Synchronized Production-Distribution Planning in the Pulp and Paper Industry (original) (raw)

This paper examines the flow synchronization problem between a manufacturing location and multiple destinations. Multiple products can be shipped from the manufacturing location to different locations via multiple transportation modes. These transportation modes may have ...

In the current competitive world, technological advancement does not guarantee that the machineries do not break down during their life. A machine's condition can turn into an out-of-control condition gradually or at once. This implies that the products would not have their potential perfect quality. Besides, such situation questions the validity of the utilized lot sizing model, in that the portion of defective products in each lot increases.

In this paper, a case is considered where a distribution center (warehouse of an auto spare parts company) receives orders regularly. Warehouse management is interested in assigning available vehicles to picked orders in such a way that lead time remains lower than a threshold, and transportation cost per unit (money) of received orders is minimized. Since the company receives orders dynamically and arrival of new orders can provide it with the opportunity to improve existing decided distribution paths, the problem better be solved several times a day in a dynamic manner.

This text proposes a mathematical programming approach to design international production-distribution networks for make-to-stock products with convergent manufacturing processes. Various formulations of the elements of production-distribution network design models are discussed. The emphasis is put on modeling issues encountered in practice which have a significant impact on the quality of the logistics network designed. The elements discussed include the choice of an objective function, the definition of the planning horizon, the manufacturing process and product structures, the logistics network structure, demand and service requirements, facility layouts and capacity options, product flows and inventory modeling, as well as financial flows modeling. Major contributions from the literature are reviewed and a number of new formulation elements are introduced. A typical model is presented, and the use of successive mixed-integer programming to solve it with commercial solvers is discussed. A more general version of the model presented and the solution method described were implemented in a commercial supply chain design tool which is now available on the market.

In this paper we consider the inventory-distribution planning under uncertainty for industrial gas supply chains through extending the continuous approximation solution strategy proposed in part I. A stochastic inventory approach is proposed and it is incorporated into a multi-period two-stage stochastic mixed-integer nonlinear programming (MINLP) model to handle uncertainty of demand and loss or addition of customers. This nonconvex MINLP formulation takes into account customer synergies and simultaneously ...