In over-subscription planning (OSP), the set of goals is not achievable jointly, and the task is to find a plan that attains the best feasible subset of goals given resource constraints. Recent classical OSP algorithm...
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In over-subscription planning (OSP), the set of goals is not achievable jointly, and the task is to find a plan that attains the best feasible subset of goals given resource constraints. Recent classical OSP algorithms ignore the uncertainly inherent in many natural application domains where OSPs arise. And while modeling stochastic OSP problems as MDPs is easy, the resulting models are too large for standard solution approaches. Fortunately OSP problems have a natural twotiered hierarchy, and in this paper we adapt and extend tools developed in the hierarchical reinforcement learning community in order to effectively exploit this hierarchy and obtain compact, factored policies. Typically, such policies are suboptimal, but under certain assumptions that hold in our planetary exploration domain, our factored solution is, in fact, optimal. Our algorithms work by repeatedly solving a number of smaller MDPs, while propagating information between them. We evaluate a number of variants of this approach on a set of stochastic instances of a planetary rover domain, showing substantial performance gains.
This paper is concerned with algorithms for the logical generalisation of probabilistic temporal models from examples. The algorithms combine logic and probabilistic models through inductive generalisation. The induct...
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Global supply chain management presents some special challenges and issues for manufacturing companies in planning production: these challenges are different from those discussed in domestic production plans. Globally...
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Global supply chain management presents some special challenges and issues for manufacturing companies in planning production: these challenges are different from those discussed in domestic production plans. Globally loading production among different plants usually involves substantial uncertainty and great risk because of uncertain market demand, fluctuating quota costs incurred in the global manufacturing process, and shortening lead times. This study proposes a dual-response production loading strategy for two types of plants - company-owned and contracted - to hedge against the short lead time and uncertainty, and to be as responsive and flexible as possible to cope with the uncertainty and risk involved. Three types of robust optimization models are presented: the robust optimization model with solution robustness, the robust optimization model with model robustness, and the robust optimization model with the trade-off between solution robustness and model robustness. A series of experiments are designed to test the e. effectiveness of the proposed robust optimization models. Compared with the results of the two-stage stochastic recourse programming model, the robust optimization models provide a more responsive and flexible system with less risk, which is particularly important in the current context of global competitiveness.
We consider a stochastic version of the classical multi-item Capacitated Lot-Sizing Problem (CLSP). Demand uncertainty is explicitly modeled through a scenario tree, resulting in a multi-stage mixed-integer stochastic...
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We consider a stochastic version of the classical multi-item Capacitated Lot-Sizing Problem (CLSP). Demand uncertainty is explicitly modeled through a scenario tree, resulting in a multi-stage mixed-integer stochastic programming model with recourse. We propose a plant-location-based model formulation and a heuristic solution approach based on a fix-and-relax strategy. We report computational experiments to assess not only the viability of the heuristic, but also the advantage (if any) of the stochastic programming model with respect to the considerably simpler deterministic model based on expected value of demand. To this aim we use a simulation architecture, whereby the production plan obtained from the optimization models is applied in a realistic rolling horizon framework, allowing for out-of-sample scenarios and errors in the model of demand uncertainty. We also experiment with different approaches to generate the scenario tree. The results suggest that there is an interplay between different managerial levers to hedge demand uncertainty, i.e. reactive capacity buffers and safety stocks. When there is enough reactive capacity, the ability of the stochastic model to build safety stocks is of little value. When capacity is tightly constrained and the impact of setup times is large, remarkable advantages are obtained by modeling uncertainty explicitly.
We study evolutionary game dynamics in finite populations. We analyze an evolutionary process, which we call pairwise comparison, for which we adopt the ubiquitous Fermi distribution function from statistical mechanic...
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We study evolutionary game dynamics in finite populations. We analyze an evolutionary process, which we call pairwise comparison, for which we adopt the ubiquitous Fermi distribution function from statistical mechanics. The inverse temperature in this process controls the intensity of selection, leading to a unified framework for evolutionary dynamics at all intensities of selection, from random drift to imitation dynamics. We derive a simple closed formula that determines the feasibility of cooperation in finite populations, whenever cooperation is modeled in terms of any symmetric two-person game. In contrast with previous results, the present formula is valid at all intensities of selection and for any initial condition. We investigate the evolutionary dynamics of cooperators in finite populations, and study the interplay between intensity of selection and the remnants of interior fixed points in infinite populations, as a function of a given initial number of cooperators, showing how this interplay strongly affects the approach to fixation of a given trait in finite populations, leading to counterintuitive results at different intensities of selection.
For decision making problems involving uncertainty, both stochastic programming as an optimization method based on the theory of probability and fuzzy programming representing the ambiguity by fuzzy concept have been ...
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For decision making problems involving uncertainty, both stochastic programming as an optimization method based on the theory of probability and fuzzy programming representing the ambiguity by fuzzy concept have been developing in various,ways. In this paper, we focus on multiobjective linear programming problems with random variable coefficients in objective functions and/or constraints. For such problems, as a fusion of these two approaches, after incorporating fuzzy goals of the decision maker for the objective functions, we propose an interactive fuzzy satisficing method for the expectation model to derive a satisficing solution for the decision maker. An illustrative numerical example is provided to demonstrate the feasibility of the proposed method. (C) 2002 Elsevier Science B.V. All rights reserved.
This paper addresses Operating Rooms (ORs) planning problem with elective and emergency surgery demands. The planning problem is considered as a stochastic optimization problem in order to minimize overtime costs and ...
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This paper addresses Operating Rooms (ORs) planning problem with elective and emergency surgery demands. The planning problem is considered as a stochastic optimization problem in order to minimize overtime costs and patients’ related costs. An “almost” exact method combining Monte Carle simulation and mixed integer programming is presented, and convergence properties are investigated. Several heuristics and meta-heuristics are then proposed. Numerical experimentation is conduced to compare different optimization methods.
In this paper we study two stage problems of stochastic convex programming. Solving the problems is very hard. A L-shaped method for it is given. The implement of the algorithm is simple, so less computation work is n...
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In this paper we study two stage problems of stochastic convex programming. Solving the problems is very hard. A L-shaped method for it is given. The implement of the algorithm is simple, so less computation work is needed. The result of computation shows that the algorithm is effective.
Vertex cover problem is not only a famous problem in graph theory, but also a problem employed to model many real-life situations. In this paper, the minimum weight vertex cover problem with stochastic weights is stud...
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Vertex cover problem is not only a famous problem in graph theory, but also a problem employed to model many real-life situations. In this paper, the minimum weight vertex cover problem with stochastic weights is studied. We propose for the first time the concepts of expected minimum weight vertex cover, α-minimum weight vertex cover and the most minimum weight vertex cover. According to different decision criteria, three types of models: expected value model, chance-constrained programming and dependent-chance programming are formulated. We produce a hybrid intelligent algorithm integrating stochastic simulation and genetic algorithm to solve the models. Finally, a numerical example is given to illustrate the effectiveness of the algorithm.
We consider a dynamic planning problem for the transport of elderly and disabled people. In particular, we focus on a decision to take one day ahead: which requests should be served with own vehicles, and which reques...
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