Warehouse Location Research Articles

Simple Multi-Attribute Rating Technique for Warehouse Location Selection (Case Study: PT. PPT)

Indonesia’s Manufacturing Industry, encompassing processing and non-oil and gas processing, grew in Q3 of 2022, contributing 17.88% and 16.10% to GDP. Transportation and storage played a significant part, with a 15.93% year-on-year growth in Q1 2023, highlighting the sector’s importance. PT. PPT is a sub-holding of one of Indonesia’s companies. The company manages to trade PP/PE significantly from more sources (import, local producer, or local trader). This research will determine and analyze the criteria for the selection of warehouse locations for PT. PPT analyzes the weight of each criterion at each location in the warehouse location selection process for PT. PPT and obtain the priority location for PT. PPT to build a new warehouse. This research aims to guide and help the decision makers to choose and find a suitable location based on the Centre of Gravity (CoG) analysis conducted by a third-party consultant. The five locations resulting from the Centre of Gravity (CoG) as alternatives will be analysed with Kepner-Tregoe Analysis in the decision-making process, using Situation Appraisal defined in Business Issues. The author then employs a Rich Picture and Fishbone Diagram to assess the scenario or the process of select the new warehouse. The decision analysis was carried out in eight stages using the one of Multi-Criteria Decision Making (MCDM) method, which is the Simple Multi-Attribute Rating Technique (SMART). Lastly, using the preceding analysis, undertake a Potential Problem Analysis. The major criterion is the distance to the customer, which aligns with the company’s emphasis on customer satisfaction. Demand growth affects warehouse capacity, with more growth necessitating a larger facility. Distance from suppliers reduces delivery lead time, and the size of the land must accommodate demand growth and product capacity for PT. PPT.

Optimization of routing problems in manufacturing supply

The optimal design of a suitable material supply process is important for the continuous, smooth operation of manufacturing processes, which includes the transportation and handling of work pieces, components, tools, equipment, pallets, packaging materials, and measuring instruments. The most common form of implementation of these material handling processes, especially in mass production, is the milkrun supply. In this paper, the author presents a method to optimise the milkrun material supply processes in manufacturing. The author gives a brief overview of research results related to milkrun-based material supply solutions. An Excel Solver-based optimization method is presented that is suitable to support the design of an optimal material supply process in any manufacturing environment by determining optimal material supply routes and optimal number of milkrun trolleys, depending on the supply demand, location of warehouse and manufacturing cells and the capacity of the milkrun trolleys.

Relocation of a warehouse for a cosmetic company: A case study

Warehouses are pivotal components within a company's supply chain, handling crucial functions such as receiving, storing, and delivering goods to end customers. While extensive research exists on the strategic location of warehouses in network optimization, there is a notable dearth of research on how to relocate a warehouse from one site to another, particularly while minimizing associated risks. This study sheds light on a low-risk approach undertaken by a multi-level marketing (MLM) company in Malaysia during the relocation of their warehouse. The study primarily addresses several key constraints: (i) Completion of the warehouse relocation exercise in 4.5 months; (ii) Termination of their warehouse staff services during the relocation; (iii) Elimination of disruption in the order fulfillment service level; (iv) Management of temperature-sensitive goods during the transfer and storage; (v) Seamless linkage between the third-party logistics (3PL) warehouse management system (WMS) and the company's systems. The primary objective of this study is to propose a resilient strategy for the relocation of their warehouse, taking into account these key constraints faced by the company.

