Multiple Warehouses Research Articles

Optimal Policies and Heuristics to Match Supply with Demand for Online Retailing

Problem definition: We consider an online retailer selling multiple products to different zones over a finite horizon with multiple periods. At the start of the horizon, the retailer orders the products from a single supplier and stores them at multiple warehouses. The retailer determines the products’ order quantities and their storage quantities at each warehouse subject to its capacity constraint. At the end of each period, after random demands in the period are realized, the retailer chooses the retrieval quantities from each warehouse to fulfill the demands of each zone. The objective is to maximize the retailer’s expected profit over the finite horizon. Methodology/results: For the single-zone case, we show that the multiperiod problem is equivalent to a single-period problem and the optimal retrieval decisions follow a greedy policy that retrieves products from the lowest-cost warehouse. We design a nongreedy algorithm to find the optimal storage policy, which preserves a nested property: Among all nonempty warehouses, a smaller-index warehouse contains all the products stored in a larger-index warehouse. We also analytically characterize the optimal ordering policy. The multizone case is unfortunately intractable analytically, and we propose an efficient heuristic to solve it, which involves a nontrivial hybrid of three approximations. This hybrid heuristic outperforms two conventional benchmarks by up to 22.5% and 3.5% in our numerical experiments with various horizon lengths, fulfillment frequencies, warehouse capacities, demand variations, and demand correlations. Managerial implications: A case study based on data from a major fashion online retailer in Asia confirms the superiority of the hybrid heuristic. With delicate optimization, the heuristic improves the average profit by up to 16% compared with a dedicated policy adopted by the retailer. The hybrid heuristic continues to outperform the benchmarks for larger networks with various structures. Funding: X. Li is supported by the Singapore Ministry of Education [Tier 1 Grant 23-0619-P0001]. Y. F. Lim is grateful for the support from the Singapore Management University under the Maritime and Port Authority Research Fellowship and the Singapore Ministry of Education [Tier 1 Grant MSS23B001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0394 .

A robust optimization to dynamic supplier decisions and supply allocation problems in the multi-retail industry

The research focuses on multi-retail distribution with a strategic distribution network. Challenges include intense competition, logistical and transportation complexities requiring robust infrastructure like warehouses and efficient supply chain management, as well as operational inefficiencies and distribution costs. To address these issues, a model is applied to make strategic decisions, such as determining the necessary number of facilities to minimize total supply chain network operational costs and infrastructure for retail distribution. The outcome is a model that introduces a novel approach to enhancing supply chain efficiency and effectiveness from production to distribution stages, thereby reducing system costs, including ordering costs and inventory handling. Costs and loss costs resulting from remaining products produced can be minimized by considering networks, multiple suppliers, multiple warehouses, Distribution Centers (DC), multiple retailers, and multiple products, factoring in the distances between facilities in the network. Subsequently, comprehensive testing of inspection, distribution, and retail parameters is conducted, with a focus on specific periods and product types. When applying this model, certain characteristics need to be considered regarding the importance of selecting efficient suppliers of goods, such as procurement, performance improvement, and the number of supply chains and supply chain systems. This research introduces novelty in production methods that can lead to increased customer satisfaction, sales, market share, profit margins, more effective brand advertising, and revenue streams. In this process, the research undergoes a training, testing, and validation process in forming a strategic multi-retail distribution network model, spanning a total of 52 epochs. This process yields accuracy values for training at 90 %, testing at 92 %, and validation at 94 %

A proactive transshipment model for prototype parts logistics in the automotive industry

AbstractIn the logistics of prototype parts in the automotive industry, unique parts are stored in warehouses and provided to assembly service providers to ensure a timely assembly of the associated prototype vehicles. As the allocation of the assembly orders to the assembly service providers may change before the start of the assembly, the shipments are planned at short notice. This article considers the short-term task of planning shipments of individual prototype parts from multiple warehouses to multiple assembly service providers. The current literature on planning shipments focuses mainly on parts available in batch sizes larger than one. However, as prototype parts are individual, storing them in adequate warehouses is crucial. A mixed-integer linear programming model for planning transshipments of parts between the warehouses and shipments to the assembly service providers to minimize overall costs is presented. The model enables the utilization of spare capacities of planned transports for a proactive transshipment of parts between warehouses. To this end, the future shipment costs of parts to assembly service providers are approximated to determine suitable warehouses. A numerical study shows that the proactive approach leads to a considerable cost reduction.

