Depot Location Research Articles

Optimal design and operation of a second-generation biofuels supply chain

This article investigates how climate influences the value of adding preprocessing depots to a second-generation biorefinery’s supply chain. This is vital because humidity determines the amount of dry matter loss—exponential decay of energy content—suffered by biomass stored without preprocessing. The large volume of biomass required to fuel a biorefinery poses challenges in storage and in transportation. Further complications arise because the biomass is produced seasonally by hundreds of growers. Thus, recent failures of biorefineries may have been avoided, and future success may be achieved, by adding an intermediate layer to a biorefinery’s supply chain. A rigorous climate-based analysis of cost functions for each potential grower/depot pair leads to a stochastic program that optimizes the locations of depots, the assignment of growers to depots, and the volume of biomass stored fieldside vs. the volume stored as pelleted feedstock at depots. A computational study reveals that the humidity of the climate has much greater influence on the value of adding pre-processing depots to a second-generation biofuel supply chain than does transportation consolidation or any other parameter, endogenous or exogenous. Under favorable circumstances, our process reduces a biorefinery’s costs by over 30%, on average.

Optimal depot locations for humanitarian logistics service providers using robust optimization

When a disaster strikes an area, often international assistance is requested to help responding to and recovering from the disaster. The response is often characterized by the dispatch of relief items to the affected regions via depots from Humanitarian Logistics Service Providers (HLSPs). One of the challenges an HLSP faces is how to organize its network of depots. Important questions are how many depots should be opened and what should their locations be? We develop a method that uses historical data to determine depot locations that minimize the cost of transportation and minimize the maximum response time to a disaster. We examine the trade-off between the two objectives using the Pareto front. Furthermore, we use robust optimization to find solutions that are robust against uncertainty in the location and scale of future disasters. We provide a case study of the United Nations Humanitarian Response Depot (UNHRD), a large globally operating HLSP. Among other things, we compute potential cost savings that could be obtained when expanding the network with additional depots. Also, we show the need for using robust optimization when looking for solutions that are robust against the location and scale of future disasters. We generalize the approach by applying it to the independent disaster database EM-DAT, in which we focus on examining the added value of using a robust optimization framework. The first result holds for all Uncapacitated Facility Location Problems (UFLP) with uncertain demand, which are used to find the optimal number of depots. We conclude that incorporating the likely advantage of larger networks having less uncertainty in the expected cost of transportation, enhances the decision-making regarding the optimal number of depots. Even if at some point increasing the number of depots allowed to be opened may not decrease the expected cost of transportation significantly, it is likely to reduce the uncertainty in this expected cost of transportation. For the second result, we consider UFLP used for finding optimal locations of a given number of depots. We conclude that solutions based on nominal and robust optimization may perform similar on individual years that are not included in the optimization, but robust solutions guarantee a smaller cost of transportation for worst case scenarios. This uncertainty reduction is especially valuable when the solution has to be robust against extreme scenarios.

A Balanced Strategy for the FFBS Operator Integrating Dispatch Area, Route, and Depot Based on Multimodel Technologies

Bicycle scheduling is the essential strategy for balancing the demand for the public bicycle system (PBS). Existing literature pays more attention to bike scheduling models and their solutions, but seldom discusses the dispatch area and depot center. Reasonable dockless public bicycle dispatch area and optimal dockless bike dispatch depot location in the service area were discussed from the existing shared bicycle operation data in this paper. We proposed a feasible framework including bike trip network segmentation, mean-shift clustering based on the point position, VRP model, genetic algorithm, and TOPSIS evaluation method. The effectiveness and superiority of the division of the dispatch area are verified. The main evidence for this article is (1) although the cycling networks of bicycles are different at different times of the day, the results of community division are relatively stable and have great similarities. (2) The plan of the dispatch area has impacted on the operation efficiency of the PBS. For a scheduling area, the target value of the optimal scheduling strategy corresponding to different dispatch centers is obviously different. Therefore, the location of the dispatch center has a great impact on the quality of the scheduling strategy. The dispatch area determined by bike trip OD community detection has stable characteristics of scheduling costs. (3) This work is an attempt to combine big data and model technology to assist city management. We build a feasible framework to serve a balanced strategy for FFBS which can provide reasonable dispatch area, optimal dispatch depot location, dispatch truck’s route length, load action, and time window. Our proposed framework provides new ideas for regional traffic dispatching for the traffic management department and FFBS operator, which has certain practical reference significance.

