Optimal Facility Location Research Articles

Location Planning of Emergency Medical Facilities Using the p-Dispersed-Median Modeling Approach

This research employs a spatial optimization approach customized for addressing equitable emergency medical facility location problems through the p-dispersed-median problem (p-DIME). The p-DIME integrates two conflicting classes of spatial optimization problems, dispersion and median problems, aiming to identify the optimal locations for emergency medical facilities to achieve an equitable spatial distribution of emergency medical services (EMS) while effectively serving demand. To demonstrate the utility of the p-DIME model, we selected Gyeongsangbuk-do in South Korea, recognized as one of the most challenging areas for providing EMS to the elderly population (aged 65 and over). This challenge arises from the significant spatial disparity in the distribution of emergency medical facilities. The results of the model assessment gauge the spatial disparity of EMS, provide significantly enhanced solutions for a more equitable EMS distribution in terms of service coverage, and offer policy implications for future EMS location planning. In addition, to address the computational challenges posed by p-DIME’s inherent complexity, involving mixed-integer programming, this study introduces a solution technique through constraint formulations aimed at tightening the lower bounds of the problem’s solution space. The computational results confirm the effectiveness of this approach in ensuring reliable computational performance, with significant reductions in solution times, while still producing optimal solutions.

Sustainable management of EOL tyres: A remanufacturing supply chain network design

End-of-life (EOL) objects such as tyres have been categorised as hazardous to the environment due to heavy metals and chemicals. To manage the recovery and remanufacturing operations of these EOL items, an effective network design is necessary. In this paper, we developed a mixed integer linear programming (MILP) model to minimize the total cost of the proposed EOL tyre remanufacturing supply chain network. Since, the problem is Non-deterministic Polynomial time hardness (NP-hard) in nature, we propose a prediction model that evaluates the operational feasibility of vehicle allocation and an evolutionary algorithm-based technique is employed to identify optimal facility locations, material flows among facilities, and demand distribution to individual automobiles for near-optimal solutions. Numerical experiments were conducted on a leading tyre remanufacturing company as a case study, and the results were compared with several evolutionary algorithms found that the Modified Genetic Algorithm (MGA) outperforms other algorithms. We demonstrate how, in each case, an optimal cost-effective location for a factory is found based on the cost and demand of the products. The study findings can assist governments and industry stakeholders in developing successful EOL tyre management strategies.

Repurposing existing skeletal spatial structure (SkS) system designs using the Field Information Modeling (FIM) framework for generative decision-support in future construction projects

Skeletal spatial structure (SkS) systems are modular systems which have shown promise to support mass customization, and sustainability in construction. SkS have been used extensively in the reconstruction efforts since World War II, particularly to build geometrically flexible and free-form structures. By employing advanced digital engineering and construction practices, the existing SkS designs may be repurposed to generate new optimal designs that satisfy current construction demands of contemporary societies. To this end, this study investigated the application of point cloud processing using the Field Information Modeling (FIM) framework for the digital documentation and generative redesign of existing SkS systems. Three new algorithms were proposed to (i) expand FIM to include generative decision-support; (ii) generate as-built building information modeling (BIM) for SkS; and (iii) modularize SkS designs with repeating patterns for optimal production and supply chain management. These algorithms incorporated a host of new AI-inspired methods, including support vector machine (SVM) for decision support; Bayesian optimization for neighborhood definition; Bayesian Gaussian mixture clustering for modularization; and Monte Carlo stochastic multi-criteria decision making (MCDM) for selection of the top Pareto front solutions obtained by the non-dominant sorting Genetic Algorithm (NSGA II). The algorithms were tested and validated on four real-world point cloud datasets to solve two generative modeling problems, namely, engineering design optimization and facility location optimization. It was observed that the proposed Bayesian neighborhood definition outperformed particle swarm and uniform sampling by 34% and 27%, respectively. The proposed SVM-based linear feature detection outperformed k-means and spectral clustering by 56% and 9%, respectively. Finally, the NSGA II algorithm combined with the stochastic MCDM produced diverse “top four” solutions based on project-specific criteria. The results indicate promise for future utilization of the framework to produce training datasets for generative adversarial networks that generate new designs based only on stakeholder requirements.

