Warehouse Size Research Articles

A matheuristic for a multi-period three-echelon network design problem with temporary capacity acquisition

This study addresses a three-echelon network design problem that determines the location and size of new warehouses, the removal of company-owned warehouses, the inventory levels of multiple products at the warehouses, and the assignment of suppliers as well as customers to warehouses over a multi-period planning horizon. A distinctive feature of our problem is that new warehouses operate with modular capacities that can be expanded or reduced over several periods, the latter not necessarily having to be consecutive. Moreover, in every period, the demand of a customer for a given product has to be satisfied by a single warehouse. The problem arises in the context of warehousing-as-a-service, a business scheme that offers flexible conditions for temporary capacity leasing. The associated fixed warehouse lease cost reflects economies of scale in the capacity size and the length of the lease contract. We develop a mixed-integer linear programming formulation and propose a matheuristic to solve this problem, which exploits the structure of the optimal solution of the linear relaxation to successively assign customers to open warehouses and fix other binary variables related to warehouse operation. Additional variable fixing rules are also developed, based on a scheme for managing inventories at warehouses and using the product quantities provided by suppliers. Numerical experiments with randomly generated large-sized instances reveal that the proposed matheuristic outperforms a general-purpose solver in 74% of the instances by identifying higher quality solutions in a substantially shorter computing time.

Perancangan dan Penempatan Alat Pemadam Kebakaran Jenis Sprinkler

The fire suppression ability of this sprinkler installation compared to other fire extinguishing equipment has been proven to be more effective. Because the sprinkler system does not require an operator when operating, fires can be prevented earlier before causing greater losses. For this reason, the implementation of sprinkler systems in buildings is crucial. According to KEPMEN PU NO. 10/KPTS/2000, a building must have elements that can maintain structural stability to a certain level during a fire. This research was conducted directly in the field, and the necessary data was obtained independently through interviews with relevant parties. The author conducted research observations in the archive warehouse to obtain both primary and secondary data. Primary data included field documentation and calculations. Planning calculations were done directly by calculating the number of sprinklers, water requirements, and reservoirs to determine the precise needs. Based on the survey, it was found that the size of the archive warehouse is 48 x 48 m, totaling 2304 m2. The maximum distance between sprinklers used was 4.6 m. Therefore, the effective distance between sprinkler heads is 193 units. The result of the calculation for the water supply for the sprinkler system obtained 34,139.77 liters, with the total overall cost for the equipment and materials used being Rp. 63,121,925.74. Based on the data and calculation results regarding the planning and design of fire protection equipment in open active spaces adhering to the NFPA-13 Standard for the Year 2019, it is expected to provide comfort and safety against the threat of fire incidents.

Usulan Perbaikan Layout Area Gudang Kering Logistik Menggunakan Metode Dedicated Storage di PT. Starfood International

PT. Starfoood International is a medium-sized industry engaged in fish processing which is used for snacks that have been exported to foreign countries. The company has a warehouse size of 20 meters wide x 50 meters long. Currently, the company is experiencing problems related to the arrangement of goods that are less organized and the company itself does not apply good layout procedures, which has an impact on the process of picking up goods which takes a relatively long time. The special storage method is a layout preparation method, in which the storage of products is arranged according to the number of import and export operations in the shortest distance. Through the use of this method, finished products that need to be stored will occupy the right place, making it easier for employees to store and remove products for production so that product flows smoothly. From the results of calculations using the dedicated storage method in the warehouse, the factors that influence 4 factors are Box (frozen fish), Box (Surimih), plastic (frozen fish), plastic (Surimih), for goods that are often in and out of the product manufacturing process at the factory placed behind the production door so that it doesn't take a long time and the items are arranged neatly, the calculation of the dedicated storage method is able to provide closer distance changes and make it easier for employees to find items that are often used.

An investigation of contributing factors for warehouse location and the relationship between local attributes and explanatory variables of Warehouse Freight Trip Generation Model

The location of warehouses is strategic for urban goods distribution, and the decision factors are subjective. This paper aims to analyse the contributing factors to warehouse location and describe the association between local attributes and the explanatory variables of a Warehouse Freight Trip Generation Model (WFTGM). We developed this study using data from 42 warehouses located in Belo Horizonte (Brazil). The results indicated that the accessibility to/from warehouses and the land cost are the most critical factors contributing to warehouse location decision. Additionally, the accessibility of trucks for delivery has a strong positive correlation with the accessibility of freight vehicles for loading. The land cost negatively correlates with all factors except the employee's accessibility, indicating an antagonistic relationship with other variables. Considering the Warehouse Freight Trip Generation Model, the warehouse-size related variables provided the best-fit models. Additionally, the warehouse size is not independent of the neighbourhoods' population and distance to the main ring roads traversing this city. The results support the development of public policies related to new warehouses' licensing process and offer guidance on warehouses' location in urban areas.

