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A simulation-based optimization approach to design optimal layouts for block stacking warehouses
Abstract and Figures
Storing pallets of products on top of one another on the floor of a warehouse is called block stacking. The arrangement of lanes, aisles, and cross-aisles in this storage system affects both utilization of the storage space and material handling costs; however, the existing literature focuses exclusively on lane depths and their impact on space utilization. This paper fills this gap and studies the optimal layout design for block stacking, which includes determining the numbers of aisles and cross-aisles, bay depths, and cross-aisle types. We show that lane depths affect material handling cost in addition to space utilization and develop a simulation-based optimization algorithm to find optimal layouts with respect to both of these objectives. We also propose a closed-form solution to the optimal number of aisles in a layout. The results of a case study in the beverage industry show that the resulting layout can save up to ten percent of the operational costs of a warehouse. We present the computational efficiency of the algorithm and some insights into the problem through an exhaustive experimental analysis based on various test problems that cover small- to industrial-sized warehouses.
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... Layout design optimization impacts the design of many products and industrial systems [11]. The research objects were mainly facilities, products, and enterprises/factories [12]. Existing studies mostly focus on the facility layout problem [12,13]. ...
... The research objects were mainly facilities, products, and enterprises/factories [12]. Existing studies mostly focus on the facility layout problem [12,13]. Patsiatzis et al. [14] studied the single-floor process plant layout problem, considering the piping cost, property damage, and cost of protection devices. ...
... Constraint (10) and (11) restrict each enterprise from being placed in one location. Constraint (12) guarantees that each enterprise can only be assigned to one building. To explain Constraint (13), a clear illustration is provided. ...
With the advent and development of Industry 4.0 and 5.0, manufacturing modes have changed and numerous newly complicated and integrated village-level industrial parks have emerged in the Southeast of China, where several enterprises are gathered in the same multistory building. The number of floors and surrounding enterprises can have an impact on accident risk. To reduce the overall risk level of industrial parks, the layout of enterprises with different risks needs to be well designed and optimized. However, to date, limited studies have been conducted to emphatically consider safety and optimize the enterprise layout at an industrial area level, and most studies focus on the cost of the layout. Therefore, this study proposed three biobjective mathematical optimization models to obtain the trade-off between minimizing risk and maximizing rental profit. Risk factors include the enterprise location and the association risk; the enterprise inherent safety risks are not considered. To solve this problem, a specific linearization strategy was proposed and an epsilon-constraint method was applied to obtain Pareto-optimal solutions. Subsequently, an industrial park in Shunde, China, was considered as a case study to verify the performance of the proposed models and methods. Finally, a sensitivity analysis of critical parameters was conducted. The critical factors influencing the objective functions were also analyzed to provide valuable managerial insights.
... Chen et al. [32] proposed a hierarchical two-stage-exchange method to minimize the total dispersion degree in large-scale transshipment (commercial cars). Derhami et al. [33] presented a block-stacking method by bay depth, cross-aisle types and the number of aisles and cross-aisles in the beverage industry, and the resulting layout reduced operation costs by 10%. Ghomri et al. [28] proposed an analytical method that took into account various items' physical parameters, such as length, width, and depth. ...
This paper aimed to introduce multiple-rack strategies in miniload automated storage and retrieval systems (AS/RSs), which included first fit (FF) and best fit (BF) assignment methods based on a matrix real-coded genetic algorithm (MRCGA) in the storage and retrieval process. We validated the probability occurrence of item sizes as a contributory factor in multiple-rack strategies, and compared their capacities, utilization of units and space by equal probabilities or the 80/20 law. According to the analytical methods, BF showed a reduction of more than 11.2% than FF on travel distance, and Type B-FF, Type B-BF and Type C-BF were better able to meet high-density requirements. These strategies provide diversified storage and retrieval solutions for the manufacturing and express delivery industry.
... Similarly, [2] studied the optimal layout design for block stacking. Minimising the travel distance of a picking tour is often considered an imperative factor in improving warehouse operation efficiency. ...
