We consider an M/G/∞ exchangeable-item repair system with spares and ample servers to which arriving customers bring groups of random size B identical items for repair. An exact formula for the waiting time distribution and a computationally efficient approximation are presented for this system. From this, we not only have the standard fillrate, but a practical extension of this concept to a 'f...

We seek to optimize the production planning of a three-echelon remanufacturing system under uncertain input data. consider multi-stage stochastic integer programming approach and use scenario trees represent information structure. introduce new dynamic formulation that relies on partial nested decomposition tree. then propose approximate dual algorithm based this decomposition. Our numerical re...

This paper considers a two-echelon distribution system which consists of a single warehouse serving N (possibly non-identical) retailers that face discrete stochastic demand of the customers. The warehouse orders from an exogenous supplier with ample stock. Orders arrive at the warehouse after a fixed leadtime. The warehouse satisfies, if possible, the replenishment requests from the retailers....

The municipal solid waste (MSW) disposal system is the key for building smart city. In MSW system, allocated among plants in first echelon, and then derivatives (incineration residues RDF) are between markets second echelon. two-echelon optimal allocation of two objectives, cost environmental impact, should be considered. Considering uncertainty this paper constructs a grey fuzzy multi-objectiv...

We consider a two echelon supply chain with a single retailer and a single manufacturer. Inventory replenishment policies at the retailer level transmit customer demand variability to the manufacturer, sometimes even in an amplified form (known as the bullwhip effect). When production is inflexible at the manufacturing level, significant costs may be incurred by ramping up and down production l...

We have used Reinforcement Learning together with Monte Carlo simulation to solve a multi-period production planning problem in a two-stage hybrid manufacturing process (a combination of build-to-plan with build-to-order) with a capacity constraint. Our model minimizes inventory and penalty costs while considering real-world complexities such as different component types sharing the same manufa...

We have used Reinforcement Learning together with Monte Carlo simnlation to solve a multi-period production planning problem in a westage hybrid manufacturing process (a combination of build-to-plan with build-to-order) with a capacity constraint. Our model minimizes inventory and penalty costs while considering real-world complexities such as different component types sharing the same manufact...

We consider a supply chain consisting of one manufacturer (capacitated supplier) and two retailers. We characterize the manufacturer’s optimal production policy under selective-information sharing, in which the manufacturer receives demand and inventory information from only one of the two retailers. We show that the manufacturer’s optimal production policy is a state-dependent base-stock polic...

In this paper, a three-echelon supply chain model is analyzed to determine strategies to reduce the supply chain system dynamics. Uniqueness of this research stems from the use of multiple models with varying degrees of detail representing the same supply chain. The significance of a detailed supply chain model on the quality of result is made clear. Factors employed to build an abstract to a d...