DISCRETE-EVENT SIMULATION MODELLING OF SEA COAL TERMINAL WAREHOUSE USING ANYLOGIC

The issues of simulation modeling of complex transport nodes and systems are considered in the paper. It has been determined that warehouses in seaports act as buffers and smooth out the resulting difference in incoming and outgoing cargo traffic between land and sea transport. When designing offshore coal terminals, choosing the size, layout and equipment of a warehouse is one of the cornerstones. The basic principles for the placement of coal piles, depending on the size and capacity of an open warehouse, the size of ship lots and the average shelf life are provided in the paper. One of the main indicators that determine the correctness of choosing the size and layout of an open warehouse is the coefficient of utilization of the warehouse capacity. Formulas for its calculation are given. The principles of stacking coal in piles are further defined in the paper. There are four main laying methods - conical, chevron, layering and windowed. Next, the dependence of the coal storage density on the mass of the stack and the utilization rate of the warehouse on the average storage time of the cargo is determined. It is concluded that it is possible to fully assess the impact of stochastic events occurring in the terminal warehouse only with the use of simulation modeling. Further, it is determined that mixing and homogenization operations can be carried out in open warehouses. The differences and principles of these processes are given. Approaches to the selection of warehouse equipment and cyclical machines are identified, the advantages and disadvantages of the various types of warehouse machines are indicated. Definitions of technical and operational performance of machines are given. The Anylogic environment is chosen for modeling. Using environment elements, a digital copy of the coal terminal warehouse, serving the material flow - bulk cargoes, is created. Further, a detailed explanation of the principles and approach to creating a digital model is given, the internal and external links of the elements are clarified. In conclusion, information about the options for setting up and managing the model, and brief information about the results obtained are given.

Combined replenishment and warehousing with contract commitments and flexibilities

AbstractThis paper studies a combined replenishment and warehouse size planning problem. A firm (3PW user) decides the replenishment quantities over a finite planning horizon to satisfy the time‐varying demands of a product. The firm has no warehouse and contracts with a third‐party warehousing (3PW) provider for storage space over the planning horizon. In the warehousing contract, the planning horizon is partitioned into a number of sessions, and each session also consists of multiple periods. The firm decides a stationary base storage size (BSS) of each session and has an opportunity to adjust the BSS at the beginning of each session. The firm also has the option to rent additional storage size beyond the BSS by paying higher rental cost in each individual period. We explore structural properties and develop a strongly polynomial‐time algorithm by applying geometric techniques to solve the firm's replenishment and warehouse sizing problem. We also conduct numerical experiments and provide important insights into the 3PW user's decisions and the 3PW contract design.

Optimal Warehouse Location and Size In Practice

Optimal Warehouse Location and Size In Practice

Materialized View Selection Using Self-Adaptive Perturbation Operator-Based Particle Swarm Optimization

A data warehouse is a central repository of time-variant and non-volatile data integrated from disparate data sources with the purpose of transforming data to information to support data analysis. Decision support applications access data warehouses to derive information using online analytical processing. The response time of analytical queries against speedily growing size of the data warehouse is substantially large. View materialization is an effective approach to decrease the response time for analytical queries and expedite the decision-making process in relational implementations of data warehouses. Selecting a suitable subset of views that deceases the response time of analytical queries and also fit within available storage space for materialization is a crucial research concern in the context of a data warehouse design. This problem, referred to as view selection, is shown to be NP-Hard. Swarm intelligence have been widely and successfully used to solve such problems. In this paper, a discrete variant of particle swarm optimization algorithm, i.e. self-adaptive perturbation operator based particle swarm optimization (SPOPSO), has been adapted to solve the view selection problem. Accordingly, SPOPSO-based view selection algorithm (SPOPSOVSA) is proposed. SPOPSOVSA selects the Top-K views in a multidimensional lattice framework. Further, the proposed algorithm is shown to perform better than the view selection algorithm HRUA.

