we present a new model and a new approach for solving fuzzylinear programming (flp) problems with various utilities for the satisfactionof the fuzzy constraints. the model, constructed as a multi-objective linearprogramming problem, provides flexibility for the decision maker (dm), andallows for the assignment of distinct weights to the constraints and the objectivefunction. the desired solutio...

Probabilistic or stochastic programming is a framework for modeling optimization problems that involve uncertainty.In this paper, we focus on multi-objective linear programmingproblems in which the coefficients of constraints and the righthand side vector are fuzzy random variables. There are several methodsin the literature that convert this problem to a stochastic or<b...

in this research, we address the application of uncertaintyprogramming to design a multi-site, multi-product, multi-period,closed-loop supply chain (clsc) network. in order to make theresults of this article more realistic, a clsc for a case study inthe iron and steel industry has been explored. the presentedsupply chain covers three objective functions: maximization ofprofit, minimization of n...

in this study, a new stochastic model is proposed to deal with a multi-product, multi-period, multi-stage, multi-site production and transportation supply chain planning problem under demand uncertainty. a two-stage stochastic linear programming approach is used to maximize the expected profit. decisions such as the production amount, the inventory level of finished and semi-finished product, t...

this paper presents a new mathematical model for a bi-objective job shop scheduling problem with sequence-dependent setup times that minimizes the weighted mean completion time and the weighted mean tardiness time. for solving this multi-objective model, we develop a fuzzy multi-objective linear programming (fmolp) model. in this problem, a proposed fmolp method is applied with respect to the o...

In this paper, a Multi-Choice Stochastic Bi-Level Programming Problem (MCSBLPP) is considered where all the parameters of constraints are followed by normal distribution. The cost coefficients of the objective functions are multi-choice types. At first, all the probabilistic constraints are transformed into deterministic constraints using stochastic programming approach. Further, a general tran...

classical deterministic inventory models consider the demand rate to be either constant or time-dependent but independent from the stock status. however, for a certain type of inventory, the demand rate may be in-fluenced by the stock level. also in many real-life problems, some products such as fruits, vegetables, phar-maceuticals and volatile liquids continuously deteriorate to evaporation, o...