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By early January 2023, the COVID-19 pandemic had caused >6.7 million deaths worldwide (1), and severe socioeconomic hardship (2-4), particularly for racial minorities (5,6). Children experienced pandemic-related school closures that [...]

Abstract. Package carriers use sophisticated automated sorting facilities to efficiently process inbound packages and sort them to their down line destinations. During each of several daily processing windows, primary sorters [...]

Diligent preparation and effective countermeasures are critical to mitigating future influenza pandemics. The 1918 influenza pandemic, the most severe in recent history, resulted in [approximately equal to] 50 million deaths [...]

Abstract. We consider two types of convex approximations of two-stage totally unimodular integer recourse models. Although worst-case error bounds are available for these approximations, their actual performance has not yet [...]

Oil and gas companies are drilling and developing fields in the Arctic Ocean, which is an environment with ice floes. These companies must protect their platforms from ice floe collisions. [...]

The infrastructure of the United States, and the world, is increasingly interconnected. The ongoing integration of systems--including transportation, energy, water, communications, finance and more--has been central to their growth in [...]

We take a novel approach to decision problems involving binary activity-selection decisions competing for scarce resources. The literature approaches such problems by forming an optimal portfolio of activities. However, often practitioners instead form a rank-ordered list of activities and select those with the highest priority. We account for both viewpoints. We rank activities considering both the uncertainty in the problem parameters and the optimal portfolio that will be obtained once the uncertainty is revealed. We use stochastic integer programming as a modeling framework, and we apply our approach to a facility location problem and a multidimensional knapsack problem. We develop two sets of cutting planes to improve computation. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1865. Keywords: priority lists; activity-selection problem; stochastic programming; cutting planes History : Received December 20, 2011; accepted February 17, 2013, by Dimitris Bertsimas, optimization. Published online in Articles in Advance May 15, 2014., 1. Introduction Resource-constrained activity-selection problems (RCASPs) involve binary activity-selection decisions, which compete for scarce resources. The bulk of the current literature approaches RCASPs by forming an optimal portfolio of activities [...]

Influenza pandemics occur when novel strains of the influenza virus emerge in human populations and spread worldwide (1). There were 3 influenza pandemics in the 20th century (1918, 1957, and [...]

We consider a multiperiod system operation problem with two conflicting objectives, minimizing cost and risk. Risk sterns from uncertain disruptions to the system during operation. Whereas a general model would [...]

We consider capital investments under uncertainty. A typical approach to this problem, when the problem parameters are assumed known, is via a multi-knapsack model. This model takes as input annual budgets as well as the cost streams and profit--i.e., net present value (NPV)--of each project. Its output is a portfolio of projects with the highest total NPV, observing yearly budget constraints. We argue that such a portfolio fails to hedge against uncertainties in the budgets, the cost streams, and the profits. As an alternative, we propose a model that forms an optimal priority list of projects, incorporating multiple scenarios for these input parameters. We apply our approach to two sets of example projects from the South Texas Project Nuclear Operating Company. DOI: 10.1080/00137910903338545, INTRODUCTION When practitioners plan for capital budgeting they often form a priority list of candidate projects, by scoring the projects individually, using economic measures like net present value, benefit-investment ratio, [...]

We describe two stochastic network interdiction models for thwarting nuclear smuggling. In the first model, the smuggler travels through a transportation network on a path that maximizes the probability of evading detection, and the interdictor installs radiation sensors to minimize that evasion probability. The problem is stochastic because the smuggler's origin-destination pair is known only through a probability distribution at the time when the sensors are installed. In this model, the smuggler knows the locations of all sensors and the interdictor and the smuggler 'agree' on key network parameters, namely the probabilities the smuggler will be detected while traversing the arcs of the transportation network. Our second model differs in that the interdictor and smuggler can have differing perceptions of these network parameters. This model captures the case in which the smuggler is aware of only a subset of the sensor locations. For both models, we develop the important special case in which the sensors can only be installed at border crossings of a single country so that the resulting model is defined on a bipartite network. In this special case, a class of valid inequalities reduces the computation time for the identical-perceptions model. Keywords: Network interdiction, integer programming, stochastic programming, 1. Introduction We develop stochastic network interdiction models designed to locate radiation sensors, which detect gamma and neutron emissions from nuclear material, at critical border crossings in the Former Soviet [...]

