We propose a scenario decomposition algorithm for stochastic 0-1 programs. The algorithm recovers an optimal solution by iteratively exploring and cutting-off candidate solutions obtained from solving scenario subproblems. The scheme is applicable to quite general problem structures and can be implemented in a distributed framework. Illustrative computational results on standard two-stage stoch...

a linear programming model has been presented for finding an appropriate planning in order to maximize hotel revenue. in this model, a special planning horizon has been considered that includes several busy days of a year. there are several reservation periods that may start a few months earlier. each period of reservation may have different prices and so result different incomes. hotel custome...

In this paper we discuss computational complexity and risk averse approaches to two and multistage stochastic programming problems. We argue that two stage (say linear) stochastic programming problems can be solved with a reasonable accuracy by Monte Carlo sampling techniques while there are indications that complexity of multistage programs grows fast with increase of the number of stages. We ...

We present recent developments in two-stage mixed-integer stochastic programming with regard to application in power production planning. In particular, we review structural properties, stability issues, scenario reduction and decomposition algorithms for two-stage models. Furthermore, we describe an application to stochastic thermal unit commitment.

in any supply chain, distribution planning of products is of great importance to managers. with effective and flexible distribution planning, mangers can increase the efficiency of time, place, and delivery utility of whole supply chain. in this paper, inventory routing problem (irp) is applied to distribution planning of perishable products in a supply chain. the studied supply chain is compos...

The paper considers modeling of risk-averse preferences in (multi-stage) stochastic programming problems using risk measures. We utilize the axiomatic foundation of coherent risk measures and deviation measures in order to develop simple representations for these measures that facilitate their incorporation into stochastic programs. It is demonstrated that the developed representations allow fo...

Since the emergence of power market, the target of power generating utilities has mainly switched from cost minimization to revenue maximization. They dispatch their power energy generation units in the uncertain environment of power market. As a result, multi-stage stochastic programming has been applied widely by many power generating agents as a suitable tool for dealing with self-scheduling...

This paper proposes an integrated methodology and its solution for supplier selection problem. A twostage stochastic programming model and fuzzy TOPSIS methods are consolidated in this methodology. After pre-research phase, in the second part of the methodology, fuzzy TOPSIS is used for ranking potential suppliers considering qualitative data under fuzzy environment. In the third part of the me...

In the context of public transportation system, improving the service quality and robustness through minimizing the average passengers waiting time is a real challenge. This study provides robust stochastic programming models for train timetabling problem in urban rail transit systems. The objective is minimization of the weighted summation of the expected cost of passenger waiting time, its va...

We show that a number of problems in Artificial Intelligence can be seen as Stochastic Constraint Optimization Problems (SCOPs): problems that have both a stochastic and a constraint optimization component. We argue that these problems can be modeled in a new language, SC-ProbLog, that combines a generic Probabilistic Logic Programming (PLP) language, ProbLog, with stochastic constraint optimiz...