Relative logistics sprawl: Measuring changes in the relative distribution from warehouses to logistics businesses and the general population

The logistics sprawl – warehousing and distribution centers relocating farther away from central urban areas to the urban periphery – has been widely examined in multiple global trade gateways. The spatial shifts have been attributed to the systematic and geographic restructuring of goods production and distribution systems at the global scale. More recently, growing online shopping sales and demand for instant delivery have put tremendous pressure on online retail businesses to locate order fulfillment centers in urban areas with direct access to the consumer markets. Has the trend of sprawling warehouses reversed? Have warehouses been relocating closer to consumer demand? In this research, I use a relative distribution measure to quantify the spatial distribution of warehouses relative to the logistics sector, the goods movement sector, and the general population over time. The main data source is ZIP Code Business Patterns from 2003 to 2016 with the study area of the 64 largest US metropolitan areas. In 2016, warehouses were most closely distributed relative to logistics sectors (38.6 km), followed by goods movement sectors (41.2 km), and population (43.5 km). From 2003 to 2016, the distance between warehouses and related businesses has increased, but the extent varies with respect to metropolitan and warehouse size. The trend of increasing relative distance from warehouses to related businesses stopped since the economic recession in 2008. Relative distribution measures are significantly correlated with internal ground shipment ton-kilometers but only in large trade gateways.

OPTIMIZATION PLAN FOR EXCESS WAREHOUSE STORAGE IN E-COMMERCE–BASED PLANT SHOPS: A CASE STUDY FOR CHINESE PLANT INDUSTRIAL

The rapid development of e-commerce in China has played a critical role in the development of the national economy and ongoing modernization. The plant industry is unique among industries that employ e-commerce sales models because its products exhibit special characteristics such as high death and damage rates. Therefore, its e-commerce and logistical requirements are stricter than in other industries and, as a result, excess warehouse storage can be extremely difficult for e-commerce–based plant shops to manage. Numerous studies have indicated the need to identify a product’s most up-to-date market conditions, as well as the type, function, and size of warehouses. Therefore, based on a case study, this study proposes an optimization plan for solving excess warehouse storage in e-commerce–based plant shops. First, sales volume data of the case company, Enterprise A, were analyzed to predict future sales. Then, entropy and the technique for order preference by similarity to an ideal solution were used to construct the decision-making model. Finally, a cloud warehouse–based optimization plan was proposed to solve excess warehouse storage in e-commerce–based plant shops. This plan can serve as a reference for decision-makers or executives in e-commerce–based plant shops when handling excess warehouse storage.

Efikasnost politike skladištenja proizvoda prema kriterijumu klasifikacije i dimenzijama skladišta

The paper focuses on identifying the influence of warehouse size to effectiveness of products storage policy. In the paper different storage policy was analysed. To evaluate the effectiveness of selected order-picking warehouses the dedicated software for simulation of orders picking process was used. The effectiveness of product allocation planning was evaluated by the total time that is needed for the order-picking process. For the route planning it was assumed that the nearest product was appointed as next to pick. In our research, a discrete event simulation analysis by using special software called PickupSimulo was performed. In this research different warehouse sizes were investigated. The layout ranged from 2.188 m2 to 22.021 m2. Based on the simulation study it is possible to support warehouse managers for choosing the best products storage policy. This will reduce retrieval time and improve the effectiveness of order picking.

Materialized View Selection Using Set Based Particle Swarm Optimization

A data warehouse is a central repository of historical data designed primarily to support analytical processing. These analytical queries are exploratory, long and complex in nature. Further, the rapid and continuous growth in the size of data warehouse increases the response times of such queries. Query response times need to be reduced in order to speedup decision making. This problem, being an NP-Complete problem, can be appropriately dealt with by using swarm intelligence techniques. One such technique, i.e. the set-based particle swarm optimization (SPSO), has been proposed to address this problem. Accordingly, a SPSO based view selection algorithm (SPSOVSA), which selects the Top-K views from a multidimensional lattice, is proposed. Experimental based comparison of SPSOVSA with the most fundamental view selection algorithm shows that SPSOVSA is able to select comparatively better quality Top-K views for materialization. The materialization of these selected views would improve the performance of analytical queries and lead to efficient decision making.

