جستجوی مقالات مرتبط با کلیدواژه "two-stage stochastic programming" در نشریات گروه "صنایع"

Due to the increasing complexity of uncertainty and its impact on the supply chain network, many researchers have resorted to coping approaches with data uncertainty. In addition, the occurrence of any disruption in the supply chain networks can cause irreparable damage. Therefore, adopting appropriate strategies to increase the level of the supply chain network resilience toward any disruptive events seem to be necessary.

In this paper, a multi-objective, multi-period, and scenario-based mathematical model is presented in which objective functions of delivery time and total network cost are minimized, and to increase network resilience, non-resilience measures are also minimized. Furthermore, a Two-Stage Stochastic Programming (TSSP) approach has been utilized to overcome the uncertain nature of the input parameters. Goal programming has also been used to transform the model into a single-objective one.

In order to prove the model's applicability, the real-world data of a case study of Mashhad has been implemented. Eventually, according to the validation and sensitivity analysis results, the proposed uncertain model has clear superiority over the deterministic model.

Originality/Value: 

This paper presents a multi-objective linear mathematical model for designing the Pharmaceutical Supply Chain (PSC) network under the COVID-19 situation. Two indicators of time and resilience as optimization tools have been considered simultaneously.

Location-allocation in a supply chain is a strategic level decision due to high cost, impossibility of change and the scope of its impact on other decisions and activities, and the selection of suppliers and production and transportation policies is at a tactical level. Integrating different levels of supply chain decisions helps overall costs reduction and performance improvement. To this end, in this paper, a two-stage mean-conditional value at risk model is used to allocate locations and calculate the flow of materials and manufactured products of a multi-product-multi-level supply chain. In this model, distributors and suppliers face the possibility of disruption, and to prevent disruption on the side of suppliers, they can be equipped by spending additional costs. Sources of uncertainty in the model include shipping costs, end-user demand, and the possibility of disruption to distribution centers and suppliers. The research model uses the conditional value at risk along with the risk aversion factor to control the risk of long distances. The designed model is eventually transformed into a single-level linear programming with the help of Monte Carlo simulation. At the end, while solving a numerical example, the model is implemented and the related sensitivity analysis is presented.</em>