Optimization of Warehouse Selection with SWOT and AHP Methods in the Pulogadung Industrial Area

This study aims to analyze the factors that influence the determination of location and warehouse space in Pulogadung Industrial Estate, Jakarta, using a combined SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) and AHP (Analytical Hierarchy Process). The research method applied is qualitative. Participants in this study were leaders of companies operating in the Pulogadung Industrial Estate. Data was collected through the use of SWOT questionnaires and AHP questionnaires given to respondents. SWOT data is analyzed using Excel software, while AHP data is analyzed using Super Decision software. The results revealed that factors that affect the determination of warehouse location and space in the Pulogadung Industrial Estate include proximity to the main road, centralized warehouse location, proximity to the goods distribution terminal, proximity to the port, and efficiency of warehouse maintenance costs. SWOT and AHP analysis show the importance of the determinants of the location of the Pulogadung Industrial Estate, Jakarta. The most important "strength" factor is the location of the warehouse close to the main road, while the "weakness" factor that needs to be avoided is the high cost of renting the warehouse. The "opportunity" factor that must be exploited is the application of modern technology, and the "threat" factor that needs to be avoided is the emergence of more efficient warehouse maintenance costs in competitors' warehouses. This research provides important insights for companies operating in the Pulogadung Industrial Estate in determining the location of their warehouses.

The application of multi factor method in the site selection of reserve warehouse for nuclear and radiation emergency

This article explains the power generation of civilian nuclear power plants and the development trend of nuclear technology utilization. The construction of regional nuclear and radiation emergency monitoring material reserve warehouse (hereinafter referred to as the “reserve warehouse”) can ensure nuclear and radiation safety. According to the requirements for emergency monitoring of nuclear accidents and radiation accidents, the article summarizes several factors for site selection. The article conducts research on candidate locations for reserve warehouse and emergency monitoring demand location in China (hereinafter referred to as the “demand locations”). The multi factor method was applied to the location selection of reserve warehouses, and the following conclusions were obtained: (1) The multi factor method is feasible. It has high computational accuracy and fast speed. The site selection model has a significant optimization effect on the location of reserve warehouses. (2) The site selection model proves that the location of reserve warehouses need to consider multiple factors. When the number of reserves is 3–8, about 1–2 cities which is the location of nuclear power plants are selected as candidate cities. (3) When the number of reserve warehouse is 8, the maximum distance is less than 2400 km, meeting the distance limit set for nuclear and radiation emergency monitoring. Considering comprehensive road conditions and other factors, China needs to establish at least 5 reserve warehouses to achieve an emergency ideal situation with a maximum distance of 1600 km.

Including operational costs in warehouse location problems: A case study in USA

There is vast research on particular aspects of warehouse design - layout, material handling, order picking, and operating policies - when, in fact, the decisions involved in the process are interrelated. In this paper, we develop an engineering economics framework for warehouse operations that decomposes the cost structure of the operation and lays out the relationships between them. Our framework decomposes operational costs into four exogenous characteristics (wages, leasing costs, cost of capital, and access to technology) that depend on the geographic location of the warehouse and two operational requirements (throughput and storage capacity). Using these six parameters, and publicly available information, practitioners can estimate the total operational cost of the warehouse for potential locations in facility location analysis, which have been traditionally limited to transportation costs. In our case study, we use our framework to establish a rank of preferable warehouse locations in terms of operational costs among logistics clusters in the United States of America.

Optimisation of multi-level logistics network for agricultural products under community group buying model

This paper introduces a rapidly developing new online retail model, community group buying, and proposes a three-level agricultural logistics network optimisation model. Under the community group buysing model, it is necessary to use different types of vehicles for transportation of agricultural products with different temperature control requirements. The study establishes a multi-objective mixed integer programming model with the objectives of shortest transportation time and minimum total cost, taking into account the freshness penalty cost incurred during transport. The multi-objective problem is transformed into a single objective by normalisation and weighting methods. According to the calculation for the actual case, this paper solves the problems of community group buying grid warehouse location, multiple vehicles use strategy, loading capacity, transportation path optimisation and self-pickup station demand allocation. In addition, through sensitivity analysis, the management insights of community group buying enterprises are obtained: (1) The community group buying enterprises should use brokers of the community group buying model to enhance customer stickiness; (2) The enterprises should focus on developing business in less developed regions; (3) The enterprises need to adjust the proportion of time efficiency and logistics costs according to the real situation.

