A Hybrid model for locating new emergency facilities to improve the coverage of the road crashes

We propose an emergency facility-locating model aimed at increasing the coverage of emergency demand throughout the city. The proposed model takes into account the status and location of the emergency facilities in the network and identifies locations suitable for the construction of new facilities. Here, Data Envelopment Analysis (DEA) and Maximum Coverage Location Problem (MCLP) have been combined in a single model. To do so, design problem and evaluation problem are considered concurrently to maximize the efficiency of services provided by emergency facilities across the city in response to the demand. Moreover, the total emergency demand in each district was considered in relation to the population density, the fatal, injurious, and property damage only (PDO) crashes. The coverage area of each emergency facility was assumed to be proportional to the average ambulance speed in the surrounding road network during rush hours. The available budget was included in the model to let the model function under various fiscal conditions. Model input variables consisted of average number of mortalities, injuries and PDO crashes as well as the population density of each urban district. The output variables of the model included the coverage share of proposed emergency centers and hospitals equipped with ambulances. The model was tested on the network of Tehran (Iran). It is recommended to add the location of some emergency centers and hospitals to the network. Moreover, the results showed that ten urban districts had efficiency problem in provision of emergency services.     

Reconfiguring a set of coverage-providing facilities under travel time uncertainty

We study networks of facilities that must provide coverage under conditions of uncertainty with respect to travel times and customer demand. We model this uncertainty through a set of scenarios. Since opening new facilities and/or closing existing ones is often quite expensive, we focus on optimal re-configuration of the network, that is finding a facility set that achieves desired thresholds with respect to expected and minimal coverage, while retaining as many of the existing facilities as possible. We illustrate our model with an example of Toronto Fire Service. We demonstrate that relocating just a few facilities can have the same effect as opening a similar number of new ones. We develop exact and approximate solution approaches and test them with computational experiments. Algorithm based on Tabu Search (with certain novel components) appears to be particularly successful for this problem. We also analyze the multi-objective version of the problem, where the expected and minimum coverage levels are treated as objectives in addition to the objective of maximizing the number of pre-existing facilities in the final location set.     

A healthcare facility location problem for a multi-disease,multi-service environment under risk aversion

This paper presents a stochastic optimisation model for locating walk-in clinics for mobile populations in a network. The walk-in clinics ensure a continuum of care for the mobile population across the network by offering a perpetuation of services along the transportation lines, and also establishing referral systems to local healthcare facilities. The continuum of care requirements for different diseases is modelled using coverage definitions that are designed specifically to reflect the adherence protocols for services for different diseases. The risk of not providing the required care under different realisations of health service demand is considered. In this paper, for a multi-disease, multi-service environment, we propose a model to determine the location of roadside walk-in clinics and their assigned services. The objective is to maximise the total expected weighted coverage of the network subject to a Conditional-Value-at-Risk (CVaR) measure. This paper presents developed coverage definitions, the optimisation model and the computational study carried out on a real-life case in Africa.     

Siting a facility in continuous space to maximize coverage of a region

Siting facilities in continuous space such that continuously distributed demand within a region is optimally served is a challenging location problem. This problem is further complicated by the non-convexity of regions typically encountered in practice. In this paper a model for maximizing the service coverage of continuously distributed demand through the location of a single service facility in continuous space is proposed. To address this problem, theoretical conditions are established and associated methods are proposed for optimally siting a service facility in a region (convex or non-convex) with uniformly distributed demand. Through the use of geographic information systems (GIS), the developed approach is applied to identify facility sites that maximize regional coverage provided limitations on facility service ability.     

The maximal covering location problem with accessibility indicators and mobile units

We study the Maximal Covering Location Problem with Accessibility Indicators and Mobile Units that maximizes the facilities coverage, the accessibility of the zones to the open facilities, and the spatial disaggregation. The main characteristic of our problem is that mobile units can be deployed from open facilities to extend the coverage, accessibility, and opportunities for the inhabitants of the different demand zones. We formulate the Maximal Covering Location Problem with Accessibility Indicators and Mobile Units as a mixed-integer linear programming model. To solve larger instances, we propose a matheuristic (combination of exact and heuristic methods) composed of an Estimation of Distribution Algorithm and a parameterized Maximal Covering Location Problem with Accessibility Indicators and Mobile Units integer model. To test our methodology, we consider the Maximal Covering Location Problem with Accessibility Indicators and Mobile Units model to cover the low-income zones with Severe Acute Respiratory Syndrome Coronavirus 2 patients. Using official databases, we made a set of instances where we considered the poverty index, number of population, locations of hospitals, and Severe Acute Respiratory Syndrome Coronavirus 2 patients. The experimental results show the efficiency of our methodologies. Compared to the case without mobile units, we drastically improve the coverage and accessibility for the inhabitants of the demand zones.     

