The rapid development and wide acceptance of microgrids call for new methodologies to comprehensively model all the active components within microgrids and specifically focus on islanding requirements when the main grid power is not available. To ensure a high level of reliability of the interconnected microgrid (MG) network, an optimal scheduling model is proposed that minimizes the day-ahead cost of the MGs, while considering existing operational constraints. The original problem is decomposed into two main operating conditions, namely, grid-connected and resilient operations. The objective is to ensure a sufficient online capacity for all the MGs in the network in the case of an emergency islanding situation, such as during an extreme weather event. The exact time and duration of these events are generally unknown, thereby resulting in having to consider a number of possible islanding scenarios for each event. In this paper, optimal scheduling is determined to ensure feasible islanding for all the probable scenarios of the event, preserving load shedding as the last resort. The optimization model has been implemented on different layouts of the modified IEEE 123-bus test system for different events during a period of 24 hours. Besides proposing resiliency-oriented day-ahead scheduling for multiple MGs, cost analysis and comparison among all the test cases are also presented. The results obtained prove the proposed day-ahead scheduling algorithm to be beneficial for the MG owners, especially when networking with neighboring MGs.

Uncertainties in demand causes recurrent changes and revisions in a production plan, which may create anxiety in organizations and a state of instability throughout the supply chain. This study proposes a bi-objective aggregate production planning model (BO-APP), where plan stability is incorporated as a second objective alongside the traditional cost objective. The proposed BO-APP model is compared here to a classical single objective aggregate production planning model (APP) as well as to an alternate model called flexible requirements profile-based aggregate production planning model (FRP-APP), which aims to control plan stability by introducing dynamically changing bounds over the planning horizon. Numerical results on five industry-based cases show that both the BO-APP and FRP-APP models consistently generate plans with significantly less variability compared to the traditional APP model, while resulting in comparable planning costs. In addition, the BO-APP shows a better cost and stability performance combination, while the FRP-APP seems to give more flexibility to the planners for controlling the stability level.

While a computerized simulation can be useful to gather user inputs during the new service development (NSD) process, it requires the active involvement of users/customers and can be costly and time-consuming compared to other practices such as surveys, focus groups, and interviews. However, when the service is rendered in the machine-to-machine (M2M) environment, the NSD process can exploit a simulation platform to assess service alternatives without customer involvement. This article contributes to the NSD research by identifying the services in the M2M environment as the suitable application area where a simulation platform can be effectively and efficiently implemented for data collection and analysis. We consider the autonomous charging service to demonstrate how a simulation-based analytical framework can help developing a new service in the M2M environment. As the electric vehicle (EV) industry has been evolving tremendously over the past decade, it becomes necessary to undertake significant modernization and autonomy adoption in the future charging infrastructure. Since the currently deployed charging infrastructure has not been designed to service autonomous EVs (AEVs) in an unsupervised manner, service plans to upgrade this infrastructure need to be developed to serve AEVs without any human intervention. Numerical experiments demonstrate how the simulation platform helps analyze autonomous charging station alternatives, provides recommendations for operational decisions, and creates a potential value for stakeholders and businesses during the NSD process.

This paper considers a let scheduling problem for highway construction projects under an anticipated funding scenario. Highway construction projects typically require large-scale funds, and rely on the revenue from state and federal funding sources. The revenues mainly come from transportation-related taxes and fees. Such revenues can radically change over time due to the uncertainty of economy, and the Department of Transportations (DOTs) need to cope with such funding changes to avoid any overdraft or unhealthily excessive cash balances. A mathematical optimization model is proposed as a means of decision support to determine how to revise the let schedule when the future revenue is expected to change. A case study of the North Carolina DOT (NCDOT) is presented to demonstrate the application of the proposed model, where major types of projects having construction costs more than $10 million are analyzed. A formula to evaluate the impact of changing let dates is presented and a constrained logistic model that estimates the construction costs of individual projects is proposed. As a recommendation for utilizing the proposed model, three practical ways are summarized including the rolling-horizon-based let scheduling, funding change analysis, and the assessment of funding project risks.

