of the single-target system; and the multi-period multi-objective system is more in line with the real situation. Meanwhile, the mitigation strategies, such as maintaining emergency inventory and implementing lateral transshipment, can significantly enhance the performance of the system in disruptions. This paper mainly contributes the follows: Firstly, it provides a reference for configuration and optimisation of a cross-region closed-loop supply chain under disruptions and uncertainties. Secondly, the comprehensive trade-off of multi-objective and multi-period in this paper is especially suitable for perishable production with short cycles, large fluctuations of supply and demand.

Optimisation-in-the-loop simulation of multi products single vendor-multi buyers supply chain systems with reactive lateral transhipment

Considering that batik is one of the most popular products in Indonesia, it is important to analyse the supply chain system for batik products. In reality, the supply chain system for batik products enables orders between buyers to receive products more rapidly, allowing them to anticipate stock outs and obtain lower ordering costs than when ordering from vendors. It is referred to as reactive lateral transshipment. This paper discusses the development of a simulation-based stochastic optimisation model for a batik product supply chain system with multiproducts and single vendor-multi buyers. The utilised solution searching algorithm is a modified Genetic Algorithms (GA) executed in-loop with the developed simulation-based stochastic model. The results demonstrate that the proposed modified GA is able to provide a global optimum solution, allowing the proposed simulation-based stochastic model to reduce the joint total cost (JTC) of the investigated supply chain system by up to 19% when compared to the local optimisation model in each supply chain party.

Location–allocation problem in the emergency logistics system considering lateral transshipment strategy

This paper explores the problem of designing an efficient emergency logistics system while considering lateral transshipment strategy. Firstly, a two-stage mixed-integer programming model is proposed to characterize the location–allocation problem in an emergency logistics system with the aim of minimizing the total cost. Secondly, we propose an improved Benders decomposition framework for solving the emergency logistics system model. The numerical results reveal that our algorithm can find the optimal solution efficiently for large-scale instances, outperforming the commercial programming solver Gurobi. Finally, using Poyang Lake in China as a case study, we demonstrate that the lateral transshipment strategy can reduce the total cost of the emergency logistics system by 8.3% through comparing with the one without considering the lateral transshipment strategy. At the same time, we argue that the equity factor prevents the affected people from being evacuated to more distant shelters. The contribution of our work can be summarized as follows: an improved Benders decomposition algorithm is developed to investigate the optimal design of location–allocation strategy in the three-level emergency logistics network, with the consideration of lateral transshipment strategy and equity factor.

When is it worth it for two hospital network pharmacies to operate as an integrated inventory system in the presence of supply chain disruptions?

Hospital pharmacies stock perishable drugs that experience supply chain disruptions. A potential solution to alleviate the negative effects of shortages caused by these disruptions is lateral transshipments (i.e., sharing of inventory; integrated inventory system) between hospital network pharmacies with independent suppliers. However, it is unclear when it is beneficial to operate as an integrated inventory system. We create a modeling framework to solve for the integrated inventory policies in a two-hospital network pharmacy inventory system. We find that (i) to benefit from an integrated inventory system, the lateral transshipment cost must be sufficiently less than the shortage cost; sufficiently largely influenced by the duration of and time between supply chain disruptions. We find (ii) hospital network pharmacies need to consider the duration of and time between supply chain disruptions when selecting a hospital network pharmacy with which to share inventory. The integrated inventory policies demonstrate that perishable inventory systems with supply chain disruptions may benefit from sharing inventory. In our hospital network pharmacy setting, this contradicts the strict regulations in current practice that generally prohibit hospital network pharmacies from sharing drugs or make it very difficult for hospital network pharmacies to stay compliant when sharing drugs outside of their network.