A two stage stochastic programming for asset protection routing and a solution algorithm based on the Progressive Hedging algorithm

In this paper, a two-stage stochastic programming model is developed for the asset protection routing problem (APRP) to be employed in anticipation of an escaped wildfire. In this model, strategic and tactical decisions are considered in a two-stage setting. The locations of protection depots are determined, taking into account the routing decisions under different possible scenarios. To solve the proposed model, the Frank–Wolfe Progressive Hedging decomposition approach is employed. A realistic case study set in south Hobart, Tasmania, is considered. In this study, the scenarios for uncertain parameters are generated based on real data, considering different sources of uncertainties such as wind direction and speed and total monthly rainfall. Computational experiments have been conducted to demonstrate the solution algorithm’s efficiency in solving the asset protection routing problem with a two-stage stochastic framework. The numerical results suggest that more assets with higher values can be protected by considering the proposed two-stage stochastic programming model. The value of the approach is particularly significant where resources are limited, and uncertainty levels are high. Moreover, the model and solution procedure can be applied to other disaster situations in which protection activities occur.

The nth-plant scenario for blended feedstock conversion and preprocessing nationwide: Biorefineries and depots

The sustainability of the biofuel industry depends on the development of a mature conversion technology on a national level that can take advantage of the economies of scale: the nth-plant. Defining the future location and supply logistics of conversion plants is imperative to ultimately transform the nation’s renewable biomass resources into cost-competitive, high-performance feedstock for production of biofuels and bioproducts. Since the US has put restrictions on production levels of conventional biofuels from edible resources, the nation needs to plan for the widespread accessibility and development of the cellulosic biofuel scenario. Conventional feedstock supply systems will be unable to handle cellulosic biomass nationwide, making it essential to expand the industry with an advanced feedstock supply system incorporating a distributed network of preprocessing depots and conversion plants, or biorefineries. Current studies are mostly limited to designing supply systems for specific regions of the country. We developed a national database with potential locations for depots and biorefineries to meet the nation’s target demand of cellulosic biofuel. Blended feedstock with switchgrass and corn stover (harvested by either a two- or three-pass method) are considered in a Mixed Integer Linear Programming model to deliver on-spec biomass that considers both, a desired quantity and quality at the biorefinery. The model solves for a network of varying size depots that supply to biorefineries of 725,000 dry tons/year. A total delivered feedstock cost that is less than $79.07/dry tons (2016$) is evaluated for years 2022, 2030, and 2040. In 2022, 124 depots and 59 biorefineries could be supplied with 42.8 million dt of corn stover and switchgrass. In 2030 and 2040, the total accessible biomass could increase to 215% and 393% respectively when compared to 2022. However, an $8/dry tons reduction in targeted delivery cost could reduce total accessible biomass by 67%. Kansas, Nebraska, South Dakota and Texas were identified as potential states with a strong biofuel economy given that they had six or more biorefineries located in all scenarios. In some scenarios, Colorado, Alabama, Georgia, Minnesota, Mississippi and South Carolina would greatly benefit from a depot network as these could only deliver to a biorefinery in a nearby state. To elaborate the impact of a nationwide consideration, the findings were compared with existing literature for different US regions. We also present results for biorefinery capacities that are double, triple and quadruple in size.

MAPPING OF RESTAURANT DISTRIBUTION IN THE WONOKROMO DISTRICT AREA USING GOOGLE PLATFORM

Geographic Information System (GIS) or Geographic Information System (GIS) is a technology that is a tool and is essential for storing, manipulating, analyzing, and displaying natural conditions and often functions as a way to map an area with the help of data. attribute and spatial. The city of Surabaya is one of the big cities in Indonesia with a population of around 2,874,314 people in 2020. With a population of 2,874,314 people, many Surabaya residents meet their daily needs by trading, one of which is by opening a restaurant or depot. restaurant. There are so many restaurants or restaurant depots scattered in the city of Surabaya, as time goes on, there are more restaurants or restaurant depots and there are also more flavors, special foods, and so on. On this occasion, I tried to map several restaurants or restaurant depots in the city of Surabaya, one of which is in the Wonokromo sub-district. In this research, I use an application that is very easy to use in creating a path map that will usually be connected frequently to track the path from a certain point. The application is Google My Maps, the application will be used to create a path from the starting point to the end point. The purpose of this research is to help someone to find out the location of restaurant points or restaurant depots that are rarely known in the Wonokromo sub-district.