The uneven workload distribution and working time utilisation create a bottleneck, leading to inefficient utilisation of capacity and increased costs. A bottleneck is a limiting and risk factor for any business entity. In the case of a distribution warehouse, the bottleneck limits its ability to meet the requirements for sending an order within the required time limit. Delays at any phase of a distribution process may result in non-compliance with customer requirements. In solving capacity problems and bottlenecks elimination, computer simulations and optimisation are often used. The article presents a basic simulation analysis of workload distribution and work times, useful for logistics companies, thus for the area of human and financial resources. In the article, the use of simulations in the ExtendSim9 program to eliminate the bottleneck is discussed. The bottleneck is solved by experiments on a simulation model when optimal workers assignment to individual workplaces of the warehouse is sought. The two final proposals for workers allocation, with the current and increased number of workers, are compared in workforce utilisation and system stability. The simulation method allows verification of the proposals' impacts in advance and practically with no financial costs.
... Wang [16] proposed a model to minimize the piping investment, pump power, land, and floor construction costs. Derhami [17] focused on reducing the operational costs of warehouses by layout. The park leases plants to the enterprise, and rent is the primary profit source of the park. ...
In the new-type clustered industrial park, the closer distance between enterprises leads to risk aggregation, and the layout of enterprises affects the safety and economy of the park. However, previous studies have often paid insufficient attention to safety, and few studies have considered park profits. To address this issue, a bi-level three-dimensional layout optimization model was proposed to minimize the overall association risk of the park and maximize the rental profit. In particular, this article explained the enterprise association risks and provided calculation formulas, considering multiple risk types. To solve the proposed nonlinear model, a specific variable conversion method was presented to reduce the problem scale. Subsequently, an improved genetic algorithm was developed and applied to obtain the layout results. Furthermore, a case study of an industrial park was conducted, and the computational results indicated the validity of the model and methods. Finally, two different scenarios were implemented, and critical parameters were tested to provide valuable management insights.
... This issue was addressed by Stanković and Božić in [13], who proposed a linear programming model (LP model) to optimize the truck-to-gate assignment and designed a simulation model of inbound docking operations to quantify the respective reduction in waiting time in docking operations, which were the KPIs set in that case. Generally, these simulation methods have been widely used in designing and optimizing logistics processes and systems [14][15][16][17][18][19][20], since the time when researchers started needing to use general-purpose computer language (such as C) to write program code that would enable the performance of simulation experiments [21], until the present, when advanced simulation software packages (such as FlexSim) provide user-friendly development interfaces supported by automatic program-code generation. ...
As energy consumption constantly gains importance, it has become one of the major issues in managing logistics systems. However, it is ranked against other company priorities, and the rationalization for investing in energy needs to be justified by the savings achieved. A solution for reducing energy consumption via electric forklifts for performing docking operations at distribution centers, which requires no investments in infrastructure or equipment, is outlined in this paper. The solution is based on optimizing inbound dock door allocation, and the energy savings are quantified using a simulation model. A case study of a local FMCG distributor’s logistics center was conducted to collect the data and information needed for modeling inbound docking operations and performing simulation experiments. The optimal dock door allocation was obtained using a linear programming method using an MS Excel spreadsheet optimizer (Solver), while the simulation of the docking operations was carried out using FlexSim simulation software. The experimental results show that the solution outlined in this paper enables savings in the electric energy consumption of forklifts of between 12.8% and 14.5%, compared to the empirical solution applied by the company in the case study. The intended contribution of this paper is not limited to presenting an applicable solution for energy savings in performing logistics processes, but also aims to draw the attention of more researchers and companies to the ways in which logistics processes are managed and performed in terms of raising energy efficiency.
... Utilizar el espacio cúbico y no solamente la superficie de almacenamiento, utilizar donde sea posible la ubicación aleatoria en vez de ubicación fija de productos, reducir la cantidad de productos obsoletos y minimizar la cantidad de pasillos. 3. Minimizar movimiento: Ubicar los productos de mayor movimiento en las "zonas doradas", que son las zonas que generan la menor distancia recorrida [3]. Si es posible, separar el inventario en reserva y delantero. ...
