Depot Location Research Articles

An Integrated Approach for Ammunition Depot Location Selection and Ammunition Distribution Network Design Based on P-Median and Vehicle Routing Problems

During the war, the safe provision of the required ammunition at the desired place and time can affect the fate of the war and even the country. Determining the number and locations of ammunition depots needed in the war is as important as planning their distribution. Ammunition supply has always been a force multiplier for military units. Although the timely delivery of ammunition to the unit is not sufficient for success on its own, it is an important factor. These two issues are directly related to each other. This study presents a two-stage methodology to solve the problems in question. In the first stage, it was decided which of the 3 different types of depots, whose establishment locations were known with the p-median model, would be opened, and the unit-depot assignments were determined. In the second stage, the distribution network design for the distribution of ammunition from the ammunition depot to the deployed artillery units was realized with the Capacity-Constrained Vehicle Routing Problem (CCVRP) model. This methodology was tested for a fictitious situation where the geographical location and numerical information of the depots and artillery batteries were generated generically. By solving the mathematical model in the General Algebraic Modeling System (GAMS) program, it was determined which depot should distribute how many vehicles to which artillery batteries, and the results were presented visually.

Design of a reverse logistics network for medical waste considering location and routing decisions

Medical waste generated in healthcare facilities is categorized as hazardous due to its infectious, toxic, or radioactive properties, posing substantial risks to human health and the environment. This research proposes a bi-objective Mixed-Integer Linear Programming model for designing reverse logistics networks that enable both economical and safe medical waste management. Said model determines optimal quantity, depot locations, and routes for transporting waste from its generating points (hospitals) to the treatment and disposal sites (depots).This research addresses a bi-objective location routing problem, with multiple depots, time windows, capacity constraints, and variated waste types, across a multi-period horizon. It is innovative because no previous works have successfully dealt with such a combination of pragmatic features. The Location Routing Problem is an already well-known NP-Hard problem, with the additional aforementioned features further adding to its complexity. Thus, a genetic algorithm to solve large-sized realistic instances within reasonable computing times had been developed.Numerical experiments considered 20 random instances contrasting exact and approximate solution approaches. Also, a medical waste collection case study of a public hospital network in Atlántico Department, Colombia, delivering a Pareto frontier solution set, was introduced. The results demonstrated the efficacy of the proposed genetic algorithm in successfully addressing small instances of the problem, delivering outcomes comparable to the Mixed-Integer Linear Programming model. Furthermore, it yields good-quality solutions in a reduced computational time for larger instances deemed unfeasible for the Mixed-Integer Linear Programming model.This article contributes to both scientific literature and practitioners by presenting a decision-making tool designed to address the medical waste reverse logistics challenge under realistic scenarios. It approaches the issue from a bi-objective standpoint, jointly considering economic factors and biomedical safety. Additionally, the article introduces realistic and pertinent assumptions that have not been explored in previous literature.

A reinforcement learning guided hybrid evolutionary algorithm for the latency location routing problem

The latency location routing problem integrates the facility location problem and the multi-depot cumulative capacitated vehicle routing problem. This problem involves making simultaneous decisions about depot locations and vehicle routes to serve customers while aiming to minimize the sum of waiting (arriving) times for all customers. To address this computationally challenging problem, we propose a reinforcement learning guided hybrid evolutionary algorithm following the framework of the memetic algorithm. The proposed algorithm relies on a diversity-enhanced multi-parent edge assembly crossover to build promising offspring and a reinforcement learning guided variable neighborhood descent to determine the exploration order of multiple neighborhoods. Additionally, strategic oscillation is used to achieve a balanced exploration of both feasible and infeasible solutions. The competitiveness of the algorithm against state-of-the-art methods is demonstrated by experimental results on the three sets of 76 popular instances, including 51 improved best solutions (new upper bounds) for the 59 instances with unknown optima and equal best results for the remaining instances. We also conduct additional experiments to shed light on the key components of the algorithm.

