Facility location under demand uncertainty: Response to a large-scale bio-terror attack

In the event of a catastrophic bio-terror attack, major urban centers need to efficiently distribute large amounts of medicine to the population. In this paper, we consider a facility location problem to determine the points in a large city where medicine should be handed out to the population. We consider locating capacitated facilities in order to maximize coverage, taking into account a distance-dependent coverage function and demand uncertainty. We formulate a special case of the maximal covering location problem (MCLP) with a loss function, to account for the distance-sensitive demand, and chance-constraints to address the demand uncertainty. This model decides the locations to open, and the supplies and demand assigned to each location. We solve this problem with a locate-allocate heuristic. We illustrate the use of the model by solving a case study of locating facilities to address a large-scale emergency of a hypothetical anthrax attack in Los Angeles County.     

Modeling integrated supply chain logistics in real-time large-scale disaster relief operations

The goal of this research is to develop a comprehensive model that describes the integrated logistics operations in response to natural disasters. We propose a mathematical model that controls the flow of several relief commodities from the sources through the supply chain and until they are delivered to the hands of recipients. The structure of the network is in compliance with FEMA's complex logistics structure. The proposed model not only considers details such as vehicle routing and pick up or delivery schedules; but also considers finding the optimal locations for several layers of temporary facilities as well as considering several capacity constraints for each facility and the transportation system. Such an integrated model provides the opportunity for a centralized operation plan that can eliminate delays and assign the limited resources to the best possible use.A set of numerical experiments is designed to test the proposed formulation and evaluate the properties of the optimization problem. The numerical analysis shows the capabilities of the model to handle the large-scale relief operations with adequate details. However, the problem size and difficulty grows rapidly by extending the length of the operations or when the equity among recipients is considered. In these cases, it is suggested to find fast solution algorithms and heuristic methods in future research.     

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.     

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.     

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.     

Scheduling of re-entrant flow shops

We propose and develop a scheduling system for a very special type of flow shop. This flow shop processes a variety of jobs that are identical from a processing point of view. All jobs have the same routing over the facilities of the shop and require the same amount of processing time at each facility. Individual jobs, though, may differ since they may have different tasks performed upon them at a particular facility. Examples of such shops are flexible machining systems and integrated circuit fabrication processes. In a flexible machining system, all jobs may have the same routing over the facilities, but the actual tasks performed may differ; for instance, a drilling operation may vary in the placement or size of the holes. Similarly, for integrated circuit manufacturing, although all jobs may follow the same routing, the jobs will be differentiated at the photolithographic operations. The photolitho-graphic process establishes patterns upon the silicon wafers where the patterns differ according to the mask that is used.The flow shop that we consider has another important feature, namely the job routing is such that a job may return one or more times to any facility. We say that when a job returns to a facility it reenters the flow at that facility, and consequently we call the shop a re-entrant flow shop. In integrated circuit manufacturing, a particular integrated circuit will return several times to the photolithographic process in order to place several layers of patterns on the wafer. Similarly, in a flexible machining system, a job may have to return to a particular station several times for additional metal-cutting operations.These re-entrant flow shops are usually operated and scheduled as general job shops, ignoring the inherent structure of the shop flow. Viewing such shops as job shops means using myopic scheduling rules to sequence jobs at each facility and usually requires large queues of work-in-process inventory in order to maintain high facility utilization, but at the expense of long throughput times.In this paper we develop a cyclic scheduling method that takes advantage of the flow character of the process. The cycle period is the inverse of the desired production rate (jobs per day). The cyclic schedule is predicated upon the requirement that during each cycle the shop should perform all of the tasks required to complete a job, although possibly on different jobs. In other words, during a cycle period we require each facility to do each task assigned to it exactly once. With this requirement, a cyclic schedule is just the sequencing and timing on each facility of all of the tasks that that facility must perform during each cycle period. This cyclic schedule is to be repeated by each facility each cycle period. The determination of the best cyclic schedule is a very difficult combinatorial optimization problem that we cannot solve optimally for actual operations. Rather, we present a computerized heuristic procedure that seems very effective at producing good schedules. We have found that the throughput time of these schedules is much less than that achievable with myopic sequencing rules as used in a job shop. We are attempting to implement the scheduling system at an integrated circuit fabrication facility.     

