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About 10,000 papers for “facility location optimization”

Spatial statistics Simulation Robust optimization Facility location

David Afshartous A, Yongtao Guan B, Anuj Mehrotra C et al. · 2008

a b s t r a c t We study the problem of suitably locating US Coast Guard air stations to respond to emergency distress calls. Our goal is to identify robust locations in the presence of uncertainty in distress call locations. Our analysis differs from the literature primarily in the way we model this uncertainty. In our optimization and simulation based methodology, we develop a statistical model and demonstrate our procedure using a real data set of distress calls. In addition to guiding strategic decisions of placement of various stations, our methodology is also able to provide guidance on how the resources should be allocated across stations.

Cited by 0

Facility Location Optimization for Last-mile Delivery

Brittany C. Collins, Hao Wang · DSpace@MIT (Massachusetts Institute of Technology) · 2019

Technological breakthroughs and consumer preferences have led to e-commerce growth in the United States. Given the continued rise in e-commerce demand, the final leg of transportation of goods to the end recipient, or the “last-mile,” is of increasing importance to freight services companies. The last- mile is one of the most challenging and complex aspects of business for such companies, who must manage the cost of getting items to consumers’ doorsteps against an increasing expectation for level of service and delivery times. The sponsor company seeks to become more competitive by optimizing their delivery network through exploring alternative facility location options to reduce the cost to service last-mile deliveries. Seven metropolitan areas were chosen based on volume and significance to the business: San Francisco, Los Angeles, Chicago, Dallas, Atlanta, Newark and New York City. The research hypothesis was that creating a more strategically-located network of cross-docks, each more proximal to common delivery points, would generate cost efficiencies through a reduction in overall travelling distance. The analytical objective was to explore the tradeoff between the additional facilities’ operating costs and the implied savings on purchased transportation costs. We implored a two-step methodology to explore this tradeoff: a center of gravity analysis to identify potential facility locations based on the concentration of customer demand, and a mixed integer linear programming (MILP) model to identify the cost optimal solution given the balance between the transportation cost reduction and the new facilities’ lease costs. The model output resulted in a shortlist of facilities, locations, and costs for each region. The average distance to serve the customer fell by 14 miles (40%) compared to the baseline average distance of 35 miles, and only 8% of customers remained greater than 50 miles away from a cross-docking facility compared to 14% in the baseline. The proposed facilities were $15 per-square-foot-per-year less than baseline lease fees on a weighted average basis. The key takeaway from the facility location optimization model was that adding facilities reduced the overall network cost by 23%, as the reduction in transportation cost to serve the historical demand outweighed the incremental facility lease fees.

Cited by 1Open access

Distributionally Robust Optimization Utilizing Facility Location Problems

Elijah Kling · 2021

Facility location problems are a used in widespread application in transportation, freight, supply chain, and logistics problems. Models can be developed as deterministic, where all parameters are known, or robust, where a parameter has uncertainty. This thesis explores a new method for developing robust formulation and compares the implications of assuming values for this uncertain parameter. Two models are solved, and both are compared against their deterministic counterparts using numerical analysis. By manipulating the input parameters and considering real world implications of the solutions, either the robust or deterministic can show better performance.

Cited by 0Open access

Metaheuristics for solving facility location optimization problem

Babatunde Opesemowo, Chika Yinka-Banjo · Journal of Computer Science and Its Application · 2020

The increasing demand of optimization to various aspect of real-life complex combinatorial problems have seen a directly increase proportion of the use of metaheuristics as a technique many researchers applied to solve such problems. This is as a result of its flexibility, yet a complex approach that takes into consideration domain specific entities on how the problems are solve. One of such combinatorial problem applicable to real life issues is the facility location problem. Hence, this study sought to describe the concept of facility location problem, the techniques used to solve FLP (facility location problem) and real life application of FLP. Particle swarm optimization, genetic algorithm and tabu search are the most common metaheuristics used as solution tools to solving facility location problem from the review carried out. From the review, we recommended the application of more hybridized techniques to solve FLP in other to achieve better result.Keywords: Metaheuristics, Facility Location Problem (FLP), Optimization

Cited by 5

PARALLEL OPTIMIZATION ALGORITHM FOR COMPETITIVE FACILITY LOCATION

Algirdas Lančinskas, Pilar M. Ortigosa, Julius Žilinskas · Mathematical Modelling and Analysis · 2015

A stochastic search optimization algorithm is developed and applied to solve a bi-objective competitive facility location problem for firm expansion. Parallel versions of the developed algorithm for shared- and distributed-memory parallel computing systems are proposed and experimentally investigated by approximating the Pareto front of the competitive facility location problem of different scope. It is shown that the developed algorithm has advantages against its precursor in the sense of the precision of approximation. It is also shown that the proposed parallel versions of the algorithm have almost linear speed-up when solving competitive facility location problems of different scope reasonable for practical applications.

Cited by 8Open access

Discrete compettitive facility location : Modelling and optimization approaches

Athanasia Karakitsiou, Athanasios Migdalas · Epubl LTU · 2014

Competitive facility location problems are concerned with thefollowing situation: a firm wants to locate a predefined num-ber of facilities to serve customers locate in some region wherethere already exist (or will be) other firms offering the sameservice. Both new and existing firms compete for optimizingtheir market share of profit. A discrete version of such problemsarises when it is assumed that there is a ( rather small ) finitenumber of candidate locations and the markets consist of pointdemands. We review modelling and optimization approaches forthis type of problems and we emphasize and develop the bi-levelprogramming methodology.

