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Centralized versus market-based approaches to mobile task allocation problem: State-of-the-art
Centralized approach has been adopted for finding solutions to resource allocation problems (RAPs) in many real-life applications. On the other hand, market-based approach has been proposed as an alternative to solve the problem due to recent advancement in ICT technologies. In spite of the existence of some efforts to review the pros and cons of each approach in RAPs, the studies cannot be directly applied to specific problem domains like mobile task allocation problem which is characterised with high level of uncertainty on the availability of resources (workers). This paper aims to review existing studies on task allocation problems(TAPs) focusing on those two approaches and their comparison and identify major issues that need to be resolved for comparing the two approaches in mobile task allocation problems. Mobile Task Allocation Problem (MTAP) is defined and its problematic structures are explained in relation with task allocation to mobile workers. Solutions produced by each approach to some applications and variations of MTAP are also discussed and compared. Finally, some future research directions are identified in order to compare both approaches in function of uncertainty emerging from the mobile nature of the MTAP
Multiobjective strategies for New Product Development in the pharmaceutical industry
New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems
Multiobjective strategies for New Product Development in the pharmaceutical industry
New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems
Chance-Constrained Outage Scheduling using a Machine Learning Proxy
Outage scheduling aims at defining, over a horizon of several months to
years, when different components needing maintenance should be taken out of
operation. Its objective is to minimize operation-cost expectation while
satisfying reliability-related constraints. We propose a distributed
scenario-based chance-constrained optimization formulation for this problem. To
tackle tractability issues arising in large networks, we use machine learning
to build a proxy for predicting outcomes of power system operation processes in
this context. On the IEEE-RTS79 and IEEE-RTS96 networks, our solution obtains
cheaper and more reliable plans than other candidates
Designing IS service strategy: an information acceleration approach
Information technology-based innovation involves considerable risk that requires insight and foresight. Yet, our understanding of how managers develop the insight to support new breakthrough applications is limited and remains obscured by high levels of technical and market uncertainty. This paper applies a new experimental method based on “discrete choice analysis” and “information acceleration” to directly examine how decisions are made in a way that is behaviourally sound. The method is highly applicable to information systems researchers because it provides relative importance measures on a common scale, greater control over alternate explanations and stronger evidence of causality. The practical implications are that information acceleration reduces the levels of uncertainty and generates a more accurate rationale for IS service strategy decisions
Radio Spectrum and the Disruptive Clarity OF Ronald Coase.
In the Federal Communications Commission, Ronald Coase (1959) exposed deep foundations via normative argument buttressed by astute historical observation. The government controlled scarce frequencies, issuing sharply limited use rights. Spillovers were said to be otherwise endemic. Coase saw that Government limited conflicts by restricting uses; property owners perform an analogous function via the "price system." The government solution was inefficient unless the net benefits of the alternative property regime were lower. Coase augured that the price system would outperform the administrative allocation system. His spectrum auction proposal was mocked by communications policy experts, opposed by industry interests, and ridiculed by policy makers. Hence, it took until July 25, 1994 for FCC license sales to commence. Today, some 73 U.S. auctions have been held, 27,484 licenses sold, and 17 billion in U.S. welfare losses have been averted. Not bad for the first 50 years of this, or any, Article appearing in Volume II of the Journal of Law & Economics.
Analyzing Policy Risk and Accounting for Strategy: Auctions in the National Airspace System
We examine the potential for simple auction mechanisms to efficiently allocate arrival and departure slots during Ground Delay Programs (GDPs). The analysis is conducted using a new approach to predicting strategic behavior called Predictive Game Theory (PGT). The difference between PGT and the familiar Equilibrium Concept Approach (ECA) is that PGT models produce distribution-valued solut tion concepts rather than set-valued ones. The advantages of PGT over ECA in policy analysis and design are that PGT allows for decision-theoretic prediction and policy evaluation. Furthermore, PGT allows for a comprehensive account of risk, including two types of risk, systematic and modeling, that cannot be considered with the ECA. The results show that the second price auction dominates the first price auction in many decision-relevant categories, including higher expected efficiency, lower variance in efficiency, lower probability of significant efficiency loss and higher probability of significant efficiency gain. These findings are despite the fact that there is no a priori reason to expect the second price auction to be more efficient because none of the conventional reasons for preferring second price over first price auctions, i.e. dominant strategy implementability, apply to the GDP slot auction setting.auction, ground delay program, entropy, predictive game theory, strategic risk
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