Search Results (69)

Currently, supermarket-run shopping centers, known as “Big Marts,” monitor sales information for every single item in order to predict potential customer demand and update inventory management. Anomalies and general trends are commonly discovered through data warehouse mining using a range of machine learning techniques, and businesses such as Big Marts can use the obtained data to forecast future sales volumes. Compared to other research publications, this one forecasted sales with higher accuracy using machine learning models including KNN (K Nearest Neighbors), Naïve Bayes, and Random Forest. To adapt the proposed business model to anticipated outcomes, the sales forecast is based on Big Mart sales for various stores. Using different machine learning methods, the data that is produced may then be used to predict potential sales volumes for retailers such as Big Marts. The projected cost of the suggested system includes the following identifiers: price, outlet, and outlet location. In order to facilitate data-driven decision-making in retail operations and help Big Marts optimize their business models and effectively satisfy anticipated demand, this study emphasizes the importance of incorporating cutting-edge machine learning approaches. Full article

In the context of the rapid development of e-commerce, the selection of optimal land plots for the construction of warehouse complexes that meet environmental, technical, and political requirements has become increasingly relevant. This task requires a comprehensive approach that accounts for a wide range of factors, including transportation accessibility, environmental conditions, geographic features, legal constraints, and more. Such an approach enhances the efficiency and sustainability of decision-making processes. This article presents a solution to the aforementioned problem that employs the use of land suitability maps generated by aggregating multiple evaluation criteria. These criteria represent the degree to which each land plot satisfies the requirements of various stakeholders and are expressed as suitability functions based on attribute values. Attributes describe different characteristics of the land plots and are represented as layers on a digital terrain map. The criteria and their corresponding attributes are classified as either quantitative or binary. Binary criteria are aggregated using the minimum operator, which filters out plots that violate any constraints by assigning them a suitability score of zero. Quantitative criteria are aggregated using the second-order Choquet integral, a method that accounts for interdependencies among criteria while maintaining computational simplicity. The criteria were developed based on statistical and environmental data obtained from an analysis of the Samara region in Russia. The resulting suitability maps are visualized as gradient maps, where land plots are categorized according to their degree of suitability—from completely unsuitable to highly suitable. This visual representation facilitates intuitive interpretation and comparison of different location options. These maps serve as an effective tool for planners and stakeholders, providing comprehensive and objective insights into the potential of land plots while incorporating all relevant factors. The proposed approach supports spatial analysis and land use planning by integrating mathematical modeling with modern information technologies to address pressing challenges in sustainable development. Full article

As consumer awareness grows and regulations regarding the quality and safety of perishable goods become stricter, careful management of environmental conditions throughout the supply chain is becoming essential. Among these factors, storage temperature plays a crucial role in preserving the physicochemical characteristics of products. Therefore, an effective approach to ensure quality and safety up to the final customer is to continuously monitor the temperature within warehouses, using specific location-mapping techniques and stocking optimization methods. This study proposes a dynamic optimization model for the storage location assignment problem, integrating both temperature and humidity constraints into the placement of stock-keeping units. The model operates under a multi-period, multi-product framework and leverages real-time sensor data to account for spatial temperature stratification and environmental variability within the warehouse, contributing to the reduction in the energy consumption. Two alternative optimization strategies are explored: one focused on minimizing thermal and humidity stress, and another targeting the reduction in average storage cycle time. A detailed what-if analysis is conducted across three scenarios, varying warehouse fill rates and incoming load volumes, in order to prove the effectiveness of the proposed model in a real-data context. The results show that the approach minimizing environmental stress consistently outperforms traditional methods in quality-related metrics, maintaining superior objective function values. Full article

Pre-disaster and post-disaster allocation strategies are widely investigated as the single optimization problem in humanitarian supply chain management, while integrated decisions including the above two problems are seldom discussed in the existing literature. Here, this paper proposes a mixed-integer programming model to determine these decisions, including the location of central warehouses and emergency storage points and the quantities of relief items pre-deployed and distributed. Specially, two preferences regarding costs and cost-resilience are considered, and a comparison of two models concerning the above preferences is performed. The results are as follows: (i) When the impact of disasters is at a relatively low or moderate level, the cost-oriented model can reduce the government’s financial burden and increase the coverage of relief items. However, when the severity of the disaster is high, the cost resilience-oriented model can respond to the needs of victims within the shortest time, although these needs cannot be completely met. (ii) Increasing the initial inventory level of emergency storage points and enhancing the victims’ tolerance time through social support can effectively reduce the total costs, while increasing the transportation speed can effectively reduce the response delay time. (iii) Adjusting the unit penalty cost can make the total penalty costs and transportation costs decline within a certain range, but such an adjustment has no influence on the response delay time. This paper not only proposes an integrated framework for pre- and post-disaster allocation decisions but also highlights the importance of incorporating resilience into relief item allocation in disaster contexts. Full article

