Lot-sizing for inventory systems with product recovery

We study inventory systems with product recovery. Recovered items are as-good-as-new and satisfy the same demands as new items. The demand rate and return fraction are deterministic. The relevant costs are those for ordering recovery lots, for ordering production lots, for holding recoverable items in stock, and for holding new/recovered items in stock. We derive simple formulae that determine the optimal lot-sizes for the production/procurement of new items and for the recovery of returned items. These formulae are valid for finite and infinite production rates as well as finite and infinite recovery rates, and therefore more general than those in the literature. Moreover, the method of derivation is easy and insightful

Determining optimal disassembly and recovery strategies

We present a stochastic dynamic programming algorithm for determining the optimal disassembly and recovery strategy, given the disassembly tree, the process dependent quality distributions of assemblies, and the quality dependent recovery options and associated profits for assemblies. This algorithm generalizes the one proposed by Krikke et al. \\cite{Krikke98} in two ways. First, there can be multiple disassembly processes. Second, partial disassembly is allowed. Both generalizations are important for practise

Profitability of price promotions if stockpilling increases consumption

Price promotions induce consumers to purchase higher-than-usual quantities, resulting in higher stocks that lead to increased consumption. We show for a stylized model with a single shop and a single loyal customer that because of this stockpiling effect, promotions can be profitable even if they do not attract extra customers

The multiple-job repair kit problem

The repair kit problem is that of finding the optimal set of parts in the kit of a repairman. An important aspect of this problem, in many real-life situations, is that several job-sites are visited before a kit is restocked. In this paper, we present two heuristics for solving the multiple-job repair kit problem. Both heuristics can be used to determine a solution under the service-objective (minimal holding cost for a required job-fill rate) as well as the cost-objective (minimal expected total cost, including a penalty cost for each `broken' job). The `Job Heuristic (JH)' almost always determines the exact optimal solution, as is shown in an extensive numerical experiment. However, it can not (easily) be used in cases where several parts of the same type may be needed on a job, or part failures are dependent, or the number of jobs in a tour varies. The `Part Heuristic (PH)' is simpler and easy to use in these cases also. In fact, it can be applied in a spreadsheet software package, as we illustrate. The numerical experiments show that it s leads to near-optimal solutions (average `cost error' of less than 0.1 per cent). Therefore, the PH is an excellent method for solving repair kit problems in practise

Hybrid Lateral Transshipments in a Multi-Location Inventory System

In managing networks of stock holding locations, two approaches to the pooling of inventory have been proposed. Reactive transshipm nts respond to stockouts at a location by moving inventory from elsewhere within the network, while proactive redistribution of stock seeks to minimise the chance of future shocks. This paper is the first to propose a hybrid approach in which transshipments are viewed as an opportunity for stock redistribution. We adopt a quasi-myopic approach to the development of a strongly performing hybrid transshipment policy. Numerical studies which utilise dynamic programming and simulation testify to the benefits of using transshipments proactively. In comparison to a purely reactive approach to transshipment, service levels are improved while a reduction in safety stock levels is achieved. The aggregate costs incurred in managing the system are significantly reduced, especially so for large networks facing high levels of demand.

Logistic planning and control of reworking perishable production defectives

We consider a production line that is dedicated to a single product. Produced lots may be non-defective, reworkable defective, or non-reworkable defective. The production line switches between production and rework. After producing a fixed number (N) of lots, all reworkable defective lots are reworked. Reworkable defectives are perishable, i.e., worsen while held in stock. We assume that the rework time and the rework cost increase linear with the time that a lot is held in stock. Therefore, N should not be too large. 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. For a given N, we derive an explicit expression for the average profit (sales revenue minus costs). Using that expression, the optimal value for N can be determined numerically

Rework and postponement: a comparison of bottling strategies

This paper presents the results of a case study in a batch production facility for biological vaccines. The problem considered is that of finding the best bottling strategy for produced batches. A batch can be bottled directly after production, after positive intermediate test results, or after positive final test results. Strategies that start the bottling process quickly after production, have the advantages of a low capacity requirement for production tanks and of a small throughput time if all test results are positive. However, a production batch can only be reworked as long as it has not been bottled. So fast bottling reduces the possibilities for rework and therefore reduces the production yield. We present performance measures for comparing the different strategies and derive closed-form expressions for them. We illustrate the results obtained for the considered case

The Newsboy Problem with Resalable Returns

We analyze a newsboy problem with resalable returns. A single order is placed before the selling season starts. Purchased products may be returned by the customer for a full refund within a certain time interval. Returned products are resalable, provided they arrive back before the end of the season and are undamaged. Products remaining at the end of the season are salvaged. All demands not met directly are lost. We derive a simple closed-form equation that determines the optimal order quantity given the demand distribution, the probability that a sold product is returned, and all relevant revenues and costs. We illustrate its use with real data from a large catalogue/internet mail order retailer