Designing a block stacked warehouse for dynamic and stochastic product flow: a scenario-based robust approach

The problem considered in this paper is motivated from a real life case. Conventional models in designing block stacked warehouse assume uniform and deterministic inflow and outflow of products in specific quantities and time intervals. These assumptions would lead to underestimation of space requirement for the case. Also, the possibility of leasing additional warehouse space during seasonal spikes was found to be unexplored in block stacking design literature. In this study, we develop a scenario-based model to address the decisions of warehouse size, lane depth and hiring of temporary additional warehouse capacity when flow of products is stochastic and dynamic in nature. A sequential decision model is presented with future operating scenarios assumed to branch out in the form of a tree. On comparison with conventional block stacking design methods, the model is found to provide robust solution in stochastic and dynamic environments in terms of risk reduction and infeasibility avoidance. Further, the model gives additional flexibility to the designer to trade-off model and solution robustness with predicted cost.

Estimating Warehouse Rental Price using Machine Learning Techniques

Boosted by the growing logistics industry and digital transformation, the sharing warehouse market is undergoing a rapid development. Both supply and demand sides in the warehouse rental business are faced with market perturbations brought by unprecedented peer competitions and information transparency. A key question faced by the participants is how to price warehouses in the open market. To understand the pricing mechanism, we built a real world warehouse dataset using data collected from the classified advertisements websites. Based on the dataset, we applied machine learning techniques to relate warehouse price with its relevant features, such as warehouse size, location and nearby real estate price. Four candidate models are used here: Linear Regression, Regression Tree, Random Forest Regression and Gradient Boosting Regression Trees. The case study in the Beijing area shows that warehouse rent is closely related to its location and land price. Models considering multiple factors have better skill in estimating warehouse rent, compared to singlefactor estimation. Additionally, tree models have better performance than the linear model, with the best model (Random Forest) achieving correlation coefficient of 0.57 in the test set. Deeper investigation of feature importance illustrates that distance from the city center plays the most important role in determining warehouse price in Beijing, followed by nearby real estate price and warehouse size.

Joint optimization of dynamic lot and warehouse sizing problems

We consider an integrated model of inventory and warehousing decisions for a single product. In this model, dynamic lot size decisions are considered under constraints of the warehouse size that can be either increased or decreased at the beginning of each period. The ordering cost is typically a fixed cost and the holding cost is assumed to be a linear function. Each adjustment of the warehouse size incurs a fixed-plus-linear adjustment cost. A lease cost has to be paid for the warehouse size in each period. We provide an efficient algorithm to solve the problem. Further, we present computational experiments that show the efficiency of the algorithm and the effectiveness of the model.

An Evaluation of Cross-Efficiency Methods, Applied to Measuring Warehouse Performance

In this paper method and practice of cross-efficiency calculation is discussed. The main methods proposed in the literature are tested not on a set of artificial data but on a realistic sample of input-output data of European warehouses. The empirical results show the limited role which increasing automation investment and larger warehouse size have in increasing productive performance. The reason is the existence of decreasing returns to scale in the industry, resulting in sub-optimal scales and inefficiencies, regardless of the operational performance of the facilities. From the methodological perspective, and based on a multidimensional metric which considers the capability of the various methods to rank warehouses, their ease of implementation, and their robustness to sensitivity analyses, we conclude to the superiority of the classic Sexton et al. (1986) method over recently proposed, more sophisticated methods.

Dynamic warehouse size planning with demand forecast and contract flexibility

This paper develops a dynamic warehouse planning model incorporating demand forecast and contract flexibility, and addresses how demand forecast and contract flexibility affect warehouse size planning. In this model, a manager announces a nominal size of the warehouse space to rent before the planning horizon begins (strategic decision), and determines the ordering quantity and actual warehouse size during the horizon (operational decision). In particular, the manager can adjust the actual warehouse size within a range according to dynamically updating demand forecast during the horizon, which reflects the contract flexibility. We start with the characterisation of the operational decision. For any given nominal size, we show the monotonicity of optimal inventory replenishment and warehousing decisions w.r.t. demand forecast and contract flexibility. However, this monotonicity does not necessarily hold for the strategic choice of the nominal size. Finally, a case study is presented to investigate the interaction between demand forecast information and contract flexibility. We find that the value of demand forecast can be enhanced as the contract flexibility improves. However, more forecasted demands do not imply higher value of contract flexibility.