Warehouses are vital in linking production to consumption, often using a forward–reserve layout to balance picking efficiency and bulk storage. However, replenishing the forward area from reserve storage is prone to delays and congestion, especially during high-demand periods. This study investigates the strategic use of buffer areas—intermediate zones between forward and reserve locations—to enhance flexibility and reduce bottlenecks. Although buffer zones are common in practice, they often lack a structured decision-making framework. We address this gap by developing an optimization model that integrates demand forecasts to guide daily replenishment decisions. To handle the computational complexity arising from large state and action spaces, we implement an approximate dynamic programming (ADP) approach using certainty-equivalent control within a rolling-horizon framework. A real-world case study from an automotive spare parts warehouse demonstrates the model’s effectiveness. Results show that strategically integrating buffer zones with an ADP model significantly improves replenishment timing, reduces direct picking by up to 90%, minimizes congestion, and enhances overall flow of intra-warehouse inventory management. Full article

This study develops a calibrated multiscale simulation of three lightweight industrial warehouses located in Tecámac, Mexico, to evaluate the dual role of rooftop photovoltaic (PV) arrays as renewable energy generators and passive thermal modifiers. Dynamic energy models were developed using EnergyPlus via Ladybug Tools v. 1.8.0 and calibrated against 2021 real-world electricity billing data, following ASHRAE Guideline 14. Statistical analyses conducted in RStudio v2024.12.1 Build 563 confirmed significant passive cooling effects induced by PV integration, achieving up to 15.3 °C reductions in peak indoor operative temperatures and improving thermal comfort rates by approximately 10 percentage points. While operational energy savings were evident, the primary focus of this research was on the multiscale modeling of thermal performance enhancement in composite metallic-PV roofing systems under semi-arid climatic conditions. These results provide new insights into computational approaches for optimizing passive thermal performance in lightweight industrial envelopes. Full article

This paper examines the impact of two storage policies—dedicated storage (D-SLAP) and randomized storage (R-SLAP)—on warehouse operational efficiency. It integrates the Storage Location Assignment Problem (SLAP) with the unrelated parallel machine scheduling problem (UPMSP), which represents the scheduling of the material handling equipment (MHE). This integration is intended to elucidate the interplay between storage strategies and scheduling performance. The considered evaluation metrics include transportation cost, average waiting time, and total tardiness, while accounting for product arrival and demand schedules, precedence constraints, and transportation expenses. Additionally, considerations such as MHE eligibility, resource requirements, and available storage locations are incorporated into the analysis. Given the complexity of the combined problem, a tailored Non-dominated Sorting Genetic Algorithm (NSGA-II) was developed to assess the performance of the two storage policies across various randomly generated test instances of differing sizes. Parameter tuning for the NSGA-II was conducted using the Taguchi method to identify optimal settings. Experimental and statistical analyses reveal that, for small-size instances, both policies exhibit comparable performance in terms of transportation cost and total tardiness, with R-SLAP demonstrating superior performance in reducing average waiting time. Conversely, results from large-size instances indicate that D-SLAP surpasses R-SLAP in optimizing waiting time and tardiness objectives, while R-SLAP achieves lower transportation cost. Full article

This study addresses the multi-robot task allocation (MRTA) problem for logistics robots operating in zone-picking warehouse environments. With the rapid growth of e-commerce and the Fourth Industrial Revolution, logistics robots are increasingly deployed to manage high-volume order fulfillment. However, efficiently assigning tasks to multiple robots is a complex and computationally intensive problem. To address this, we propose a five-step optimization framework that reduces computation time while maintaining practical applicability. The first step calculates and stores distances and paths between product locations using the A* algorithm, enabling reuse in subsequent computations. The second step performs hierarchical clustering of orders based on spatial similarity and capacity constraints to reduce the problem size. In the third step, the traveling salesman problem (TSP) is formulated to determine the optimal execution sequence within each cluster. The fourth step uses a mixed integer linear programming (MILP) model to allocate clusters to robots while minimizing the overall makespan. Finally, the fifth step incorporates battery constraints by optimizing the task sequence and partial charging schedule for each robot. Numerical experiments were conducted using up to 1000 orders and 100 robots, and the results confirmed that the proposed method is scalable and effective for large-scale scenarios. Full article

