We investigate two stage supply chain optimization coordination with Supply-Hub operation mode for assembly manufacturing enterprise. Because all parts delivery of all suppliers are integrated at Supply-Hub, all needed parts by the production line are selected, packaged and then sent to the manufacturer by Supply-Hub. We applied queuing theory and basic inventory strategy to model this system and derive the optimization solution for decentralized decision and centralized decision separately. Then coordination inventory strategy is obtained by comparing decentralized decision and centralized decision. Due to inventory risk shifting from manufacturers to suppliers with Supply-Hub operation mode, backorder and holding cost subsidy contracts are used for coordination that incites suppliers to set basic inventories in favor of whole supply chain operation cost reduction. And numerical examples of three suppliers and one manufacturer are given to illustrate the effectiveness of the coordination strategy and the condition to gaining Pareto improving for whole system with the collaborative strategy.
For assembly system, randomness and variableness make it impossible to control the inventory accurately specially. Because the negative effect of them have ripple effects. So we investigate reorder point optimization strategy of assembly manufacturing system with random demand and random lead time. We seek the manufacturers order strategy for the minimum integration of the supply chain inventory cost. And we use scale benefits parameters of suppliers in this system to reflect actual influence of the lot's scale. Numerical example of two components assembly system is given to illustrate the effectiveness of the reorder point strategy. From the numerical simulation, it is can be seen backorder rate and order scale effect will impact the total supply chain cost dramatically.
In this paper, we investigate operational decision optimization problem of assembly manufacturing capacitated supply chain. Open equations of this system in all planning periods are set up. Delivery time to clients form a time window that limits point to assembly manufacturer delivery start and end time. The system model divide planning time into many equally periods, and decisions can vary with time. Ability constraint in the process of supply chain operation, time constraints and assembly production constrain are all considered in the inventory control dynamic batch optimization model. We solve the optimization problem by hybrid mixed integral optimization and SQP algorithm. At last, we examine a set of numerical examples that reveal the insights into the dynamic inventory control policy and the performance of such an assembly type inventory mode.
Abstract. Order strategy optimization of multiple suppliers assembly manufacturing supply chain system under the random demand and random lead time is investigated in this paper. Manufacturers order strategy is sought for in order to minimize supply chain joint cost. Quantity ratio of different components due to assembly requirement is considered in optimization model. And scale benefits parameters of suppliers are used to reflect actual influence of the order's scale. Numerical example of two suppliers and one manufacturer supply chain is given to illustrate the order strategy.
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