ANALYSIS OF COST AND TIME EFFICIENCY IN CONTAINER DISTRIBUTION BETWEEN CONTAINER TRUCK AND FREIGHT TRAIN FROM INDUSTRIAL AREA TO PORT

Jakarta International Container Terminal (JICT) in Tanjung Priok Port , which serves export and import containers, can serve up to 5500 TEUs daily. However, no more than 1% of the total containers are transported by freight trains, meaning that 99% of container distribution from industrial areas is carried by trailer trucks. This condition causes a long queue at the container terminal entrance, resulting in delays in the services as well. Currently, Tanjung Priok Port is connected to rail transportation from Cikarang dry port with the frequency of twice a day with 30 flatcars (FC) for each trip. The capacity of one series of freight trains from Cikarang dry port to Tanjung Priok Port can reach 30 Feus or 60 Teus. However, the container distribution using freight trains is still less optimal because of the double handling at the JICT emplacement which is located outside JICT and the use of trailer trucks to move the containers from the area to JICT. The cost of transporting export and import containers always increases year by year because of the slow transportation due to inefficiency of the container depot location in Tanjung Priok area. In this research, the method used to determine the efficiency of container transportation is divided into three scenarios. The first scenario is when the container is fully transported by truck from the industrial area. The second scenario is when the container is transported by train and handled twice while entering JICT. Finally, the third scenario is when the JICT emplacement is located inside the JICT area, changing the business process of containers coming in and out, needing no stacking in the JICT container yard, relocating the container depot placement, consolidating containers, and automating equipment at JICT to reduce the cost and time of containers for export and import. After analyzing the costs and time of the three scenarios, it was found that the third scenario could reduce travel costs and shorten container time when exporting and importing. The 3rd scenario can reduce the cost by almost 50% from the 1st and 2nd scenarios due to container consolidation.

A lightweight genetic algorithm with variable neighborhood search for multi-depot vehicle routing problem with time windows

A multi-depot green vehicle routing problem with time windows considering customer satisfaction (MDGVRPTW-CS) was constructed. To optimize it, we proposed a lightweight genetic algorithm with variable neighborhood search (LGAVNS), which takes the genetic algorithm as the upper algorithm and the variable neighborhood algorithm as the neighborhood search algorithm. In our constructed algorithm, the neighborhood search operators were applied to the optimal gene instead of all genes. Additionally, an enhanced crossover operator was developed to facilitate the effective transmission of gene information. Finally, we extended and optimized the parameters of the algorithm. Experimental results demonstrated that our approach exhibited significant advantages in terms of optimization effectiveness and convergence speed compared to six state-of-the-art algorithms, particularly when addressing MDGVRPTW-CS in large-scale scenarios. Moreover, the lightweight nature of our algorithm can be utilized for optimizing both the number and location of depots while further exploring the optimization potential of MDGVRPTW-CS.

Determination of optimal depot location for a capacitated vehicle routing problem (CVRP) based on gross vehicle weight

The fuel consumption of a vehicle depends on its gross weight, which varies as goods are successively unloaded at each wholesaler site or distribution centre within a transportation network. There exist multiple routes and many wholesalers with different demand quantities within the network. Again, transportation cost varies according to the route and the depot location (DL) from which the vehicle departs. Hence, this article aims to determine the optimal DL for Ly-Foods Ltd.'s transportation network, utilising a capacitated vehicle to fulfil wholesalers’ demands, based on gross vehicle weight (GVW). First, we develop some analytical and mathematical models (mixed-integer programming-MIP) to determine the minimum transportation cost for each location. Second, we verify our models for other instances and compare our results with some heuristic results. We find the optimal location for the depot for the transportation network of Ly-Foods, which saves a maximum of 10.43 litres of fuel in a trip. This research holds significance for the manufacturing or distribution companies aiming to set up depots within a new transportation network. Furthermore, our models are applicable to transportation networks featuring multiple routes and numerous wholesalers, where the management is planning to shift DL with the changes in wholesalers’ demands.

Collaborative optimization of depot location, capacity and rolling stock scheduling considering maintenance requirements

Generally, when optimizing a rolling stock schedule, the locations of the depots, or places in the network where the composition changes and maintenance occurs, are assumed known. The locations where maintenance is performed naturally influence the quality of any resulting rolling stock schedules. In this paper, the problem of selecting new depot locations and their corresponding capacities is considered. A two-stage mixed integer programming approach for rolling stock scheduling with maintenance requirements is extended to account for depot selection. First, a conventional flow-based model is solved, ignoring maintenance requirements, to obtain a variety of rolling stock schedules with multiple depot locations and capacity options. Then, a maintenance feasible rolling stock schedule can be obtained by solving a series of assignment problems by using the schedules found in the first stage. The proposed methodology is tested on real-life instances, and the numerical experiments of different operational scenarios are discussed.

Differentiation of Adipose Tissue Mesenchymal Stem Cells into Endothelial Cells Depends on Fat Depot Conditions: Regulation by miRNA.