La presente investigación es desarrollada en una empresa productora de alimento balanceado en sus divisiones de Salud Animal, que produce alimento balanceado para animales; y Acuacultura, que produce alimento balanceado para camarones. El producto es manipulado por montacarguistas y la unidad de carga es de cuarenta y setenta sacos por pallet, respectivamente. La compañía tiene como objetivo reducir el índice de reproceso de producto terminado que es generado principalmente por la ruptura de sacos y el derrame de producto durante las actividades de recepción, acomodo, almacenamiento, reabastecimiento, preparación y despacho en la bodega de producto terminado. Este desperdicio en forma de reproceso es un problema porque se requiere de espacio para almacenar producto reprocesado, mano de obra para reprocesar sacos y lo más importante, representa costos adicionales para la empresa. Por medio de la aplicación de la filosofía de almacenamiento esbelto y la metodología DMAIC fue posible reducir en 51.6% el índice de reproceso de producto terminado.
Metode heuristik merupakan teknik pencarian solusi dengan waktu yang cepat dan mendekati nilai optimal. Metode block stacking adalah metode peletakan barang dengan cara ditumpuk secara vertikal dan horizontal. Pada penelitian ini dilakukan perhitungan jumlah konsumsi energi di setiap penumpukan barang pada proses put away dan picking di area gudang. Pada kasus di area gudang, perlu diperhatikan posisi penumpukan barang berdasarkan aspek volume dan berat. Penumpukan barang yang tidak sesuai, dimana barang yang lebih berat ditempatkan di bagian atas dari tumpukan akan menimbulkan risiko pada kerusakan barang yang berada di bawahnya, kerugian biaya inventori dan tingginya konsumsi energi material handling forklift. Algoritma yang digunakan dimulai dari menghitung jumlah kombinasi penumpukan yang bergantung pada banyaknya permintaan, memperhatikan kapasitas penumpukan yang bergantung pada ukuran barang dan ukuran pallet, menentukan syarat pemilihan barang untuk ditumpuk berdasarkan volume, berat dan konsumsi energi pada material handling forklift. Oleh karena itu fokus utama dari penelitian ini adalah melakukan perancangan algoritma tata letak kombinasi penumpukan barang dengan memperhatikan jenis barang terhadap energi dan menguji beberapa algoritma lainnya. Dari hasil perancangan yang sudah dilakukan, kriteria pemilihan algoritma yang digunakan berdasarkan jumlah konsumsi energi yang terkecil. Hasil penelitian menunjukkan bahwa algoritma 1 menghasilkan jumlah konsumsi energi terkecil, yaitu 38,880,767 joule, sedangkan algoritma 2 menghasilkan jumlah konsumsi energi sebesar 56,272,982 joule dan algoritma 3 yaitu sebesar 75,148,190 joule.
Warehousing management is essential for many companies involved in the supply chain. The optimal arrangement and operation of warehouses play important role in companies, allowing them to maintain and increase their competitiveness. One of the main goals of warehousing is the reduction of costs and improvement of efficiency. Determination of the ideal warehouse layout is a special optimization case. In the article, a procedure is applied to the optimization of the allocation of bins in an automated warehouse with special characteristics. The initial layout of the warehouse, as well as the automated platforms constraints the search, as well as define the time needed to move goods inside the warehouse. The definition of time needed to move goods, with the analysis of historical data, allows the definition of a mathematical model of the operation of the warehouse. Using that model, an optimization procedure based on the well-known hill-climbing algorithm is defined. Experimental results show increments in the efficiency of the warehousing operations.
This paper deals with the combined implementation of the optimal redistribution of warehouse stations through layout analysis, as well as the use of lean techniques in warehouse operations of an air cargo company. The study highlights the effort to improve operations within the warehouse through the use of existing lean tools that allow eliminating waste (activities, materials, among others) that do not generate operational value, as well as improving and expanding the warehouse's storage capacity using the plant distribution tools and block stacking in order to maximize the allowed load and maintain the proper order that allows the continuous flow of operations within the air cargo warehouse.
