Facility Location-allocation Problem Research Articles

The processing facility location-allocation problem for tunnel slag during tunnel construction

The processing facility location-allocation problem for tunnel slag during tunnel construction

A planar facility location–allocation problem with fixed and/or variable cost structures for rural electrification

A planar facility location–allocation problem with fixed and/or variable cost structures for rural electrification

Optimized density and locations of stroke centers for improved cost effectiveness of mechanical thrombectomy in patients with acute ischemic stroke

BackgroundDespite the proven cost effectiveness of mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS) due to large vessel occlusion, treatment within 6 hours from symptom onset remains inaccessible...

Service Operation Vessels Fleet Size and Mix Location Routing for the Maintenance of an Offshore Floating Wind Farm

Mixed-integer linear programming is adopted to translate the routing of service operation vessels that support the logistic aspects of the maintenance of offshore floating wind farms into mathematical language. The models attempt to help the decision-makers by providing quantified tools to screen out the optimal planning for preventive maintenance. The models search for the optimal offshore base location, vessel’s routing per day, vessel’s capacity, and vessel fleet composition that minimize the total fixed and variable infrastructure cost. The integration of the vehicle fleet size and mix problem, facility location–allocation problem, and vehicle-routing problem with time window advances the state of the art. A realistic case study is shown, and the results and discussions demonstrated that the practical insights of the solutions, as well as the identification of the route patterns through a navigation route table, may improve the decision planning of preventive maintenance.

Hierarchical tri-level optimization model for effective use of by-products in a sugarcane supply chain network

Developing a solution algorithm for tri-level facility location–allocation problem is always challenging due to the inherent complexity of these problems. The proposed sugarcane supply chain network is a tri-level modeling approach formulated based on the static Stackelberg game between producer, distribution center, sugarcane industry, compost unit, biorefinery unit and market in the framework. The network emphasizes the use of massive amount of the by-products generated in the sugarcane industry. These by-products are excellent raw materials for compost unit and biorefinery units. In such cases decisions are made in the hierarchy. By reviewing the proposed algorithms in the past, it has been realized that the shortcomings of the algorithm could be improved by introducing some efficient search mechanism in the algorithms. In this context, a strong local search mechanism based on social engineering optimizer is developed to intensify search space more carefully. Two-hybrid algorithms, GASEO based on Genetic algorithm and social engineering optimizer, and KASEO based on Keshtel algorithm and social engineering optimizer is proposed. To appraise the performance of the proposed algorithm, we pervasively discussed the parameter tuning using Taguchi approach. The ANOVA and Tukey post-hoc test ensures that the performance of the proposed GASEO is appreciable over other algorithms.

Modeling Emergency Logistics Location-Allocation Problem with Uncertain Parameters

In order to model the emergency facility location-allocation problem with uncertain parameters, an uncertain multi-objective model is developed within the framework of uncertainty theory. The proposed model minimizes time penalty cost, distribution cost and carbon dioxide emissions. The equivalents of the model are discussed via operational laws of uncertainty distribution. By employing the goal attainment technique, a series of Pareto-optimal solutions are generated that can be used for decision-making. Finally, several numerical experiments are presented to verify the validity of the proposed model and to illustrate decision-making strategy.

Facility Location-Allocation Problem for Emergency Medical Service With Unmanned Aerial Vehicle

This paper models an operation problem of an unmanned aerial vehicle for the emergency medical service (UEMS) system. The model is set up as a location-allocation problem. The coverage distance and capacity of the UEMS facility are modeled as functions of UAVs assigned. The allocation of the demand point is constrained by the variable coverage distance of each facility. The robust optimization approach is used over the cardinality-constrained uncertain demand, which leads to a nonlinear optimization problem. The UEMS location-allocation problem (ULAP) is reformulated to a solvable problem. An extended formulation and corresponding branch-and-price (B&P) algorithm are also proposed, which strengthen the linear programming relaxation bound. The subproblem of the B&P algorithm is defined as a robust disjunctively constrained integer knapsack problem. Two solution approaches of mixed-integer linear programming reformulation and decomposed dynamic programming are designed for the subproblem. To provide time-efficient solutions for large-scale problems, a restricted master heuristic (RMH) is proposed based on the extended formulation. In computational experiments, the B&P algorithm provided a strong lower bound, and the RMH could find an effective feasible solution within an applicable computation time.