The construction industry in the United States and other parts of the world has been facing several challenges, including a shortage of skilled workers. A review of the relevant body of knowledge indicates that one of the key reasons for this problem is the absence of human resource management strategies for construction workers at project, corporate, regional, or industry levels. This paper addresses the issues of workforce training and allocation on construction projects. It presents a framework to optimize the investment in, and to make the best use of, the available workforce with the intent to reduce project costs and improve schedule performance. A linear program model, entitled the Optimal Workforce investment Model, is built to provide an optimization-based framework for matching supply and demand of construction labor most efficiently through training, recruitment, and allocation. Given a project schedule or demand profile and the available pool of workers, the suggested model provides human resource managers a combined strategy for training the available workers and hiring additional workers. The input data to the proposed model consists of a certain available labor pool, cost figures for training workers in different skills, the cost of hiring workers, hourly labor wages, and estimates of affinities between the different considered skills. The objective of the model is to minimize labor costs while satisfying project labor demands. Results from application of the model to typical situations are presented, and recommendations for future developments are made. CE Database subject headings: Construction industry: Optimization: Training; Employees; Computer programming.

We develop a class of lower bounds on the expectation of a convex function. The bounds utilize the first two moments of the underlying random variable, whose support is contained [...]

Bayesian forecasting models provide distributional estimates for random parameters, and relative to classical schemes, have the advantage that they can rapidly capture changes in nonstationary systems using limited historical data. Unlike deterministic optimization, stochastic programs explicitly incorporate distributions for random parameters in the model formulation, and thus have the advantage that the resulting solutions more fully hedge against future contingencies. In this paper, we exploit the strengths of Bayesian prediction and stochastic programming in a rolling-horizon approach that can be applied to solve real-world problems. We illustrate the methodology on an employee production scheduling problem with uncertain up-times of manufacturing equipment and uncertain production rates. Computational results indicate the value of our approach., 1. Introduction We consider a production planning problem in which a nominal weekday employee schedule is published at the beginning of each month in the face of uncertain production rates [...]

We consider stochastic vehicle routing problems on a network with random travel and service times. A fleet of one or more vehicles is available to be routed through the network [...]

Multiskilling is a workforce strategy that has been shown to reduce indirect labor costs, improve productivity, and reduce turnover. A multiskilled workforce is one in which the workers possess a range of skills that allow them to participate in more than one work process. In practice, they may work across craft boundaries. The success of multiskilling greatly relies on the foreman's ability to assign workers to appropriate tasks and to compose crews effectively. The foreman assigns tasks to workers according to their knowledge, capabilities, and experience on former projects. This research investigated the mechanics of allocating a multiskilled workforce and developed a linear programming model to help optimize the multiskilled workforce assignment and allocation process in a construction project, or between the projects of one company. It is concluded that the model will be most useful in conditions where full employment does not exist; however, it is also useful for short term allocation decisions. By running the model for various simulated scenarios, additional observations were made. For example, it is concluded that, for a capital project, the benefits of multiskilling are marginal beyond approximately a 20% concentration of multiskilled workers in a project workforce. Benefits to workers themselves become marginal after acquiring competency in two or three crafts. These observations have been confirmed by field experience. Extension of this model to allocation of multifunctional resources, such as construction equipment, should also be possible. DOI: 10.1061/(ASCE)0733-9364(2002)128:2(103) CE Database keywords: Construction industry; Personnel management; Optimization.