The efficiency of emergency response operations is critically dependent on the strategic storage and allocation of emergency supplies. Proper management of these resources reduces economic impacts and ensures prompt availability in crises. This study addresses the challenges and existing practices in emergency reserve warehousing, with a specific focus on a Fangshan District case study. It introduces optimized storage strategies and principles for storage location assignment, taking into account both planar and three-dimensional storage configurations. The study employs two pallet types to establish basic assumptions and formulates two models: one for standard pallets in three-dimensional storage and another for special pallets in planar storage, including scenarios for their combined usage. Utilizing an advanced non-dominated genetic algorithm (NSGA-II) with an elite strategy, the study conducts simulations and analyses of these models under various scenarios. The findings indicate that the application of the second scenario significantly improves storage location optimization in emergency reserve warehouses. Full article

As product diversity continues to expand in today’s market, there is an increasing demand from customers for unique and varied items. Meeting these demands necessitates the transfer of different sub-product components to the production line, even within the same manufacturing process. Lean manufacturing has addressed these challenges through the development of kitting systems that streamline the handling of diverse components. However, to ensure that these systems contribute to sustainable practices, it is crucial to design and implement them with environmental considerations in mind. The optimization of warehouse layouts and kitting preparation areas is essential for achieving sustainable and efficient logistics. To this end, we propose a comprehensive study aimed at developing the optimal layout, that is, creating warehouse layouts and kitting preparation zones that minimize waste, reduce energy consumption, and improve the flow of materials. The problem of warehouse location assignment is classified as NP-hard, and the complexity increases significantly when both storage and kitting layouts are considered simultaneously. This study aims to address this challenge by employing the genetic algorithm (GA) and Ant Colony Optimization (ACO) methods to design a system that minimizes energy consumption. Through the implementation of genetic algorithms (GAs), a 24% improvement was observed. This enhancement was achieved by simultaneously optimizing both the warehouse layout and the kitting area, demonstrating the effectiveness of integrated operational strategies. This substantial reduction not only contributes to lower operational costs but also aligns with sustainability goals, highlighting the importance of efficient material handling practices in modern logistics operations. This article provides a significant contribution to the field of sustainable logistics by addressing the vital role of kitting systems within green supply chain management practices. By aligning logistics operations with sustainability goals, this study not only offers practical insights but also advances the broader conversation around environmentally conscious supply chain practices. Full article

The growing demand for bottled beverages has led to the search for optimal configurations that represent the lowest costs. Using crossdocking techniques reduces storage costs, these costs being the main ones in the logistics of distribution of products to the consumer. However, it is vitally important to consider the ergonomics of the workers who are subjected to the loading and unloading of products to meet the demands. Various ailments have been reported to the authorities, and it is imperative to address them for decision making. Likewise, the best arrangement of the products within these fast warehouses is associated with the relationship between the number of times a worker travels to pick up the product from the place where it is located to the loading area and the distance. In this work, the distribution from the production plants and the crossdocking to the distribution centers are proposed jointly and in each distribution center the best arrangement of the products is determined, as well as the ergonomics of those involved, considering the best scheme that represents the lowest cost. The results show the best distribution of products as well as the crossdocking that must be installed to meet the demands of the distribution centers. Full article