In block stacking warehouses, pallets of stock keeping units (SKUs) are stacked on top of one another in lanes on the warehouse floor. A conventional layout consists of multiple bays of lanes separated by aisles. The depths of the bays and the number of aisles determine the storage space utilization. Using an analytical model, we show that the traditional lane depth model underestimates accessibility waste and therefore does not provide an optimal lane depth. We propose a new model of wasted storage space and embed it in a mixed integer program to find the optimal bay depths. The model improves space utilization by allowing multiple bay depths and allocating SKUs to appropriate bays. Our computational study shows the proposed model is capable of solving large-scale problems with a relatively small optimality gap. We use simulation to evaluate performance of the proposed model on small to industrial-sized warehouses. We also include a case study from the beverage industry.
In this paper, order assignment, order batching and picker routing problems with multiple pickers in a wave picking warehouse of a major US third party logistics company is studied and modeled mathematically. The proposed mathematical model is solved using an exact algorithm. Since the exact algorithm suffers from long CPU time, a Lagrangian decomposition heuristic combined with particle swarm optimization (LD-PSO) algorithm is proposed, which performs well for small size waves. To solve large-scale problems, a hybrid parallel simulated annealing and ant colony optimization (PSA-ACO) is presented. The proposed methods are tested using the warehouse data. The results are compared against the minimum makespan impact (MMI) heuristic that is currently being used in the warehouse and a state-of-the-art variable neighborhood descent (VND). While PSA-ACO slightly outperforms VND, for picking large waves, PSA-ACO and VND can improve the makespan by approximately 7.8% and 6.9% over MMI respectively.
Storage racks in a unit-load warehouse typically have slots of equal height, whereas the unit-loads themselves have heights that vary significantly. The result of this mismatch is unused vertical space and storage spaces larger than they otherwise could be. We propose the use of storage racks with multiple slot heights to better match the distribution of pallet heights. The slot profile design problem seeks the best set of slot heights and their corresponding quantities such that a desired service level is met, where service level is the probability that all pallets present in a period can be stored. Using data from several companies, we found that the potential space savings of using multiple slot heights are between 29% and 45%.
Storing pallets of Stock Keeping Units (SKUs) on top of one another in lanes on a warehouse floor is known as block stacking. This storage system is widely used in manufacturing systems and distribution centers. The arrangement of lanes in the layout of this system significantly impacts utilization of the storage space and transportation costs. Existing research that studies the layout for this system focuses exclusively on determining the optimal lane depth with respect to space utilization and ignores transportation costs. In this study, we develop a simulation model that computes several performance metrics to evaluate both of these objectives for a warehouse layout. It aims to take the stochastic variations exist in the real world situation into account. Designing the layout based on the historical data distinguishes this model from the analytical models in the systems with high level of uncertainty, where determining the required parameters for analytical models are difficult due to the high variations. We verified the model using the existing analytical models and developed an experimental analysis to show the trade-off between the space utilization and transportation costs in the layout design problem.
Block stacking storage is an inexpensive storage system widely used in manufacturing systems where pallets of stock keeping units (SKUs) are stored in a warehouse at the finite production rates. However, determining the optimal lane depth that maximises space utilisation under a finite production rate constraint has not been adequately addressed in the literature and is an open problem. In this research, we propose mathematical models to obtain the optimal lane depth for single and multiple SKUs where the pallet production rates are finite. A simulation model is used to evaluate performance of the proposed models under stochastic uncertainty in the major production parameters and the demand.
In this paper, we provide a method to generate a three-dimensional detailed design of fishbone layouts. This method takes the desired storage capacity and returns the location (x,y,z) of each opening of the warehouse in such a way that the total operational cost - area cost and material handling cost - of the warehouse is minimal. We model the arrangement of the openings using mathematical finite sequences and represent a fishbone layout in terms of four primary characteristics. Next, we develop an algorithm that generates a detailed design of a fishbone layout given values of its four primary characteristics. Then, we present an optimization model that finds the values for the four primary characteristics that minimize the total operational cost of the warehouse. Finally, we solve the optimization model using a genetic algorithm. Our results suggest that in 91.74% of the cases, our optimization procedure reaches a near optimum point - deviated only by 0.587% - in a reasonable computational time (maximum 4.5. min). This paper aims to diminish dependence upon experts and human decision making in the process of implementing a fishbone layout on greenfield projects, and fulfills an identified need of warehouse practitioners by integrating the most recent advances on non-traditional layouts and detailed warehouse design.
