Facility Location Research Articles

Extensions to Competitive Facility Location with Multi-purpose Trips

Extensions to Competitive Facility Location with Multi-purpose Trips

Implementasi Algoritma Rivest Shamir Adleman (RSA) Untuk Keamanan Data Rekam Medik Penyakit Pasien Rumah Sakit

Medical records are records of the who, what, where, and how of patient care while in hospital. One aspect of the use of medical records is the documentation aspect, namely medical records contain important information that is useful for various parties. Medical records contain data regarding past and present health and contain health professional notes regarding the patient's current condition in the form of physical findings, results of diagnostic and therapeutic procedures and patient responses. Medical record documents belong to doctors, dentists and health service facilities, while the contents of medical records belong to the patient. What can be given, recorded or duplicated by the patient is a summary of the medical record. Patients have the right to know the contents of medical records, however, the entire medical record file can only be held by authorized health/medical records officers and cannot leave the location of the health facility. This aims to maintain medical confidentiality (because medical information can be misused) and prevent medical records from being lost or damaged. To overcome this problem, a computerized system is needed that is able to secure medical record data so that unauthorized parties cannot read it. One technique that can be used to secure medical record data is cryptography. Cryptography comes from Greek which consists of the words kryptos which means hidden and graphia which means something written so that cryptography can be interpreted as something written in secret or hidden.. In applying the Rivest Shamir Adleman (RSA) method in securing medical record data at home Mitra Sejati Medan Hospital, namely by inserting program coding from Shamir Adleman's Rivest method into the programming language used. Next, form a public and private key, then carry out the encryption process for medical record data

Location selection for a lumber drying facility via a hybrid Pythagorean fuzzy decision-making approach

The strategic selection of facility locations plays a critical role in optimizing operational efficiency, reducing costs, and enhancing customer satisfaction, thereby contributing significantly to the success and competitiveness of businesses. In this study, an interval-valued Pythagorean fuzzy decision-making framework is proposed to select the best location for the lumber drying industry. A four-level hierarchical model is devised with four main criteria, 16 subcriteria, and five alternatives. The opinions of different experts are gathered to obtain input data. The weights of the criteria are calculated using the interval-valued Pythagorean fuzzy analytic hierarchy process (AHP) method. The interval-valued Pythagorean fuzzy weighted aggregated sum product assessment (WASPAS) method is employed to evaluate the alternative locations. A sensitivity analysis is conducted to support the validity of the model results. The study concludes by revealing the optimal location for the lumber drying industry in Turkey. This study presents its novelty by formulating the lumber drying facility location selection problem as a complex fuzzy multicriteria decision-making problem and integrating the Pythagorean fuzzy AHP and WASPAS methods to solve the problem.

Fixed set search matheuristic applied to the knapsack problem with forfeits

This paper focuses on solving the knapsack problem with forfeits (KPF). This variation of the knapsack problem includes soft conflicts or forfeits, where forfeit pairs consist of two items and an associated penalty, which must be subtracted from the total profit if both items in the pair are chosen to be jointly in the knapsack. The proposed method combines the fixed set search (FSS) metaheuristic’s learning mechanism with integer programming to solve subproblems. A new ground set of elements for the KPF is introduced to augment the information provided by the fixed set, and the method for creating fixed sets is adjusted to increase the diversity of solutions. The conducted computational experiments show that the proposed method significantly outperforms current state-of-the-art methods and finds a large number of best-known solutions for the standard test instances. Moreover, the method performs well across a wide range of parameter values. The proposed approach does not significantly exploit any specific properties of the KPF and could potentially be applied to other 0–1 problems, such as the minimum vertex cover problem or the facility location problem, without significant modifications. Additionally, the method’s simplicity makes it suitable for hybridization with other metaheuristic approaches or the incorporation of specific KPF properties to improve its performance.

A 3-Approximation Algorithm for Uniform Capacitated Facility Location Problem with Penalties

We introduce an algorithm based on local search for “Uniform Capacitated Facility Location Problem with Penalties (UCFLPP)” and analyze the same. The algorithm is same as given by Korupolu et al. for the “Uniform Capacitated Facility Location Problem (UCFLP)”. Aggarwal et al. in their work proved that the algorithm given by Korupolu et al. is a 3-factor approximation algorithm for UCFLP. We extend idea of Aggarwal et al. to show that the same algorithm works for the problem with penalty incorporated. It has the same approximation guarantee for UCFLPP, as given by them. This improves upon the current best of 5.732 factor for the problem, which is an LP based algorithm by Lv and Wu.