A Two-Warehouse Lot Sizing Problem for Defective Items with a Completely Backlogged Shortage Under Limited Storage Capacity for Rented Warehouses

Determining an economic order quantity across inventory management is vital for any production or distribution company. There is an assumption in classical lot-sizing problems that the inventories are stored in a single warehouse with unlimited capacity. However, the mentioned assumption may be unrealistic, and in many markets, owned warehouses (OW) have limited capacity, therefore, in some cases, a rented warehouse (RW) is considered for storing goods to create more flexibility in terms of space capacity. In this study, we proposed a more realistic extension of a two-warehouse system, which intends to investigate an optimal EOQ considering limited storage for both OW and RW. This paper contributes to considering multiple RWs with limited capacity. For this purpose, a two-objective MINLP lot-sizing problem with a discreet period and finite time horizon is taken into account. The complexity of the model is to determine if there is a need to rent one or multiple warehouses regarding limited storage on OWs and RWs as well as budget restrictions to hire one or multiple warehouses. Also, all parameters are considered to be stochastic which means they follow a specific probability distribution. As the capacity of RWs and the total available budget to rent them are random parameters, the constraint on budget, as well as RW’s capacity in a period, can be formulated as chance constraints. Finally, four multi-objective decision-making methods namely Goal programming, Lp-Metric, Goal Attainment, and Augmented E-Constraint are utilized to solve the concerned problem. Then, the best method is chosen based on graphical, statistical, and TOPSIS analysis. Moreover, the Lagrangian Relaxation method is applied since solving this optimization model in large-size problems requires a considerable amount of time. In the end, the effect of changing the rates of three parameters on objective function values is evaluated through sensitivity analysis.

Analysis of Supply Chain Management under the Impact of Epidemic

Based on this epidemic, the author found that the supply chain is a very important issue for every country. Therefore, this paper aims to analyze the supply chain management of countries and companies under the epidemic. This paper will reflect well on the measures and actions taken by some countries in response to the new outbreak, and its importance is that it is the first relatively effective summary and analysis of supply shortages. The paper focuses on presenting data and comparing measures, ranking and comparing them, and then selecting the best. The results found that government response to this phase of the great wave of supply chain disruption requires the introduction of a series of solution policies. For companies, it is necessary to improve digitalization, enhance supply chain coordination, and consider the layout of warehouses and multiple warehouses on the basis of ensuring survival. For logistics service providers, it is necessary to increase business flexibility, increase transportation options, improve accessibility, and prepare themselves for emergencies and preparedness.

Optimization of emergency allocation of necessities of life based on fractal perspective.

The supply and reserves of emergency necessities of life are important for emergency management in disaster events. The scope of the necessities of life changes with social development, and their reserves and allocation in sudden disaster events continually face new challenges. Timely distribution of the necessities of life during disasters is critical to saving lives and maintaining social order. Therefore, this study proposes a fractal multi-level distribution network (FMDN) optimization model with multiple warehouse points, multiple emergency distribution centers, and multiple disaster points from the perspective of fractal theory. The FMDN model considers the influence of road damage on vehicle speed and the dynamic change in demand at the affected points. The FMDN model aims to minimize the operating costs of a distribution network, including the cost of building emergency reserve points, transportation costs, and penalty costs for lack of demand. Numerical experiments verify the feasibility of the model. The FMDN model is solved using LINGO software programming, and an optimal distribution path and quantity are obtained. Analyzing the numerical example results shows that the model is suitable for emergencies and has good applicability.

Production, distribution, and capacity planning for an integrated buyer-vendor system incorporating different production and shipping scenarios

Supply chain management generally seeks to reduce costs, improve customer satisfaction, and gain a competitive advantage for all members of the chain. If decisions are made independently, there can be no guarantee that network members will reach the optimal decision as a whole. In integrated buyer-vendor optimization models, decisions are made centrally, and different parts are integrated and coordinated. This paper examines two different scenarios, namely Continuous Production-Instantaneous Shipping and Continuous Production-Gradual Shipping. In each scenario, an integrated inventory model with multiple warehouse capacity levels is proposed under uncertain conditions. We solved both mathematical models using proposed exact algorithm. Based on the calculated results, capacity planning leads to proper inventory management, efficient use of space, and making the most appropriate decision to have the least chain cost. Furthermore, the outputs of the models show that limited access to transport fleet leads to an increase in inventories and the subsequent reduction of stock-out effects requires effective changes in plans and management measures.