An integrated optimization model for planning supply chains' resilience and business continuity under interrelated disruptions: a case study

PurposeThe adverse interactions between disruptions can increase the supply chain's vulnerability. Accordingly, establishing supply chain resilience to deal with disruptions and employing business continuity planning to preserve risk management achievements is of considerable importance. The aforementioned idea is discussed in this study.Design/methodology/approachThis study proposes a multi-objective optimization model for employing business continuity management and organizational resilience in a supply chain for responding to multiple interrelated disruptions. The improved augmented e-constraint and the scenario-based robust optimization methods are adopted for multi-objective programming and dealing with uncertainty, respectively. A case study of the automotive battery manufacturing industry is also considered to ensure real-world conformity of the model.FindingsThe results indicate that interactions between disruptions remarkably increase the supply chain's vulnerability. Choosing a higher fortification level for the supply chain and foreign suppliers reduces disruption impacts on resources and improves the supply chain's resilience and business continuity. Facilities dispersion, fortification of facilities, lateral transshipment, order deferral policy, dynamic capacity planning and direct transportation of products to markets are the most efficient resilience strategies in the under-study industry.Originality/valueApplying resource allocation planning and portfolio selection to adopt preventive and reactive resilience strategies simultaneously to manage multiple interrelated disruptions in a real-world automotive battery manufacturing industry, maintaining the long-term achievements of supply chain resilience using business continuity management and dynamic capacity planning are the main contributions of the presented paper.

Inventory cooperation strategies between one supplier and two recycling centers

AbstractDue to environmental and economic incentives, reverse supply chains have developed. In a reverse supply chain, due to the uncertainty of recycling, the probability of out‐of‐stock is high for recycled parts. In inventory management, transshipment and substitution are often used to avoid various consequences caused by stock shortages. This paper deals with a single‐echelon inventory system with two recycling centers and one supplier. Given demand characteristics, replenishment methods, and sharing rules, four inventory models: general strategy, lateral transshipment between the two recycling centers, substitution between recycling centers, and the supplier and the multi‐strategy are constructed. In the numerical example, Mathematica 11.3 is used to solve the models. Results show that lateral transshipment does not necessarily reduce the cost of recycling centers, while the substitution strategy and the multistrategy are better than the other two, among which the multistrategy is usually better than the substitution, unless the recycling center faces low replenishment rates with high demand rates. These conclusions can provide some management implications for recycling companies.

Cluster-based lateral transshipments for the Zambian health supply chain

Many low- and middle-income countries, including Zambia, suffer from unreliable distribution of health commodities leading to high variation in service levels across health facilities. Our work investigates how transshipment can improve system-wide service levels, equity across facilities, and average inventory levels. We focus on the distribution of malaria medicines in Zambia’s public pharmaceutical supply chain, which is heavily impacted by the rainy season leading to seasonality and uncertainty in demand and lead times. We use the more advanced deep reinforcement learning method Proximal Policy Optimization to develop transshipment policies and compare their performance with currently available, easy-to-use heuristics. To ensure that the model applies to settings of a realistic scale, we adopt a policy architecture that effectively decouples the policy’s complexity from the problem dimensions. We find that deep reinforcement learning is mainly useful in resource-constrained environments where system-wide inventory is scarce. When sufficient inventory is available, transshipment heuristics are more appealing from an overall cost-effectiveness perspective. Based on our numerical experiments, we formulate policy insights that can support policymakers in a humanitarian health context.

A robust optimization approach for the production-routing problem with lateral transshipment and outsourcing

Despite the fact that there is a large body of literature on the Production Routing Problem (PRP), we were struck by the dearth of research on outsource planning and lateral transshipment. This paper presents a mixed-integer linear programming model for incorporating outsourcing, lateral transshipment, back ordering, lost sales, and time windows into production routing problems. Then a robust optimization model will be introduced to overcome the detrimental effects of demand uncertainty. Considering the scale and complexity of the suggested problem, addressing it in a reasonable time was a challenge. Therefore, three matheuristic algorithms, including Genetic Algorithm (GA), Simulated Annealing (SA), and Modified Simulated Annealing (MSA), are developed for solving large-scale problems. Eventually, computational experiments on disparate instances are performed, and the results show the effectiveness and efficiency of the proposed algorithms. In other words, our recommended algorithms outperform the CPLEX solver in terms of the quality and time of obtaining the solutions.