A fast-response mathematical programming approach for delivering disaster relief goods: an earthquake case study

ABSTRACT This paper tackles a complex logistics challenge of disaster management, encompassing warehouse location, pre-disaster inventory planning, routing, and post-disaster relief supply delivery. We establish an iterative process for optimizing relief distribution to shelters. Adaptable warehouse inventory reallocation responds to fluctuating demands, guided by a two-phase mathematical programming approach. In the first phase, a two-stage stochastic programming (TSSP) model determines optimal warehouse and shelter locations and inventory levels. In the subsequent phase, we introduce a mixed-integer programming (MIP) model to minimize the overall delivery time by making routing decisions. To streamline the process, we introduce a novel enumeration algorithm that trims down route options by considering unavailable links, effectively transforming the MIP model into an assignment-based model. This innovation results in a noticeable 74% reduction in solution time. Further efficiency is achieved by developing a branch-and-cut algorithm for swift MIP resolution. A real-world case study confirms the practicality of our approach.

Providing an integrated multi-depot vehicle routing problem model with simultaneous pickup and delivery and package layout under uncertainty with fuzzy-robust box optimization method

This paper modeled and solved an integrated multi-depot vehicle routing problem (MDVRP) with simultaneous pickup and delivery (SPD) with package layout under unpredictable pickup, delivery, and transfer costs. The model described in this paper is divided into two stages. In the first stage, the SCA algorithm is used to optimize the package dimensions (a collection of commodities consumers need). The NSGA II and MOALO algorithms are used in the second stage to optimize the three objective functions of 1 simultaneously) minimizing total costs, 2) minimizing co2 emissions, and 3) minimizing the maximum working hours of drivers based on the optimal dimensions (length, width, and height) obtained from solving the first stage model. Determining the quantity and ideal location of possible warehouses, the best route for trucks to take to deliver and collect customer items, and the distribution of customers to warehouses are the key goals of the second stage. Since the model is unclear, the problem's uncertainty parameters are controlled using a novel fuzzy-robust box optimization (FRBO) technique. This technique, which combines the advantages of fuzzy programming with robust box-based optimization, produces excellent results when used to optimize objective functions. The numerical calculations in the numerical example show that the total network costs and CO2 emissions increased in the second stage in the presented model with an increasing uncertainty rate. At the same time, the maximum working hours of drivers decreased due to the shortened communication route and the number of vehicles increasing. Finally, the MOALO algorithm was used to resolve a case study at Safir Broadcasting Company because of its excellent efficiency in resolving the created model, the findings of which revealed the presence of 13 potential effective solutions. The quantity of greenhouse gas emissions rose by 1.11%, the overall expenditures climbed by 1.72%, and the number of hours that drivers worked fell by 11.98% when the uncertainty rate was raised from 0.5 to 0.7, according to research on the FRBO.

A new bi-stage interactive possibilistic programming model for perishable logistics distribution systems under uncertainty

Setting the optimal vehicle routes and the optimal level of inventory to avoid shortage, in addition to reducing costs of transportation, are two of the main objectives in developing the distribution systems of perishable goods. Despite its importance, an area that still requires more investigation is the simultaneous consideration of hub location selection and routing and warehousing operations. In other words, addressing decisions related to the locations of the warehouses, vehicle routing, and warehouse management at the same time could improve the outcome. Therefore, this paper presents a new non-linear mixed-integer model for warehouse location selection, in addition to an integrated multi-objective mixed-integer model for routing and warehousing operations. Moreover, features of real operations are considered, such as dynamic conditions, order picking, and delivery, customer prioritization, time windows for customers, and different perishable goods. Minimizing the operations' costs and time under uncertain conditions is achieved by proposing a possibilistic-robust optimization algorithm. The optimization approach regards the weight of each objective using the opinions of experts by using a novel hesitant fuzzy approach. Finally, a case study is presented, and the results are compared with the actual data along with validating the model by using several numerical examples. The results have shown that the method can perfectly form the supply chain of perishable goods. Furthermore, the optimization method can provide the experts with more flexibility in finding a compromise solution.

