Optimal Facility Location Research Articles

Оптимизация расположения зарядных станций для электромобилей на примере г. Тюмени

The development of electric vehicles necessitates the creation of charging stations` infrastructure, which, given the small size of the fleet, requires solving the problem of the optimal location of appropriate facilities. The article presents an analysis of scientific approaches in the area under consideration, which has made it possible, simultaneously with an analysis of the features of the functioning of the charging stations` infrastructure, to develop an algorithm for the optimal location of charging stations in the cities of the Russian Federation. To achieve the results of the work, the methods of system analysis and methods of optimal location of socially significant objects were applied. In the future, the approach is going to be applied in the algorithm of the methodology for substantiating the parameters of the urban charging stations` infrastructure for electric vehicles.

A Linear Algorithm for a Minimax Location of a Path-Shaped Facility with a Specific Length in a Weighted Tree Network

This paper considers the problem of locating a path-shaped facility of a specific size on a weighted tree network. The criterion for optimality used in this paper is the minimax criterion in which the weighted distance to the farthest vertex from the facility is minimised. The minimax criterion gives acceptable results from the point of view of the fast response for the clients who are located far away from the facility. This location problem usually has applications in computer science, information science, and operations research. An O(n) time algorithm is proposed for finding the optimal location of a path-shaped facility of a bounded length on a weighted tree network, where n is the number of vertices in the tree.

Increasing supply chain resilience through efficient redundancy allocation: a risk-averse mathematical model.

The COVID-19 pandemic has created significant uncertainty in all areas of life, including supply chains (SCs). This paper presents a new risk-averse mixed-integer nonlinear problem mathematical model for the design and planning of a two-echelon resilient SC network. Disruption events, which can partially or completely reduce the available capacity, are included in the model. The model’s objective is to minimise the total costs by determining the optimal facility location and capacity, allocation flows and resilience actions for hedging against disruption risk. A solution procedure is tested through computational experiments, and managerial insights were formed based on a numerical example for several disruption configurations, with a specific case of long-term crises similar to the COVID-19 pandemic. The results showed that recovery activities are the most efficient actions to take for a short-term disruption event. Besides, proactive resilience investment in a protection system and flexibility enhancement allows the SC to handle the disruption period with a limited increase in network building costs and overcapacity.

A Hybrid Agent-Based Simulation and Optimization Approach for Statewide Truck Parking Capacity Expansion

Abstract Truck parking shortages are a critical concern for both the trucking industry and truck drivers in the USA. To tackle growing shortages, parking capacity expansions are needed. This paper presents a hybrid agent-based simulation and optimization approach to model truck movements and driver behaviors to determine feasible locations for truck parking facility capacity expansions across a state. The simulation model considers the driving limit and rest requirements of hours-of-service regulations set forth by the federal regulations. By leveraging observed historical truck GPS data, agent profiles are created, capturing truck trip origins, necessary stops, trip destinations, and parking behaviors. The simulation model estimates parking facility utilization over time by identifying where, when, and how long truck drivers rest. The estimated parking usage data is then fed into a maximal coverage capacitated multiple facility location optimization with multiple service model to deduce capacity expansion locations given budgetary restrictions. Ultimately the model recommends where and how many parking spaces to add to accomplish a certain level of parking overcrowding. The simulation and optimization approaches are finally integrated into a map-based, user adaptable decision support tool.

РОЗВИТОК ПРАВОВОГО РЕГУЛЮВАННЯ КОНЦЕСІЇ У СФЕРІ ПОВОДЖЕННЯ З ВІДХОДАМИ

The article considers the peculiarities of legal regulation of concession relations in field of waste management in the context of the new Law of Ukraine «On Concession». The main purpose of this paper is to substantiate proposals for the development of the above-mentioned legal regulation. The paper offers to establish special performance indicators, the achievement of which will determine the amount of fee for the operational readiness of waste facility (paid to concessionaire by public partner). Such indicators are increase in volume of separate collection and recycling of waste, reducing volume of biodegradable waste disposal, as well as non-pretreated household waste disposal. The procedure for analyzing the effectiveness of public-private partnership in the form of a concession is specified, taking into account the principles of waste hierarchy, self-sufficiency and proximity. The waste hierarchy will allow a fuller assessment of the environmental, resource, energy, social and other efficiencies of potential concession projects. The principle of proximity can be an important criterion for determining the optimal location of waste facility to be built by concessionaire. It is proposed to approve model concession agreements for the construction and management of waste facilities. By analogy with model lease agreements of state property, all conditions established by model concession agreements for the construction and management of waste facilities it is advisable to divide into changeable (recommended) and unchangeable (mandatory). It is argued the direct inclusion of waste legislation in the legislation, which is not subject to the guarantees of the rights of concessionaire.