A Single-Product Multi-Period Inventory Routing Problem under Intermittent Demand

Demand fluctuations and uncertainty bring challenges to inventory management, and intermittent demand patterns increase the risk of inventory backlogs and raise inventory holding costs. In previous studies on inventory routing problems, different variants have been proposed to cope with complicated industrial scenarios. However, there are few studies on inventory routing problems with intermittent demand patterns. To solve this problem, we introduce a lateral transshipment strategy and build a single-product multi-period inventory routing mixed integer programming model to reduce customers’ inventory backlogs, balance regional inventory, reduce inventory holding costs, and improve inventory management efficiency. Furthermore, we design an adaptive large-neighborhood search algorithm with new operators to improve the solving efficiency. The experimental results show that an appropriate transshipment price can reduce the share of distribution costs. Another finding is that higher-capacity vehicles lead to higher revenue. Our findings not only expand the scope of the IRP domain but also provide actionable management insights for business practitioners.

The "Lateral Transshipment" Is a Cooperative Tool for Optimizing the Profitability of a Distribution System

The "Lateral Transshipment" Is a Cooperative Tool for Optimizing the Profitability of a Distribution System

Designing a Resilient–Sustainable Supply Chain Network of Age-Differentiated Blood Platelets Using Vertical–Horizontal Transshipment and Grey Wolf Optimizer

Blood platelets are a typical instance of perishable age-differentiated products with a shelf life of five days (on average), which may lead to significant wastage of some collected samples. At the same time, a shortage of platelets may also be observed because of emergency demands and the limited number of donors, especially during disasters such as wars and the COVID-19 pandemic. Therefore, developing an efficient blood platelet supply chain management model is highly necessary to reduce shortage and wastage. In this research, an integrated resilient-sustainable supply chain network of perishable age-differentiated platelets considering vertical and horizontal transshipment is designed. In order to achieve sustainability, economic cost, social cost (shortage), and environmental cost (wastage) are taken into account. A reactive resilient strategy utilizing lateral transshipment between hospitals is adopted to make the blood platelet supply chain powerful against shortage and disruption risks. The presented model is solved using a metaheuristic based on a local search-empowered grey wolf optimizer. The obtained results demonstrate the efficiency of the proposed vertical-horizontal transshipment model in reducing total economic cost, shortage, and wastage by 3.61%, 30.1%, and 18.8%, respectively.

Designing a resilient retail supply network for fresh products under disruption risks.

The retail sector supplies the daily fresh products and increasingly plays a key role in the stability and livability of cities. However, public health events such as COVID-19 have caused frequent product shortages in recent years. The risk of fresh product shortages not only causes retailers to lose profits, but also affects the normal life of residents. In this paper, we address the problem of designing a resilient retail supply network for fresh products under the supply disruption risks and propose a bi-objective mixed-integer programming model. This model can help retailers to select suppliers, distribution centers and transportation routes under different scenarios and implement three resilience strategies, namely, priority supply, multiple sourcing and lateral transshipment. We use the ε -constraint method to transform the multi-objective problem into a single objective model and develop a Lagrangian relaxation algorithm to solve the different scale instances. This model is solved for a real-life case of a supermarket to obtain managerial insights. In the case study, this paper shows the set of Pareto fronts with different inventory periods, maximum allowed decay and decay rate. We calculate the expected total cost under targeted disruption scenarios and evaluate the effectiveness of these resilience strategies when implemented concurrently or separately. Our results identify the most critical suppliers and distribution centers that should be fortified. We elaborate that more resilience strategies are not always better and managers need to take appropriate resilience strategies according to their own problems.

Development of a blood supply chain network design model by considering lateral transshipment and blood substitution

blood supply chain network design is an approach to design an efficient blood supply chain network. This study developed a model for determining the best Blood Supply Chain Network Design solution that considering costs and social sustainability. The developed model was divided into four echelons: blood donation centers, blood banks, regional hospitals, and demand points. The objective of this research was to minimize total supply chain costs while maximize job opportunities. This study considered the concept of lateral transshipment and blood substitution at the third echelon, namely the regional hospital, to ensure that demand was met. The concept of lateral transshipment enabled regional hospitals that having excess of blood unit to satisfy blood supplies to other regional hospitals. Furthermore, blood substitution was a response to emergency conditions, specifically a shortage of appropriate blood product units. Blood substitution scheme is allowed in this research, that is, each type of blood product with a certain group can not only meet its own demand, but can also be used as a replacement for the same product with other compatible groups. a multi-objective possibilistic mixed integer linear programming model was developed. According to the obtained results, the optimal number of facilities to satisfy the uncertain demand, blood flow between supply chain echelons, network cost, and the number of jobs created can be discovered by the model.