Depot Location Research Articles

Environmental assessment of a product-service system for renting electric-powered tools

To address increasing environmental sustainability concerns among consumers, many companies have developed approaches to provide functions, rather than products through product-service systems (PSS). This study evaluates a use-oriented tool rental service from Husqvarna, called ‘Tools for you,’ with the aim to identify critical processes to improve the sustainability of the offering. The environmental implications of the system are assessed using life cycle assessment for the annual service of one electric chainsaw and compared with a conventional sales alternative. The results suggest that rental service is influenced extensively by the location of the rental depot. Furthermore, while the impacts of the product and accessories, infrastructure, waste management, and use are reduced compared to the sales alternative, their contribution is only minor compared to environmental impacts from transportation. The results are also sensitive to the methodological choices, where the lifetime of the products, data choices, transportation assumptions, maintenance intervals, and other user-related variables for the use of the products have a significant influence on the results. The conclusions confirm and extend previous assertions on the challenges of applying LCA to PSS and add to the emerging literature on sustainable business models through empirical evidence from a case study. The findings also point to the holistic insights required to optimize the potential environmental sustainability of the services for Husqvarna and other retailers interested in adopting use-oriented business models. Future research could focus on the geographical differences of the rental lockers worldwide, models for optimizing their location, in addition to further input on user behavior, and the role of refurbishment and remanufacturing for more robust analyses of the sustainability of PSS offerings.

A Discrete Particle Swarm Optimization to Solve the Put-Away Routing Problem in Distribution Centres

Put-away operations typically consist of moving products from depots to allocated storage locations using either operators or Material Handling Equipment (MHE), accounting for important operative costs in warehouses and impacting operations efficiency. Therefore, this paper aims to formulate and solve a Put-away Routing Problem (PRP) in distribution centres (DCs). This PRP formulation represents a novel approach due to the consideration of a fleet of homogeneous Material Handling Equipment (MHE), heterogeneous products linked to a put-away list size, depot location and multi-parallel aisles in a distribution centre. It should be noted that the slotting problem, rather than the PRP, has usually been studied in the literature, whereas the PRP is addressed in this paper. The PRP is solved using a discrete particle swarm optimization (PSO) algorithm that is compared to tabu search approaches (Classical Tabu Search (CTS), Tabu Search (TS) 2-Opt) and an empirical rule. As a result, it was found that a discrete PSO generates the best solutions, as the time savings range from 2 to 13% relative to CTS and TS 2-Opt for different combinations of factor levels evaluated in the experimentation.

Route Planning for Agricultural Machines with Multiple Depots: Manure Application Case Study

Capacitated field operations involve input/output material flows where there are capacity constraints in the form of a specific load that a vehicle can carry. As such, a specific normal-sized field cannot be covered in one single operation using only one load, and the vehicle needs to get serviced (i.e., refilling) from out-of-field facilities (depot). Although several algorithms have been developed to solve the routing problem of capacitated operations, these algorithms only considered one depot. The general goal of this paper is to develop a route planning tool for agricultural machines with multiple depots. The tool presented consists of two modules: the first one regards the field geometrical representation in which the field is partitioned into tracks and headland passes; the second one regards route optimization that is implemented by the metaheuristic simulated annealing (SA) algorithm. In order to validate the developed tool, a comparison between a well-known route planning approach, namely B-pattern, and the algorithm presented in this study was carried out. The results show that the proposed algorithm outperforms the B-pattern by up to 20.0% in terms of traveled nonworking distance. The applicability of the tool developed was tested in a case study with seven scenarios differing in terms of locations and number of depots. The results of this study illustrated that the location and number of depots significantly affect the total nonworking traversal distance during a field operation.

Depot Location Analysis for Capacitated Vehicle Routing Problem: A Case Study of Solid Waste Management

This paper presents an optimized solution to locate a depot on multiobjective instances of Capacitated Vehicle Pouting Problem (CVRP) using firefly algorithm (FA). The main objective of a depot location routing problem (LRP) is to obtain the optimal position to locate a depot in other to serve a set of customers ensuring the minimum possible total travelled distance across a search space. In this paper, the instances of solid waste management were created to simulate a real-life scenario of CVRP. This was formulated into a multiobjective optimization problem considering various depot positioning. Firefly Algorithm (FA) which is a metaheuristic technique was employed to navigate the travel path towards an optimal depot placement for solving the LRP model. Various depot positions which includes random, optimized, centered and eccentric were evaluated. Results showed that the optimized depot positioning approach obtained the best depot position as against the other possible positions. Results when compared with two metaheuristics approach Unified Hybrid Genetic Search (UHGS) and Iterated Local Search with Set Partitioning (ILS-SP) presented in literature also showed that, optimized depot positioning obtained the best results and FA can compete effectively with other metaheuristics approaches.