The development of obesity is associated with substantial modulation of adipose tissue (AT) structure. The plasticity of the AT is reflected by its remarkable ability to expand or reduce in size throughout the adult lifespan, which is linked to the development of its vasculature. This increase in AT vasculature could be mediated by the differentiation of adipose tissue-derived stem cells (ASCs) into endothelial cells (ECs) and form new microvasculature. We have already shown that microRNA (miRNA)-145 regulates the differentiation of ASCs into EC-like (ECL) cells. Here, we investigated whether ASCs-differentiation into ECs is governed by a miRNAs signature that depends on fat depot location and /or the metabolic condition produced by obesity. Human ASCs, which were obtained from white AT by surgical procedures from lean and obese patients, were induced to differentiate into ECL cells. We have identified that miRNA-29b-3p in both subcutaneous (s)ASCs and visceral ASCs and miRNA-424-5p and miRNA-378a-3p in subcutaneous (s)ASCs are involved in differentiation into EC-like cells. These miRNAs modulate their pro-angiogenic effects on ASCs by targeting FGFR1, NRP2, MAPK1, and TGF-β2, and the MAPK signaling pathway. We show for the first time that miRNA-29b-3p upregulation contributes to ASCs' differentiation into ECL cells by directly targeting TGFB2 in both sASCs and visceral ASCs. Moreover, our results reveal that, independent of sASCs' origin (obese/lean), the upregulation of miRNA-378a-3p and the downregulation of miRNA-424-5p inhibit MAPK1 and overexpress FGFR1 and NRP2, respectively. In summary, both the adipose depot location and obesity affect the differentiation of resident ASCs through the expression of specific miRNAs.

The Electric Vehicle Scheduling Problem for Buses in Networks with Multi-Port Charging Stations

As more and more cities try to reduce their CO2 emissions, public transport fleets are undergoing a transition from conventional to electric vehicles. To complete this shift, there is a need to build the required charging infrastructure. When the first electric buses were adopted, the charging stations were mostly built in the locations of large bus depots. However, in recent years, there has been a crowding problem in the charging stations resulting in queuing and unnecessary delays. In this study, we explore the potential of replacing single-port charging stations with multi-port charging stations that can serve multiple vehicles at once with a reduced charging rate. Because the charging rate reduces with the number of ports, we develop a mixed-integer linear program to determine the charging schedules of bus fleets in order to reduce the overall delays in the bus network. The novel formulation is tested in benchmark instances of various sizes demonstrating the improvement potential.

Facility Location Selection for Ammunition Depots based on GIS and Pythagorean Fuzzy WASPAS

The purpose of this study is to determine depot locations where expired ammunition will be controlled before being sent to recycling facilities. Expiration of ammunition means that using, transporting and even storing that ammunition where it is located poses a greater risk. For this reason, it is important to determine facility locations so that ammunition is stored in places that will least harm the environment and human health. The criteria to be used for ammunition depot location selection were determined through literature review, various researches and expert opinions. The proposed model is based on the combined use of Geographic information system (GIS) and multi-criteria decision making. For an example application of the model, a generic study on a district basis in Turkey is presented. Candidate depot locations were determined using GIS with the help of 6 main criteria and 18 sub-criteria. Then, candidate depot locations were ranked by the Pythagorean Fuzzy Set-based WASPAS (Weighted Aggregated Sum Product Assessing) method, taking into account the opinions of military experts for the main criteria. WASPAS method selected location A1 as the most suitable ammunition depot location. The results show that the proposed methodology can be practically applied.

Joint optimization of location and allocation for spare parts depots under ($ r, Q $) inventory policy

<p>The ability to replace failed spare parts in time directly affects the supportability level of equipment systems. The selection of spare parts' depot locations, inventory mode, and allocation are often separate and independent operations. However, in these situations, the total supply cost is usually relatively high with the consideration of spare parts shortage and maintenance delays. Therefore, this article dealt with a depot location-inventory-allocation problem based on the $ (r, Q) $ inventory method and analyzed a combined network of centralized spare part depot locations, inventory, and allocation. Meanwhile, considering the convenience and speed of spare parts transportation brought about by the improvement of transportation capacity, a network is proposed to adopt a centralized storage and point-to-point allocation strategy for parts replacement, which reduces supportability costs without affecting supply efficiency. An optimization model has been developed that reduces the overall cost of support, including inventory, construction, transportation, and logistics. Three equipment support efficiency metrics were used as constraints in this model to assess the location of open depots: selection availability, fill rate, and predicted downtime. Additionally, due to the knowledge asymmetry, there are some shortage issues which always lead to extra expenditure. The model also introduces uncertain distribution to demand measurement and adopts a genetic algorithm for model solving. Ultimately, a numerical instance was developed so as to verify our results.</p>

Dataset of U.S. School Bus Depots

This technical note describes the methods used to create a dataset of school bus depot locations in the United States. There are nearly half a million school buses in the country but almost no public information about where they are kept. Environmental justice literature describes how school bus depots may create air pollution hotspots for underserved communities that lead to health harms and may require grid infrastructure upgrades for future bus electrification.