Arc routing based compact formulations for picker routing in single and two block parallel aisle warehouses

Order picking is the most expensive and labor-intensive warehouse activity. The objective of the order picking problem (OPP) is to collect the items on the pick list in a sequence that minimizes the total travel time. While the literature has generally modeled the OPP as a special case of the traveling salesman problem, this paper presents arc routing-based binary integer programming formulations for the OPP in single- and two-block parallel-aisle warehouses, by taking into account the special properties of the graph corresponding to both warehouse layouts. These formulations depend on replacing the subtour elimination constraints with a much smaller number of disconnectivity elimination constraints, which significantly reduces the integrality gap. Our computational experiments show that the proposed formulation solves large instances within significantly short computing times when compared with its counterparts in the literature for single- and two-block parallel-aisle warehouses. The efficiency of these formulations implies that not only can they be used to solve the OPP in a timely manner, but they can also be incorporated into integrated models that consider multiple warehouse decision problems at the operational level. More importantly, when compared to other state-of-the-art formulations, the extensibility of our proposed model makes it an ideal candidate for further research in this field.

Reliability-Driven Multiechelon Inventory Optimization With Applications to Service Spare Parts for Wind Turbines

There are multiple warehouses in a multiechelon inventory system, and the size of the state space increases exponentially with the number of warehouses. Therefore, the curse of dimensionality becomes unavoidable when performing steady-state analysis. Most existing studies calculate the inventory cost or supply chain reliability based on specific assumptions. For example, it often assumes that the lead time is either zero or an integral multiple of the review period, and that each warehouse adopts a base-stock policy. This article considers a more practical and prevalent situation where the lead time is less than a review period, and a more general (s, S) strategy is adopted. The curse of dimensionality during steady-state analysis is alleviated by decomposing transition probabilities. Then, the cost and supply chain reliability are derived from steady-state distributions. Finally, a case study involving spare part inventory of wind turbines is considered. Nondominated inventory strategies are obtained using the particle swarm optimization method to strike a balance between costs for the wind turbine manufacturer and wind farm owners.

Multi-objective optimization of the auto-carrier loading and routing problem in an automotive logistics company

This paper presents a real-world auto-carrier loading and routing problem encountered by an automotive logistics company in China. The problem requires determining a set of routes for auto-carriers to transport vehicles from warehouses to dealers subject to several loading and routing constraints. Four objectives are considered, including the maximization of the number of loaded urgent vehicles and the number of loaded vehicles, and the minimization of the number of visits to dealers and travel distance. To solve such a multi-objective problem, we develop a two-stage iterated local search heuristic with the lexicographic optimization principle. Expressly, we assume that all vehicles are stored in one warehouse and solve the resulting problem as the first stage. Then the best-found solution is improved by considering the multiple warehouse constraints in the second stage. The effectiveness of the proposed heuristic is validated by comparing it with a commercial solver and the company’s approach. Moreover, computational results provide managerial insights for decision-makers when considering the objectives and their priorities in different scenarios.

Research on Multi-product Order Splitting and Distribution Route Optimization Of "Multi-warehouse in One Place"

In recent years, large online supermarkets have become a new trend in the development of e-commerce. Due to the limited storage capacity of a single warehouse, many large online supermarkets, such as Jingdong and Tmall, often adopt the warehouse layout of "one place and multiple warehouses" to quickly respond to customer needs, and the sorting and distribution tasks of orders are completed by the warehouse. At the same time, due to the change of people's lifestyle, customer demand presents the characteristics of "one order with multiple products" and "one order with multiple quantities", which makes the split fulfillment of orders become a common phenomenon. In this paper, under the condition that the warehouse is not out of stock in the layout of one place and many warehouses, aiming at the split execution problem of multi-category orders, the split order method is based on the combination of "minimum split order rate" and "principle of proximity". An order splitting optimization model considering both category and quantity splitting is established, and a set of initial order batch splitting schemes is formed to achieve the first optimization of multi-category order splitting. Secondly, the PLBH-LNS method is used to generate a better initial distribution scheme considering the customer preset time window limit and vehicle-mounted capacity constraint. Finally, with the goal of minimizing the total order performance cost, the solution idea of two-stage method is used for reference, based on the initial order splitting scheme and distribution scheme, the improved two-stage genetic algorithm is used to generate the optimal order allocation scheme and distribution scheme from the alternative schemes, and the global optimization of the splitting and distribution process is realized. The experimental results show that compared with the order splitting strategy using simple rules in practice, the PLBH-LNS method can reduce the average order splitting cost by 12.48%, which provides a new idea and effective auxiliary decision support for the order splitting problem of large online supermarkets.