A green multi-facilities open location-routing problem with planar facility locations and uncertain customer

A green stochastic open location-routing problem (GSOLRP) is generally a combined location-routing problem and set of green vehicles (e.g., hybrid vehicles corresponding to customers’ demands and traffic limitations) under uncertainty. The environmental topics in production systems strive to reduce their costs by finding efficient locations for depots and optimize routing vehicles to transport manufactured goods to depots with the least CO2 pollution possible. In this model, the stochastic locations are assumed as an effective parameter on the cost of the supply chain and its rival. The proposed method determines depots opening decision, vehicle allocation, and path design in such a stochastic location and minimizes the quantity of CO2 emissions by supply chain management. A robust optimization approach is used for stochastic customers’ location. Then, a hybrid meta-heuristic method of combining the imperialist competitive algorithm (ICA) and variable neighborhood search (VNS) is developed to solve large-scale problems.

Online-to-offline mobile charging system for electric vehicles: Strategic planning and online operation

Limitations such as the driving range, a lack of charging facilities, and the inconvenience of charging services hamper the further market development of EVs. With the development of mobile Internet, an alternative charging option for EVs, i.e., online-to-offline (O2O) mobile charging services, can be established to increase the charging convenience for EV users. This study aims to investigate the strategic planning and online operation of such mobile charging service systems. At the operational level, we model the MC dispatching as a dynamic vehicle routing problem and adopt a scenario-sampling-based online policy to operate the MCs in real time. At the planning level, we construct a simulation-based optimization framework to design the number and locations of depots, MC fleet size, and battery size. Numerical tests are conducted to evaluate the performance of the proposed planning and operation methodologies, from which some planning and managerial policy insights are derived.

The Model for Location Routing Problem with Roaming Delivery Locations

The Location Routing Problem with Roaming Delivery Locations (LRPRDL) is a model that represents company activities in delivering products to final customers. Direct delivery to final customers has increased significantly over the growth of e-commerce in the world. E-commerce or business-to-customer companies are urged to increase their last-mile distribution efficiency to survive in the global competition. For that purpose, the LRPRDL model was proposed to increase the efficiency of the company’s last-mile distribution. The model aims to minimize the sum of open depots and transportation costs by determining the number and location of depots and the shipping routes. The LRPRDL was implemented in an instance with four depot candidates, 15 customers, and six vehicles. The instance was solved to the optimality by using a public solver Gurobi. Furthermore, this research conducted a sensitivity analysis on the open depots and fuel costs, customer demand, and radius. The study indicated that customer’s demand and radius have a significant impact on the purchase decision.

Scheduling spatially distributed jobs with degradation: Application to pothole repair

This paper considers scheduling spatially distributed jobs with degradation. A mixed integer programming (MIP) model is developed for the linear degradation case in which no new jobs arrive. Properties of the model are analyzed, following which three heuristics are developed, enhanced greedy, chronological decomposition and simulated annealing. Numerical tests are conducted to: (i) establish limits of the exact MIP solution, (ii) identify the best heuristic based on an analysis of performance on small problem instances for which exact solutions are known, (iii) solve large problem instances and obtain lower bounds to establish solution quality, and (iv) study the effect of three key model parameters. Findings from our computational experiments indicate that: (i) exact solutions are limited to instances with less than 14 jobs; (ii) the enhanced greedy heuristic followed by the application of the simulated annealing heuristic yields high quality solutions for large problem instances in reasonable computation time; and (iii) the factors “degradation rate” and “work hours” have a significant effect on the objective function. To demonstrate applicability of the model, a case study is presented based on a pothole repair scenario from Buffalo, New York, USA. Findings from the case study indicate that scheduling spatially dispersed jobs with degradation such as potholes requires: (i) careful consideration of the number of servers assigned, degradation rate and depot location; (ii) appropriate modeling of continuously arriving jobs; and (iii) appropriate incorporation of equity consideration.

An integrated location-routing problem with post-disaster relief distribution

Quick and wise decisions made regarding depot location and vehicle routing in emergency logistics play an important role in the relief of affected areas after a disaster. We address an integrated location-routing problem with post-disaster relief distribution, seeking to design an assignment system for a fleet of homogeneous rescue vehicles from a set of candidate depot locations to deliver relief supplies to affected areas after a disaster. Each affected area is associated with a soft time window, during which it is expected to receive the relief supplies. Two objective functions are involved: the penalty for time window violation, and the total operational cost comprising the depot opening cost, vehicle fixed cost, and transport cost. The overall objective to find the opened transfer depots, the number of vehicles used, and the route of each used vehicle so as to identify the approximate Pareto frontier comprising the trade-offs between the conflicting objectives. To achieve this, we develop a hybrid ant colony optimization algorithm for which we use particles as operators to more widely search for enabled depots among alternative ones and then assign clients to them so that ants can find the most effective and balanced vehicle routes for every selected depot. We conduct extensive numerical experiments to assess the performance of the developed algorithm by comparing with three algorithms. The numerical results confirm the efficacy of the developed method in terms of its computational efficiency and solution quality.

