Decentralized supply chain management is found to be significantly relevant in today’s competitive markets. Production and distribution planning is posed as an important optimization problem in supply chain networks. Here, we propose a multi-period decentralized supply chain network model with uncertainty. The imprecision related to uncertain parameters like demand and price of the final produc...

decentralization allows channel members to make their decisions, independently. in this decentralized channels, members may have different power to make its decisions. hence, decisions of each member may influence on the other members’ decisions. to model such situations, bi-level programming models are useful. in this study, a supply chain including one manufacturer and multiple competitive re...

Supplier selection and material procurement planning are the most important issues in supply chain management. This decision is complicated when the buyers face with discount price schemes. In real situation, each supplier may apply different methods such as different types of discount schedules and various types of payment in order to increase market share. In this situation, buyers try to sel...

Pricing is one of the major aspects of decision making in supply chain. In the previous works mostly a centralized environment is considered indicating the retailers cannot independently apply their decisions on the pricing strategy. Although in a two-echelon decentralized environment it may be possible that supply chain contributors have encountered with different market power situations which...

Some of the real life decisions are made in decentralized manner under uncertainty. Stochastic bi-level linear programming problems are used to handle such decision making problems. In this paper, we have proposed stochastic bi-level linear programming problems where some of the right hand side parameters of the constraints in both first (leader) and second level (follower) as normal and log-no...

In this paper, a bi-objective pharmaceutical supply chain network under uncertainty demand and transportation costs is modeled and developed. To control the uncertainty parameters, the robust optimization method is considering. The main objective of this paper determines the number and location of potential facilities such as drug manufacture centers and drug distribution centers by considering...

A novel data-driven stochastic robust optimization (DDSRO) framework is proposed for optimization under uncertainty leveraging labeled multi-class uncertainty data. Uncertainty data in large datasets are often collected from various conditions, which are encoded by class labels. Machine learning methods including Dirichlet process mixture model and maximum likelihood estimation are employed for...

Distributed energy resources (DERs) including distributed generators (DGs) and controllable loads (CLs) are managed in the form of several microgrids (MGs) in active distributions networks (ADNs) to meet the demand locally. On the other hand, some loads of distribution networks (DNs) can be supplied by retailers which participate in wholesale energy markets. Therefore, there are several decisio...