Stochastic home care transportation with dynamically prioritized patients: An integrated facility location, fleet sizing, and routing approach

We study a home health care (HHC) problem that is characterized by prioritized patients and uncertain demands. In practice, HHC supply chain networks often struggle to meet high demand due to a shortage of service vehicles. Additionally, disruptions caused by natural calamities and pandemics (e.g., COVID-19) further compound these challenges, necessitating the consideration of real-life characteristics such as patient priorities, infrastructure locations, and transportation of medical supplies with uncertain demands. To formulate the problem, we propose a multi-depot and multi-period chance-constrained optimization model with precedence constraints, assuming that the demand quantities for medical supplies are random variables. Since patients’ medical conditions vary in severity, the priority of each patient is translated into a time-dependent potential healthcare cost that changes dynamically over the planning horizon. The solution to the proposed model determines the optimal locations for the base Mobile Health Facilities (MHFs) and the fleet size of HHC vehicles, and generates scheduling and routing plans to visit patients within specified time windows. We propose a unique three-phase solution approach, integrated with stochastic simulation, to address the problem. We then assess the robustness of the proposed model based on a realistic case of HHC service provision in Hong Kong and explore the optimal values for two model parameters, namely the Vehicle Threshold Index and the MHF Threshold Index. The performance evaluation tests show that the proposed solution method is efficient and effective for solving real-world problems.

River Morphological Change and Sedimentation Countermeasures Analysis in Miu River

One major floods problem in Miu River is huge amount of sediment which transported along the river channel. The decreasing of river capacity has been analysed and some channel courses changes also detected. These problems caused river morphological change and it is necessary to find comprehensive solution which not only decreasing the flood damage but also consider appropriate approach to natural environment. The complexity of these problems and possible solutions to overcome flood and sedimentation problem were investigated. Increasing of river capacity by channel excavation, protecting riverbank with revetment and construction of sediment control facilities were proposed as main solution. However, it is necessary to evaluate the effectiveness of these efforts by analysed sediment control facilities location and dimension of the structures. Some alternatives were investigated in order to find most effective solution to solve flood and sedimentation problem in Miu River.

Deep learning enhanced mixed integer optimization: Learning to reduce model dimensionality

This work introduces a framework to address the computational complexity inherent in Mixed Integer Programming (MIP) models by harnessing the potential of deep learning. By employing deep learning, we construct problem-specific heuristics that identify and exploit common structures across MIP instances. We train deep learning models to estimate complicating binary variables for target MIP problem instances. The resulting reduced MIP models are solved using standard off-the-shelf solvers. We present an algorithm for generating synthetic data enhancing the robustness and generalizability of our models across diverse MIP instances. We compare the effectiveness of (a) feed-forward neural networks (ANN) and (b) convolutional neural networks (CNN). To enhance the framework's performance, we employ Bayesian optimization for hyperparameter tuning, aiming to maximize the occurrence of global optimum solutions. We apply this framework to a flow-based facility location allocation MIP formulation that describes long-term investment planning and medium-term tactical scheduling in a personalized medicine supply chain.

Elderly care facility location in the face of the climate crisis: A case study in Canada

The climate crisis poses a dual threat to the environment and human health, with older adults being particularly vulnerable. This study aims to establish a network of elderly care facilities designed to respond to climate-induced disasters—an essential element in creating resilient cities that address the challenges of elderly care within the context of the climate crisis. First, we formulate an equation grounded in expert opinions to estimate demand in each region based on factors such as the elderly population, average age, frailty, and proximity to healthcare service nodes. We then introduce an Integrated Coverage and Distance Facility Location problem (ICDFL), a novel approach tailored to address the unique needs of seniors during disasters while considering governmental budget constraints. The ICDFL, therefore, simultaneously pursues two key objectives: (i) maximizing coverage while respecting a maximum allowable coverage radius, and (ii) minimizing overall travel distance. The first objective ensures the resilience of elderly health, guiding healthcare policymakers, while the second considers transportation and service costs faced by government agencies. To address dual-objective optimization, we utilize the epsilon-constrained optimization technique, which enables precise management of both coverage and distance goals through an iterative problem-solving approach. We apply the proposed model to a real-world case study in southern Ontario, Canada, to validate its effectiveness. This is an important aspect of the study, as Canada’s warming trend is happening twice as fast as the rest of the world and its elderly population is expanding at an alarming rate. The results of our model provide guidance to policy makers and healthcare planners in improving emergency preparedness, thus creating a sustainable community for our growing elderly population and improving the well-being of older people during climate-induced disasters.

