in this paper, firstly by using a mixed linear programming a new model of locating facilities with limited capacity is presented to design a closed-loop supply chain in a multi-product and multi-period mode. then, using a robust optimization approach, the proposed model decreases in non-deterministic expansion. the results show that the proposed model can handle facility capacity in a closed lo...

The aim of this paper is to propose a robust reliable bi-objective supply chain network design (SCND) model that is capable of controlling different kinds of uncertainties, concurrently. In this regard, stochastic bi-level scenario based programming approach which is used to model various scenarios related to strike of disruptions. The well-known method helps to overcome adverse effects of disr...

Forward inference techniques such as sequential Monte Carlo and particle Markov chain Monte Carlo for probabilistic programming can be implemented in any programming language by creative use of standardized operating system functionality including processes, forking, mutexes, and shared memory. Exploiting this we have defined, developed, and tested a probabilistic programming language intermedi...

 We consider a generalization of the classical quadratic assignment problem, where coordinates of locations are uncertain and only upper and lower bounds are known for each coordinate. We develop a mixed integer linear programming model as a robust counterpart of the proposed uncertain model. A key challenge is that, since the uncertain model involves nonlinear objective function of the ...

index tracking is the process of developing a portfolio that reproduces the performance of an index. the tracker portfolio has relatively good diversity and low turnover and low transaction costs. in this paper we applied a binary programming model for index tracking problem. in this model the number of assets for portfolio construction is defined by portfolio manager. the robust optimization f...

Real-world Decision-Theoretic Planning (DTP) is a very challenging research field. A common approach is to model such problems as Markov Decision Problems (MDP) and use dynamic programming techniques. Yet, two major difficulties arise: 1dynamic programming does not scale with the number of tasks, and 2the probabilistic model may be uncertain, leading to the choice of unsafe policies. We build h...

A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. This makes it hard to understand the underlying pr...

We provide an RKHS based inverse problem formulation[15] for analytically deriving the optimal function approximation when probabilistic information about the underlying regression is available in terms of the associated correlation functions as used in [9, 8]. On the lines of Poggio and Girosi[9], we show that this solution can be sparsified using principles of SVM and provide an implementatio...

in traditional portfolio selection model coefficients often are certain and deterministic, but in real world these coefficients are probabilistic. so decision maker cannot estimate them exactly. financial optimization is one of the most attractive areas in decision under uncertainty. in the portfolio selection problem the decision maker considers simultaneously conflicting objectives such as ra...

perishability of blood products as well as uncertainty in demand amounts complicate the management of blood supply for blood centers. this paper addresses a mixed-integer linear programming model for blood platelets production planning while integrating the processes of blood collection as well as production/testing, inventory control and distribution. whole blood-derived production methods for...