The anticipated wide-spread deployment of unsupervised Autonomous Vehicles (AVs) across the globe will reshape the existing vehicle-related services (e.g., refueling/charging, parking, car washing, etc.) into new forms. Especially, a new paradigm of the refueling/charging mechanism can offer new economies and service opportunities. It has the potential to make a great impact on the current gasoline station industry given its enormous market size (e.g., annual sales more than $418 billion in 2016), as the AVs become mainstream modes of transportation. We propose a resilient and secure layered architecture of completely automated charging/refueling stations for unsupervised AVs. To demonstrate the feasibility of the architecture, we develop an analytical framework using a bottom-up approach. Then, we demonstrate the operation of a charging station as an essential component of the proposed architecture. The main goal of charging station's operation is to optimize scheduling of electric vehicles for their charging service. A divide-and-conquer strategy is employed for such scheduling optimization at the operational level real-time decision-making. In this optimization, the objective function is to minimize the sum of charging completion times of all vehicles in the queue. A mixed-integer linear programming model is considered to solve this online optimal scheduling procedure. An illustrative example of the scheduling solution that is obtained by a Matlab code combined with the Gurobi optimization solver is presented.

A "reverse bullwhip effect in pricing (RBP)" occurs when an amplification of price variability takes place moving from the upstream suppliers to the downstream customers in a supply chain. In this study, we investigate RBP conditions for supply chains where joint replenishment and pricing decisions are made. Commencing with a single-stage supply chain in which a retailer faces a random and price-sensitive demand, we extend the results to a multi-stage supply chain using a leader-follower game theoretical framework. We discuss RBP conditions for supply chains where newsvendor and continuous review inventory policies are employed, and present numerical examples for commonly used demand functions.

Demand uncertainty can cause frequent changes in production plans, which create nervousness in manufacturing companies. Traditional methods used for stabilizing production plans do not provide the adequate flexibility in production plans to handle the random demand. Flexibility Requirements Profile (FRP) is an alternative stabilizing approach, where flexible bounds are enforced on production plans in order to maintain a desired degree of flexibility. In this study, we incorporate FRP into conventional aggregate planning, which is formulated as a mixed-integer linear program with additional constraints to reflect the FRP requirements. To ascertain the effectiveness of the proposed method, several structural results are presented along with a comprehensive numerical study using a design of experiments framework with examples from automotive and textile industries. Based on production costs and production plan stability, the effectiveness of FRP-based aggregate planning is compared to traditional aggregate planning without FRP as well as to FRP planning without optimization. The results show that aggregate planning with FRP can consistently identify more stable production plans without significantly sacrificing the cost objective.

Conservation Voltage Reduction (CVR) schemes and renewable energy based distributed generation (DG) can be coordinated to maintain lower node voltages for reducing load consumption and line losses in distribution systems. In addition, smart inverter interfaced DGs are significant resources to maintain the lowest possible voltages at the loading points and adjacent nodes. This paper describes a method of coordination of distributed smart inverters and existing network infrastructure (i.e., capacitor banks and step voltage regulator) of radial distribution system for precise CVR planning. This helps to keep the source voltage and LTC stepper in a range which can maintain the voltage profile within the lower ANSI band (0.95 p.u. - 1.0 p.u) throughout the feeder. A mixed integer nonlinear Programming (MINLP) formulation is used to co-ordinate the whole network to keep the voltage in the desired service range. The IEEE 37 bus test system is used to validate the method and to compare with the outcome of conventional inverter based DGs for CVR deployment.