Random utility demand models and service location

This paper considers the problem of the optimal location of facilities when the demand behavior is described by a random utility choice model. By means of duality theory of mathematical programming, it is shown how a wide family of such problems can be mapped into an entropy maximizing problem, which is usually easy to solve numerically. Theory and methods are discussed in details for the problem of locating a single type of facilities, and the extension to some kinds of systems with many types of facilities is outlined.     

考虑人口因素的公共应急服务设施选址问题研究

研究了考虑人口因素的公共应急服务设施选址问题,建立了该问题数学模型,分别设计了求解该问题的精确算法和启发式算法,并通过具体的案例进行了求解及分析,得到了满意的结果。     

基于消费者环保意识的闭环物流网络优化研究

通过引入消费者的环保意识来反映其对不同回收率产品的需求函数,并考虑了回收产品质量的不确定性,建立了闭环物流网络优化模型,其目标是计划期内利润最大化,以此来确定该网络中各设施的数量、位置、设施间的物流量以及最佳回收率。最后通过一个算例验证了模型的有效性并分析了公众的环保意识对最佳回收率的影响。     

Data center supply chain configuration design: A two-stage decision approach

Data centers are special-purpose facilities that enable customers to perform cloud based real-time online transactions and rigorous computing operations. Service levels of data center facilities are characterized by response time between query and action, which to a large extent depends on data center location and data travel distance. Another aspect of service level is resource up-time availability, which is determined by data center configuration. Data center location and configuration decisions are, therefore, of great significance to ensure uninterrupted operations in customers of manufacturing and service industries relying on cloud-based computing resources. In this study, following a grid-based location approach, we present two mixed integer linear programming models for capacitated single-source data center location-allocation problems. The first model provides optimal locations, capacities and configurations of data centers, and allocation of demands to open facilities when there is no existing facilities in the region. Our second model considers the decision problem of meeting new demand when the existing demand is met by the already opened facilities. We term these newly arrived demand as replication demand, which results either from emergence of new users of existing customers at distant locations in the future, or as a means of increasing data resilience by creating data replication as a backup. To solve the decision problem for meeting primary and replication demand optimally, we propose a two-stage decision algorithm. The algorithm provides optimal locations, capacities and configurations for new data centers, capacity addition decisions to the existing facilities and subsequent allocation of demands. Both models and solution algorithm are implemented using AMPL programming language and solved with CPLEX solver. The models are found to be scalable and capable to provide high quality solutions in reasonable time.     

基于GIS的启发式物流配送中心选址方法的研究

本文针对配送中心选址规划问题，综合考虑影响选址决策的各种因素。利用GIS的空间分析技术，在电子地图上分析定性因素得出了一系列选址候选点，在此基础上建立整数规划选址模型，采用聚类的方法对客户分群，用贪婪取走启发算法得出最佳配送中心选址地点。最后通过实例证明方法的有效性。     

Concept‐Based Bayesian Model Averaging and Growth Empirics

In specifying a regression equation, we need to specify which regressors to include, but also how these regressors are measured. This gives rise to two levels of uncertainty: concepts (level 1) and measurements within each concept (level 2). In this paper we propose a hierarchical weighted least squares (HWALS) method to address these uncertainties. We examine the effects of different growth determinants taking explicit account of the measurement problem in the growth regressions. We find that estimates produced by HWALS provide intuitive and robust explanations. We also consider approximation techniques which are useful when the number of variables is large or when computing time is limited.     

Improving access to health facilities in Nouna district,Burkina Faso

The Nouna health district in Burkina Faso, has a population of approximately 275,000 people living in 281 villages, and is served by 25 health facilities, as of 2006. For many people, the time and effort required in traveling to a health facility, which may demand a journey of many kilometers over poor roads on foot, is a deterrent to seeking proper medical care. In this study we examine how access to health facilities in Nouna may be improved by considering the configuration of the road network in addition to the locations of the facilities. We model the situation as a facility location–network design problem and draw conclusions about how best to improve the physical access of the health facilities. Our model shows the extent to which access can be improved when the road network is considered along with facility locations, in contrast to facility locations considered alone.     

