We discuss in this paper statistical inference of sample average approximations of multistage stochastic programming problems. We show that any random sampling scheme provides a valid statistical lower bound for the optimal value of the true problem. However, in order for such lower bound to be consistent one needs to employ the conditional sampling procedure. We also indicate that fixing a fea...

Metric inequalities, cutset inequalities and Benders feasibility cuts are three families of valid inequalities that have been widely used in different algorithms for network design problems. This article sheds some light on the interrelations between these three families of inequalities. In particular, we show that cutset inequalities are a subset of the Benders feasibility cuts, and that Bende...

We discuss in this paper statistical inference of sample average approximations of multistage stochastic programming problems. We show that any random sampling scheme provides a valid statistical lower bound for the optimal (minimum) value of the true problem. However, in order for such lower bound to be consistent one needs to employ the conditional sampling procedure. We also indicate that fi...

In recent years, the market of electric vehicles (EVs) has developed rapidly across world, and recycling a large number their spent power batteries become an urgent challenge today. The resulting closed-loop supply chain (CLSC) have been considerably studied under different aspects. However, there is lack research investigating vehicle (EVBs) network design uncertainty. This paper focuses on is...

This paper describes a complete and efficient solution to the stochastic allocation and scheduling for Multi-Processor System-on-Chip (MPSoC). Given a conditional task graph characterizing a target application and a target architecture with alternative memory and computation resources, we compute an allocation and schedule minimizing the expected value of communication cost, being the communica...

This paper describes a complete and efficient solution to the stochastic allocation and scheduling for Multi-Processor System-on-Chip (MPSoC). Given a conditional task graph characterizing a target application and a target architecture with alternative memory and computation resources, we compute an allocation and schedule minimizing the expected value of communication cost, being the communica...

(Extended Abstract) Stochastic programming is an optimization technique that incorporates random variables as parameters. Because it better reflects the uncertain real world than its traditional deterministic counterpart, stochastic programming has drawn increasingly more attention among decision-makers, and its applications span many fields including financial engineering , health care, commun...

In order to implement the cellular manufacturing system in practice, some essential factors should be taken into account. In this paper, a new mathematical model for cellular manufacturing system considering different production factors including alternative process routings and machine reliability with stochastic arrival and service times in a dynamic environment is proposed. Also because of t...

Industries conduct the Sales and Operations Planning (S&OP) to balance demand supply aligned business targets. This study aims at proposing a model an algorithm for tactical chain planning admitting uncertainty reflecting peculiar S&OP aspect of rolling horizon planning. Therefore, two-stage stochastic programming is developed solved via multi-cut Benders decomposition algorithm. The solution m...