An Assessment of Accessibility from a Socially Sustainable Urban Mobility Approach in Mass Transit Projects: Contributions from the Northern Central American Triangle

This article aims to address the lack of research on the social dimension of sustainability, also known as social sustainability, in urban mobility projects, primarily in cities of the Global South. It proposes a strategy to partially assess social sustainability, focusing on accessibility, which is one of the key dimensions for conducting such an evaluation. To this end, a comparative analysis of three study cases is conducted in the capital cities of the Northern Central American Triangle (NCAT) before and after the construction of bus rapid transit (BRT) projects between 2000 and 2020. Accessibility is evaluated through equity and spatial efficiency indicators obtained through geographical information system (GIS) modeling, including layers representing transportation networks, populated areas, and locations of basic urban facilities. The result is an unprecedented assessment of accessibility in the NCAT capitals, which shows how the Guatemala City BRT project has improved the city’s social sustainability by reducing access times to basic urban facilities, mainly public health clinics and educational facilities, and narrowing the inequality gap as compared to projects in San Salvador and Tegucigalpa, the other capital cities in the NCAT. Additionally, it is emphasized that this methodology can be replicated in the Global South while considering the scarcity of information and the use of open-source software in the process.

Optimization of Shared Electric Scooter Deployment Stations Based on Distance Tolerance

The proliferation of shared electric scooters (E-scooters) has brought convenience to urban transportation but has also introduced challenges such as disorderly parking and an imbalance between supply and demand. Given the current inconsistent quantity and spatial distribution of shared E-scooters, coupled with inadequate research on deployment stations selection, we propose a novel maximal covering location problem (MCLP) based on distance tolerance. The model aims to maximize the coverage of user demand while minimizing the sum of distances from users to deployment stations. A deep reinforcement learning (DRL) was devised to address this optimization model. An experiment was conducted focusing on areas with high concentrations of shared E-scooter trips in Chicago. The solutions of location selection were obtained by DRL, the Gurobi solver, and the genetic algorithm (GA). The experimental results demonstrated the effectiveness of the proposed model in optimizing the layout of shared E-scooter deployment stations. This study provides valuable insights into facility location selection for urban shared transportation tools, and showcases the efficiency of DRL in addressing facility location problems (FLPs).

Accelerated Benders decomposition and local branching for dynamic maximum covering location problems

The maximum covering location problem (MCLP) is a key problem in facility location, with many applications and variants. One such variant is the dynamic (or multi-period) MCLP, which considers the installation of facilities across multiple time periods. To the best of our knowledge, no exact solution method has been proposed to tackle large-scale instances of this problem. To that end, in this work, we expand upon the current state-of-the-art branch-and-Benders-cut solution method in the static case, by exploring several acceleration techniques. Additionally, we propose a specialised local branching scheme which exploits the separability of the problem by time period. This scheme uses a novel distance metric in its definition of subproblems and features a new method for efficient and exact solving of the subproblems. These methods are then compared through extensive computational experiments, highlighting the strengths of the proposed methodologies.

Ecology of emergency care in lower-tier healthcare providers in Ghana: an empirical data-driven Bayesian network analytical approach.

Thehealthcare landscape in Ghana is primarily composed of lower-tier providers, which serve asthe initial point of contact for most medical emergencies. This study aimed to assess the emergency care preparedness and readiness of primary healthcare providers using a robustevaluation approach.A multicentre retrospective cross-sectional study was conductedon 460 healthcare facilities using the standardised Health Facilities Emergency Preparedness Assessment Tool (HeFEPAT). Data were analysed via Bayesian Belief network.Emergency preparedness was associated with facility location, type, ownership, and in-charge personnel.Over 70% of facilities lacked specialised emergency/critical care personnel. Although 65% of in-charges reported protocol knowledge, only 7.8% could execute cardiopulmonary resuscitation. 90% of facilities lacked onsite defibrillators, and over 80% had no cerebrovascular accident medications. Road traffic accident protocols were largely unavailable, with an estimated53% probability of lacking such protocols. Private-owned facilities were more likely to lack protocols for road traffic accidents (76% vs 20% probability) and general acute care (62% vs 32%)compared to government-owned facilities.Significant gaps in emergency preparedness were identified across the studied health facilities, indicating limited capacity to manage critical situations effectively. Urgent investments in emergency medicine training, essential resources, and evidence-based protocols are needed. Standardised emergency preparedness assessments should be implemented for accreditation and quality improvement. Further research can inform the development of national guidelines and targeted interventions to strengthen emergency response capacities.