Improving the Logistics of Institutional Postal Services

Postal services are often considered by the different institutions as a back-up service. Taking into account that in case of large geographical coverage these services can generate significant costs, it is important to optimize the postal service functions within these institutions and to develop the already existing conventional postal services. Within the frame of this article the authors review the logistics processes of the postal services at the University of Miskolc to propose improvements to the processes of postal services focusing on delivery. The main regulatory elements and the logistics processes of the institutional postal services are discussed and some critical remarks are described focusing on three main areas: the facility location optimization of the institutional postal services, the lack of use of modern technologies and the lack of modern administrative facilities. On the basis of these criticisms, the authors define recommendations for improving institutional postal services. Regarding the facility location optimization problem, a mathematical model and an optimization algorithm is described to evaluate the efficiency of the logistics processes of delivery services. The proposed methodology supports a cost-based analysis of different solution variants and a priority-based differentiation between institutions and offices. The suggested approach is tested with a real scenario, where the numerical results validate the efficiency of the evaluation of optimization algorithm. The second improvement direction is the application of a pneumatic tube system. The authors show the most important parts of the proposed pneumatic tube system, which can lead to decreased labor cost and increased service level.

On-demand last-mile distribution network design with omnichannel inventory

E-commerce delivery deadlines are getting increasingly tight, driven by a growing ‘I-want-it-now’ instant gratification mindset of consumers and the desire of online and omnichannel retailers to capitalize on the growth of on-demand e-commerce. On-demand deliveries with delivery deadlines as tight as one or two hours force companies to rethink their last-mile distribution network, since tight delivery deadlines require decentralization of order picking and inventory holding to ensure close proximity to consumers. This fundamentally changes the strategic design process of last-mile distribution networks. We study the impact of incorporating inventory order-up-to level decisions into the strategic design process of last-mile distribution networks with tight delivery deadlines. We develop an approximate inventory model by including an estimate of the cost of late delivery and additional transportation due to local stock-outs in a newsvendor formulation. Such local stock-outs require an order to be delivered from a more distant facility, which may lead to late delivery and additional transportation cost. We integrate our approximate inventory model and a location–allocation mixed-integer program that determines optimal facility locations, associated order-up-to inventory levels, and fleet composition, into a metamodel simulation-based optimization approach. Our numerical analyses demonstrate that pooling the additional online inventory with brick-and-mortar (B&M) inventories leads to cannibalization by the B&M network and higher B&M service levels. However, the pooling benefits to the online network outweigh the cost of inventory cannibalization. Furthermore, we show under which circumstances omnichannel retailers may have an incentive to consolidate online inventory in specific B&M facilities.

A method for determining the optimal number and location of biomass energy facilities

The site selection of biomass energy facilities has always been a key part of energy spatial planning. The site suitability evaluation criteria of the existing studies are not comprehensive. On the other hand, most of the existing studies are to determine the only site, while less research on the multiple-facility planning. The aim of this paper is to identify the most effective number and location for biomass energy facilities. To achieve this objective, the Geographic Information System (GIS) is utilized to perform the following tasks: Generate a site suitability map for potential biomass energy facilities and identify suitable site candidates. The standardization of site suitability evaluation indicators is based on fuzzy logic, and indicator weights are determined based on the Analytic Hierarchy Process (AHP) evaluation of experts' opinions. 2. Develop planning schemes for biomass energy facilities for various number of proposed facilities, and subsequently determine the optimal scheme using multi-objective fuzzy comprehensive evaluation. The weight of each indicator is again determined using the AHP method. Following the analysis, it was found that in the case study of Fuxin City, the plans of 1 and 40 biomass energy facilities can achieve the lowest cost and the highest energy self-sufficiency level. However, both options have potential drawbacks that must be considered. The plan of 30 energy facilities has the highest comprehensive benefits, corresponding to the 30,919.75 yuan of transport cost (3748 yuan lower than the average transport cost) and 75.49% of energy self-sufficiency (67.21% of the average value). This work maximizes the comprehensive positive impacts in economic, environmental and social aspects.