A hybrid variable neighborhood search approach for the multi-depot green vehicle routing problem

The Multi-Depot Green Vehicle Routing Problem (MDGVRP) is an extension of the well-known Green Vehicle Routing Problem (GVRP) where a fleet of alternative fuel-powered vehicles (AFVs) are used to serve the customers. GVRP consists of determining AFV tours such that the total distance travelled is minimum. The AFVs depart from the depot, serve a set of customers, and complete their tours at the depot without exceeding their driving range and the maximum tour duration. AFVs may refuel en-route at public refueling stations. In MDGVRP, the AFVs are dispatched from different depot locations and may refuel during the day at any depot or refueling station. We formulate MDGVRP as a mixed integer linear programming model and develop a hybrid General Variable Neighborhood Search and Tabu Search approach by proposing new problem-specific neighborhood structures to solve the problem effectively. We assess the performance of our method using the GVRP dataset from the literature. Our results show that the proposed method can provide high quality solutions in short computation times. Then, we extend these instances to the multi-depot case and compare our solutions for small-size instances with the optimal solutions. We also report our results for large-size problems and investigate the trade-offs associated with operating multiple depots and adopting different refueling policies to provide further insights for both academicians and practitioners.

Trajectory estimation model for a solid body with an irregular shape undergoing extremely high aerodynamic forces

A generalized (6DOF) model for evaluating fragment trajectory elements is defined, which incorporates a novel model for estimating the projected surface of the body and novel model for estimating aerodynamic force and moment. This 6DOF model is developed on the basis of differential equations of the center of mass motion and movement around the center of mass (currently no known model incorporates movement of the body around its center of mass), and can model the parameters that play an essential role in movement of the bodies with irregular shape through the atmosphere. In our model the basic parameters (i.e. body dimensions) can be arbitrarily changed in the initial part of the analysis, and based on their values and values of initial kinematic parameters (initial velocity, position, orientation), trajectories can be determined (as well as other parameters: velocities, orientation) in a relatively short amount of time. The calculation of the complete trajectory of the fragments can be used in a number of applications: the analysis of the effects of the fragments (i.e. the safety analysis of the location of the ammunition depots, due to the potential explosion of the projectile) or in the estimation of a danger zones when demining larger quantities of the munition. Also, from the point of view of the parameters of the lethal zone of HE projectiles, it is generally important to estimate the trajectory of the fragments in the range up to 50m, so this model can be used to model such a scenario also. This model could also be potentially used wherever there is a need to calculate flight mechanics parameters of irregularly shaped bodies. Generalized (6DOF) model for estimation of an irregularly shaped body trajectory is implemented in a computer program, written in MatLab. Based on the model, the trajectory calculations were performed for the complete trajectory and for shorter distances to the center of the explosion, with varied geometric-inertial parameters and initial kinematic conditions for the given fragment.

Trajectory estimation model for a solid body with an irregular shape undergoing extremely high aerodynamic forces