Benders decomposition for the discrete ordered median problem

Ordered median optimization has been proven to be a powerful tool to generalize many well-known problems from the literature. In Location Theory, the Discrete Ordered Median Problem (DOMP) is a facility location problem where clients are first ranked according to their allocation cost to the nearest open facility, and then these costs are multiplied by a suitable weight vector λ. That way, DOMP generalizes many well-known discrete location problems including p-median, p-center or centdian. In this article, we also allow negative entries of λ, allowing us to derive models for better addressing equity and fairness in facility location, for modeling obnoxious facility location problems or for including other client preference models. We present new mixed integer programming models for DOMP along with algorithmic enhancements for solving the DOMP to optimality using mixed integer programming techniques. Specifically, starting from state-of-the-art formulations from the literature, we present several Benders decomposition reformulations applied to them. Using these approaches, new state-of-the-art results have been obtained for different ordered weighting vectors.

There is no place to go in “America's Finest City”: Basic sanitation deprivation is punishment in San Diego, California, USA

Safe and consistent access to water, sanitation, and hygiene (WaSH) resources is an internationally recognized human right. Yet in the U.S., access to public toilets, handwashing facilities, potable water, and menstrual supplies is grimly limited, especially for unhoused people. This article develops the concept of “sanitation justice” and argues for its urgent consideration alongside calls for racial, gender, social, and environmental justice. We advance this argument via a case study analyzing three novel data sources on WaSH access in San Diego, California's second-largest city, with a focus on public restrooms: geospatial data on facility locations, infrastructure assessments, and interviews with unhoused people on their restroom needs and challenges. We present a conceptual model, informed by the prior literature and our data, for visualizing how deliberate sanitation deprivation in the U.S. urban environment functions as a punishment regime inflicted on city residents, unhoused residents especially, to the detriment of public health and safety. As our data show, WaSH access is the most basic way governments can support not only public health but also social and economic mobility. We conclude with a vision of sanitation justice that centers the voices of those most affected by sanitation deprivation.

Siberia and the Far East in the Project “The Map of Bookstores in Russia”

Assessing the state of the bookselling infrastructure by creating an interactive map of the country’s bookstores in order to identify the potential for its development and search for ways to optimize it is an important factor in improving the information exchange of the book industry. The article analyzes the results of the first stage (2023) of monitoring bookselling enterprises in the Siberian-Far Eastern region within the framework of the project “The map of bookstores of Russia” (“The Cultural Map of Russia. Literature. Reading”). The research methodology is based on a combination of comparative typological analysis of monitoring data and the landscape-reconstructing approach. Statistical data on the number of bookselling facilities and their availability in federal districts and cities of Russia are summarized. The information on the location and number of bookselling facilities in the cities of Siberia and the Far East is studied in detail, leaders and outsiders are identified. The representation of federal, regional, and local network formations and independent bookstores in the region is considered. The activities of leading enterprises operating in the region for many years and recently opened bookstores are evaluated. The all-Russian trends and regional features of the development of the bookselling infrastructure are determined. The analysis of monitoring data for bookselling facilities in Siberia and the Far East showed the following. The modern regional bookselling infrastructure, along with its inherent all-Russian features, is characterized by: expansion and strengthening of the position of the federal bookselling network “The City of Read”; significant growth of the Internet channel due to the ongoing consolidation of the main players and the growing popularity of marketplace platforms. At the same time the bookselling infrastructure has a number of regional features. They are the reduction in the activities of the oldest regional network formations, successful adaptation and expansion of local large and medium-size network formations; the emergence and successful operation of special children’s bookstores-clubs, as wekk as club-backroom format stores.