Cited by 0Open access

On Some Optimization Problems in Obnoxious Facility Location?

Zhongping Qin, Yingzhuo Xu, Binhai Zhu · 2015

Abstract. In this paper we study the following general MaxMin-optimization problem concerning undesirable (obnoxious) facility loca-tion: Given a set of n sites S inside a convex region P, construct m garbage deposit sites Vm such that the minimum distance between these sites Vm and the union of S and Vm, Vm[S, is maximized. We present a general method using Voronoi diagrams to approximately solve two such problems when the sites S’s are points and weighted convex polygons (correspondingly, Vm’s are points and weighted points and the distances are L2 and weighted respectively). In the latter case we generalize the Voronoi diagrams for disjoint weighted convex polygons in the plane. Our algorithms run in polynomial time and approximate the optimal solutions of the above two problems by a factor of 2. 1

Cited by 0

Facility Location Optimization Model for COVID-19 Resources

Katherine A. Klise, Michael Bynum · 2020

In response to anticipated resource shortfalls related to the treatment and testing of COVID-19, many communities are planning to build additional facilities to increase capacity. These facilities include field hospitals, testing centers, mobile manufacturing units, and distribution centers. In many cases, these facilities are intended to be temporary and are designed to meet an immediate need. When deciding where to place new facilities many factors need to be considered, including the feasibility of potential locations, existing resource availability, anticipated demand, and accessibility between patients and the new facility. In this project, a facility location optimization model was developed to integrate these key pieces of information to help decision makers identify the best place, or places, to build a facility to meet anticipated resource demands. The facility location optimization model uses the location of existing resources and the anticipated resource demand at each location to minimize the distance a patient must travel to get to the resource they need. The optimization formulation is presented below. The model was designed to operate at the county scale, where patients are grouped per county. This assumption can be modified to integrate other scales or include individual patients.

Cited by 5Open access

Facility Location Problem with Cost and Radius Optimization

Chao Yang · 2012

The facility location problem with cost and radius optimization(FLCRO) has a wide range of applications in emergency response,logistics,maintenance service as well as express delivery.By considering the relationship of response time,service benefits and service costs,the problem seeks a decision on whether the distant customers are brought into the range of service radius or not.Both service cost and service radius are taken into consideration in the model.We construct a mixed 0-1 integer programming model for the problem and present a 7.853+e-approximation algorithm for FLCRO on a plane.We also provided a heuristic algorithm based on benders' decomposition which is very effective in solving FLCRO.

Cited by 0

Spatial Analysis-Driven Facility Location Optimization for Vertiports

ERKEK, Elif, Lindner, Martin, Fricke, Hartmut · Zenodo (CERN European Organization for Nuclear Research) · 2025

<p><strong>Abstract</strong><br>Innovative Air Mobility (IAM) integrates vertical take-off and landing (VTOL)-capable aircraft (VCA), specifically electric VTOLs (eVTOLs), into urban and suburban transportation systems and infrastructure. The success of this integration depends on the strategic placement of takeoff/landing sites (vertiports), representing central nodes for IAM operations. This study presents a two-step optimization approach for allocating demand from urban subareas to potential vertiport locations. In the first step, spatial analysis and Anselin Local Moran’s I clustering method are used to identify candidate vertiport points. In the second step, a linear location allocation model is developed to assign these demand points to the most suitable vertiports while considering operational constraints such as VTOL flight range. A case study in Saxony, Germany, demonstrates how demand distribution and accessibility influence vertiport selection. By integrating technological and urban planning constraints, this study provides a structured approach to optimizing IAM networks, supporting systematic and scalable IAM infrastructure planning.<br><br><strong>Note</strong><br>This manuscript has also been submitted to EasyChair Preprints (currently under processing).</p>

Cited by 0Open access

Location cost based method for traffic rescue facility location determination and optimization

Chai Ga · 2014

Based on the combined consideration on location costs and practical constraints from current rescue points,a location cost based method was proposed to improve the practicability and scientific value of traffic rescue facility location selection.By changing the penalty parameters and maintaining the existing rescue points selectively,a location selection plan meeting the requirements of all the constraints is constructed.Keeping the number of existing and new rescue point a constant,a unique solution is generated with minimal total weighted rescue time.Considering the difference in construction costs of candidate rescue points,the total construction cost can be controlled precisely.Compared with traditional methods,twice more existing locations can be preserved through this method and multi-objective optimization can be achieved in terms of total construction cost and total weighted rescue time,realizing effectively the balance between cost and efficiency and having the ability of real world implementation.

Cited by 0

Facility Location and Supply Chain Optimization for a Biorefinery

Ian Michael Bowling, José María Ponce‐Ortega, Mahmoud M. El‐Halwagi · Industrial & Engineering Chemistry Research · 2011

This paper presents a systematic approach for the optimal production planning and facility placement of a biorefinery. A structural representation is first developed to include sources of biomass feedstock, distributed preprocessing hubs, and centralized processing facilities to produce desired products and byproducts. An optimization formulation is developed to determine the optimal supply chain, size, operational strategies, and location of the biorefinery and preprocessing hub facilities. The model considers simultaneously the optimal selection of different configurations considering the specific location configuration (centralized and/or distributed), selection of biomass, and processing facilities to determine the one with the maximum overall net profit. The objective function considers the overall sales and the costs for the feedstocks, transportation costs, capital costs for the facilities, and the operational costs for the facilities. The model also considers nonlinear economy-of-scale behavior of the capital-cost functions that are reformulated using disjunctive models to yield convex relationships to guarantee a global optimal solution. The proposed model was applied to two case studies.

Cited by 180