Location and Design of Urban Facilities considering Competitors’ Responses: A Case Study of Putuo District, Shanghai

In view of the problem of competitive facility location in urban planning, the response of competitors to the new facility is considered. We use Huff’s probability model to describe the market share of all facilities which depends on its service quality and its distance from customers. To maximize the market share of facilities, a two-stage method (quality decision stage and location decision stage) is adopted, which takes into account the responses of competitors. In the stage of quality decision, the competitive decision-making process is simulated as a game process and solved by Nash equilibrium. The solution of the quality decision process can be expressed as a function of the new facility’s location which can be obtained by polynomial approximation. In the location decision stage, we apply the interval analysis based on branch and bound algorithm to determine the optimal location of new facility. Then, we use the randomly generated numerical experiment to verify the effectiveness of the model and algorithm. Finally, we apply this model and algorithm to the location of a new shopping mall in Putuo district, Shanghai.

A systematic review of charging infrastructure location problem for electric vehicles

ABSTRACT With the evolving demand for sustainable mobility, adequate charging infrastructure for electric vehicles (EVs) has been growing steadily and wireless power transfer (WPT) technology has been seen as an efficient alternative for EV charging while maintaining seamless traffic flow. This paper reviews the modelling challenges in terms of both static (plug-in) charging (SC) and wireless charging (WC) facilities in a transportation network in terms of system integration, focusing on the evolution of the WPT technology. The first part of the paper provides an overview of all the major progress and achievements made by different research organisations in the area of WPT technology for EV charging. These technologies are ranked based on two indices, namely technological readiness level and system readiness level. The optimal location of WC facilities comes with more design and operational issues than conventional static charging facilities. However, they are similar in terms of the infrastructure modelling approach to locate these charging facilities, as the overall goal is to maximise the network flow and minimise the overall system cost. The second part of the paper assesses different modelling approaches used to analyse the network and locate the charging infrastructure for static and WC facilities. The economic feasibility of the technology is an important consideration for successful system integration as well as the overall performance of the system. As such, this paper also provides a synopsis of different socio-economic studies related to the WC infrastructure allocation problem. Finally, future research directions in this field are discussed based on the knowledge gaps identified from the existing literature.

A robust optimization model of the field hospitals in the sustainable blood supply chain in crisis logistics

Crisis management is one of the significant challenges when a disaster occurs. Post disaster operations mainly focus on medical treatments. One of the general requirements of medical treatments of field hospitals in disaster is blood transfusion. This way, configuring a blood transfusion network to handle the appropriate assignments and distribution is proved to influence the type, number and severity of recent crises. Therefore, obtaining effective solutions, containing determining the optimal location of facilities of field hospitals, identifying the optimal capacity of facilities and transportation, blood transfusion routes to reduce costs and injustices in this area in times of crisis is essential. Hence, in this study, we proposed a model, intend for allocating blood types and regarding the lifetime of blood to tackle with field hospitals in the disaster sites. Furthermore, the coverage of the demand for blood types for the establishment of field hospitals and the balanced distribution of blood between these hospitals from the blood collection level to the establishment of field hospitals with optimal routing of blood transfusion at the lowest cost is considered. The proposed model for the crisis response management phase is modelled applying a multi-objective mathematical model under uncertain conditions. To cope with the uncertain data arisen in the disaster condition, the robust optimization approach is applied. Then, to solve the proposed model, a hybrid method based on Genetic Algorithm and Multi-Choice Goal Programming (GAMCGP) under uncertainty is developed. In addition, the proposed model is successfully applied to a real- world blood supply chain in Mazandaran, northern part of Iran, and the results validate the performance of the proposed model. It also proves that the suggested model can be considered as a new approach for blood supply chain tactical management in the crisis logistics.