Field path planning using capacitated arc routing problem

Duration of Agricultural operations is a crucial issue in fleet management as it directly affects the operation costs. Agricultural fleet usually traverses paths, covering the whole field to complete the operation. The agricultural operations are not necessarily performed efficiently due to the various variables including field shape, presence of obstacles in the field, number of vehicles, and their specifications. In this study, agricultural operations are modeled as a capacitated arc routing problem (CARP). CARP is a combinatorial optimization problem, which determines the harvest paths in a field and finds the best order of traversing. By dividing the paths into two categories of required and non-required, CARP demonstrates a field as a graph. Using CARP, no extra or invalid edge on the graph would be needed. As CARP is an NP-Hard problem, a solution algorithm based on adaptive large neighborhood search (ALNS) is employed to solve large instances of the problem under study. The proposed ALNS algorithm is applied to well-known problem instances as well as a realistic case study to achieve the optimal harvesting pattern efficiently. The computational results demonstrate that the ALNS algorithm surpasses the conventional harvesting pattern. The amount of improvement obtained by the proposed method could vary due to the several factors, including the depot location, vehicle capacity, and field shape.

Hierarchical pre-positioning of emergency resources for a chemical industrial parks concentrated area

Accidents in chemical industrial parks always cause fateful damage which can be reduced greatly by providing emergency resources sufficiently and timely. One effective way to enhance the emergency response capacity and agility is by pre-positioning of emergency resources for the potential accidents. The Yangtze River Delta of China is a large region where a large number of chemical industrial parks are concentrated. According to the distributing characteristics of demand points in this region, a mathematical model of hierarchical pre-positioning of emergency resources is proposed to ensure that accidents in all chemical industrial parks in this region can be responded timely and effectively. Considering accident domino effect and minimizing the total cost, the model gives optimal decisions of pre-positioning emergency resources, including the location and inventory of depots. The innovative hierarchical pre-positioning method greatly reduces the total cost in the premise of sufficient preparation for supplying emergency resources. Finally, in a visual graph of the Yangtze River Delta, the model is applied and the result shows its applicability.

An exact algorithm for inland container transportation network design

In this paper, we investigate the inland depot location problem of the inland transportation system. In inland container transportation, empty containers are transported between depots and consignees/shippers, and empty containers should be repositioned after/before inbound/outbound full containers. We build a robust mathematical model that focuses on determining when and where consignees/shippers are assigned to. In addition, inter-depots empty container repositioning is implemented considering demand uncertainty. This paper proposes a branch-and-price algorithm that is based on Lagrangian relaxation and column generation. We show the optimality condition of the pricing subproblem and construct a simpler formulation in this paper. Computational experiments are performed with test instances that mimic real life. Our results also show that considering time compatibility of full and empty container routes is closer to reality and increases the utilization of empty containers in depots. The proposed algorithm yields promising solutions compared with CPLEX.

Transport analytics in action: A cloud-based decision support system for efficient city bus transportation

Optimising city bus transport operations helps conserve fuel by providing the urban transport service as efficiently as possible. This study develops a Cloud-based Decision Support System (C-DSS) for transport analytics. The C-DSS is based on an intelligent model on location of depots for opening new depots and/or closing a few existing depots and allocation of city-buses to depots. The C-DSS is built on the Cloud Computing architecture with three layers and includes an efficient and simple greedy heuristic algorithm. Using modern information and communications technology tools, the proposed C-DSS minimizes the cost of city bus transport operations and in turn to reduce fuel consumption and CO2 emissions in urban passenger transport. The proposed C-DSS is demonstrated for its workability and evaluated for its performance on 25 large scale pseudo data generated based on the observation from Bangalore Metropolitan Transport Corporation (BMTC) in India.

Optimization of a Capacitated Vehicle Routing Problem for Sustainable Municipal Solid Waste Collection Management Using the PSO-TS Algorithm.