The Qinghai Plateau has a complex geographical environment and vast amounts of land with a sparse population, dispersed settlements, and a low traffic density. In the face of major disasters, the rational layout of emergency material reserve warehouses is crucial for reducing disaster losses, ensuring regional stability, and quickly restoring production and life. This paper starts by considering the rationality and timeliness of the location selection of provincial emergency material reserve warehouses, considering the distance costs of emergency material transportation on the Qinghai Plateau. By using a traffic accessibility analysis model combined with a location–allocation model and an L-A maximum coverage model, this study optimizes the location selection of emergency material reserve warehouses on the Qinghai Plateau. The research results show the following: (1) On the basis of the existing Golmud Depot and Chengxi Depot in Qinghai Province, it is necessary to add four more depots, i.e., the Yushu Depot, Gande Depot, Ping’an Depot, and Tongde Depot, to achieve the timely and efficient supply of emergency materials. (2) After the optimization, the layout of the six provincial emergency material reserve warehouses can achieve full coverage of Qinghai Province within 8 h in the event of major disasters, increasing the coverage by 20% compared to the original layout; the new plan allows for emergency material transportation to cover 87% of Qinghai Province within 4 h, an increase of 28% compared to before. (3) The optimized location selection plan for emergency material reserve warehouses saves 139 min of time costs, and the transportation efficiency is increased by 46% compared to the previous plan. The optimized location selection plan for emergency material reserve warehouses is instructive for the construction of emergency material reserve warehouses on the Qinghai–Tibet Plateau. Full article

Natural disasters (e.g., floods, earthquakes) significantly impact citizens, economies, and the environment worldwide. Due to their sudden onset, devastating effects, and high uncertainty, it is crucial for emergency departments to take swift action to minimize losses. Among these actions, planning the locations of relief supply distribution centers and dynamically allocating supplies is paramount, as governments must prioritize citizens’ safety and basic living needs following disasters. To address this challenge, this paper develops a three-layer emergency logistics network to manage the flow of emergency materials, from warehouses to transfer stations to disaster sites. A bi-objective, multi-period stochastic integer programming model is proposed to solve the emergency location, distribution, and allocation problem under uncertainty, focusing on three key decisions: transfer station selection, upstream emergency material distribution, and downstream emergency material allocation. We introduce a multi-armed bandit algorithm, named the Geometric Greedy algorithm, to optimize transfer station planning while accounting for subsequent dynamic relief supply distribution and allocation in a stochastic environment. The new algorithm is compared with two widely used multi-armed bandit algorithms: the $ϵ$-Greedy algorithm and the Upper Confidence Bound (UCB) algorithm. A case study in the Futian District of Shenzhen, China, demonstrates the practicality of our model and algorithms. The results show that the Geometric Greedy algorithm excels in both computational efficiency and convergence stability. This research offers valuable guidelines for emergency departments in optimizing the layout and flow of emergency logistics networks. Full article

This study explored the optimization of groupage intercity delivery using a two-stage genetic algorithm (GA) framework, developed with the BaumEvA Python library. The primary objective was to minimize the transportation costs by strategically positioning regional branch warehouses within a logistics network. In the first stage, the GA selected optimal branch warehouse locations from a set of candidate cities. The second stage addressed the vehicle routing problem (VRP) by employing a combinatorial GA to optimize the delivery routes. The GA framework was designed to minimize the total costs associated with intercity and last-mile deliveries, factoring in warehouse locations, truck routes, and vehicle types for last-mile fulfillment while ensuring capacity constraints are adhered to. By solving both line haul and last-mile delivery subproblems, this solution adjusted variables related to warehouse placement, cargo volumes, truck routing, and vehicle selection. The integration of such optimization techniques into the logistics workflow allowed for streamlined operations and reduced costs. Full article

Aimed at the high cost of cold chain distribution of fresh agricultural products within a specified time window, a joint optimization method based on a bi-level programming model for cold chain logistics is proposed for the location of front warehouses and distribution path planning. At the upper level of the bi-level programming model, k-means clustering analysis is used to obtain all accurate information about alternative locations for the front warehouse for site selection, thereby providing the corresponding foundation for the lower level algorithm. At the lower level of the model, a fusion algorithm of particle swarm optimization (PSO) and a genetic algorithm (GA) is used for solving. To accelerate the convergence speed of the population and lower the running time of the algorithm, the parameter values in the algorithm are determined adaptively. An adaptive hybrid algorithm combining the particle swarm optimization algorithm and the genetic algorithm (APSOGA) is used to reallocate the location information on backup points for the front-end warehouse, ultimately determining the facility location of the front-end warehouse and planning the end path from the front-end warehouse to the customer point, achieving joint optimization of the front-end warehouse’s location and path. A comparative analysis of algorithm optimization shows that using the APSOGA hybrid algorithm can reduce the total cost of the logistics network by 14.57% compared to a traditional single-algorithm PSO solution and reduce it by 5.21% compared to using a single GA. This proves the effectiveness of the APSOGA hybrid algorithm in solving location and path planning problems for cold chain logistics distribution companies. Full article