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...

Stochastic programming models are large-scale optimization problems that are used to facilitate decision-making under uncertainty. Optimization algorithms for such problems need to evaluate the expected future costs of current decisions, often referred to as the recourse function. In practice, this calculation is computationally difficult as it involves the evaluation of a multidimensional inte...

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...

Large corporations fund their capital and operational expenses by issuing bonds with a variety of indexations, denominations, maturities and amortization schedules. We propose a multistage linear stochastic programming model that optimizes bond issuance by minimizing the mean funding cost while keeping leverage under control and insolvency risk at an acceptable level. The funding requirements a...

In this paper we consider multistage stochastic programs with endogenous parameters where investment strategies are considered to reduce uncertainty, and time-varying distributions are used to describe uncertainty. We present the proposed ideas in the context of the planning of process networks with uncertain yields. We propose a new mixed-integer/disjunctive programming model which is reformul...

Consider (typically large) multistage stochastic programs, which are defined on scenario trees as the basic data structure. It is well known that the computational complexity of the solution depends on the size of the tree, which itself increases typically exponentially fast with its height, i.e. the number of decision stages. For this reason approximations which replace the problem by a simple...

We propose a decomposition method for the solution of a dynamic portfolio optimization problem which fits the formulation of a multistage stochastic programming problem. The method allows to obtain time and nodal decomposition of the problem in its arborescent formulation applying a discrete version of Pontryagin Maximum Principle. The solution of the decomposed problems is coordinated through ...

Abstract This study examines the influence maximization (IM) problem via information cascades within random graphs, topology of which dynamically changes due to uncertainty user behavior. leverages discrete choice model (DCM) calculate probabilities existence directed arc between any two nodes. In this IM problem, DCM provides a good description and prediction behavior in terms following or not...

It is the purpose of this paper to present a methodology of analysis which identifies optimum types and combination of unit treatment processes from the range of available alternatives. The objective is to identify the optimum combination and efficiencies of various unit processes in multistage plant; then meet design criteria. Optimality, as used herein, is defined as meeting a given treatment...

We consider the multistage stochastic programming problem where uncertainty enters the right-hand sides of the problem. Stochastic Dual Dynamic Programming (SDDP) is a popular method to solve such problems under the assumption that the random data process is stagewise independent. There exist two approaches to incorporate dependence into SDDP. One approach is to model the data process as an aut...