We present a new approach to the design and implementation of probabilistic programming languages (PPLs), based on idea stochastically estimating probability density ratios necessary for inference. By relaxing usual PPL constraint that these densities be computed exactly, we are able eliminate many common restrictions in current PPLs, deliver language that, first time, simultaneously supports f...

This paper presents a decision making approach for mid-term scheduling of large industrial consumers based on the recently introduced class of Stochastic Dominance (SD)- constrained stochastic programming. In this study, the electricity price in the pool as well as the rate of availability (unavailability) of the generating unit (forced outage rate) is considered as uncertain parameters. Th...

This paper presents an application to the multi-objective stochastic transportation problem in fuzzy environment. In this paper, we focus on our attention to multiobjective stochastic transportation problem involving an inequality type of constraints in which all parameters ( supply and demand ) are log-normal random variable and the objectives are non-commensurable and conflicting in nature. A...

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...

The transportation network design problem (NDP) with multiple objectives and demand uncertainty was originally formulated as a spectrum of stochastic multi-objective programming models in a bi-level programming framework. Solving these stochastic multi-objective NDP (SMONDP) models directly requires generating a family of optimal solutions known as the Pareto-optimal set. For practical implemen...

The centralized management approach provides a general view to set a better coordination between the elements of the supply chain, and look for the equilibrium between the stock and the shipped quantities. This paper describes a stochastic model predictive control algorithm to optimize the warehousing cost and the shipping cost on the same time. We consider a multi-level and multi-product suppl...

This paper presents a framework for the design and optimization of multi-product batch processes under uncertainty with environmental considerations. The uncertainties and environmental impacts are discussed. The profit and environmental impacts are considered as biobjectives for batch plant design. The problem, thus, is formulated as a multi-objective stochastic programming problem. It can be ...

preventive maintenance scheduling is to perform a series of tasks that prevents or minimizes production breakdowns and improves the reliability. mathematical models have been developed to solve the preventive maintenance scheduling problem. there are several limitations in the prior work in this area of research. craft combinations are assumed to be given. the craft combination problem concerns...

Multi-stage stochastic programming problems arise in many practical situations, such as production and manpower planning, portfolio selections and so on. In general, the deterministic equivalences of these problems can be very large, and may not be solvable directly by general-purpose optimization approaches. Sequential quadratic programming methods are very effective for solving medium-size no...

Reverse logistics planning plays a crucial role in supply chain management. Stochasticity different parameters along with time horizon can be challenge solving reverse problems. This paper proposes multi-stage, multi-period lot sizing decisions under uncertainties. The main uncertain factors are return and demand quantities, quality. Moment matching method was adopted to generate discrete set o...