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ABSTRACTEmergency response preparedness requires strategic and operational planning. As natural disasters, disease outbreaks, and bioterrorism continue to threaten public health and the global economic system, policy makers and businesses develop plans for improving their readiness. These emergencies present challenging problems that require evaluation of several trade‐offs due to insufficient resources and capacity with limited response time pressure. Finding an optimal strategy to these problems can be computationally difficult, since they are formulated as large‐scale service network design models with location‐allocation decisions. The facility location problems in disaster management literature are typically known as ‐hard problems. Therefore, heuristic algorithms can help find practically feasible solutions, but decision makers may have to sacrifice one of their objectives while trying to satisfy multiple of them. This paper presents a flexible algorithm to evaluate trade‐offs in resource scarce situations with a given response time. We formulate the location‐allocation problem with capacity and time constraints, and the objective of minimizing the service time for individuals in an affected area. Due to the complexity to solve the problem for large‐scale scenarios, the presented algorithm relaxes capacity and time constraints, simultaneously, and presents flexibility to assess trade‐offs. A modified NSGA‐II algorithm is used with a penalty function formulated to leverage resources. We analyze how values of the penalty parameters interact with a number of open sites in defining an efficient resource allocation strategy. Therefore, decision makers can expand their choices to design an emergency response network with their preferences taken into consideration.

AbstractProject portfolio selection (PPS) is a complex problem faced by major companies whenever there are multiple funding opportunities with insufficient budget to fund them all. In this paper, we present our work on a PPS decision support tool that has become a fundamental part of the project portfolio decision process at Intel Corporation across its largest product and market divisions. The paper builds on a previous publication that outlines the decision support tool's bicriteria optimization model by providing a solution procedure that is capable of solving real‐life PPS problems within time frames acceptable to decision makers, as well as providing further details on the data collection and the decision‐making process. We also report on various analysis and visualization tools that have been built to allow decision makers to interact with promising solutions provided by the decision support tool. One of the contributions of the paper is to present a typology of the important dependencies between projects that needs to be considered, and provide details on how they are incorporated in the decision support tool's optimization engine. We discuss important implementation details on the decision‐making process and the agents involved, and conclude by describing real‐life experiences on how the framework can enable intuitive decision‐making when choosing portfolios that best satisfy the organization's business goals.

ABSTRACTWe present the Integrated Preference Functional (IPF) for comparing the quality of proposed sets of near‐pareto‐optimal solutions to bi‐criteria optimization problems. Evaluating the quality of such solution sets is one of the key issues in developing and comparing heuristics for multiple objective combinatorial optimization problems. The IPF is a set functional that, given a weight density function provided by a decision maker and a discrete set of solutions for a particular problem, assigns a numerical value to that solution set. This value can be used to compare the quality of different sets of solutions, and therefore provides a robust, quantitative approach for comparing different heuristic, a posteriori solution procedures for difficult multiple objective optimization problems. We provide specific examples of decision maker preference functions and illustrate the calculation of the resulting IPF for specific solution sets and a simple family of combined objectives.

ABSTRACTMotivated by the technology division of a financial services firm, we study the problem of capacity planning and allocation for Web‐based applications. The steady growth in Web traffic has affected the quality of service (QoS) as measured by response time (RT), for numerous e‐businesses. In addition, the lack of understanding of system interactions and availability of proper planning tools has impeded effective capacity management. Managers typically make decisions to add server capacity on an ad hoc basis when systems reach critical response levels. Very often this turns out to be too late and results in extremely long response times and the system crashes. We present an analytical model to understand system interactions with the goal of making better server capacity decisions based on the results. The model studies the relationships and important interactions between the various components of a Web‐based application using a continuous time Markov chain embedded in a queuing network as the basic framework. We use several structured aggregation schemes to appropriately represent a complex system, and demonstrate how the model can be used to quickly predict system performance, which facilitates effective capacity allocation decision making. Using simulation as a benchmark, we show that our model produces results within 5% accuracy at a fraction of the time of simulation, even at high traffic intensities. This knowledge helps managers quickly analyze the performance of the system and better plan server capacity to maintain desirable levels of QoS. We also demonstrate how to utilize a combination of dedicated and shared resources to achieve QoS using fewer servers.

ABSTRACTWe investigate the impact of the number of human–computer interactions, different interaction patterns, and human inconsistencies in decision maker responses on the convergence of an interactive, evolutionary multiobjective algorithm recently developed by the authors. In our context "an interaction" means choosing the best and worst solutions among a sample of six solutions. By interaction patterns we refer to whether preference questioning is more front‐, center‐, rear‐, or edge‐loaded. As test problems we use two‐ to four‐objective knapsack problems, multicriteria scheduling problems, and multiobjective facility location problems. In the tests, two different preference functions are used to represent actual decision maker preferences, linear and Chebyshev. The results indicate that it is possible to obtain solutions that are very good or even nearly optimal with a reasonable number of interactions. The results also indicate that the algorithm is robust to minor inconsistencies in decision maker responses. There is also surprising robustness toward different patterns of interaction with the decision maker. The results are of interest to the evolutionary multiobjective (EMO) community actively developing hybrid interactive EMO approaches.