Performance evaluation of a two-echelon inventory system with network lost sales

Inventory systems are largely analyzed in the literature under the common assumption of backorders due to the complexity of lost sales. In this paper, we consider a two-echelon inventory system composed of a central warehouse and multiple local warehouses subject to lost sales. The demand faced by each local warehouse is a Poisson process and the stock in the warehouses is controlled according to a continuous review base-stock policy. This system has been analyzed in the literature under deterministic or exponential lead-times at the central warehouse, deterministic lead times at the local warehouses and approximate performance evaluations have been proposed for two cases: (1) the demand is lost if no items are available in the local warehouse, the central warehouse, or in the pipeline in between (i.e., a waiting time threshold for incoming demand equal to the local warehouse lead time), and (2) when there is a waiting time threshold less than the local warehouse lead time. Based on a queuing network representation of the system, we extend the performance analysis of the system in the first case by considering generally distributed lead times both at the central and local warehouses and by providing the exact closed-form expressions for the inventory performance measures. In the second case, we provide new approximate solutions under generally distributed lead times at the central warehouse. We numerically show that our exact and approximate solutions perform equally or better than those presented in the literature under deterministic lead times.

Актуальные вопросы в грузовой и коммерческой работе на железнодорожных станциях

Purpose: To consider the issue of interaction between carriers and counterparties using new approaches aimed at attracting customers to the transport services market, with the creation of a single mechanism based on digitalization. Methods: A number of works explore the issues of the location of warehouse areas. In them, the task is solved by various methods, such as the heuristic definition of the “center of gravity” for enterprises, the multi-criteria Choquet integral, the method of hierarchy analysis consisting in pairwise comparisons, as well as the author’s methods of economic and geographical modeling of transport systems, reflecting the geometric idea of the approach in transport and logistics research with the differentiation of “areas of influence”. Results: The necessity of creating a single mechanism of interaction between the carrier and the counterparty is indicated. The implementation of the set aspects is played by the nature of transportation, taking into account the nomenclature of goods. Preference should be directed to mass categories. In solving the set goals, it is possible to use the method of linear programming (equality). The necessity of conducting an additional study of the conjuncture of an economic entity has been identified. Practical significance: The necessity of creating a single digital platform for interaction of participants in the transportation process is shown, the implementation of which will speed up the process of solving problems aimed at using new tools that contribute to improving the work of an economic entity. The presented schemes of the location of warehouses can be recommended for practical use.

Distributionally robust location-allocation with demand and facility disruption uncertainties in emergency logistics

Emergency logistics is vital to disaster relief management. In this paper we develop a distributionally robust optimization model (DROM) for optimizing the locations of distribution centres and backup warehouses, and the distribution of disaster relief supplies in emergency logistic networks by minimizing the expected total cost and the total delivery time. Based on limited historical distribution information, the model considers uncertain demand and uncertain facility disruptions, and describes their distributions through ambiguity sets. Following the adaptability and tractability of the ambiguity sets, we show that the model can be equivalently re-formulated as a mixed-integer linear program. To solve the model, we propose an exact algorithm based on Benders decomposition (BD). We also introduce an in-out Benders cut generation strategy to improve the efficiency of the BD algorithm. Finally, we perform extensive numerical studies to test the performance of the BD algorithm, ascertain the benefits of the proposed DROM over the corresponding deterministic and stochastic models, and examine the impacts of the key model parameters to gain managerial insights.