Convalescent plasma bank facility location-allocation problem for COVID-19

With convalescent plasma being recognized as an eminent treatment option for COVID-19, this paper addresses the location-allocation problem for convalescent plasma bank facilities. This is a critical topic, since limited supply and overtly increasing cases demand a well-established supply chain. We present a novel plasma supply chain model considering stochastic parameters affecting plasma demand and the unique features of the plasma supply chain. The primary objective is to first determine the optimal location of the plasma banks and to then allocate the plasma collection facilities so as to maintain proper plasma flow within the network. In addition, recognizing the perishable nature of plasma, we integrate a deteriorating rate with the objective that as little plasma as possible is lost. We formulate a robust mixed-integer linear programming (MILP) model by considering two conflicting objective functions, namely the minimization of overall plasma transportation time and total plasma supply chain network cost, with the latter also capturing inventory costs to reduce wastage. We then propose a CPLEX-based optimization approach for solving the MILP functions. The feasibility of our results is validated by a comparison study using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and a proposed modified NSGA-III. The application of the proposed model is evaluated by implementing it in a real-world case study within the context of India. The optimized numerical results, together with their sensitivity analysis, provide valuable decision support for policymakers.

Optimizing dynamic facility location-allocation for agricultural machinery maintenance using Benders decomposition

This paper focuses on optimizing a dynamic facility location-allocation problem with respect to a real-life agricultural machinery maintenance service network that is designed to achieve the prompt and reliable response to malfunctioning agricultural machinery during harvest. We consider a busy farming season divided into several time periods in which the problem is to determine where to locate temporary maintenance stations (TMSs) as well as identifying how many capacitated service-providing facilities to allocate to each TMS to satisfy maintenance demands. The problem is formulated as a mixed integer program (MIP) that seeks to minimize the total service mileage between TMSs and demand points. Additionally, considering that the service flow from a TMS to a demand point in this type of work takes place between potential district locations rather than discrete vertices, we use regional contiguity constraints to enforce agricultural production areas served by a TMS as geographically connected. To solve our MIP problem, an exact algorithm based on Benders decomposition is then developed along with several refinements. Lastly, our model and methodology are illustrated in the handling of a real-world problem in China. Computational results are presented that analyze the optimized facility location-allocation plan, examine the impact of selected parameters, demonstrate the advantage of implementing the contiguity constraints and discuss the performance of solution algorithm.

Capacitated facility location–allocation problem for wastewater treatment in an industrial cluster

We present a location–allocation problem for wastewater treatment in a cluster of processing units. The problem involves installing wastewater treatment plants of appropriate capacities at suitable locations and allocating processing units to these plants. The operational cost of treatment plants is a nonconvex function of wastewater volume and pollutant concentration. This problem is formulated as a non-convex mixed integer nonlinear programming (MINLP) problem. For the general class of this problem, solution strategies based on adapted McCormick envelopes and also piece-wise linear approximation are presented. For a special case of this problem, an exact convexification strategy is proposed and an outer approximation based branch-and-cut approach is presented as an exact solution method. For solving larger instances, we present a matheuristic solution method based on multi-start neighborhood search incorporated into a MINLP solution approach. A real-world case study is presented to illustrate the results of the model along with a multi-objective analysis.

An integrated approach based on two-objective optimization and 2SFCA model for health-care facility location-allocation problems

Public health-care facilities are essential to all communities, and their location has long been an important issue in urban planning. With the steady growth of Shenzhen’s population, the government needs to decide where health-care facilities should be located to improve the equity of accessibility, and further more to raise the residential needs and satisfaction for the entire population. As a large immigrate city, Shenzhen is a complex socio-ecological system which make it hard to find the best solution that meets all of the objectives. In this article, we establish a multi-objective model to examine the problem of where health-care facilities should be located to improve accessibility for the entire population and decrease the cost of building new facilities. In particular, the model accounts for the different requirements of varying age groups. We used data from Shenzhen and two heuristic algorithms to create the model. The strategy proposed here can be adopted in urban planning and help policymakers reach better decisions.

Multi-level facility location-allocation problem for post-disaster humanitarian relief distribution

PurposePreviously use of drones as a relief distribution vehicle was studied in several studies where required number of drones and the best locations for the relief centers were investigated. The maximum travel distance of drones without a need to recharge is limited by their endurance. Recharge stations can be used to extend the coverage area of the drones. The purpose of this paper is to find the best topology for both relief centers and recharge stations to cover a large-scale area with minimum and feasible incurred costs and waiting times.Design/methodology/approachA multi-level facility location problem (FLP) is utilized to find the optimum number of relief centers and refuel stations and their locations. It is supposed that the demand occurs according to Poisson distribution. The allocation of the demand is based on nearest neighborhood method. A hybrid genetic algorithm is proposed to solve the model. The performance of the algorithm is examined through a case study.FindingsThe proposed method delivers increased efficiency and responsiveness of the humanitarian relief system. The coverage area of the drones is extended by refuel stations, total costs of the system are reduced and the time to respond an emergency, which is an important factor in survival rate, is significantly decreased.Originality/valueThis study proposes a multi-level FLP to simultaneously account for recharge stations, relief centers and the number of required drones to cover all the demand for relief in a post-disaster period.