The online-to-offline (O2O) community supermarket is currently a popular O2O business
model in China. Owing to the small lot-size, high frequency, time-sensitive, and dynamic
arrival of online customer orders, many O2O community supermarkets face challenges in how to pick up the dynamic arrival orders and deliver them to customers with minimum makespan and delivery cost. To achieve the global optimal order fulfillment performance, we study the online integrated order picking and delivery problem for O2O community supermarket, and order pickers’ learning effects are considered to better plan the integrated problem. To propose a feasible and efficient schedule, the online algorithm 𝐀 is established, and the competitive ratio is proved to be 2 theoretically. To further verify the effectiveness and efficiency of algorithm 𝐀 in practice, we summarize the actual order fulfillment rules (named 𝐀𝟏), and conduct numerical experiments to compare algorithms 𝐀 with 𝐀𝟏. Moreover order pickers’ workforce characteristics are varied to evaluate the learning effects on the order fulfillment process. The results show that Algorithm 𝐀 performs better than 𝐀𝟏 in different situations, and considering pickers’ learning effects is significant for the accuracy and predictability of order fulfillment process.
This paper models Robotic Mobile Fulfillment Systems and analyzes their performance. A Robotic Mobile Fulfillment System is an automated, parts-to-picker storage system where robots bring pods with products to a workstation. It is especially suited for e-commerce distribution centers with large assortments of small products, and with strong demand fluctuations. Its most important feature is the ability to automatically sort inventory and to adapt the warehouse layout in a short period of time. Queueing network models are developed for both single-line and multi-line orders, to analytically estimate maximum order throughput, average order cycle time, and robot utilization. These models can be used to quickly evaluate different warehouse layouts, or robot zoning strategies. Two main contributions are that the models include accurate driving behavior of robots and multi-line orders. The results show that: 1) the analytical models accurately estimate robot utilization, workstation utilization, and order cycle time 2) maximum order throughput is quite insensitive to the length-to-width ratio of the storage area and 3) maximum order throughput is affected by the location of the workstations around the storage area.
Self-storage warehousing is a rapidly growing industry where consumers or companies can rent storage space for personal or company use, over a certain horizon. This paper addresses the question of how to design the facilities so that revenue can be maximised over a finite horizon, considering multiple demand requirements with respect to size, pricing, location within the facility, climate control, security or outside access, among others. Using a customer choice model to specify the probability of purchase for each fare product as a function of the set of fare products offered, we propose methods to design self-storage warehouses while allowing different customer choice behaviours. We model the problem as a mixed-integer program and solve it using column generation and branch-and-price algorithms. In addition, we study the impact of re-layout and methods to modify facility layout, since self-storage facilities are relatively flexible in layout and individual storage compartments can be adapted to changes in demand. We validate our model using the data of four self-storage warehouses, and show our method can improve the expected revenue by nearly 11% on average for these cases.
A major part of warehouse operations is related to the collection of parts from the warehouse which is called the Order Picking Problem. To improve order picking operations, the total travel distance and generally picking time must be reduced. In this paper, a two-level approach is proposed that determines the locations of parts in the warehouse. The first step clusters parts into part families. Four different clustering methods based on principal component analysis, singular value decomposition and Two-Step Cluster Component are applied. In the second step, four different heuristics are proposed to determine the locations of parts. In addition to the minimisation of travel distance, we also consider the minimisation of the total congestion in aisles due to multiple workers. The proposed algorithms also consider the interactions between part families to minimise intergroup movements. As a result of the implementation, we achieved more than 40% reduction in material handling compared to the current set-up of the warehouse. The applied algorithms can easily be modified to be used for warehouses with different configurations. The algorithms utilised in this case study can be helpful to researchers to become familiar with new heuristics, as well as practitioners to design improved warehouses.