Application of Multi-Criteria Decision-Making Methods in Warehouse: A Brief Review

This review paper investigates the application of various multi-criteria decision-making (MCDM) methods in the context of warehouse management, considering papers published from 2010 to date. Warehouses often face many challenges, including efficient inventory management, space optimization, proper resource allocation, and optimal supplier selection. This paper focuses on several well-known MCDM methods that are often used in the context of warehouse management. Full consistency method (FUCOM), analytic hierarchy process (AHP), the technique for order of preference by similarity to ideal solution (TOPSIS), weighted aggregated sum product assessment (WASPAS), criteria importance through intercriteria correlation (CRITIC), measurement of alternatives and ranking according to the compromise solution (MARCOS), best worst method (BMW), evaluation based on distance from average solution (EDAS), correlation coefficient and the standard deviation (CCSD), indifference threshold-based attribute ratio analysis (ITARA), and simple additive weighting (SAW) are some of the methods reviewed in the paper, as well as certain fuzzy versions of the methods. This review paper provides a brief comprehensive overview of the application of these methods in the context of warehouse management. Data collection leads to results that tell us that the methods are mainly used in solving problems during the selection of warehouse location, the selection of warehouse equipment, and also in the management of the warehouse itself and the performance of its management. It has also been seen that the methods are useful even in "green" logistics, as well as in inventory management.

Warehouse Location Optimization with Clustering Analysis to Minimize Shipping Costs in Indonesia’s E-Commerce Case

Due to the growth of the Internet economy, the popularity of online shopping has escalated in recent years. One of the largest e-commerce enterprises in Indonesia, PT. S, is the subject of the research in this article. Instead of typical e-commerce, where anybody may start a store, PT. S is concentrating on social commerce, which makes use of several resellers to offer hand-picked SME brand partners. PT. S must expand the market for inter-island or non-java-to-non-java transactions to fulfill its vision. However, PT. S will have logistical difficulty completing this job. The business used performance indicators to keep track of the logistics process’ vision and mission. Gross merchandise value, pickup time service level, and shipping time service level are a few of the performance indicators that pertain to logistics. The process of managing the supply chain will become more complex as a result of the opening of the new warehouse, and the business will need to maximize its use of various selling channels, logistical services, and supply chain management. With the aid of clustering analysis, which assesses demand similarity and proximity, the enterprise can locate a new warehouse. Durairaj and Kasinathan developed the framework template for this study in 2015. Based on the case study, literature review, and clustering method framework, the framework will be modified in several ways, particularly clustering analysis. The alteration concerns framework-integrated theories as an input and as a data source. According to the simulation’s findings, shipping costs per kilogram decreased by about 35% for five clusters. But if the corporation does not have a problem with the number of warehouses, according to the simulation’s findings, because the cost of transportation will go down as the number of clusters increases, the number of warehouses can be expanded to more than five.

Retracted: Overseas Warehouse Location of Cross-Border E-Commerce Based on Particle Swarm Optimization

Retracted: Overseas Warehouse Location of Cross-Border E-Commerce Based on Particle Swarm Optimization

Алгоритм нахождения оптимального расположения распределительных центров для доставки заказов с использованием методов неконтролируемого машинного обучения

The problem of automating the finding of centers of mass for a system of objects on the Earth's surface is considered. This task often arises, for example, in the course of optimizing the logistics network of the company in quickening the order delivery to the customers. Automation of this process contributes to the optimization of the staff involved, improves the quality of decisions on the development of logistics networks by estimating the situation based on data. The authors of the article suggest to use the modified metric K-means machine learning algorithm for determining the optimal locations of distribution centers of the delivery of goods to settlements. Such centers can significantly reduce transportation costs, as well as provide a high level of customer service quality. The research is also touches upon the more complicated cases of using the chosen method for finding warehouse locations, since the process depends on the logistics strategy of the company. The data were analyzed and a set of features suitable for the clustering algorithm as weights was determined. The data were processed and transformed to apply the algorithm. There has been developed a class for finding distances between points on the highways. The optimal number of clusters is calculated. The clustering algorithm has been modified in terms of calculating distances between objects. With the modified algorithm, a set of candidate points for the location of distribution centers was obtained.