An agent-based simulation and logistics optimization model for managing uncertain demand in forest supply chains

This paper aims to model and minimize transportation costs in collecting tree logs from several regions and delivering them to the nearest collection point. This paper presents agent-based modeling (ABM) that comprehensively encompasses the key elements of the pickup and delivery supply chain model and presents the units as autonomous agents communicating. The modeling combines components such as geographic information systems (GIS) routing, potential facility locations, random tree log pickup locations, fleet sizing, trip distance, and truck and train transportation. ABM models the entire pickup and delivery operation, and modeling outcomes are presented by time series charts such as the number of trucks in use, facilities inventory, and travel distance. In addition, various simulation scenarios are used to investigate potential facility locations and truck numbers and determine the optimal facility location and fleet size.

A Fermatean fuzzy MCDM method for selection and ranking Problems: Case studies

The fuzzy set theory has been evolving to represent the uncertainty in the real-world decision-making environment. Literature has been steadily expanding to incorporate subjective judgments and ambiguous information in the decision-making process, aiming to enhance the reliability and flexibility of data representation. Fermatean Fuzzy Sets (FFSs), a recent extension of intuitionistic fuzzy sets, address the limitations associated with membership functions and the representation of hesitation in multi-criteria decision-making (MCDM) methods. The aim of this study is to examine the performance of FFSs in exploiting uncertainty in selection and ranking decisions in MCDM problems. The performance of FFSs is investigated for Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) which is extended with Stepwise Weight Assessment Ratio Analysis (SWARA) method for criteria evaluations. This study is designed to present a comparative analysis for three different types of fuzzy set definitions: classical fuzzy sets with fuzzy triangular numbers, Intuitionistic Fuzzy Sets (IFSs), and FFSs, for MCDM problems under uncertainty. The proposed methodology is applied to two real case studies: (i) to rank Turkish research universities for performance assessment and (ii) to select the optimal facility location for a company in the beverage industry. To assess the effectiveness of the FFSs in multiple criteria selection and ranking decisions, a comparative analysis is conducted on two real-world problems. The results show that FFSs provide valuable insight especially for multiple criteria ranking problems. The comparative analysis highlights the effectiveness of Fermatean Fuzzy SWARA-TOPSIS method and its potential for practical ranking applications.

Presenting a Model for Locating and Allocating Multi-Period Hubs and Comparing It With a Multi-Objective Imperialist Competitive Algorithm

Abstract Recently, air pollution has received much attention as a result of reflections on environmental issues. Accordingly, the hub location problem (HLP) seeks to find the optimal location of hub facilities and allocate points for them to meet the demands between source-destination pairs. Thus, in this study, decisions related to location and allocation in a hub network are reviewed and a multi-objective model is proposed for locating and allocating capacity-building facilities at different time periods over a planning horizon. The objective functions of the model presented in this study are to minimize costs, reduce air pollution by diminishing fuel consumption, and maximize job opportunities. In order to solve the given model, the General Algebraic Modeling System (GAMS) along with innovative algorithms are utilized. The results presented a multi-objective sustainable model for full-covering HLP, and provided access to a hub network with minimum transport costs, fuel consumption, and GHG (greenhouse gas) emissions, and maximum job opportunities in each planning horizon utilizing MOICA (multi-objective imperialist competitive algorithm) and GAMS to solve the proposed model. The study also assessed the performance of the proposed algorithms with the aid of the QM, MID, SM, and NSP indicators, acquired from comparing the proposed meta-heuristic algorithm based on some indicators, proving the benefit and efficiency of MOICA in all cases.

Points-of-Dispensing (PODs) 위치를 고려한 긴급물류창고 위치 선정

The significance of choosing optimal locations for emergency facilities is emphasized both in natural disasters and public health emergencies like pandemics. To ensure effective management of emergency logistics, procurement, and distribution, it is crucial to consider the establishment of facilities such as Points-of-Dispensing (PODs) that facilitate fast and efficient distribution. Consequently, when deciding on the location of logistics warehouses as part of emergency logistics management, it is essential to take into account the positioning of temporary facilities like PODs. We propose an integer programming model that integrates the Maximal Covering Location Problem (MCLP) and includes capacity constraints. Additionally, we conducted a case study in Incheon Metropolitan City to compare the outcomes. Through this case analysis, we present the optimal subset of temporary logistics warehouses, enabling the development of an optimal location-distribution plan. The study holds three main implications: (1) it provides an optimized selection of locations that aids in the decision-making process for policies; (2) the empirical analysis using real data allows for its application in similar disaster management situations; (3) the proposed model can be utilized to determine the positioning of various public facilities