A multi-layer congested facility location problem with consideration of impatient customers in a queuing system

This study presents a novel multi-objective model for a facility location-allocation problem with congestion in a multi-layer network. The proposed model contemplates the existence of impatient customers within a classical queuing system, namely M/M/1. The network is configured by determining the optimal number of facilities in each layer and allocation of customers to the facilities in a hierarchy facilities problem. Regarding the first objective function of the model, we minimize the sum of aggregate costs related to the customers’ waiting times, as well as the costs imposed by the impatience of the customers. In the second objective function, the costs associated with the sum of aggregate idleness of facilities are minimized. The proposed multi-objective multi-layer facility location-allocation (MLFLA) problem is solved by an evolutionary approach according to the Pareto envelope-based selection algorithm II (PESA-II). Parameters of the algorithm are tuned by Taguchi method. Then, the performance of the algorithm is reported by four performance metrics introduced for multi-objective optimization problems. The computational run times of 10 test problems demonstrate that the solution methodology is capable of solving the problem with large network sizes.

Hierarchical supplement location-allocation optimization for disaster supply warehouses in the Beijing–Tianjin–Hebei region of China

Research on the emergency facility location-allocation problem has important theoretical and practical significance for responses to large-scale emergencies. During the actual planning of disaster supply warehouses (DSWs), reconstruction and expansion projects usually need to fully exploit different levels of designated facilities to achieve resource integration. This article presents a hierarchical supplement location-allocation (HSLA) optimization model for DSWs in both the preparedness and response phases to address the above concerns. The model minimizes the total number and cost of DSWs for the facility location problem and the total distance for delivering supplies at all levels for the supply allocation problem. A modified particle swarm optimization algorithm is applied to derive solutions to the HSLA model. The model is demonstrated for the Beijing–Tianjin–Hebei (BTH) region in China. The results show that twelve of the 180 counties, which account for 6.03% of the total area of the BTH region are not covered by county-, prefectural- and provincial-level DSWs. In addition, three sets of optimization schemes for the location-allocation problem for DSWs at the three levels and one final mixed optimal scheme (one county-level DSW, two prefectural-level DSWs, and one provincial-level DSW should be newly built or upgraded in four counties) are suggested based on the supplemental optimization site selection. This model is believed to remain more consistent with the knowledge of DSW planners and policy makers by incorporating the multi-level coverage and supplemental site selection concept into the location-allocation problem. The method can be easily duplicated to expand or build new emergency facilities at different levels in addition to DSWs. The results provide a scientific reference for DSW location-allocation planning in the BTH region.

Crowdsourcing planar facility location allocation problems

Facility location allocation is key to success of urban design, mainly in designing transport systems, finding locations for warehouse, fire stations and so on. The problem of determining locations of k facilities so that provides service to n customers, also known as p-median problems, is one of the well-known $$\mathcal NP$$ -hard problems. Several heuristics have been proposed to solve location allocation problems, each of which has several limitations such as accuracy, time and flexibility, besides their advantages. In this paper, we propose to solve the p-median problems using crowdsourcing and gamification techniques. We present a crowdsourced game, called SolveIt, which employs wisdom and intelligence of the crowd to solve location allocation problems. We have presented a data model for representing p-median problems, designed and implemented the game and tested it using gold standards generated using a genetic algorithm tool. We have also compared the results obtained from SolveIt with the results of a well-known approach called Cooper. The evaluations show the accuracy and superiority of the results obtained from SolveIt players. We have also discussed the limitations and possible applications of the proposed approach.

An improved artificial bee colony for facility location allocation problem of end-of-life vehicles recovery network

Reverse logistics is indispensable for resources reuse and circular economy, and a reverse logistics network optimization problem for end-of-life vehicles is studied frequently. Recent researches have focused on the material flow for different end-of-life vehicles. However, the primary question for an end-of-life vehicles recovery network is to determine optimal network nodes. To account for it, we considered a facility location allocation problem of end-of-life vehicles recovery network, and established a mathematical model to solve it. The model is used to achieve the minimization of cost for deciding optimal locations of end-of-life vehicles recovery network. The facility location allocation problem is a non-deterministic polynomial complete problem proved with increase in the number of candidate locations. This type of problem usually handled by a metaheuristics. Therefore, we proposed a valid novel approach based on artificial bee colony to solve the problem. Artificial bee colony is an optimization method that imitates bee behavior. Also, the proposed algorithm is applied to two different scale real-life cases, and some comparisons with several presented algorithms are presented to illustrate the effectiveness of the presented method.

