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In this paper a class of discrete optimization problems with uncertain costs is discussed. The uncertainty is modeled by providing a discrete scenario set, in which each scenario represents a possible realization of the element costs (the problem parameters). It is assumed that a partial information about scenario occurrence probabilities is also available. Namely, each such a probability is known...
In this paper, we are interested in a production planning process in collaborative supply chains. More precisely, we consider supply chains, where actors use Manufacturing Resource Planning process (MRPII). Moreover, these actors collaborate by sharing procurement plans.We focus on a supplier, who applies the Periodic Order Quantity (POQ) rule to plan a production integrating the uncertain procurement...
The paper deals with a single-item production planning problem with uncertain demands modeled by fuzzy intervals whose membership functions are possibility distributions for the values of the uncertain demands. Optimization criteria, in the setting of possibility theory, that lead to choose robust production plans under fuzzy demands are given. Algorithms for determining optimal robust production...
This paper deals with a class of combinatorial optimization problems with uncertain costs. The uncertainty is modeled by specifying a scenario set containing a finite number of possible realizations of the costs called scenarios. Additionally, a possibility distribution on the scenario set can be defined. Two robust models, namely the min-max and two-stage, for hedging against uncertainty of the costs...
In this paper, a class of sequencing problems with uncertain parameters is discussed. The uncertainty is modeled by the usage of fuzzy intervals, whose membership functions are regarded as possibility distributions for the values of unknown parameters. It is shown how to use possibility theory to find robust solutions under fuzzy parameters; this paper presents a general framework, together with applications,...
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