Ali Koc

Two exact formulations for disassembly line balancing problems with task precedence diagram construction using an AND/OR graph

In this paper, the disassembly line balancing problem, which involves determining a line design in which used products are completely disassembled to obtain useable components in a cost-effective manner, is studied. Because of the growing demand for a cleaner environment, this problem has become an important issue in reverse manufacturing. In this study, two exact formulations are developed that utilize an AND/OR Graph (AOG) as the main input to ensure the feasibility of the precedence relations among the tasks. It is also shown that traditional task precedence diagrams can be derived from the AOG of a given product structure. This procedure leads to considerably better solutions of the traditional assembly line balancing problems; it may alter the approach taken by previous researchers in this area.

Prioritizing Project Selection

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.

OPRO: Precise emergency preparedness for electric utilities

Electric utilities spend a large amount of their resources and budget on managing unplanned outages, the majority of which are driven by weather. The weather is the largest contributing factor for power outages faced by the population in the United States and several other countries. A major ongoing effort by utilities is to improve their emergency preparedness process, in order to 1) reduce outage time, 2) reduce repair and restoration costs, and 3) improve customer satisfaction. We present an approach called Outage Prediction and Response Optimization (OPRO) to improve emergency preparedness by combining a) localized and highly accurate weather prediction, b) damage prediction, c) infrastructure health-aware damage hotspot analysis, and d) optimal resource planning. The combination of these capabilities can enable utilities to initiate their storm preparation process 1 to 2 days in advance of the storm and precisely plan their resource schedules and escalation stance. This would be a profound change to the business process of utilities, which today tends to be reactionary once the storm hits. We describe these capabilities and their effectiveness in terms of metrics relevant to a utility, the related use cases, and the overall business process that brings them together in the context of a real U.S. utility.

Optimization Approaches to Security-Constrained Unit Commitment and Economic Dispatch with Uncertainty Analysis

At the heart of the future smart grid lie two related challenging optimization problems: unit commitment and economic dispatch. The contemporary practices such as intermittent renewable power, distributed generation, demand response, etc., induce uncertainty into the daily operation of an electric power system, and exacerbate the ability to handle the already complicated intermingled problems. We introduce the mathematical formulations for the two problems, present the current practice, and survey solution methods for solving these problems. We also discuss a number of important avenues of research that will receive noteworthy attention in the coming decade.

Optimizing Project Prioritization Under Budget Uncertainty

We consider a problem commonly faced in the nuclear power industry, involving annual selection of plant capital investments under the constraints of a limited and uncertain budget. When the budget is assumed known, a typical approach to such problems is built on a multi-dimensional knapsack model. This model takes as input the available budget in each year, the stream of liabilities induced by selecting each project, and the profit, i.e., net present value (NPV), of each project. The goal is to select the portfolio of projects with the highest total NPV, while observing the budget constraint for each year, as well as any additional constraints. We show that a portfolio selected in this manner can fail to hedge against uncertainties in the budget. While the budget may be known at the beginning of the planning period, external events can cause this to change as time unfolds, and hence the funds that will actually be allocated over time are typically uncertain. So, we propose a model that forms an optimal priority list of projects, incorporating multiple budget scenarios. The model is applied to example projects from the South Texas Project Nuclear Operating Company (STPNOC).

Prioritization via Stochastic Optimization

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. n nData, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1865 . n nThis paper was accepted by Dimitris Bertsimas, optimization.

Optimal scenario tree reductions for the stochastic unit commitment problem

Scenario tree reductions of multi-period stochastic processes have been used as an important technique in obtaining good approximate solutions of multi-period convex stochastic programs. The scenario reduction step is aimed often at optimal approximation of the underlying stochastic process. We provide a new fast computationally cheap scenario tree reduction procedure and describe its approximation capabilities. Our context is the stochastic Unit Commitment Problem, the stochastic version of a problem that is at the heart of many modern energy markets. Its solution determines wholesale contracts between energy producers and energy consumers a day before actual transactions. We show that the new technique performs better than earlier prescriptions in obtaining approximations to the original program. However, these techniques of approximating only the underlying distributions without attention to the cost functions may produce weaker approximations of the optimal solution value; we provide a couple of illustrations to this point.

Project Prioritization via Optimization

We consider a problem commonly faced in industry, involving annual selection of plant capital investments. A typical approach to such a problem uses a multi-knapsack formulation, which takes as input the available budget in each year, the stream of liabilities induced by selecting each project, and the profit, i.e., net present value, of each project. The goal is to select the portfolio of projects with the highest total net present value, while observing the budget constraint for each year, as well as any additional constraints. A portfolio selected in this manner can fail to hedge against uncertainties in the budget, the liability stream and the profit. So, we propose a model that forms an optimal priority list of projects, incorporating multiple scenarios for these input parameters. Our model is not a simplistic ranking scheme. Structural and stochastic dependencies among the projects are key to our approach. We apply our methods on a set of example projects from South Texas Project Nuclear Operating Company.Copyright

Optimal spatio-temporal emergency crew planning for a distribution system

A common problem that distribution utilities grapple with is planning crew levels on a day-to-day basis, especially in the face of large weather events, while accounting for complex business constraints. This paper proposes a method for optimally planning hourly crew staffing levels across different organizations (service centers, local contractors, mutual aid crews) and different crew types. The goal is to estimate these staffing levels over different shifts on a time range of days, in a way as to optimize the overall Estimated Time to Restoration (ETR) while maximizing crew efficiency, and honoring business constraints such as labor rules, organizational structure, business processes and public safety. The proposed method uses a constraint programming based task scheduling to capture these complex business constraints and objectives, and solve for an optimal solution. The paper demonstrates how this crew-planning tool can be used for what-if scenario analysis to evaluate different escalation scenarios and aid in decisionmaking.