Variable selection and functional form uncertainty in cross-country growth regressions

Regression analyses of cross-country economic growth data are complicated by two main forms of model uncertainty: the uncertainty in selecting explanatory variables and the uncertainty in specifying the functional form of the regression function. Most discussions in the literature address these problems independently, yet a joint treatment is essential. We present a new framework that makes such a joint treatment possible, using flexible nonlinear models specified by Gaussian process priors and addressing the variable selection problem by means of Bayesian model averaging. Using this framework, we extend the linear model to allow for parameter heterogeneity of the type suggested by new growth theory, while taking into account the uncertainty in selecting explanatory variables. Controlling for variable selection uncertainty, we confirm the evidence in favor of parameter heterogeneity presented in several earlier studies. However, controlling for functional form uncertainty, we find that the effects of many of the explanatory variables identified in the literature are not robust across countries and variable selections.     

A credibility-based multi-objective temporary logistics hub location-allocation model for relief supply and distribution under uncertainty

Disaster response operations revolve around uncertainties. While uncertainties arising due to randomness can be avoided for post-disaster location problem, those arising because of impreciseness may persist long after the disaster's occurrence. Despite the uncertainties and lack of sufficient information about the extent of the damage, disaster response facilities must be established quickly after the occurrence of the disaster. Moreover, the decisions of whether to open, where to locate, and when to open disaster response facilities are based on the amount and quality of information available during the decision-making period. To address these issues, we develop a multi-objective location-allocation model for relief supply and distribution that accounts for the imprecise and time-varying nature of different parameters and time-varying coverage, while also accommodating the subjective attributes necessary to enable establishment and operation of the temporary logistics hubs (TLHs). A credibility-based fuzzy chance-constrained programming model is employed to account for the impreciseness inherent in predicting parameter values during disaster response. The results show where, when, and how many TLHs to open and how to allocate relief supplies. Meanwhile, the sensitivity analysis provides a broader understanding of the impact of limiting the number of TLHs as well as the confidence level and the spread of the symmetric triangular fuzzy numbers on the attainment of the model objectives.     

Challenges in service network expansion: An application in donated breastmilk banking in South Africa

Neonatal infections are the leading cause for neonatal deaths in developing countries, resulting in more than 1.5 million infant fatalities annually. The most effective prevention against neonatal infections is exclusive breastfeeding. However, reasons such as maternal death during birth, maternal illnesses such as HIV and TB-meningitis, and lack of rooming-in facilities prevent the infant to be breastfed by his/her mother. One way to mitigate these infections is by supplying pasteurized donor-expressed breastmilk. In this paper, we consider the network expansion of the donated breastmilk distribution supply chain in South Africa. As with the distribution of most public sector and humanitarian relief goods and services, the transportation of donated breastmilk is hampered by the inherent uncertainty in the environment, and by the fact that in addition to efficient usage of resources, distribution should be made in an equitable manner. We incorporate uncertainty into our models by means of multiple scenarios, which are determined based on different assumptions about population size, HIV prevalence, and status of public health in the country, income, and education. We consider various equity-based objectives and propose rounding-based heuristics to solve these. We focus on two delivery schemes; one which uses out-and-back transportation, and one that makes multiple stops on the delivery route. Using computational experiments, we analyze the trade-offs between the objectives as well as the effects of various public health policies, network expansion budget, and assumptions on supply and demand. We also describe the teaching materials resulting from this paper, which include a case study, a supply/demand estimation tool, and an interactive decision support tool.     

A socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: An interactive possibilistic programming approach for a real case study

In uncertain circumstances like the COVID-19 pandemic, designing an efficient Blood Supply Chain Network (BSCN) is crucial. This study tries to optimally configure a multi-echelon BSCN under uncertainty of demand, capacity, and blood disposal rates. The supply chain comprises blood donors, collection facilities, blood banks, regional hospitals, and consumption points. A novel bi-objective Mixed-Integer Linear Programming (MILP) model is suggested to formulate the problem which aims to minimize network costs and maximize job opportunities while considering the adverse effects of the pandemic. Interactive possibilistic programming is then utilized to optimally treat the problem with respect to the special conditions of the pandemic. In contrast to previous studies, we incorporated socio-economic factors and COVID-19 impact into the BSCN design. To validate the developed methodology, a real case study of a Blood Supply Chain (BSC) is analyzed, along with sensitivity analyses of the main parameters. According to the obtained results, the suggested approach can simultaneously handle the bi-objectiveness and uncertainty of the model while finding the optimal number of facilities to satisfy the uncertain demand, blood flow between supply chain echelons, network cost, and the number of jobs created.     