Safety stock placement with market selection under load-dependent lead times

We study the problem of safety stock placement in a supply chain with market selection decisions. A manufacturer with deterministic, load-dependent lead time supplies multiple warehouses, each serving multiple retailers. Each retailer has access to a set of potential markets with different characteristics. Serving more markets increases revenues, but also increases the manufacturer’s lead time, resulting in higher inventory costs. Adopting the Guaranteed Service Approach, we present a nonlinear mixed-integer programming model and reformulate it to eliminate integer variables related to service times at warehouses. We then propose a successive piecewise linearization algorithm and a mixed-integer conic quadratic formulation to solve the resulting nonlinear binary formulation. Computational experiments show that the successive piecewise linearization algorithm outperforms two state-of-the-art solvers, BARON and CPLEX, which are used to solve instances of the original formulation and the mixed-integer conic quadratic reformulation, respectively. The value of incorporating load-dependent lead times is greatest when capacity is limited relative to available demand. The benefit of integrating market selection and safety stock decisions is greatest when capacity is limited and marginal revenue is relatively low.

A spatio-temporal constrained hierarchical scheduling strategy for multiple warehouse mobile robots under industrial cyber–physical system

The industrial cyber–physical system (ICPS) framework can reduce the computational load and improve task efficiency. This paper studies the ICPS-based scheduling strategy for multi-warehouse mobile robots (multi-WMRs). First of all, the possible congestion problem is considered in topological map modeling, which is transformed into a new path time cost index. Second, each robot independently executes the path planning algorithm, which realizes distributed path computation and takes time cost and destination distance into account. The improved task assignment strategy includes task evaluation and decision-making, which are considered part of the planning and help to improve task efficiency. Finally, the complete scheduling process is applied to the novel ICPS architecture, including cost evaluation, path planning, task assignment, and collision avoidance. In numerical experiments, the task efficiency has been increased by 24.8% to recent research and 14.59% to previous work. The average congestion time is reduced by 28.41%, and the planning time is reduced to 10.13% of the traditional method.

Generalized Path Planning for UTM Systems With a Space-Time Graph

Motivated by the increased use of UAS in commercial applications, in this paper we tackle the problem of path planning when requests are submitted by UAS managed by different operators. We propose the new problem of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">generalized path planning</i> for UAS Traffic Management, where the UAS path is described by operators with a sequence of waypoint groups and a solution trajectory must pass through a waypoint in each group. This problem is typical for applications where multiple charging stations and pickup/drop-off locations are distributed in a flight area. Our solution builds upon prior work on discretized space-time graph path planning and proposes a novel multi-source/multi-destination graph search algorithm that generates collision-free trajectories for pre-flight CDR. Our efficient algorithm has runtime proportional to the number of groups and avoids combinatorial explosion. We apply our mechanism to the energy-constrained UAS package delivery problem with multiple warehouses and battery charging stations. Simulation results show that our algorithm is efficient and scalable with the number of requests and graph size. The addition of charging stations and the option for multiple warehouses increases the request admission ratio and reduces the overall trajectory duration, effectively improving both the planner’s quality of service and the efficiency of airspace usage.

Applied routing problem for a fleet of delivery drones using a modified parallel genetic algorithm

More and more experts agree that in the near future, most freight traffic will be carried out using automated systems, and of them drone delivery is considered to be the most promising. Drone delivery would benefit by independence from the limitations of transport infrastructure and road conditions and would ensure cargo delivery with rapid turnaround times, as well as a significant reduction of environmental impact. The technical capabilities of unmanned aerial vehicles improve year by year, so the task of coordinating drones and effectively planning routes is relevant and in great demand. The development of such technologies will help reduce transportation costs and improve customer service through faster delivery. This article discusses the applied routing problem for a fleet of drones with limited load capacity for the delivery of heterogeneous goods with the possibility of loading in multiple warehouses from an international optimization competition. The solution includes new approach based on a mixed dimensional parallel genetic algorithm (MDPGA) for finding rational routes for delivering goods to various customers and an assignment problem to reduce the dimension depending on the number of warehouses.