Industrial location and urban macroform prediction model based on cellular automata and multi-criteria decision-making methods (ILAUFM)

The relationships between industrialization and urban regions, as well as technological advancements, innovations, and new work patterns and organizations, significantly impact the structuring of cities. Industrial sectors can become key drivers in shaping the form and macrostructure of a city over time. By examining and evaluating the existing system of structures and relations in the settlements in order to “estimate the land use differentiations” that may arise in the future and “to determine the spatial location of industrial facilities or sites,” a model has been developed. This model aims to predict potential “land use differentiations” and determine the “spatial location selection of industrial facilities or sites” in the future based on the analysis and evaluation of existing structures and relational systems in settlements. The model consists of the integration of the LUCAM, which is based on a cellular automata operating system, along with the “GIS-Based Integrated Industrial Area Location Selection Model,” which evaluates all data under the main headings of natural structure characteristics, socio-economic structure characteristics, and built environment characteristics using weighted overlay analysis. In this article, the general structure of the model developed within the scope of the research and İnegöl sampling are explained. The outputs of this model can be used for alternative separation for logistics centers, residential areas, secondary centers, reinforcement areas, and industrial sites in different settlements. In addition, in the research, a detailed model was designed by integrating decision-makers (central administrations, local administrations, registered users, NGOs) into the urban planning process. The proposed model can be used as a tool in designing transportation and other urban systems (distribution order of equipment areas, green area system, grading of centers, etc.).

A robust optimization model for multi-objective blood supply chain network considering scenario analysis under uncertainty: a multi-objective approach

Annually, different regions of the world are affected by natural disasters such as floods and earthquakes, resulting in significant loss of lives and financial resources. These events necessitate rescue operations, including the provision and distribution of relief items like food and clothing. One of the most critical challenges in such crises is meeting the blood requirement, as an efficient and reliable blood supply chain is indispensable. The perishable nature of blood precludes the establishment of a reserve stock, making it essential to minimize shortages through effective approaches and designs. In this study, we develop a mathematical programming model to optimize supply chains in post-crisis scenarios using multiple objectives. Presented model allocates blood to various demand facilities based on their quantity and location, considering potential situations. We employ real data from a case study in Iran and a robust optimization approach to address the issue. The study identifies blood donation centers and medical facilities, as well as the number and locations of new facilities needed. We also conduct scenario analysis to enhance the realism of presented approach. Presented research demonstrates that with proper management, crises of this nature can be handled with minimal expense and deficiency.

Designing a reliable-sustainable supply chain network: adaptive m-objective ε-constraint method

AbstractIn the current era emphasizing sustainability and circularity, supply chain network design is a critical challenge for making reliable decisions. The optimization of facility location-allocation inventory problems (FLAIPs) holds the key to achieving dependable product delivery with reduced costs and carbon emissions. Despite the importance of these challenges, a substantial research gap exists regarding economic, reliability, and sustainability criteria for FLAIPs. This paper aims to fill this gap by introducing a multi-objective mixed-integer linear programming model, focusing on configuring a reliable sustainable supply chain network. The model addresses three key objectives: minimizing costs, minimizing emissions, and maximizing reliability. A notable contribution of this research lies in elaborating on five levels of a supply chain network catering to the delivery of multiple products across various periods. Another novelty is the simultaneous incorporation of economic, environmental, and reliability objectives in the network design—a facet rarely addressed in prior research. Results highlight that varying demand levels for each facility lead to altered trade-offs between objectives, empowering practitioners to make diverse decisions in facility location allocation. The proposed mathematical model undergoes validation through numerical examples and sensitivity analysis of parameters. The paper concludes by presenting theoretical and managerial implications, contributing valuable insights to the field of sustainable supply chains.

Designing a sustainable plastic bottle reverse logistics network: A data-driven optimization approach

Management of recovery options for plastic beverage containers involves some challenges. Most materials of used containers are recycled and are used to produce new products. The locations of collection facilities are the strategic decisions, affecting the amount collected and the total cost of a Reverse Logistics Network (RLN). In this study, a multi-objective (MO) optimization model is introduced to configure a plastic beverage container RLN, considering economic, environmental, and social objectives. This study also implements a scenario-based possibilistic approach to handle the uncertainty of the parameters. Furthermore, a data-driven fuzzy optimization framework is developed to consider the overlapping and multi-clustered characteristics of historical data samples. The application of the proposed method is demonstrated by considering a network in Vancouver, Canada. The numerical results reveal that the optimal configuration of the RLN resulting from the proposed MO model exhibits significant sensitivity to fluctuations in costs, demands, and the prioritization of the objective functions. Additionally, the proposed data-driven framework can incorporate decision makers' preferences when tuning the conservatism degree of uncertain parameters and the preference level of different objectives of the MO model. Moreover, the developed data-driven algorithm can reduce over-conservatism by 14% and guarantee the feasibility of optimal solutions compared to other data-driven strategies.