Modified and Improved Algorithm for Finding a Median Path with a Specific Length (ℓ) for a Tree Network

The median path problem (min-sum criterion) is a common problem in graph theory and tree networks. This problem is open to study because its applications are growing and extending in different fields, such as providing insight for decision-makers when selecting the optimal location for non-emergency services, including railroad lines, highways, pipelines, and transit routes. Also, the min-sum criterion can deal with several networks in different applications. The location problem has traditionally been concerned with the optimal location of a single-point facility at either a vertex or along an edge in a network. Recently, numerous investigators have investigated this classic problem and have studied the location of many facilities, such as paths, trees, and cycles. The concept of the median, which measures the centrality of a vertex in a graph, is extended to the paths in a graph. In this paper, we consider the problem of locating path-shaped facilities on a tree network. A new modified and improved algorithm for finding a median single path facility of a specified length in a tree network is proposed. The median criterion for optimality considers the sum of the distances from all vertices of the tree to the path facility. This problem under the median criterion is called the ℓ-core problem. The distance between any two vertices in the tree is equal to the length of the unique path connecting them. This location problem usually has applications in distributed database systems, pipelines, the design of public transportation routes, and communication networks.

Assessing trauma center accessibility in the Southeastern region of the U.S. to improve healthcare efficacy using an anti-covering approach.

Accessibility to trauma centers is vital for the patients of severe motor vehicle crashes. Many vehicle crash fatalities failed to reach the proper emergency medical services since the accident location was far away from trauma centers. The spatial discordance between the service coverage area of trauma centers and actual locations of motor vehicle accidents delays the definitive medical care and results in death or disability. Many fatalities would have been prevented if the patients had a chance to get proper treatment in time at Southeastern region of the U.S. Also, the accessibility to trauma centers from the actual locations of motor vehicle accidents is different in the Southeastern region. This research aimed to facilitate the accessibility to trauma centers for severe motor vehicle crash patients in the Southeastern region. The analyses are conducted to assess current trauma center accessibility and suggest the optimal locations of future trauma centers using the Anti-covering location model for trauma centers (TraCt model). This study found that existing trauma centers failed to serve many demands, and the actual coverages of the current locations of trauma centers over potential demands are highly different in each Southeastern state. TraCt model is applied to each Southeastern state, and its solutions provide better coverage for demand locations. However, the TraCt model for each state tends to choose too many facilities, with excessively supplied facilities across the Southeastern region. The excessive service supply issue is addressed by applying the TraCt Model to a broader spatial extent. TraCt model applied to the entire Southeastern region and most of the demand, over 98% covered by the service coverage of optimal facility locations with 15 additional facilities. This research proves that the GIS and TraCt model applied to the broader spatial extent works well with increasing trauma medical service beneficiaries while providing a minimum number of additional facilities.

Primary Health Care Accessibility in Zomba, Malawi: A Spatial Modelling Approach

Abstract. Access to primary health care (PHC), as the first point of care, is an essential factor in influencing the health status of a society. In Malawi, an individual is considered to have access to health care if they are within an 8 km radius to a health facility. Based on this, the Health Sector Strategic Plan (HSSP) II, 2017–2022 puts access to PHC in Malawi at 90 %. However, a Demographic Health Survey (DHS) reports that 56 % of women find distance as a major barrier. As such the aim of the study was to model spatial access to health of households in the district. The study used the 2 Step Floating Catchment Area (2FCSA) with Gaussian decay function method to model health care access and the greedy algorithm to find optimal locations for new facilities. The study found that 62% households have the potential of walking 60 minutes to the facility, a figure 28 % lower than the one reported by HSSP. With 2SFCA scores ranging from 0 to 0.19, the results show that 98 % and 57 % of the households scored between 0.031 to 0.046 for urban and rural areas respectively. These scores imply that urban households have better spatial accessibility than rural households. The results provide a spatially objective PHC accessibility data to inform policy direction.