Incorporating recycling into post-disaster debris disposal

Although large amounts of disaster-generated debris significantly strain landfill capacities, until recently existing policy provided no financial incentive to consider other disposal alternatives such as recycling. In 2007, the U.S. Federal Emergency Management Agency (FEMA) released a new pilot program that provides incentives for communities to recycle by allowing them to retain revenue from the sale of disaster debris. This first-ever policy offers significant financial benefits for communities seeking to cleanup in an environmentally responsible way but requires reexamining existing assumptions and decision processes that are based on prior reimbursement programs. This paper presents a decision model with recycling incentives for locating temporary disposal and storage reduction (TDSR) facilities in support of disaster debris cleanup operations. A facility location model is proposed to incorporate the unique assumptions, objectives, and constraints of disaster recovery in light of FEMA’s new policy.     

Managing returnable transport items in a vendor managed inventory system

This paper addresses a closed-loop inventory routing problem with demand uncertainty, which manages the delivery operations to customers and pick-up operations of empty returnable transport items (RTIs) from customers. The problem involves decisions regarding forward and backward vehicle routes, the delivery and collection quantities, the amount of production in terms of the number of filled RTIs, and the number of RTIs produced/ bought by the vendor during the defined planning horizon. The problem considers the holding costs, fixed cost of operating vehicles, fuel consumption cost, producing/buying costs, cleaning costs of RTIs, and loading and unloading costs. We formulate the problem as a mixed integer linear programming model and propose a Relax and Fix based solution approach to solve large instances. We conduct extensive analyses on a case study derived from a fruit and vegetable distribution network and several hypothetical instances. Our analyses investigate the effects of several problem parameter changes (i.e., average collection rate, customer service level, cost of buying crate and handling cost) on the total logistics cost. Additional numerical analyses are performed to demonstrate the usage of the model for evaluating the cost of being more green and environmental-friendly. Moreover, experiments on relatively large-scaled problems allow us to demonstrate the potential benefits of the proposed heuristic     

Robust and stable flexible blood supply chain network design under motivational initiatives

To improve the coordination of blood supply and demand, the efficient design of the blood supply chain network (BSCN) is a proper strategy. In this regard, this paper is the first-ever study to strive for a simultaneous investigation on three interdependent challenges of the BSCN: (1) donors motivation, (2) optimizing location and capacity decisions, and (3) controlling the reliability and robustness of the network under combinatorial risk. As in reality, blood donors play a critical role in the BSC; this study undertakes motivational initiatives to encourage blood donors for maintaining sufficient blood supply. Advertisement, education and medical credits are the directions incorporated to construct the motivational function. To observe the efficiency as the most critical factor while evaluating the pool of location alternatives for establishing facilities, an augmented version of data envelopment analysis (DEA) is utilized. In addition, a mixed integer programming model is proposed by which the simultaneous location and capacity decisions are supported. The model is also extended to handle the combinatorial risk of uncertainty as well as disruption. Thus, a novel mixed possibilistic-stochastic flexible robust programming (MPSFRP) is developed. Eventually, the proposed model is implemented in a real case study to assess its practicality and then provide applicable insights for administrators.     

The flow-refueling location problem for alternative-fuel vehicles

Beginning with Hodgson (Geogr.Anal.22(1990) 270), several researchers have been developing a new kind of location-allocation model for “flow capturing.” Instead of locating central facilities to serve demand at fixed points in space, their models aim to serve demand consisting of origin-destination flows along their shortest paths. This paper extends flow-capturing models to optimal location of refueling facilities for alternative-fuel (alt-fuel) vehicles, such as hydrogen fuel cells or natural gas. Existing flow-capturing models assume that if a flow passes just one facility along its path, it is covered. This assumption does not carry over to vehicle refueling because of the limited range of vehicles. For refueling, it may be necessary to stop at more than one facility in order to successfully refuel the entire path, depending on the vehicle range, the path length, and the node spacing. The Flow Refueling Location Model (FRLM) optimally locates p refueling stations on a network so as to maximize the total flow volume refueled. This paper presents a mixed-integer programming formulation for the nodes-only version of the problem, as well as an algorithm for determining all combinations of nodes that can refuel a given path. A greedy-adding approach is demonstrated to be suboptimal, and the tradeoff curve between number of facilities and flow volume refueled is shown to be nonconvex.