This research presents a model for optimal allocation of Iranian oil and gas resources in sanction condition based on stochastic linear multi-objective programming. The general policies of the resistive economy include expanding exports of gas, electricity, petrochemical and petroleum products, expanding the strategic oil and gas reserves, increasing added value through completing the petroleum...

introduction: in the present study, dealing with water deficit challenges for gorgan river basin has been considered. golestan province's economy is dependent on agriculture but the occurrence of drought periods reduced the agricultural production and consequently the region's economy is in crisis. therefore, performing studies for programming and management of the water resources of the provin...

In this chapter, we will study algorithms for both two-stage as well as multi-stage stochastic mixed-integer programs. We present stagewise (resourcedirective) decomposition methods for two-stage models, and scenario (pricedirective) decomposition methods for multi-stage models. The manner in which these models are decomposed relies not only on the specific data elements that are random, but al...

Stochastic Constraint Programming is an extension of Constraint Programming for modelling and solving combinatorial problems involving uncertainty. A solution to such a problem is a policy tree that specifies decision variable assignments in each scenario. Several solution methods have been proposed but none seems practical for large multi-stage problems. We propose an incomplete approach: spec...

This paper presents an algorithm for solving a class of large scale nonlinear programming problem which is originally derived from the multi-stage stochastic convex nonlinear programming. Using the Lagrangian-dual method and the Moreau-Yosida regularization, the primal problem is neatly transformed into a smooth convex problem. By introducing a self-concordant barrier function, an approximate g...

Problem statement: The most important character within optimization problem is the uncertainty of the future returns. Approach: To handle such problems, we utilized probabilistic methods alongside with optimization techniques. We developed single stage and two stage stochastic programming with recourse. The models were developed for risk adverse investors and the objective of the stochastic pro...

In today’s competitive business environment, the design and management of supply chainnetwork is one of the most important challenges that managers encounter. The supply chain network shouldbe designed for satisfying of customer demands as well as minizing the total system costs. This paper presentsa multi-period multi-stage supply chain network design problem under demand uncertainty. The prob...

Dynamic lot sizing problem is one of the significant problem in industrial units and it has been considered by  many researchers. Considering the quantity discount in  purchasing cost is one of the important and practical assumptions in the field of inventory control models and it has been less focused in terms of stochastic version of dynamic lot sizing problem. In  this paper, stochastic dyn...

The paper discusses the application of multi-stage stochastic optimization for managing and optimizing expected returns versus risk, and contrasts static (single-stage) versus dynamic (multi-stage) portfolio optimization. We present how to best fund a pool of similar fixed rate mortgages through issuing bonds, callable and non-callable, of various maturities using stochastic optimization. We di...

In this paper, we propose a new model for designing integrated forward/reverse logistics based on pricing policy in direct and indirect sales channel. The proposed model includes producers, disposal center, distributers and final customers. We assumed that the location of final customers is fixed. First, a deterministic mixed integer linear programming model is developed for integrated logistic...