Analytical Optimization for the Warehouse Sizing Problem Under Class-Based Storage Policy

The aim of this paper study the impact of class-based storagepolicy basedon the optimal configuration of U-flow single command warehouses using aon an ABC product classification. For that purpose, the authors proposea non linear optimization model to minimize the expected travel distanceof the warehouse and use analytical methods to solve it.The most important contribution is to provide a mathematical proof thatregardless of the storage policy (It does not matter the specifics of theturnover pattern of the products), the optimal warehouse has a width thatis the double of its length, and the pick and deposit point should be locatedin the middle of the width of the warehouse.In addition, authors perform a sensitivity analysis that indicates that theoptimal solution is robust, meaning that a certain deviation from the op-timum layout does not impose a significant penalty on the expected traveldistance of the warehouse.

Sizing, pricing and common replenishment in a headquarter-managed centralized distribution center

Purpose – Commonly shared logistics services help manufacturing companies to cut down redundant logistics investments while enhance the overall service quality. Such service-sharing mode has been naturally adopted by group companies to form the so-called headquarter-managed centralized distribution center (HQ-CDC). The HQ-CDC manages the common inventories for the group’s subsidiaries and provides shared storage services to the subsidiaries through appropriate sizing, pricing and common replenishment. Apart from seeking a global optimal solution for the whole group, the purpose of this paper is to investigate balanced solutions between the HQ-CDC and the subsidiaries. Design/methodology/approach – Two decision models are formulated. Integrated model where the group company makes all-in-one decision to determine the space allocation, price setting and the material replenishment on behalf of HQ-CDC and subsidiaries. Bilevel programming model where HQ-CDC and subsidiaries make decisions sequentially to draw a balance between their local objectives. From the perspective of result analysis, the integrated model will develop a managerial benchmark which minimizes the group company’s total cost, while the bilevel programming model could be used to measure the interactive effects between local objectives as well as their final effect on the total objective. Findings – Through comparing the numerical results of the two models, two major findings are obtained. First, the HQ-CDC’s profit is noticeably improved in the bilevel programming model as compared to the integrated model. However, the improvement of HQ-CDC’s profit triggers the cost increasing of subsidiaries. Second, the analyses of different sizing and pricing policies reveal that the implementation of the leased space leads to a more flexible space utilization in the HQ-CDC and the reduced group company’s total cost especially in face of large demand and high demand fluctuation. Research limitations/implications – Several classical game-based decision models are to be introduced to examine the more complex relationships between the HQ-CDC and the subsidiaries, such as Nash Game model or Stackelberg Game model, and more complete and meaningful managerial implications may be found through result comparison with the integrated model. The analytical solutions may be developed to achieve more accurate results, but the mathematical models may have to be with easier structure or tighter assumptions. Practical implications – The group company should take a comprehensive consideration on both cost and profit before choosing the decision framework and the coordination strategy. HQ-CDC prefers a more flexible space usage strategy to avoid idle space and to increase the space utilization. The subsidiaries with high demand uncertainties should burden a part of cost to induce the subsidiaries with steady demands to coordinate. Tanshipments should be encouraged in HQ-CDC to reduce the aggregate inventory level as well as to maintain the customer service level. Social implications – The proposed decision frameworks and warehousing policies provide guidance for the managers in group companies to choose the proper policy and for the subsidiaries to better coordinate. Originality/value – This research studies the services sharing on the warehouse sizing, pricing and common replenishment in a HQ-CDC. The interactive decisions between the HQ-CDC and the subsidiaries are formulated in a bilevel programming model and then analyzed under various practical scenarios.

Assessing the environmental impact of integrated inventory and warehouse management

There has been considerable research on the environmental impact of supply chains but most of this has concentrated on the transport elements. The environmental impact of warehousing has received relatively little attention except within the context of distribution networks. A high proportion of total warehouse emissions emanate from heating, cooling, air conditioning and lighting and these aspects are largely related to warehouse size. This in turn is greatly influenced by inventory management, affecting stockholding levels, and warehouse design, affecting the footprint required for holding a given amount of stock. Other emissions, such as those caused by material handling equipment, are closely related to warehouse throughput and equipment choice. There is a substantial gap in the literature regarding this interaction between inventory and warehouse management and its environmental impact. The purpose of this paper is to contribute to filling this gap. Therefore, an integrated simulation model has been built to examine this interaction and the results highlight the key effects of inventory management on warehouse-related greenhouse gas emissions. In particular, it is found that decisions on supply lead times, reorder quantities, and storage equipment all have an impact on costs and emissions and therefore this integrated approach will inform practical decision making. Additionally, it is intended that the paper provides a framework for further research in this important area.