Proof of Evaluation-based energy and delay aware computation offloading for Digital Twin Edge Network

The increasing availability of Internet of Things (IoT) applications has led to the development of new technologies. Specifically, the deployment of edge servers close to IoT devices has strengthened the edge computing paradigm. With the collaboration of Mobile Edge Computing (MEC) and cloud computing, delay-sensitive and computation-intensive tasks can be offloaded to the edge/cloud servers to improve system performance in terms of the delay and energy consumption of IoT devices. However, there is a need to schedule the computation tasks for an efficient management. More importantly, the task scheduling strategy can face data tampering attacks to deliberately modify, destroy or manipulate the decisions. To solve the above problems, in this paper, we newly propose to integrate digital twin and blockchain into the edge networks. However, it is unclear (i) how energy and delay-aware computation should be combined, and (ii) which mining computations should be executed for a secure task scheduling. The state-of-the-art focuses on task scheduling and blockchain mining, separately. Therefore, we propose a novel blockchain-based digital twin-edge network architecture where our proposed algorithm solves these two challenges at the same execution. We design a three-layer system architecture, composed of physical entity layer, digital twin edge layer and blockchain layer. In the physical entity layer, we formulate an energy and delay-aware task scheduling problem. In the digital twin edge layer, we propose a novel Proof of Evaluation (PoE)-based secure energy and delay-aware task scheduling algorithm where optimization is executed by the genetic algorithm implementation of Warehouse Location Problem (WLP). In the blockchain layer, the best-found solutions are shared with the topology in a blockchain. Here, each block includes the hash of the previous block, a genetic algorithm-based solution, nonce value, and a hash of whole blocks in the blockchain. Thus, we aim to execute the computation tasks with an acceptable delay in an energy-efficient manner and prevent data tampering attacks against the optimal computation decisions. We validate the outcomes of our PoE-based secure digital twin-edge network model with extensive evaluations. Since the proposed model distributes the task not only to the local device but also to the MEC and cloud server for delay awareness, it reduces the delay but consumes more energy. Nevertheless, the additional energy consumption can be neglected against the delay reduction. The proposed scheme is also more scalable to compare with the conventional solution. The numerical results clearly show that the proposed model provides energy and delay awareness, maintaining both data integrity and trustworthiness at the same execution of algorithm.

Optimal warehouse location and size under risk of failure

We present an optimisation model for supply chain network design under risk of failure. The design parameters include a finite set of potential warehouse locations, a finite set of potential warehouse capacities, a set of branches with known demand for multiple products, a list of products available from each warehouse, and costs of land, building, processing and transportation. Each branch-product pair is assigned a primary and a secondary warehouse. If the primary warehouse fails, some portion of the demand will be supplied by a secondary warehouse. The major contribution of this paper is a novel method of scenarios and an algorithm to determine those scenarios, for the solution of instances of the optimisation model. The scenarios are novel in that they do not specify a subset of warehouses to be built, but rather they specify the number of warehouses of each capacity that are to be built. We give an upper bound on the number of scenarios, and the bound is linear in the difference between the largest and smallest failure probabilities. The effectiveness of the method of scenarios is demonstrated with its application to a case study of a Canadian company and to a fabricated example based in Europe.

Barter Credit: Warehouses as a Contracting Technology

ABSTRACTA large Brazilian agribusiness lender introduces a new contracting technology: grain warehouses. Using runner‐up warehouse locations as a control group, I find that construction of these warehouses permits a new debt contract, namely, barter credit repayable in grain. This contract increases borrowers' debt capacity and reduces borrowing costs. The effects are stronger when grain price risk is higher, for municipalities with weaker courts, and for financially constrained borrowers. These findings are consistent with barter credit reducing financial market imperfections by mitigating borrowers' output price risk.