The development of a framework for the selection of optimal sites for the location of municipal solid waste to value‐added facilities through the integration of a geographical information system and a fuzzy analytic hierarchy process

AbstractThis study develops a framework for quantification of the available municipal solid waste (MSW), defines the geographical point source locations for the MSW feedstock, and determines optimal locations for potential waste‐to‐value‐added (W2VA) facility sites. The framework developed here is applied to Canada. To determine optimal sites for W2VA facilities, a three‐stage decision‐making model comprising exclusion analysis, preference analysis, and network analysis was developed. The fuzzy analytic hierarchy process (FAHP) and geographic information systems (GIS) were used in an integrated decision‐making network to prioritize the preference factors and facility locations; with these, a land suitability map (LSM) was developed and suitable candidate sites for W2VA facilities were obtained for Canada. The identified candidate sites were then used in a network analysis to select optimal sites with minimal travel distances. This study was used to determine 10 and 15 optimal sites for W2VA facilities in Western Canada and Eastern Canada, respectively. This study prioritized optimal sites based on the minimization of transportation distances. The highest priority site in Western Canada is Site 1, located in Lethbridge County, Alberta. It is connected with 74 TSs and can receive 2.42 million tonnes of MSW. The highest priority site in Eastern Canada is Site 11, which is located in West Nipissing, Ontario, and is connected with 42 transport stations (TSs) and can receive 1 million tonnes of MSW. The adaptability of the applied decision‐making model, competency of the developed LSM, and flexibility of the network analysis provide a competent supporting tool for authorities to identify optimal locations for W2VA facilities.

A location-inventory model for the sustainable supply chain of perishable products based on pricing and replenishment decisions: A case study.

In recent decades, changes in people's lifestyles and occupations have led to new food consumption patterns around the world, with a notable growth in the demand for ready meals and meat products. Accordingly, the food industry has tried to transform the global food culture by promoting its more profitable products to become able to set a higher price for meat. Considering the short lifetime of products in perishable food supply chains, inventory decisions are considered crucial. In addition, the demand for perishable food products is greatly affected by their freshness. In this paper, we develop a multi-objective mixed-integer non-linear programming model for a four-level sustainable supply chain (SC) of a perishable product with price-dependent demand and deterioration rates. The SC consists of suppliers, a production center (PC), distribution centers (DCs), and retailers. We aim to ascertain the optimal pricing policy and cycle length to maximize profit, achieve specific social objectives, and minimize the total cost and adverse environmental impacts. The proposed model determines the deterioration rate of each product according to its expiration date, the optimal location of supply-side facilities, and the flow rate between the facilities based on the selected mode of delivery. The products are delivered to the retailers by a mechanized transportation system either directly from the PC or indirectly through the DCs. Finally, an actual case study is provided to demonstrate the applicability of the model and our theoretical results under real-world conditions. We solve the case study by a preemptive fuzzy goal programming method and perform several sensitivity analyses on the results. By performing simultaneous sensitivity analyses on the demand and expiration dates, the optimal values of the problem's parameters are determined. The optimal values help decision-makers make optimal decisions regarding the selling price of products and replenishment times. The model is applicable to supply chains of any perishable items with expiration dates.

Bi-objective robust optimisation on relief collaborative distribution considering secondary disasters

ABSTRACT Developing an effective emergency collaborative distribution system is critical to improve on-time response performance, especially considering secondary disasters. To address the challenge, this paper investigates a bi-objective robust optimisation model on relief collaborative distribution among three echelons of authorities, the province, the municipality, and the county, which aims to minimise the total travel time and the total humanitarian logistics cost simultaneously. The optimal location of relief supply facilities and the relief distribution schemes will be determined by the optimisation model, which considers uncertain demand and travel time. Moreover, two robust optimisation methods are utilised to deduce the robust counterparts of the proposed model. An epsilon-constraint-based approach is used to solve the bi-objective optimisation model. A real-world case study based on an earthquake and aftershocks with different magnitudes in Yunnan Province is provided. The results show that incorporating secondary disaster scenarios contributes to reducing the total travel time and cost. For making full use of emergency resources and preventing situations from worsening, the centralised decision scheme is more effective than the decentralised one. The uncertainty of demand of primary disaster relief has a higher impact on the optimal solution than that of travel time.