As future human space missions reach greater distances and span longer durations, designing spacecraft and habitats to better accommodate habitability functions and support crew health, performance and safety will become increasingly important. Mission planners and spacecraft designers need tools to help them better define habitable volume needs for future systems and identify mission and programmatic risks. Because spacecraft/habitat volume directly drives mass and cost, well-informed volume estimation and assessments early in the design process are key. The Spacecraft Optimization Layout and Volume (SOLV) project is a three-year project funded by a NASA Research Announcement Human Exploration Research Opportunities grant. It aims to develop a constraint-driven, optimization-based computational model that can be used by planners, designers and integrators during the early design phases to estimate and evaluate spacecraft/habitat volume based on mission attributes and critical task volumes, while providing a characterization of associated risks. This paper describes the initial efforts to develop the model architecture, including defining model inputs, outputs, variables, and constraints. It describes plans to use decision theory strategies to define and prioritize the level of information and interactions among the model factors in order to drive model logic. The SOLV model implements a “bottom-up” method of estimating volumes that considers multivariate attributes of the mission and crew tasks, an approach that aligns with a human-centered design philosophy. The model aims to support iterative design processes and help reduce design and mission risks through improvements in spacecraft volume design and operations.

This case research was designed to determine if non-urgent patient processes could be improved using a fast-track process in the emergency department of a medical center. The existing fast-track process served 40% of overall walk-in patients at the emergency department. We proposed a series of potential enhancements to patient flow. The proposed smart-track process highlights three features: (1) balancing workload, (2) a flexible re-visit option, and (3) renovation of a portion of the fast-track area into a consulting area. Simulation study results revealed that the enhanced smart-track process has the potential to reduce the average wait time of patients by 73.2% while reducing the burnout of medical staff. This study provides an example of how simulation can be used by engineering managers to make improvements to operations in the healthcare industry.

In this paper, we consider a Cost-of-Quality (CoQ) optimization problem that finds an optimal allocation of prevention and inspection resources to minimize the expected total quality costs under a prevention-appraisal-failure framework, where the quality costs in the proposed model are involved with prevention, inspection, and correction of internal and external failures. Commencing with a simple structure of the problem, we progressively increase the complexity of the problem by accommodating realistic scenarios regarding preventive, appraisal, and corrective actions. The resulting problem is formulated as a zero-one polynomial program, which can be solved either directly using a mixed-integer nonlinear programming solver such as BARON, or using a more conventional mixed-integer linear programming (MILP) solver such as CPLEX after performing an appropriate linearization step. We examine two case studies from the literature (related to a lamp manufacturing context and an order entry process) to illustrate how the proposed model can be utilized to find optimal inspection and prevention strategies, as well as to analyze sensitivity with respect to different cost parameters. We also provide a comparative numerical study of using the aforementioned solvers to optimize the respective model formulations. The results provide insights into the use of such quantitative methods for optimizing the CoQ, and indicate the efficacy of using the linearized MILP model for this purpose.

Uncertainties in supply and/or demand combined with rolling horizon planning necessitate a dynamic and flexible production planning process. Production plans are frequently updated as new information becomes available, which may result in a surplus or deficiency in production resources. Flexibility Requirements Profile (FRP) is designated to mitigate these frequent changes by enforcing bounds on production plans in order to maintain a desired degree of flexibility. FRP successfully integrates external market constraint and internal capacity constraint into the planning process; not only to establish stability in planning but also to identify the constraint that is expected to be the bottleneck in the future. In this study, we discuss stability in planning from the lean thinking perspective and explore its role in eliminating non-value added activities such as unnecessary inventory, and overproduction through the utilization of FRP.

Most forecasting models often fail to produce appropriate forecasts because they are built on the assumption that data is being generated from only one stochastic process. However, in many real world problems, the time series data are generated from one stochastic process initially and then abruptly undergo certain structural changes. In this paper, we assume that the basic underlying process is the simple state-space model with random level and deterministic drift, but is interrupted by three types of exogenous shocks; level shift, drift change, and outlier. A Bayesian procedure to detect, estimate, and adapt to the structural changes is developed and compared to simple, double, and adaptive exponential smoothing using simulated data and the U.S. leading composite index.