A Hybrid Methodology Based on Machine Learning for a Supply Chain Optimization Problem

This paper presents an advanced methodology that integrates a machine learning methodology into an optimization process. The framework of an interactive machine learning algorithm was developed to meet the challenges in solving large-scale optimization problems. An artificial neural network (ANN) is used with the knowledge gained from solving previous problems with different scenarios to define a good starting point for a solution searching process. By using an initial solution, known as “warm start”, the search space can be reduced to get more opportunity to find an optimal solution. The applicability of the proposed method was evaluated by using it to determine the optimal facility locations for a biomass supply chain problem using a real case study from Central Vietnam. The supply chain planning model is based on an optimization model, where the goal is to maximize the benefits from meeting the electricity demand minus the total cost from facility cost, penalty cost from lost demand, and operational costs form the supply chain. The structure of the ANN, the number of intermediate layers and the number of processing nodes, was determined by comparing the accuracy from different configurations. The ANN with two intermediate layers possesses the best performance from the training and testing datasets. The proposed model succeeded in predicting the facility location with more than 98% prediction accuracy. The results from our framework provide optimal solutions while saving runtime.

Optimal facility location problem on polyhedral terrains using descending paths

We study the descending facility location (DFL) problem on the surface of a triangulated terrain. A path from a point s to a point t on the surface of a terrain is descending if the heights of the subsequent points along the path from s to t are in a monotonically non-increasing order [1]. We are given a set D={d1,d2,⋯,dn} of n demand points on the surface of a triangulated terrain W and our objective is to find a set F (of points), of minimum cardinality, on the surface of the terrain such that for each demand point d∈D there exists a descending path from at least one facility f∈F to d. We present an O((n+m)log⁡m) time algorithm for solving the DFL problem, where m is the number of vertices in the triangulated terrain. We achieve this by reducing the DFL problem to a graph problem called the directed tree covering(DTC)problem. In the DTC problem, we have a directed tree B=(V,E) with a set of marked nodes M⊆V. The objective is to compute a set C⊆V of minimum cardinality, such that for every node v∈M, either v∈C or there exists a node c∈C such that v is reachable from c. We prove that the DFL problem can be reduced to DTC problem in O((m+n)log⁡m) time. The DTC problem thereafter can be solved in O(|V|) time. We also prove that the general version of the DTC problem, called the directed graph covering(DGC)problem is NP-hard on directed bipartite graphs and hard to approximate within (1−ϵ)ln⁡|M|-factor, for every ϵ>0, where |M| is the size of the set of marked nodes. We also prove that for the DGC problem, an O(log⁡|M|) factor approximation is possible and this approximation factor is tight.

Electric bus scheduling under single depot dynamic wireless charging infrastructure planning

The use of electric bus fleets has become a topical issue in recent years. This paper aims to analyze strategic bus fleet scheduling and dynamic wireless charging (DWC) planning problems based on data obtained from the transportation center at Binghamton University. More precisely, we have developed a mixed-integer mathematical model to simultaneously select the optimal location of the DWC facilities and find the optimal battery sizes of electric buses (EBs) for the system. This DWC planning model allows all the system’s EBs to travel the common routes (depot-base station-depot) safely despite their different optimal battery sizes. Additionally, we have developed a scheduling model to find the optimal number of EBs by considering the additional charging time and battery size restrictions. To allow organizations to determine bus replacement plans that will meet their fleet electrification targets in a cost-effective way, we have introduced joint and disjoint scheduling planning strategies for the current conventional bus fleet and a potential EB fleet. Further, we have reformulated the DWC model so an EB can serve several routes with uniform battery size. The results show that the joint scheduling with one optimal battery size is more cost-effective than disjoint scheduling with route-specific battery sizes.

A mathematical programming model for facility location optimization of hydrogen production from renewable energy sources

ABSTRACT Fossil fuels are the primary energy sources and meet the global energy demands. However, environmental and health problems related with these sources boosted the demand for renewable energy sources. Hydrogen, as an energy carrier has a growing potential for solving these problems. In this article, a mathematical programming model that integrates the production, storage and transportation, safety, location, and staff assignment decisions is presented considering minimization of costs. Although most of the studies focus on location, distribution, storage decisions of hydrogen energy networks, the article also includes production, safety and staff assignment decisions to make this problem more practical. Furthermore, we also investigate the set covering constraint will ensure that each region is covered by minimum number of the hydrogen facilities. The developed model ensures a balance between location, distribution, storage, production, safety and staff decisions by installing two production facilities by assigning total 9 warehouses, 22 tank trucks, 12100 km pipeline, 35 staffs under distance constraint 2000 km in regions 1 and 5. The computational results indicate that the proposed model produces effective solutions for the coverage to all region and minimum total cost for real-case situations.