Method for determining the optimal location of public catering facilities

Relevance and goals. The relevance of the research topic is due to the need for a comprehensive study of the problems of choosing a spatial location in the context of the rapid spread of digital technologies. The purpose of the study is an attempt to substantiate the feasibility of implementing a technical solution, namely the development of a digital geographic information system aimed at determining the optimal location for public catering facilities. Materials and methods. An approach is proposed for optimizing the layout of facilities as a set of three interrelated sequential elements (stages), consisting of determining the best operational-territorial unit, determining the expected rational boundaries of the consumer service market, determining the coordinates of the location of the catering facility and finding the optimal structure for the sale of products that provides the facility catering maximum profit. The advantage of the considered technical solution is to increase the accuracy of the optimal placement of public catering facilities, which ensures an increase in the speed of actions to determine the location point, including ensuring the speed of response of public catering facilities to continuous changes in dynamic competitive markets in real time; the emergence of the ability to most reliably draw conclusions about the optimal placement of a public catering facility at any time. The research methodology is based on the use of general scientific methods: description, measurement, analysis, synthesis, comparison, modeling, as well as the use of statistical methods and linear programming. Results. Creation in the future of a program that implements the proposed model and algorithms. Conclusions. The results of the study showed the possibility of applying the proposed approach with the ability to determine the expected optimal location of the catering facility with great accuracy. The scope of the research results is associated with the subsequent creation of information technology to manage the optimization of the location of public catering facilities.

Facility location planning to mitigate strategic conflict in joint operations

PurposeThis paper aims to deal with a real-life strategic conflict in joint operations (JOs) for facility location decision and planning in an oil and gas field that stretches over two countries and tries to develop a basis for mitigating such conflict.Design/methodology/approachThis paper develops a novel approach using integer linear programming (ILP) to determine optimal facility location considering technical, economic and environmental factors. Strategic decision-making in JOs is also influenced by business priorities of individual partner, sociopolitical issues and other covert factors. The cost-related quantitative factors are normalized using inverse normalization function as these are to be minimized, and qualitative factors that are multi-decision-making criteria are maximized, thus transforming both qualitative and quantitative factors as a single objective of maximization in ILP model.FindingsThe model identifies the most suitable facility location based on a wide range of factors that would provide maximum benefit in the long term, which will help decision-makers and managers.Research limitations/implicationsThe model can be expanded incorporating other quantitative and qualitative factors such as tax incentives by the government, local bodies and government regulations.Practical implicationsThe applicability of the model is not limited to JOs or oil/gas field, but is applicable to a wide range of sectors.Originality/valueThe model is transparent and based on rational and scientific basis, which would help in building consensus among the dissenting parties and aid in mitigating strategic conflict. Such type of model for mitigating strategic conflict has not been reported/used before.

Emergency resource storage facility location problem considering domino effect after a disaster

PurposeThe earth is facing challenges to work for the survival of human life during domino effect disasters. The emergency resource storage locations should be selected considering the probability of domino effect disasters. The first purpose of this study is to select the storage locations where domino effect probability is less. And second, facility development cost and transportation costs and costs for unutilized capacity have been optimized.Design/methodology/approachThe work is a multiobjective optimization problem and solved with weighted sum approach. At first, the probabilities of domino effect due to natural disasters are calculated based on the earthquake zones. Then with that result along with other necessary data, the location to set up storage facilities and the quantities of resources that need to be transported has been determined.FindingsThe work targeted a country, Bangladesh for example. The authors have noticed that Bangladesh is currently storing relief items at warehouse which is under the domino effect prone region. The authors are proposing to avoid this location and identified the optimized cost for setting up the facilities. In this work, the authors pointed out which location has high probability of domino effect and after avoiding this location whether cost can be optimized, and the result demonstrated that this decision can be economical.Originality/valueDisaster response authorities should try to take necessary proactive steps during cascading disasters. The novelty of this work is determining the locations to select storage facilities if the authors consider the probability of the domino effect. Then a facility location optimization model has been developed to minimize the costs. This paper can support policymakers to assess the strategies for selecting the location of emergency resource facilities.

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...