Random Energy-Efficient Models for Sustainable Facility Location Subject to Carbon Emission, Economical, Capacitated and Regional Constraints

An uncertainty facility location allocation (FLA) problem is a complex nonlinear optimization problem. Currently, researchers have discussed the stochastic cost/profit issues of FLA. However, rising energy conservation awareness and environmental concerns, energy-efficientcy and low-carbon emission should be regarded as key criteria in solving it. To do so, this paper presents a new FLA problem from a sustainable development point of view. By taking a typical facility service enterprise as a case, a new stochastic energy-efficient FLA model subject to carbon emission, economical, capacitated, and regional constraints is formulated. Then, stochastic simulation and scatter search are integrated as an intelligent algorithm to resolve it. Some examples are demonstrated to prove the availability of the proposed model and solution algorithm.

AN INVESTIGATION OF THE RELATIONSHIP BETWEEN FACILITY LOCATION DECISIONS, SERVICE LEVEL AND DISTRIBUTION COSTS: A PROPOSED MODEL FOR INDONESIAN LPG SUPPLY CHAIN

This paper proposes models of Indonesian multi-echelon LPG distribution system by opening new facilities (new echelon) taking into account the current facilities and investigates the implications of the proposed models to distribution components such as transportation, inventory and shortage decisions. The objectives are to determine the optimum location and allocation of LPG facilities and investigate the relation between distribution costs such as transportation, inventory cost and location decisions in Indonesian multi-echelon LPG supply chain. Capacitated fixed-charged facility location-allocation problem is used to determine the optimal solution of the proposed model. The output of the optimal solutions reported that the costs associated to the proposed distribution are lower than the associated costs in the existing distribution system. It is noted that there is US$ 106.57 million saving per year of total cost by applying the proposed distribution system. In the sensitivity analysis, it is indicated that the trade-offs between facility locations and distribution costs are exist. Results report that as the number of facility increases, total transportation and inventory cost also increase.Keywords: Facility Location; Allocation; Inventory; Transportation.

Fuzzy Goal Programming for Linear Facility Location-Allocation in a Supply Chain; The Case of Steel Industry

This paper presents a mathematical model for a facility location-allocation problem in order to design an integrated supply chain. We consider a supply chain including multiple suppliers, multiple products, multiple plants, multiple transportation alternatives and multiple customer zones. The problem is to determine a number and capacity level of plants, allocation of customers demand, and selection and order allocation of suppliers. A multi-objective mixed-integer linear programming (MOMILP) is presented with two conflicting objectives simultaneously. The first objective is to minimize the total costs of a supply chain including raw material costs, transportation costs and establishment costs of plants. The second objective function aims to minimize the total deterioration rate occurred by transportation alternatives. Finally, by applying the fuzzy goal programming, the model is solved as a single objective mixed-integer programming model. An experiment study shows that the proposed procedure can provide a promising result to design an efficient supply chain.

A multi-objective optimization approach for health-care facility location-allocation problems in highly developed cities such as Hong Kong

Public health-care facilities are essential to all communities, and their location/allocation has long been an important issue in urban planning. Given the steady growth of Hong Kong's population, new health-care facilities will need to be built over the next few years. This research examines the problem of where such health-care facilities should be located to improve the equity of accessibility, raise the total accessibility for the entire population, reduce the population that falls outside the coverage range, and decrease the cost of building new facilities. However, because urban areas such as Hong Kong are complex socio-ecological systems, the aforementioned conflicting objectives make it impossible to find one ‘best’ solution that meets all of the objectives. Therefore, this research uses a genetic algorithm based multi-objective optimization (MOO) approach to yield a set of Pareto solutions that can be used to find the most practical tradeoffs between the conflicting objectives. The MOO approach is used to optimize the location of new health-care facilities in Hong Kong for 2020. Because the MOO approach provides a set of diverse plans, planners can compare the value of each objective and the spatial distribution of facilities to analyze or select the solution that best supports their further decisions. Comparing the Pareto solutions with other solutions, it indicates that the MOO approach is a sensible choice for solving multi-objective problems of health-care facility location-allocation in Hong Kong.