Reliable design of a congested disaster relief network: A two-stage stochastic-robust optimization approach

The preparedness of humanitarian relief networks can be enhanced by pre-positioning resources in strategic locations and using them when disasters strike, a strategy that gives rise to a two-stage planning problem. This paper presents a novel two-stage stochastic-robust optimization approach for integrated planning of pre- and post-disaster positioning and allocation of relief resources, while taking into consideration the uncertainty about demand for relief services and disruptions in the relief facilities and the transportation network. The proposed approach enables planners to effectively use limited historical data and imperfect experts’ opinions to obtain robust solutions while avoiding the over-conservatism of classical robust optimization methods. The objective sought is to minimize the expected total time victims need to receive assistance, including both access time to facilities and waiting/service time in them. Congestion in relief facilities is accounted for by modeling them as queuing systems and penalizing waiting time. A decomposition method based on column-and-constraint generation is implemented to solve the problem, whereas the nonlinear terms corresponding to queuing in the second-stage problem are handled using a direct search procedure. Applicability of the proposed approach is demonstrated through a real case study and the numerical results are analyzed to draw managerial insights.     

Scientific planning of urban cordon sanitaire for desired queuing time

The outbreak of the COVID-19 pandemic disrupted ofur normal life. Many cities enforced a cordon sanitaire as a countermeasure to protect densely inhabited areas. Travelers can only cross the cordon after being checked. To minimize the waiting time in the queue, this paper proposes a method to determine the scientific planning of urban cordon sanitaire for desired queuing time, which is a significant problem that has not been explored. A novel two-stage optimization model is proposed where the first stage is the transportation system equilibrium problem to predict traffic inflow, and the second stage is the queuing network design problem to determine the allocation of test stations. This method aims to minimize the total health infrastructure investment for the desired maximum queuing time. Note that queuing theory is used to represent the queuing phenomenon at each urban entrance. A heuristic algorithm is designed to solve the proposed model where the Method of Successive Averages (MSA) is adopted for the first stage, and the Genetic Algorithm (GA) with elite strategy is adopted for the second stage. An experimental study with sensitivity analysis is conducted to demonstrate the effectiveness of the proposed methods. The results show that the methods can find a good heuristic optimal solution. This research is helpful for policymakers to determine the optimal investment and planning of cordon sanitaire for disease prevention and control, as well as other criminal activities such as drunk driving, terrorists, and smuggling.     

A multiobjective modeling approach to locating incinerators

The location of hazardous material incineration facilities is an important problem due to the environmental, social, and economic impacts that they impose. The costs associated with the facilities and the risks placed on nearby populations are important concerns as are the distributions of these costs and risks. This paper introduces a mixed-integer, multiobjective programming approach to identify the locations and capacities of such facilities. The approach incorporates a Gaussian dispersion model and a multiobjective optimization model in a GIS-based interactive decision support system that planners can access via the Internet. The proposed approach is demonstrated via a case study in central Portugal where the national government has decided to locate a large facility for the incineration of hazardous industrial waste. Due to intense local and national opposition, construction of the facility has been delayed. The system has been designed so that it can be used by decision makers with no special training in dispersion modeling, multiobjective programming, or GIS.     

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.     

Optimal pre-dispatch task assignment of volunteers in daily emergency response

In emergency response volunteer programs, volunteers in the vicinity of an emergency are alerted via their mobile phones to the scene of the event to perform a specific task. Tasks are usually assigned based on predetermined rules disregarding real-world uncertainties. In this paper, we consider some of these uncertainties and propose an optimization model for the dispatch of volunteers to emergencies, where all task assignments must be done before dispatch. This means that each volunteer must be given a task before knowing whether (s)he is available. The model becomes computationally demanding for large problem instances; therefore, we develop a simple greedy heuristic for the problem and ensure that it can produce high quality solutions by comparing it to the exact model. While the model is for a general emergency, we test it for the case of volunteers responding to out-of-hospital cardiac arrest (OHCA) incidents. We compare the results of the model to the dispatch strategies used in two ongoing volunteer programs in Sweden and in the Netherlands and use simulation to validate the results. The results show that the model most often outperforms the currently used strategies; however, the computational run times, even for the heuristic, are too high to be operationally useful for large problem instances. Thus, it should be possible to improve the outcome using optimization-based task assignments strategies, but a fast solution method is needed for such strategies to be practically useable.     

