Conflicting objectives becomes a common issue in many supply chain network optimization problems. In this paper, a new model is formulated to design a green supply chain network through a new mixed integer linear programming problem. Uncertain demand and stochastic environmental respect levels are the main parameters of the formulation. The first objective function minimizes the cost of the sup...

There are several different methods to treat uncertainty in literacy. First, sensitivity analysis which is a post-optimization method analyzing stability of generated solutions. Second, stochastic programming is a modeling approach, and this method is limited to that the uncertainty is stochastic in nature. Third, “Robust Mathematical Programming” is that a candidate solutions allowing to viola...

In this work we propose a stochastic model for the design and planning of closed-loop supply chains. Uncertainties in demand and return volumes are modelled together with uncertain transportation costs. A two-stage stochastic programming is developed and a sensitivity analysis to the worst-case probability is performed in order to test the solution robustness. Finally, in order to prove the goo...

In real-world applications, costs for products are not deterministic: neither static nor dynamic. They actually tend to be non-stationary and cross-correlated. To overcome this drawback, there have been some efforts by researchers to extend the Wagner–Whitin algorithm to consider stochastic costs. However, they assume that the information of probability density function of random costs exists. ...

This paper proposes a novel facility location model for health service network design by considering different key elements including the reliability aspects, service capacity, congestion, service quality, surrounding public infrastructures, geographical accessibility and several types of cost such as investment, transportation and operational costs. We formulate the problem as a robust scenari...

Abstract The nursing rescheduling problem is a challenging decision-making task in hospitals. However, this needs to be made stochastic setting meet uncertain demand with insufficient historical data or inaccurate forecasting methods. In study, programming model and distributionally robust are developed for the nurse multiple methods under demands. We show that these models can reformulated int...

The close interaction between the electricity market and end-users can assist demand response (DR) aggregator in handling managing various uncertain parameters simultaneously to reduce their effect on aggregator's operation. As DR main responsibility is aggregate obtained from individual consumers trade it into wholesale market. Another of proposing programs (DRPs) end-users. This article propo...

The study focuses on the dynamic multi-site capacity planning problem in the thin film transistor liquid crystal display (TFT-LCD) industry under stochastic demand. Capacity planning refers to the process of simultaneously implementing a robust capacity allocation plan and capacity expansion policy across multiple sites against stochastic demand. In addition, the demand situation in TFT-LCD man...

In this paper, global robust stability of stochastic impulsive recurrent neural networks with time-varyingdelays which are represented by the Takagi-Sugeno (T-S) fuzzy models is considered. A novel Linear Matrix Inequality (LMI)-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of uncertain fuzzy stochastic impulsive recurrent neural...