Sustainable management of municipal solid waste (MSW) collection has been of increasing concern in terms of its economic, environmental, and social impacts in recent years. Current literature frequently studies economic and environmental dimensions, but rarely focuses on social aspects, let alone an analysis of the combination of the three abovementioned aspects. This paper considers the three benefits simultaneously, aiming at facilitating decision-making for a comprehensive solution to the capacitated vehicle routing problem in the MSW collection system, where the number and location of vehicles, depots, and disposal facilities are predetermined beforehand. Besides the traditional concerns of economic costs, this paper considers environmental issues correlated to the carbon emissions generated from burning fossil fuels, and evaluates social benefits by penalty costs which are derived from imbalanced trip assignments for disposal facilities. Then, the optimization model is proposed to minimize system costs composed of fixed costs of vehicles, fuel consumption costs, carbon emissions costs, and penalty costs. Two meta-heuristic algorithms, particle swarm optimization (PSO) and tabu search (TS), are adopted for a two-phase algorithm to obtain an efficient solution for the proposed model. A balanced solution is acquired and the results suggest a compromise between economic, environmental, and social benefits.

Adipose Tissue Distribution, Inflammation and Its Metabolic Consequences, Including Diabetes and Cardiovascular Disease.

Adipose tissue plays essential roles in maintaining lipid and glucose homeostasis. To date several types of adipose tissue have been identified, namely white, brown, and beige, that reside in various specific anatomical locations throughout the body. The cellular composition, secretome, and location of these adipose depots define their function in health and metabolic disease. In obesity, adipose tissue becomes dysfunctional, promoting a pro-inflammatory, hyperlipidemic and insulin resistant environment that contributes to type 2 diabetes mellitus (T2DM). Concurrently, similar features that result from adipose tissue dysfunction also promote cardiovascular disease (CVD) by mechanisms that can be augmented by T2DM. The mechanisms by which dysfunctional adipose tissue simultaneously promote T2DM and CVD, focusing on adipose tissue depot-specific adipokines, inflammatory profiles, and metabolism, will be the focus of this review. The impact that various T2DM and CVD treatment strategies have on adipose tissue function and body weight also will be discussed.

Search and Delivery Man Problems: When are depth-first paths optimal?

Let h be a probability measure on the nodes and arcs of a network Q, viewed either as the location of a hidden object to be found or as the continuous distribution of customers receiving packages. We wish to find a trajectory starting from a specified root, or depot O that minimizes the expected search or delivery time. We call such a trajectory optimal. When Q is a tree, we ask for which h there is an optimal trajectory that is depth-first, and we find sufficient conditions and in some cases necessary and sufficient conditions on h. A consequence of our analysis is a determination of the optimal depot location in the Delivery Man Problem, correcting an error in the literature. We concentrate mainly on the search problem, with the Delivery Man Problem arising as a special case.

Container depot location problem in the frame of the Polish part of the New Silk Road

The Chinese project One Belt One Road, in Europe often referred to as the New Silk Road, triggers infrastructural investments enabling seamless transportation of goods on the Eurasian route. Since most of these goods are transported in containers, the optimal location of inland container depots, where containers wait for future repositioning after they had been emptied at the importers, is an interesting problem. In this paper conditions determining the optimal location of inland container depots in the Polish part of the NSR are analysed. They include proximity of container transhipment terminals located on the Polish stretch of the NSR, structure of highway network enabling distribution of containers by trucks to other destinations, as well as green aspects mainly referring to avoidance of city congestion caused by trucks transporting containers.

A network design problem formulation and solution procedure for intercity transit services

This study presents a network design problem formulation and solution procedure for an intercity transit service that has multiple routes and serves multiple terminals in the origin and destination cities. The proposed solution procedure consists of three steps: (1) a set of candidate terminals are selected; (2) candidate routes are generated between those terminals using a k-shortest-path algorithm; (3) a mixed integer linear programing model finds the optimal routes, terminals, frequencies, fleet size and depot locations, given a set of constraints and the objective of minimizing total passenger travel time and vehicle deadheading time. The solution procedure was implemented for a newly conceived transit service between Tucson and Phoenix in Arizona, USA. The final routes found by the model look reasonable, and a sensitivity analysis showed that the objective function is most sensitive to changes in the demand satisfaction ratio, and then to the maximum number of routes and fleet size.

Optimal Depot Locations for Humanitarian Logistics Service Providers Using Robust Optimization

Abstract 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 multi-period humanitarian logistics model considering limited resources and network availability

Increased frequency of natural disasters keeps reminding us of the importance of effective pre-planning for the post-disaster situation. Efficient and timely humanitarian logistics can make a difference between life and death of the affected people and reduce trauma. Often marked with limited resources and partially available network capacity, post-disaster response mostly fails to address the needs of all affected people. This paper presents a humanitarian logistics model that considers the limited availability of resources, equity of distribution and the available residual capacity of road network in a multi-period optimization environment. A case study based on the Minato ward of Osaka city is presented, where a mega scale Nankai trough earthquake has been predicted in near future. Using scenario analysis, the case study illustrates determination of efficient depot location, critical infrastructure and supply strategy.