A Two-Stage DSS to Evaluate Optimal Locations for Bioenergy Facilities

Research Highlights: A set of 128 potential bioenergy facility locations is established and evaluated based on the transport cost to select optimal locations. Background and Objectives: The identification of optimal facility locations to process recovered forest biomass is an important decision in designing a bioenergy supply chain at the strategic planning level. The result of this analysis can affect supply chain costs and the overall efficiency of the network, due to the low density and dispersed nature of forest biomass and the high costs associated with its logistics operations. In this study, we develop a two-stage decision support system to identify the optimal site locations for forest biomass conversion based on biomass availability, transport distance and cost. Materials and Methods: In the first stage, a GIS-based analysis is designed to identify strategic locations of potential bioenergy sites. The second stage evaluates the most cost-effective locations individually using a transportation cost model, based on the results from stage one. The sensitivity of inputs, such as maximum allowable transport cost, the distance of transport and their relations to the profit balance, and changes in fuel price are tested. The method is applied to a real case study in the state of Queensland, Australia. Results and Conclusions: The GIS analysis resulted in 128 strategic candidate locations being suggested for bioenergy conversion sites. The logistics analysis estimated the optimal cost and transportation distance of each one of the locations and ranked them according to the overall performance between capacities of 5 and 100 MW.

Robust optimization model for sustainable supply chain for production and distribution of polyethylene pipe

PurposeIn this study, the authors consider the key decisions in the design of the green closed-loop supply chain (CSLC) network. These decisions include considering the optimal location of suppliers, production facilities, distribution, customers, recycling centers and disposal of non-recyclable goods. In the proposed model, the level of technology used in recycling and production centers is taken into account. Moreover, in this paper is the environmental impacts of production and distribution of products based on the eco-indicator 99 are considered.Design/methodology/approachIn this study, the author consider the key decisions in the design of the green CLSC network. These decisions include considering the optimal location of suppliers, production facilities, distribution, customers, recycling centers and disposal of non-recyclable goods. In the proposed model, the level of technology used in recycling and production centers is taken into account. Moreover, the environmental impacts of production and distribution of products based on the eco-indicator 99 are considered.FindingsThe results indicate that the results obtained from the colonial competition algorithm have higher quality than the genetic algorithm. This quality of results includes relative percentage deviation and computational time of the algorithm and it is shown that the computational time of the colonial competition algorithm is significantly lower than the computational time of the genetic algorithm. Furthermore, the limit test and sensitivity analysis results show that the proposed model has sufficient accuracy.Originality/valueSolid modeling of the green supply chain of the closed loop using the solid optimized method by Bertsimas and Sim. Development of models that considered environmental impacts to the closed loop supply chain. Considering the impact of the technology type in the manufacture of products and the recycling of waste that will reduce emissions of environmental pollutants. Another innovation of the model is the multi-cycle modeling of the closed loop of supply chain by considering the uncertainty and the fixed and variable cost of transport.

Capacitated single‐allocation hub location model for a flood relief distribution network

AbstractIn disaster management, the logistics for disaster relief must deal with uncontrolled variables, including transportation difficulties, limited resources, and demand variations. In this work, an optimization model based on the capacitated single‐allocation hub location problem is proposed to determine an optimal location of flood relief facilities with the advantage of economies of scale to transport commodities during a disaster. The objective is to minimize the total transportation cost, which depends on the flood severity. The travel time is bounded to ensure that survival packages will be delivered to victims in a reasonable time. Owing to complexity of the problem, a hybrid algorithm is developed based on a variable neighborhood search and tabu search (VNS‐TS). The computational results show that the VNS found the optimal solutions within a 2% gap, while the proposed VNS‐TS found the optimal solution with a 0% gap. A case study of severe flooding in Thailand is presented with consideration of related parameters such as water level, hub capacity, and discount factors. Sensitivity analyses on the number of flows, discount factors, capacity, and bound length are provided. The results indicated that demand variation has an impact on the transportation cost, number of hubs, and route patterns.

Optimal positioning of dynamic wireless charging infrastructure in a road network for battery electric vehicles

Dynamic wireless charging (DWC) offers a plausible solution to extending Battery Electric Vehicle (BEV) driving range. DWC is costly to deploy and thus its locations need to be optimized. This raises a question often encountered in practice for infrastructure investment: how to determine the optimal locations of DWC facilities in a network. In this paper, we propose a sequential two-level planning approach considering the objectives of both the public infrastructure planning agency and the BEV users. Two different planners’ objectives namely, total system travel time and total system net energy consumption are considered. Besides these objectives, constraints such as agency budget, range reassurance, and equity in resource distribution are also addressed at the planner’s level. For each objective, BEV drivers respond by choosing their preferred route based on the location of DWC facilities implemented by the planner. An effective solution algorithm is utilized that has the capability of solving relatively large-scale real-world networks within a reasonable computational time. The numerical experiment and case study results provide useful insights on optimally positioning DWC infrastructure to minimize societal cost and energy.

