We also present an algorithm for finding the policy parameter values at each location that is based on the method used to solve the single location problem. In the last few years growing interest has been dedicated to supply chain management. In order to evaluate the efficiency of the proposed model, we practice computational simulation. Even if the expected fractions of the various quality levels are known, then the exact fractions when acquiring cores are still uncertain. For these reverse logistics, the main challenges are bringing together the different actors and organise the logistics in a financial feasible way. Further the paper introduces current research topics of closed-loop supply chains and provides an overview of the single papers. Therefore, N should not be too large.
We study a single item hybrid production system with manufacturing and remanufacturing. We derive expressions for the average profit per time unit, including costs for production, rework, disposal, procurement of input materials, and storage of reworkable defective lots. Secondly, with remanufacturing a second mode of supply of serviceable goods is given, so that coordination with the regular mode of procurement becomes necessary. The structure of the optimal policy is analyzed, and it is shown that under specific allocation rules a near-optimal policy with a simple structure exists. Formulas for the optimal repair and procurement batches are developed and extended to the case where there is limited storage space in both the repair and supply depots. We also compare the information required for managing this system to that required in the Clark and Scarf or Inderfurth settings, and point out how the requirements are somewhat different depending on whether remanufacturing occurs upstream or downstream. This figure does not include additional savings due to reduced disposal quantities and additional costs due to investments in recovery equipment, of which we do not have reliable estimates.
For all kinds of reasons, rework, i. The time from issue to return of an individual container is usually not known with certainty and there is a chance that the container is never returned because of loss or irrepairable damage. This is accomplished by adopting growth curve models based on the extended Kalman filter. We develop a deterministic model as well as a stochastic model under continuous review for the system, and provide numerical examples for illustration. Several algorithms proposed for their solution are described and analyzed. It is shown that this problem is equivalent to a problem of controlling a single-server queue. Inventory systems with returns are systems in which there are units returned in a repairable state, as well as demands for units in a serviceable state, where the return and demand processes are independent.
The manufacturer in the supply chain decides the order quantity of new and reused parts to minimize the total costs. Decision variables are identified including manufacturing and remanufacturing capacities and return rates and use rates for end-of-life products and optimal policies are determined. An operating model is constructed to maximize the total profits by optimally deciding the number of purchased parts from external suppliers or subcontractor, the quantity of parts to be processed at each remanufacturing facilities. Its computational complexity is only 2. In particular, we investigate whether the optimal policy inherits the basic structural properties of the simpler systems.
This monograph considers quantitative models that support decision making in Reverse Logistics. We consider a single-stage single-product production system. For periodic review control it is shown how the optimal decision rules for procurement, remanufacturing and disposal can be evaluated by exploiting the functional equations of a dynamic programming formulation. Such problems arise naturally in several applications areas, including retailing where previously sold items are returned to the point of sale and re-enter the inventory stream such returns can be viewed as negative demands , and in managing kits of spare parts for scheduled maintenance of aircraft where excess spares are returned to the depot , among other applications. On the other hand, N should not be too small either, since there are set-up times and costs associated with switching between production and rework. Returns of merchandise occur commonly in the retail and rental businesses.
Moreover, decision-makers are supported in identifying the relevant variables in reverse supply chains and in revealing the consequences of one decision regarding other parameters of the system. We consider a single-item inventory system in which the stock level can increase due to items being returned as well as decrease when demands occur. We show that if remanufactured items flow into the most upstream stage, then this is the case. The algorithm is simple and easy to understand. We show that similar results hold when remanufactured products flow into a downstream stage — however, in this case some modifications must be made. Customer demands can be satisfied from manufacturing of new products or from remanufacturing of returned products. Second, the s,Q policy that is often utilized in the real world is compared with it.
Inspired by the reverse supply chain network at Fuji Xerox Co. The dynamic programming model formulated is different than the usual ones considered in the literature because the inventory level can fluctuate up or down and convexity of the cost function is not important since simple optimal policies can be found when the cost function is nonconvex. When returns of goods and remanufacturing options have to be taken into consideration in inventory control situations, two additional sources of complexity appear in the traditional approaches of optimizing stochastic inventory control. He is also a member of the Royal Flemish Academy of Sciences. The algorithm applies to both periodic review and continuous review inventory systems.
Disposal of products is not allowed and all used products that are returned have to be accepted. In recent years, the management of closed-loop supply chains has gained importance because of increased legislation on producer respon- bility, requiring companies to take back products from customers and to organize for proper recovery and disposal. Their main assumption is that on the one hand all manufacturing and on the other hand all recovery batches have equal size. Modeling complexity is added to the supply chain coordination problem by accounting for reverse logistics activities. Further research should address the incorporation of the quality of returned products and its impact on the actual utilizable amount of product returns. The case studies deal with issues such as the structure of the networks, the relationships between the different parties involved on the networks, inventory management, planning and control, and information technology.
El desarrollo sostenible es un componente significativo de alto crecimiento para los tomadores de decisiones que tiene que ver con aspectos ambientales y sociales propios tanto de las empresas como de sus socios en la cadena logística con el fin de permanecer competitivas en los mercados actuales. We propose different formulae depending on whether lead times for production and remanufacturing are identical or not. To ensure feasibility we have introduced new constraints for the task assignment. The collected units are remanufactured or disposed of. Based on a Markovian approach, we derive essential operating characteristics of the system, and propose an algorithm to search for the optimal replenishment parameters. A model of two-echelon inventory system consisting of a warehouse and a distributor with periodic-review and with stochastic demand as well as stochastic return is considered, where the distributor must order either none or at least as much as a minimum order quantity in each time period. Using this framework, we investigate the effects of various system characteristics such as informational structure, procurement delay, demand rate, and length of the product's life cycle.