A generalized (6DOF) model for evaluating fragment trajectory elements is defined, which incorporates a novel model for estimating the projected surface of the body and novel model for estimating aerodynamic force and moment. This 6DOF model is developed on the basis of differential equations of the center of mass motion and movement around the center of mass (currently no known model incorporates movement of the body around its center of mass), and can model the parameters that play an essential role in movement of the bodies with irregular shape through the atmosphere. In our model the basic parameters (i.e. body dimensions) can be arbitrarily changed in the initial part of the analysis, and based on their values and values of initial kinematic parameters (initial velocity, position, orientation), trajectories can be determined (as well as other parameters: velocities, orientation) in a relatively short amount of time. The calculation of the complete trajectory of the fragments can be used in a number of applications: the analysis of the effects of the fragments (i.e. the safety analysis of the location of the ammunition depots, due to the potential explosion of the projectile) or in the estimation of a danger zones when demining larger quantities of the munition. Also, from the point of view of the parameters of the lethal zone of HE projectiles, it is generally important to estimate the trajectory of the fragments in the range up to 50m, so this model can be used to model such a scenario also. This model could also be potentially used wherever there is a need to calculate flight mechanics parameters of irregularly shaped bodies. Generalized (6DOF) model for estimation of an irregularly shaped body trajectory is implemented in a computer program, written in MatLab. Based on the model, the trajectory calculations were performed for the complete trajectory and for shorter distances to the center of the explosion, with varied geometric-inertial parameters and initial kinematic conditions for the given fragment.

Optimum Location Analysis for an Infrastructure Maintenance Depot in Urban Railway Networks

Urban railway networks have an important role in increasing the mobility of urban populations while decreasing congestion and pollution. Railway network extension that is caused by urban growth requires additional specialists and equipment for the operation, which causes an increase in maintenance cost. A comprehensive review of the literature regarding the application of facility location problem (FLP) in the railway domain found that there are few studies, and most of the current research involves the maintenance of railway vehicles only. In this paper, the purpose of this study is to determine the optimal location of infrastructure maintenance depot (IMD) for storing special vehicles and to support the inspection and maintenance tasks in a timely and efficient manner. For a challenging problem that should reflect the characteristic that traffic load and the deterioration of railway tracks are proportional, we proposed a method of applying maintenance weights to the p-hub median problem. To examine the proposed model, the test network was constructed by collecting information on the Seoul metropolitan subway. The results of case studies show that the number and location of IMDs are significantly affected by maintenance demand and expanded network.

An approach based on Clustering AH and VND for Supply Chain Management

In this work, we present a new approach to solve the Capacitated Location-Routing Problem (CLRP). The aim of this method is to determine the depot locations, to assign customers to facilities and to define routes for each depot to serve the associated clients. The proposed approach contains two phases, which are the constructive phase and the improvement phase. In the first phase, we select the depots to be opened, allocate the customers to open depots using a Hierarchical Ascendant (HA) method and we solve the vehicle routing problem for each depot using Sweep algorithm. In the second phase, we apply a Variable Neighborhood Descent (VND) with three structures in order to optimize the cost obtained by the first phase. Two sets of well-known instances from the literature are used to evaluate the performance of this method, and the numerical results obtained are compared with the experimental results of other methods. Results show that our method is competitive with respect to the best-known solutions (BKSs) and demonstrate its efficiency in comparison with other approaches.

A solution approach for multi‐trip vehicle routing problems with time windows, fleet sizing, and depot location

AbstractWe present a solution approach for a multi‐trip vehicle routing problem with time windows in which the locations of a prescribed number of depots and the fleet sizes must also be optimized. Given the complexity of the task, we divide the problem into subproblems that are solved sequentially. First, we address strategic decisions, which are solved once and remain constant thereafter. Depots are allocated by solving a p‐median problem and fleet sizes are determined by identifying the vehicle requirements of several worst‐case demand instances. Then, we address the operational planning aspect: optimizing the vehicle routes on a daily basis to satisfy the fluctuating customer demand. We assign customers to depots based on distance and “routing effort,” and for the routing problem we combine a tailor‐made branch‐and‐cut algorithm with a heuristic consisting of a route construction phase and packing of routes into vehicle trips. Our strategic decision models are robust in the sense that when applied to unseen data, all customers could be visited with the allocated fleet sizes and depot locations. Our operational routing methods are both time and cost‐effective. The exact method yields acceptable optimality gaps in 20 min and the heuristic runs in less than 2 min, finding optimal or near‐optimal solutions for small instances. Finally, we explore the trade‐off between depot and fleet costs, and routing costs to make recommendations on the optimal number of depots. Our solution approach was entered into the 12th AIMMS‐MOPTA Optimization Modeling Competition and was awarded the first prize.