Warehousing and distribution network design from a Third-Party Logistics (3PL) company perspective

This paper deals with the problem of optimizing the logistics network of a Third-Party Logistics (3PL) company. The goal is to minimize the cost of storage and transport operations, in the case of multiple warehouses, multiple suppliers, multiple customers, multiple products and multiple types of transportation vehicles. A new Mixed Integer Linear Program (MILP) model is proposed for this interesting problem. The related decisions include selection of (a) the warehouse(s) to store each product, (b) the inventory level per product per warehouse, (c) the warehouse(s) to serve each customer and (d) the appropriate vehicles to transport the products from the suppliers to the warehouses, and from the latter to the final customers. The model was applied in case study of a 3PL company in Greece to optimize part of its supply chain that comprises three warehouses, 23 suppliers and 53 customers. The results obtained were very encouraging, since overall warehousing and distribution costs were lowered by 10.84% compared to the way the company operates currently.

Optimization of Distribution Cost (a Case study in PT. Zamrud Bumi Indonesia)

PT. Zamrud Bumi Indonesia is a manufacturing company engaged in the processing of multipurpose liquid organic agricultural fertilizer branded Power Bumi. Distribution from warehouses to destinations that vary in distance resulted in different distribution cost budgets. Transportation models can help solve the problem of distributing products from multiple warehouses to multiple destination cities and reduce total distribution costs. This study aims to find out the total minimum cost of distribution by using the Least Cost method as the initial solution and knowing the test results of MODI method as the optimum solution at PT. Zamrud Bumi Indonesia. The results of this study showed that before using the Transportation Method, the company incurred a distribution fee of Rp. 11.600.000,- in December 2020, after using the Least Cost transportation method as the initial solution of distribution costs incurred in the amount of Rp. 9.981.250,- and modi transportation method as the optimum test resulted in the same value of Rp. 9,981,250. So the company saves distribution costs of Rp. 1.618.750,- every month

A Comprehensive Portal for Clinical and Translational Data Warehouses.

Data-driven methods in biomedical research can help to obtain new insights into the development, progression and therapy of diseases. Clinical and translational data warehouses such as Informatics for Integrating Biology and the Bedside (i2b2) and tranSMART are important solutions for this. From the well-known FAIR data principles, which are used to address the aspects of findability, accessibility, interoperability and reusability. In this paper, we focus on findability. For this purpose, we describe a portal solution that acts as a catalogue for a wide range of data warehouse instances, featuring a central access point and links to training material, such as user manuals and video tutorials. Moreover, the portal provides an overview of the status of multiple warehouses for developers and a set of statistics about the data currently loaded. Due to its modular design and the use of modern web technologies, the portal is easy to extend and customize to reflect different corporate designs and institutional requirements.

Integration of single and dual command operations in non-traditional warehouse design

Non-traditional warehouses rise as effective solutions to shorten the travelled distances to store and retrieve unit loads, adding aisles crossing the parallel racks. Multiple warehouse configurations are proposed by the literature discussing the enhancements toward standard layouts. In previous contributions, the authors introduced the diagonal cross-aisle model, concluding about its positive impact on the handling performances under single command operations. This paper extends the previous works, integrating dual command operations, through an original analytic model supporting the design of non-traditional warehouses with a couple of symmetric straight diagonal cross-aisles and random storage assignment strategy. The closed-form expressions to compute the expected cycle travel distances are provided, optimising the aisle position. An industrial case study applies the model, getting distance savings ranging from 11 to 17%, compared to standard layout and further considering the loss of storage space due to the presence of the additional aisles.

Optimization of product category allocation in multiple warehouses to minimize splitting of online supermarket customer orders

In an online supermarket, people may purchase multiple items in a single order for convenience or to obtain free delivery. Multi-item customer orders often need to be split into multiple shipments because the ordered items may be stored in different warehouses. Order splitting results in higher shipping costs. One way to reduce splitting is to store and retrieve all the items in a single warehouse. However, due to the vast volume of items sold by online supermarkets, it is difficult to build and operate such a warehouse. Another way is to optimize the allocation of products in multiple warehouses to reduce order splitting. In this paper, we propose a K-links heuristic clustering algorithm to optimize the product category allocation among multiple warehouses based on the distribution of multi-item orders to minimize the total number of order splits. Using online order and inventory data from a large Chinese online supermarket, we demonstrate that our algorithm performs well and dramatically reduces order splitting.