Machine scheduling is to assign a group of jobs to a set of machines in an efficient strategy, such that some objectives such as minimizing the makespan time are satisfied under some constraints. Considering the human uncertainty in operations, this paper assumes the processing times of the jobs are uncertain variables, and proposes an uncertain goal programming model for the machine scheduling...

In this paper, a method is suggested to solve the nonlinear interval number programming problem with uncertain coefficients both in nonlinear objective function and nonlinear constraints. Based on an order relation of interval number, the uncertain objective function is transformed into two deterministic objective functions, in which the robustness of design is considered. Through a modified po...

We generalise the optimisation technique of dynamic programming for discretetime systems with an uncertain gain function. We assume that uncertainty about the gain function is described by an imprecise probability model, which generalises the well-known Bayesian, or precise, models. We compare various optimality criteria that can be associated with such a model, and which coincide in the precis...

In this paper, an uncertain Multi-objective Multi-item Solid Transportation Problem (MMSTP) based on uncertainty theory is presented. In the model, transportation costs, supplies, demands and conveyances parameters are taken to be uncertain parameters. There are restrictions on some items and conveyances of the model. Therefore, some particular items cannot be transported by some exceptional co...

In this paper, we study the fixed charge transportation problem with uncertain variables. The fixed charge transportation problem has two kinds of costs: direct cost and fixed charge. The direct cost is the cost associated with each source-destination pair, and the fixed charge occurs when the transportation activity takes place in the corresponding source-destination pair. The uncertain fixed ...

Many real problems with uncertainties may often be formulated as Stochastic Programming Problem. In this study, Genetic Algorithm (GA) which has been recently used for solving mathematical programming problem is expanded for use in uncertain environments. The modified GA is referred as GA in uncertain environments (GAUCE). In the method, the objective function and/or the constraint are fluctuat...

We generalise the optimisation technique of dynamic programming for discretetime systems with an uncertain gain function. We assume that uncertainty about the gain function is described by an imprecise probability model, which generalises the well-known Bayesian, or precise, models. We compare various optimality criteria that can be associated with such a model, and which coincide in the precis...

We consider linear programming problems with uncertain constraint coefficients described by intervals or, more generally, possibility distributions. The uncertainty is given a behavioral interpretation using coherent lower previsions from the theory of imprecise probabilities. We give a meaning to the linear programming problems by reformulating them as decision problems under such imprecise-pr...

We generalise the optimisation technique of dynamic programming for discrete-time systems with an uncertain gain function. The main objective in optimal control is to find out how a system can be influenced, or controlled, in such a way that it its behaviour satisfies certain requirements, while at the same time maximising a given gain function. A very effective method for solving such problems...

We show in this paper that, under general conditions, any convex programming problem depending continuously upon scalar parameters, and solvable for any value of the latter in a fixed compact set (resp. open set), admits a branch of solutions which is polynomial (resp. smooth) with respect to these parameters. This result may be useful to generate tractable approximations of uncertain convex pr...