Supply planning optimization is one of the most important issues for manufacturers and distributors. Supply is planned to meet the future demand. Under the uncertainty involved in demand forecasting, profit is maximized and risk is minimized. In order to simulate the uncertainty and evaluate the profit and risk, we introduced Monte Carlo simulation. The fitness function of GA used the statistic...

Merge-in-transit involves performing value-added operations in conjunction with transportation/warehousing operations. Logistics operators have used this concept to aid customization and postponement operations and to reduce cost and improve network efficiency. This paper presents the modeling of merge-in-transit networks with special emphasis on uncertain demand conditions.

In this paper we consider the problem of controlling a multi–inventory system in the presence of uncertain demand. The demand is unknown but bounded in an assigned compact set. The control input is assumed to be also constrained in a compact set. We consider an integral cost function of the buffer level and we face the problem of minimizing the worst case cost. We show that the optimal cost of ...

A method employing decomposition techniques and Monte Carlo sampling (importance sampling) to solve stochastic linear programs is described and applied to capacity expansion planning problems of electric utilities. We consider uncertain availability of generators and transmission lines and uncertain demand. Numerical results are presented.

The pervasive use of advanced reticle enhancement technologies demanded by VLSI technology scaling leads to dramatic increases in mask costs. In response to this trend, multiple project wafers (MPW) have been proposed as an effective technique for sharing the cost of mask tooling among up to tens of prototype and low volume designs. Previous works on MPW reticle design and dicing have focused o...

Taking into account the uncertainty of real data in the planning process is a real challenge for nowadays companies. It is suggested in this communication to take into account the demand uncertainty, but also the uncertainty on the lead times, for deciding which quantities of components should be released, and when. In that purpose, the various situations that may happen are summarized in a gra...

Although Smart Grids may represent the solution to the limits of nowadays Power Grid, the turnover may not occur in the next future yet due the complex nature of energy distribution. Thus, as a more short term effort, to improve the responsiveness of the energy demand to the power grid load, more and more energy providers apply dynamic pricing schemes for grid users. Believing that dynamic pric...

We design new algorithms for the problem of allocating uncertain, flexible, and multi-unit demand online given uncertain supply, in order to maximise social welfare. The algorithms can be seen as extensions of the expectation and consensus algorithms from the domain of online scheduling. The problem is especially relevant to the future smart grid, where uncertain output from renewable generator...

Integration of renewable energy in power systems is a potential source of uncertainty, because renewable generation is variable and may depend on changing and highly uncertain weather conditions. In this paper we present and evaluate a new method to schedule power-demanding tasks with release times and deadlines under uncertainty, in order to balance demand and uncertain supply. The problem is ...

Medium-term modeling of electricity market has essential role in generation expansion planning. On the other hand, uncertainties strongly affect modeling and consequently, strategic analysis of generation firms in the medium term. Therefore, models considering these uncertainties are highly required. Among uncertain variables considered in the medium term generation planning, demand and hyd...