Funding project risk management is a process for identifying, assessing, and prioritizing project funding risks. To plan to minimize or eliminate the impact of negative events, one must identify what projects have higher risk to respond to potential project delays due to funding issues quickly. Funding project risk management becomes essential for North Carolina Department of Transportation (NCDOT) as part of Executive Management’s decision-making processes. The objectives of this research project are to (1) investigate current risk management and funding risk management business processes and practices in transportation areas, (2) analyze project risk management and funding risk management practices in departments of transportation (DOTs) of other states, (3) analyze best practices in the other states, especially California Department of Transportation’s practices, and appraise the potentials for possible implementation, and (4) develop a funding project risk management tool to assist NCDOT staff members for better decision-making in budget/cash management, funding risk management, and project management under frequent funding change environment. In this project, the research team investigated current risk management and funding risk management processes, and evaluated the best practices among DOTs in the United States. An in-depth study has been conducted to appraise the potentials for possible implementation of the best practices. A funding project risk management tool, funding risk register, has been developed with optimization and simulation capability. This register can produce a potential adjustment of the project let schedule under 15 preset funding change scenarios as well as a user-specified funding change, which can help NCDOT staff members and Executive Management Leaders to make better decisions in budget management, cash management, and project management.

Abstract The bilinear optimization (or bilinear programming) problem is a specially structured quadratic programming problem, where two sets of variables have bilinear relationships. Assuming nonempty and bounded feasible regions, we first present useful properties of this problem, and discuss two commonly used solution approaches as well as local search methods. We also illustrate how the bilinear programming formulation is related to two game‐theoretic problems, bimatrix game and sequential game. Then, detection of unboundedness of bilinear programming problems is discussed when feasible region(s) are unbounded. Finally, brief remarks on generalized formulations of bilinear program conclude this article.

Abstract In this article, we present the equivalence of Benders, Dantzig–Wolfe, and Lagrangian optimization methods for solving linear programs. In particular, we illustrate that applying Dantzig–Wolfe decomposition for solving a linear program is equivalent to employing Benders decomposition to solve its dual linear program, which is in turn equivalent to implementing a cutting plane method to solve its Lagrangian dual problem. We first demonstrate this equivalence when solving a simply structured linear program having a bounded feasible region, and then extend the results to more general cases such as an unbounded feasible region and block diagonal structure.

Conditional Value-at-Risk (CVaR) is a portfolio evaluation function having appealing features such as sub-additivity and convexity. Although the CVaR function is nondifferentiable, scenario-based CVaR minimization problems can be reformulated as linear programs (LPs) that afford solutions via widely-used commercial softwares. However, finding solutions through LP formulations for problems having many financial instruments and a large number of price scenarios can be time-consuming as the dimension of the problem greatly increases. In this paper, we propose a two-phase approach that is suitable for solving CVaR minimization problems having a large number of price scenarios. In the first phase, conventional differentiable optimization techniques are used while circumventing nondifferentiable points, and in the second phase, we employ a theoretically convergent, variable target value nondifferentiable optimization technique. The resultant two-phase procedure guarantees infinite convergence to optimality. As an optional third phase, we additionally perform a switchover to a simplex solver starting with a crash basis obtained from the second phase when finite convergence to an exact optimum is desired. This three phase procedure substantially reduces the effort required in comparison with the direct use of a commercial stand-alone simplex solver (CPLEX 9.0). Moreover, the two-phase method provides highly-accurate near-optimal solutions with a significantly improved performance over the interior point barrier implementation of CPLEX 9.0 as well, especially when the number of scenarios is large. We also provide some benchmarking results on using an alternative popular proximal bundle nondifferentiable optimization technique.

We consider a scenario with two firms determining which products to develop and introduce to the market. In this problem, there exists a finite set of potential products and market segments. Each market segment has a preference list of products and will buy its most preferred product among those available. The firms play a Stackelberg game in which the leader firm first introduces a set of products, and the follower responds with its own set of products. The leader's goal is to maximize its profit subject to a product introduction budget, assuming that the follower will attempt to minimize the leader's profit using a budget of its own. We formulate this problem as a multistage integer program amenable to decomposition techniques. Using this formulation, we develop three variations of an exact mathematical programming method for solving the multistage problem, along with a family of heuristic procedures for estimating the follower solution. The efficacy of our approaches is demonstrated on randomly generated test instances. This article contributes to the operations research literature a multistage algorithm that directly addresses difficulties posed by degeneracy, and contributes to the product variety literature an exact optimization algorithm for a novel competitive product introduction problem. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009