Two-Echelon Electric Vehicle Routing Problem with Battery Swap Stations on Real Network

This research develops an advanced optimization framework for two-echelon electric vehicle routing problems. It tackles complex road networks, time windows, split deliveries, and battery swapping stations. By integrating optimal intermediate depot locations with efficient routing, the multi-objective model, solved using the epsilon constraints method in GAMS, addresses urban delivery challenges. The model manages larger goods volumes through split deliveries and enhances urban distribution logistics with electric vehicles, balancing sustainability and economic viability. Key features include managing electric vehicle routing limitations, ensuring timely deliveries, and optimizing split deliveries to reduce trips and congestion. The multi-objective approach minimizes travel distance, energy consumption, and costs while maximizing service levels. Strategically locating depots reduces travel distances and enhances service area coverage, improving network responsiveness. In conclusion, this research offers a holistic approach to urban logistics with electric vehicles, enhancing operational efficiency and contributing to environmental and economic goals. It provides valuable tools for decision-makers aiming to improve urban distribution networks.

Depot Location for a Multi Depots Transport Problem with Electric Vehicles

Depot Location for a Multi Depots Transport Problem with Electric Vehicles

Enhancing Post-Disaster Mapping Assessment: Agent-Based Simulation Modeling Integrating Ground Vehicles and Drones (Case Study: Mount Merapi's Volcanic Eruption)

This research presents an agent-based simulation model for post-disaster location mapping, considering land vehicles and drones along with access road availability and depot locations. The study examines the impact of bridge facility damage on depot selection and time indicators. Results reveal that damage to bridge facilities affects depots differently based on their location, leading to increased total processing and completion times due to interactions between land vehicles and bridges. Depot 7 emerges as the optimal location for undamaged and KRB II and III damage scenarios based on total processing time. Depot 3 performs best for KRB III damage, while Depot 8 exhibits the shortest completion time across all scenarios. These findings emphasize the importance of selecting depots with resilient road access and alternative routes, improving post-disaster logistics efficiency.

An exact algorithm for Two-Echelon Location-Routing problem with simultaneous pickup and delivery

This study considers an extension of the two-Echelon Location-Routing Problem (2E-LRP) namely two-Echelon Location-Routing Problem with Simultaneous Pickup and Delivery (2E-LRPSPD). A Mixed Integer Programming (MIP) formulation is developed and strengthened by the use of valid inequalities to solve the 2E-LRPSPD, and then a Branch and Cut-based exact algorithm (B&C) is presented. To improve the upper bounds, Iterated Local Search and Variable Neighborhood Search-based metaheuristic algorithms, namely, ILS-VNS and VNS, are developed and embedded into the B&C. Computational results, conducted on instances produced from the literature, demonstrate that the B&C yields tight lower and upper bounds, and also is capable of solving some instances optimally with up to 50 customers and 10 satellites in a reasonable time. Furthermore, sensitivity analysis reveals that the heuristic approaches and an increase in the runtime limit assist the algorithm to reach tight the upper bounds, whereas the number of potential depots does not have any effect on the performance of the algorithm. Besides, further computational analysis on the 2E-LRPSPD with a single depot, which is studied in the literature, shows that the B&C improves some best-known solutions while proving the optimality of some instances. Finally, the B&C is applied to a two-echelon supermarket distribution system in Türkiye. The computational results show that simultaneous pickup and delivery operations, as well as depot location decisions are critical elements to consider in the design of cost-effective distribution networks.