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.     

The trade-off between the net present cost of a project and the probability to complete it on schedule

In this article we consider the probability of not completing a project on schedule (or the risk of delays) and its effect on the net present cost of the project. We propose an efficient frontier that points out to management the trade-off between low risk, early start schedules and high risk, late start schedules. Early start schedules minimize the risk of delays at the cost of early investment in project activities and material. Late start schedules delay capital outlays while increasing the risk of not completing the project on its due date.The methodology developed in this study is aimed at strategic level decision making. At this level, decisions are based on incomplete information that calls for stochastic analysis and the introduction of uncertainty. Uncertainty in project management is introduced through stochastic activity duration and stochastic lead times of resources required for the project. The commonly used CPM analysis ignores those aspects of uncertainty. PERT analysis does consider the stochastic nature of activity durations but computes only the probability to complete the project on a given date for a single schedule.A crucial decision at the strategic level of project management is when to schedule activities with high value-added. The decision makers have to trade-off the advantages of delaying such activities, thus reducing the net present cost of the project, with the disadvantages associated with increasing the probability of not completing the project on time.The number of feasible schedules in a real project is typically large and exact analysis of all possible schedules is difficult to perform, if not impossible. This article presents a heuristic procedure that generates an efficient frontier representing the risk of delays versus the net present cost of the project. The efficient frontier is a decision aid for the manager who has to choose the appropriate schedule for the project.Most computer packages for project management are based on CPM (especially packages for personal computers). Our heuristic procedure is designed to be used as an extension to CPM analysis. The procedure starts with the early start schedule developed by CPM and, using the computed slacks, tries to delay activities with high value-added one at a time. At each iteration a Monte-Carlo-type simulation is used to approximate the probability of not completing the project on time. This probability is stored along with the net present cost of the project. The result of the analysis is a set of points on the plane representing the probability of not completing the project on time versus the net present cost of the project. Each point corresponds to a specific schedule. Management can choose the most appropriate schedule for implementation based on its attitude towards risk and its financial policy.A simple example is used to illustrate the heuristic procedure. In the example, a project with six activities and two types of resources is analyzed. Five schedules are generated with net present cost ranging from $45,000 to $8,191,000 and the probability of not completing the project on time ranging from 0.0001 to 0.75. Our experience with a real project of 400 activities is reported as well.The heuristic procedure can be implemented easily on advanced “Fourth Generation” packages for project management such as IBM's Application System (AS) or Metier's Artemis system. The heuristic procedure can also be implemented on personal computers by processing the output of any CPM package by the special subroutine that is developed in this study.     

Mathematical programming for nominating exchange students for international universities: The impact of stakeholders’ objectives and fairness constraints on allocations

We consider the problem of nominating exchange students to attend international universities where places are limited. We take into account three objectives: The sending university aims to maximize the number of nominations, the students seek nomination for a highly preferred university and, finally, the receiving universities strive for excellent incoming students. Pairwise comparison of students should guarantee the following fairness: A student with higher academic achievements should be preferred over a student with lower academic achievements. We provide mathematical programming models of the nomination problem which maximize the overall objectives and guarantee different types of pairwise fairness. Several years of real data from a major school are employed to evaluate the models’ performance including a benchmark against the heuristic that is used by the school. We show analytically and experimentally that the heuristic approach fails to guarantee some pairwise fairness. Our results reveal the following four insights: First, compared to the current approach, up to 6.6% more students can be nominated with our optimization model while ensuring all pairwise fairness perspectives. Second, on average, students are nominated with better academic achievements. Third, the problem instances can be solved to optimality within a fraction of a second even for large-size instances comprising more than 500 students and about 150 schools offering nearly 450 exchange places. This is important for its use in practice. Last, up to 17.9% more students can be nominated when considering the overall objective to maximize nominations.     