Railway capacity estimation considering vehicle circulation: Integrated timetable and vehicles scheduling on hybrid time-space networks

Railway capacity is a vague concept, related to the possibility to run a maximal transport performance given a set of available resources. While most approaches focus only on infrastructure resources (i.e. availability of train paths), we include both infrastructure and vehicle resources in a capacity estimation problem. We study the railway capacity estimation problem applying an associated timetable saturation method; in other words, the capacity is related to a timetable where no additional trains can be added. We use optimization methods to find such a timetable integrating explicitly variables and constraints from vehicle circulation. A hybrid time–space network describes the integrated timetabling and vehicles scheduling problem, based on which an integer programming model can be formulated, to maximize the overall transportation performance. A Lagrangian relaxation-based decomposition algorithm is proposed to solve the problem, and is shown able to scale to large instances efficiently. The integrated scheduling problem is decomposed into a timetabling sub-problem and a vehicle circulation sub-problem by dualizing the consistency constraints linking the two. A new heuristic, based on the concept of timetable intensity, is employed to improve the quality of the feasible (non-relaxed) solution found. The experimental result shows the benefit of the approach, which can evaluate transportation performance and relate it to various fleet sizes, vehicle depot locations, and minimum headways.

Location of depots and allocation of buses to depots in urban road transport organisations: a mathematical model and greedy heuristic algorithm

Optimising the cost of operations is one of the major issues in any urban road transport organisations (URTOs). In this study, a decision problem on location of depots (adding new locations and removing existing ones) and allocation of buses to depots is considered, as observed in one of the major URTOs in India. The main focus of this research is to provide analytic methods to minimise the cost of operations comprising: 1) dead-kilometre cost; 2) fixed cost associated with introducing new depots; 3) salvage value due to closing the depots. To do so, a (0-1) mixed integer linear programming (MILP) model is proposed and its workability is demonstrated. In addition, a simple greedy heuristic algorithm is also proposed. A computational experiment is developed to understand the performance efficiency of the proposed greedy heuristic algorithm in comparison with the optimal solution. From the average and worst case analyses of the performance evaluation, it is observed that the proposed greedy heuristic algorithm provides near-optimal solution. The (0-1) MILP model and the efficient greedy heuristic algorithm proposed in this study can be used to help make better decisions on location of depots and allocation of buses to depots of URTOs in general.

Location of depots and allocation of buses to depots in urban road transport organisations: a mathematical model and greedy heuristic algorithm

Optimising the cost of operations is one of the major issues in any urban road transport organisations (URTOs). In this study, a decision problem on location of depots (adding new locations and rem...

A Hybrid Large Neighborhood Search Algorithm for Solving the Multi Depot UAV Swarm Routing Problem

This paper focuses on a modified Multi-Depot Unmanned Aerial Vehicle Routing Problem (MMDUAVRP). Comparing to classic multi-depot vehicle routing problem, our studied problem has no constraints to restrict the depot where the Unmanned Aerial Vehicle (UAV) departs and returns. This work aims to minimize the number of UAVs and total distance traveled by all UAVs. This problem is mathematically formulated in this paper and a heuristic-assignment based hybrid large neighborhood search(HLNS) is proposed to solve it. Extensive computational experiments are conducted to verify the performance of HLNS. The HLNS algorithm was first tested on Multi Traveling Salesman Problem which is a simplified version of the MMDUAVRP, and high quality solutions have been obtained. Experimental results by compared with CPLEX and other well-known algorithms suggest that our proposed algorithm provides better solutions within a comparatively shorter period of time. In addition, we also conduct sensitivity analysis on the location of depots and task points that may affect the total cost of solution.

Case Study of Transportation Benefits Using GIS in Distributed Preprocessing of Corn Stover into Crude Biobutanol