Volleyball premier league algorithm with ACO and ALNS for simultaneous pickup–delivery location routing problem

The reverse logistics problem, which involves customer returns and incurs high additional logistics costs, has become a topic of increasing interest. This problem is modeled as the location routing problem with simultaneous pickup and delivery under time constraints (LRPSPDTW), which is a more complex version of the location routing problem (LRP). Previous studies have shown that existing algorithms are unable to provide satisfactory solution accuracy for this problem. In order to address this, a novel algorithm called the hybrid volleyball premier league (HVPL) algorithm is proposed. Drawing inspiration from the competition mechanism of the original volleyball premier league (VPL) algorithm, the HVPL algorithm incorporates adaptive large neighborhood search (ALNS) and improved ant colony optimization (ACO) as sub-algorithms, allowing them to work together synergistically and strike a balance between exploration and exploitation. The learning and elimination phases of the VPL algorithm have been redesigned to enhance convergence speed. Furthermore, the Bellman’s algorithm has been enhanced to determine depot locations and construct vehicle paths for the solution. Prior to solving the LRPSPDTW problem, benchmark testing was conducted using 35 latest benchmark functions. Experimental results and Friedman mean rank test demonstrated that HVPL exhibits superior exploration and exploitation capabilities. HVPL was subsequently tested on 66 instances based on the modified Solomon’s benchmark and the NEO research group benchmarks, and the results showcased its outperformance over other methods in the majority of cases.

Integrated Planning for Depot Location and Line Planning Problems in the Intercity Railway Network with Passenger Demand Uncertainty

In this study, we present a mathematical model and solution approach for addressing the robust integrated intercity railway depot location and line planning problem (RIDLLPP), which encompasses the initial two stages of the railway planning process. Our primary objective is to identify depot locations that exhibit robustness across a range of likely future scenarios, and this is achieved by incorporating line planning decisions. The model focuses on five critical strategic determinations, namely the depot location, depot storage capacity, line operation, passenger assignment, and fleet allocation. To tackle this complex problem, we propose an iterative solution framework that combines the Differential Evolution (DE) algorithm with improved rounding heuristics (DE-IRH). To evaluate the effectiveness of our framework, we conduct a comparative analysis with the Gurobi solver using multiple medium-sized artificial instances. The results demonstrate that our proposed framework achieves an optimality gap of 4.87% while requiring less computational time. Furthermore, we validate the robustness of the model’s location choices across various input scenarios, thereby providing valuable insights for transportation planning agencies and railway companies that can inform their decision-making processes.

Municipal solid waste management using multiple disposal location-arc routing and waste segregation approach: a real-life case study in England

ABSTRACT In the realm of municipal operations, the effective management of municipal solid waste (MSW) stands out as a pivotal undertaking. It necessitates substantial allocations of fixed and variable resources and financial investments. The bulk of these expenditures are associated with the operational facets encompassing waste collection, transportation, and disposal. This research delves into the examination of multiple Disposal Location Arc Routing Problems (LARP) while considering vehicle capacity limitations and the incorporation of waste segregation. The LARP model is designed to identify the optimal locations for depots and three waste disposal sites. The optimization objectives and constraints applied to the LARP model are geared toward enhancing waste collection efficiency and minimizing costs. Additionally, a triangular fuzzy parameter is introduced to represent the demand. To put this model to the test, a real-world case study in the UK is explored to evaluate its performance and practicality. Finally, a series of sensitivity analyses are conducted, offering valuable managerial insights under varying conditions. The inclusion of waste segregation in this waste management model holds considerable significance for managers. This is particularly relevant because it proposes a more effective strategy for waste management when dealing with diverse types of waste.

Optimization of herbaceous feedstock delivery to a network of supply depots for a biorefinery in the Piedmont, USA

AbstractThe southeastern USA has the potential to be a significant producer of bio‐based products; however, research is still needed to demonstrate the most cost‐effective feedstock delivery system for this region. A logistic system that has shown promise is one utilizing a network of supply depots. This study calculated the cost to produce a stream of size‐reduced herbaceous biomass (i.e., switchgrass) for five theoretical depots in the Piedmont province. Three depots were located in south central Virginia and two in north central North Carolina. A logistics system with a 20‐bale handling unit was used for load‐out operations at 199 theoretical satellite storage locations (SSLs) within a 48 km radius of each depot location. The distribution of potential production fields and the transportation distance from SSLs to the depots were determined with spatial and network analyses. Based on an analysis of potential land cover available for feedstock production, the annual capacity per depot ranged from 80 839 to 170 830 Mg, resulting in a total annual capacity of 555 195 Mg for all five depots. Cost to deliver feedstock for 24/7 operation, 48 weeks per year ranged from 46.03 to 62.86 USD Mg−1 annual capacity. At the low end, these costs were: SSL operation (22%), truck (29%), receiving facility (26%), and debaling‐size‐reduction (23%). The principle economy‐of‐scale factors were the receiving facility and debaling‐size‐reduction costs. To minimize per‐Mg cost, depot capacity should be chosen such that equipment can be operated as close to 80% of design capacity as possible.