On the determination of the control parameters of the optimal can-order policy

Inventory Control

Simple heuristics for push and pull remanufacturing policies

Inventory policies for joint remanufacturing and manufacturing have recently received much attention. Most efforts, though, were related to (optimal) policy structures and numerical optimization, rather than closed form expressions for calculating near optimal policy parameters. The focus of this paper is on the latter. We analyze an inventory system with unit product returns and demands where remanufacturing is the cheaper alternative for manufacturing. Manufacturing is also needed, however, since there are less returns than demands. The cost structure consists of setup costs, holding costs, and backorder costs. Manufacturing and remanufacturing orders have non-zero lead times. To control the system we use certain extensions of the familiar (s,Q) policy, called push and pull remanufacturing policies. For all policies we present simple, closed form formulae for approximating the optimal policy parameters under a cost minimization objective. In an extensive numerical study we show that the proposed formulae lead to near-optimal policy parameters

Production and inventory control in complex production systems using approximate dynamic programming.

Production systems focus not only on providing enough product to supply the market, but also on delivering the right product at the right price, while lowering the cost during the production process. The dynamics and uncertainties of modern production systems and the requirements of fast response often make its design and operation very complex. Thus, analytical models, such as those involving the use of dynamic programming, may fail to generate an optimal control policy for modern production systems. Modern production systems are often in possession of the features that allow them to produce various types of product through multiple working stations interacting with each other. The production process is usually divided into several stages, thus a number of intermediate components (WIP) are made to stock and wait to be handled by the next production stage. In particular, development of an efficient production and inventory control policy for such production systems is difficult, since the uncertain demand, system dynamics and large changeover times at the work stations cause significant problems. Also, due to the large state and action space, the controlling problems of modern production systems often suffer from the curse of dimensionality