Improving the spatial accessibility of healthcare in North Kivu, Democratic Republic of Congo

Accessibility to healthcare has a direct bearing on the overall well-being of the population. Poor access to healthcare has serious consequences particularly in low and low-middle income countries (LMIC) in sub-Saharan Africa. The lack of detailed and up-to-date spatial data and health information in these regions further challenges both the accurate assessment of spatial accessibility and the determination of optimal locations of healthcare facilities that would improve health service planning. In the present study, we proposed a systematic approach to assess the spatial accessibility to healthcare and to identify optimal locations for additional healthcare facilities based on the accessibility measures. Results from a raster-based accessibility measurement showed that majority of population could not reach the nearest hospitals within 2-hours; only 25%, 50%, and 44% of population reached the nearest hospital within 2-hours under walking, motor and, bus travel scenarios, respectively. Our results also showed that the five newly proposed hospitals whose optimal locations were determined using a location-allocation model could potentially increase 11.41%, 8.29%, and 8.95% of additional population coverage for the three travel scenarios. The proposed health system evaluation approach and the health care planning based on open-source data derived from remote sensing and crowdsourcing and the spatial modeling approach has the potential to be useful in LMIC to improve overall population health.

Dynamic network flow location models and algorithms for quickest evacuation planning

Dynamic network flow problems have wide applications in evacuation planning. From a given subset of arcs in a directed network, choosing the suitable arcs for facility location with a given objective is very important in the optimization of flow in emergency cases. Because of the decrease in capacity of an arc by placing a facility in it, there may be a reduction in the maximum flow or increase in the quickest time. In this work, we consider a problem of identifying the optimal facility locations so that the increase in the quickest time is minimum. Introducing the quickest FlowLoc problem, we give strongly polynomial time algorithms to solve the single facility case. Realizing NP-hardness of the multi-facility case, we develop a mixed integer programming formulation of it and propose two polynomial time heuristics for its solution. Because of the growing concerns of arc reversals in evacuation planning, we introduce the quickest ContraFlowLoc problem and present exact algorithms to solve the single-facility case and heuristics to solve the multi-facility case, with polynomial time complexity. The solutions thus obtained here are practically important, particularly in evacuation planning, to systematize traffic flow with facility allocation minimizing evacuation time.

Multi-objective optimization of sustainable biomass supply chain network design

This paper addresses the optimal design and planning of the biomass supply chain network that encompasses flow from poultry farms to biogas facilities. A novel multi-stage solution methodology is developed to solve the sustainable biomass supply chain network design problem. Geographical Information Systems, and Analytic Hierarchy Process Techniques are used to determine the candidate location of biogas facilities. The proposed multi-objective mixed integer linear programming model is capable of making strategic decisions (optimal biogas facility locations with capacities) along with the tactical decisions (transportation network flows). The model incorporates the two objective function of maximization of the profit, and minimization of total distance between poultry farms and biogas facilities. The aim is to determine the optimal number, location, and size of the biogas facilities, as well as the network flow, and electricity generated. The applicability of the model and solution methodology is demonstrated through a case study for a poultry supply chain network in Turkey. Additionally, sensitivity analysis is conducted to account for the impact of different parameters on the model. Sensitivity analysis show that both maximum distance parameter, and purchasing prices have major impact on decisions, and financial yield.

Simultaneous Facility Location and Path Optimization in Static and Dynamic Networks

We present a framework for solving simultaneously the problems of facility location and path optimization in static and dynamic spatial networks. In the static setting, the objective is to determine facility locations and transportation paths from each node to the destination via the network of facilities such that the total cost of commodity transportation is minimized. This is an NP-hard problem. We propose a novel stage-wise viewpoint of the paths which is instrumental in designing the decision variable space in our framework. We use the maximum entropy principle to solve the resulting optimization problem. In the dynamic setting, nodes and destinations are dynamic. We design an appropriate control Lyapunov function to determine the time evolution of facilities and paths such that the transportation cost at each time instant is minimized. Our framework enables quantifying attributes of the facilities and transportation links in terms of the decision variables. Consequently, it becomes possible to incorporate application specific constraints on individual facilities, links, and network topology. We demonstrate the efficacy of our proposed framework through extensive simulations.