Location and service mix decisions for a managed health care network

For managers of managed health care organizations, the problem of designing a competitive multiple facility network cannot be solved by existing mathematical models. This paper thus presents a nonlinear integer model for determining a facilities design strategy that embodies the economic tradeoffs encompassed in a competitive strategy: minimizing cost and maximizing market share. The integrated location and service mix model determines the number, location and service offerings of facilities that maximize profitability in a two-level hierarchical referral delivery network where an organization's market share is represented by a multiplicative competitive interaction model. To demonstrate the usefulness of the proposed integrated model, a series of problems is solved by an interchange heuristic and compared to the solutions derived by a simpler approach that ignores market competition.     

A multi-purpose heuristic for communications satellite synthesis

Three satellite system synthesis problems—the satellite location problem, the signal protection problem, and the arc allotment problem—are described and formulated. In each of these problems, satellite administrations are to be allotted some portion of the geostationary orbit for the purpose of deploying their satellites, subject to angle of elevation and electromagnetic interference constraints. A simple heuristic procedure is suggested as a means for solving these three problems. Sufficient conditions for the successful application of the heuristic and solution-value bounds for each problem are presented. The heuristic is applied to each problem for a six-satellite example. Additionally, computational results are presented for an example with 183 satellites worldwide.     

Understanding and Improving Teamwork in Organizations: A Scientifically Based Practical Guide

Teams are pervasive in today's world, and rightfully so as we need them. Drawing upon the existing extensive body of research surrounding the topic of teamwork, we delineate nine “critical considerations” that serve as a practical heuristic by which HR leaders can determine what is needed when they face situations involving teamwork. Our heuristic is not intended to be the definitive set of all considerations for teamwork, but instead consolidates key findings from a vast literature to provide an integrated understanding of the underpinnings of teamwork—specifically, what should be considered when selecting, developing, and maintaining teams. This heuristic is designed to help those in practice diagnose team‐based problems by providing a clear focus on relevant aspects of teamwork. To this end, we first define teamwork and its related elements. Second, we offer a high‐level conceptualization of and justification for the nine selected considerations underlying the heuristic, which is followed by a more in‐depth synthesis of related literature as well as empirically‐driven practical guidance. Third, we conclude with a discussion regarding how this heuristic may best be used from a practical standpoint, as well as offer areas for future research regarding both teamwork and its critical considerations. © 2014 Wiley Periodicals, Inc.     

The location of public services considering the mode of travel

By reason of their generally longer travel times, transit users are not so well-served by many public facilities as are automobile drivers. This paper investigates the implications of this fact for the location of a specific type of service, public child day care. A location-allocation model is employed to determine the most accessible locations for a set of centres in Edmonton, Canada, for users of both modes. Transit is found to be capable of providing only 51% of the accessibility of the automobile, at 2.4 times the average travel time. The argument is advanced that in order to reduce the inequality of service to a minimum, public facilities should be located with the accessibilities of transit users in mind. The optimal systems are compared with Edmonton's present system which is found to be spatially inefficient and quite discriminatory in its inefficiency against transit users. This is attributed to the city's piecemeal planning policy and an inadequate understanding of the notion of accessibility. The paper concludes by recommending improvements which would improve our simple diagnostic model to the level of a useful planning device.     

Management “flavours of the month”: their role in managers' lives

Existing analyses and critiques of the succession of management fads and fashions entering managerial discourse and activities are complemented and developed by drawing on participant observation research carried out in an organization utilizing a variety of “packaged innovations”. It is shown that what are sometimes mocked as “flavours of the month” (FOMs) play a role in the double-controlproblem faced by all managers: the problem of managing their personal identities, careers and understandings at the same time as contributing to the overall control of the organization in which they are managers. Managers in the organization studied are shown generally to be critical of the “flavour of the month effect” but there is nevertheless an equivocality about how such ideas and practices can function.     

货物配装和车辆路径问题的一种联合求解方法

货物配装和车辆路径问题(VRP)都是NP难问题,启发式算法是求解此类问题的常用方法。求解单配送中心多车型的货物配送问题,需要改进传统遗传算法:首先采用双层结构表示染色体,车辆数无需事先确定;然后对随机产生的客户序列,利用三叉树算法进行货物配装确定每辆车的配装方案,从而可以知道每个客户的货物装车顺序;依据先装后卸的原则,可得每辆车的客户装车顺序的逆序便是每辆车的客户配送顺序;最终得到货物配装方案和配送方案。基于此思想,通过Java编程验证了一个实例,证明算法切实可行,对实际作业有一定借鉴作用。