HighlightsTotal transportation costs were reduced by 32% to 63% with distributed biobutanol depots.An 8 km distance to the depot manifested the most desirable transportation costs.Across regions, biomass transport costs from field to depot were similar at equivalent distances.Abstract. The transportation efficiencies of centralized biomass processing facilities were compared to a proposed distributed preprocessing network with centralized refining facilities. Centralized biomass processing was defined as transport of baled corn stover directly from the field to the refinery. Distributed preprocessing with centralized refining was defined as transport of baled corn stover from the field to a biobutanol preprocessing depot and transport of completely dewatered crude biobutanol solution from the depot to a centralized refinery. For both systems, the locations of the corn fields, as identified through the cropland data layer, and of the refinery were fixed. For the distributed system, the biobutanol depot locations were variable and depended on different maximum transport distances (8 to 80 km) from the field to the depot. In this case study, site-specific transportation costs and biobutanol production capacities were developed for different agricultural regions in Kentucky. The distributed system produced a 32% to 63% reduction in total transportation cost with decreased (50% to 90%) fuel use as compared to the centralized system. The GIS transportation model demonstrated that on-farm biofuel production could be an effective means of producing biofuel and reducing transportation costs. Keywords: Biomass transport, Depot, Distributed biomass collection, GIS location-allocation, Minimize facilities, Satellite facilities.

The traveling salesman problem with release dates and drone resupply

This paper introduces the Traveling Salesman Problem with Release Dates and Drone Resupply, which consists of finding a minimum time route for a single truck that can receive newly available orders en route via a drone sent from the depot. We assume that each order’s release date is known at the time of delivery planning. This context is common for many applications, notably last-mile logistics. We develop a Mixed-Integer Linear Program and a solution approach for larger instances based on decomposing the problem into the truck-routing and the drone-resupply decisions. Numerical experiments show that using drones for resupply can reduce the total delivery time by up to 20%. Additionally, experiments show that the decomposition can rapidly obtain high-quality solutions. For instances of 10 and 15 customers, the decomposition solved the majority to optimality, with a trivial gap. For larger instances, this approach provided lower delivery times than a traditional parcel delivery system using a truck only. Investigations on the effects of drone speed, drone capacity, depot location, constraint addition, and allowing the truck to return to the depot are studied. We consider instances up to 50 customers.

Machine-Learning Methods to Select Potential Depot Locations for the Supply Chain of Biomass Co-Firing

Coal is the second-largest source for electricity generation in the United States. However, the burning of coal produces dangerous gas emissions, such as carbon dioxide and Green House Gas (GHG) emissions. One alternative to decrease these emissions is biomass co-firing. To establish biomass as a viable option, the optimization of the biomass supply chain (BSC) is essential. Although most of the research conducted has focused on optimization models, the purpose of this paper is to incorporate machine-learning (ML) algorithms into a stochastic Mixed-Integer Linear Programming (MILP) model to select potential storage depot locations and improve the solution in two ways: by decreasing the total cost of the BSC and the computational burden. We consider the level of moisture and level of ash in the biomass from each parcel location, the average expected biomass yield, and the distance from each parcel to the closest power plant. The training labels (whether a potential depot location is beneficial or not) are obtained through the stochastic MILP model. Multiple ML algorithms are applied to a case study in the northeast area of the United States: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Multi-Layer Perceptron (MLP) Neural Network. After applying the hybrid methodology combining ML and optimization, it is found that the MLP outperforms the other algorithms in terms of selecting potential depots that decrease the total cost of the BSC and the computational burden of the stochastic MILP model. The LR and the DT also perform well in terms of decreasing total cost.

Routing automated lane-guided transport vehicles in a warehouse handling returns

Faced with high return rates, many e-commerce retailers are considering novel technical solutions to expedite the processing of returned items in their warehouses. One such solution consists of lane-guided transport (LGT) vehicles. These small, electric vehicles follow optical markers on the floor, picking up boxes of returned items at a depot and dropping them off at workstations, releasing the logistics workers to focus on the productive task of actually processing the items instead of carrying them through the warehouse. These types of systems are simple to set up from a technical perspective; however, the routes on the warehouse floor still need to be carefully planned. This gives rise to the following routing problem. Given a set of stations to be served from multiple depots by a fleet of LGT vehicles, which stations doing what type of work should be visited on what route? Only one route per depot is allowed, but multiple vehicles may use the same route. Moreover, since routes cannot be changed on short notice, we consider an infinite planning horizon where the demand rate of the stations depends on the type of work they are assigned to do (e.g., handling defective items or refurbishing). We develop a decomposition heuristic, which solves instances derived from industry data to near-optimality in less than a minute. We also show that the depot location is rather unimportant for the overall system performance, but that the depot count can have a significant influence.