Kuik, R. (Roelof)
http://repub.eur.nl/ppl/12189/
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http://repub.eur.nl/
RePub, Erasmus University RepositoryA single-period inventory placement problem for a supply system with the satisficing objective
http://repub.eur.nl/pub/38800/
Fri, 01 Feb 2013 00:00:01 GMT<div>Chung, A.C.S.</div><div>Flynn, B.J.</div><div>Kuik, R.</div><div>Staliński, P.</div>
Consider the inventory placement problem in an N-stage supply system facing a stochastic demand for a single planning period. Each stage is a stocking point holding some form of inventory (e.g.; raw materials, subassemblies, product returns or finished products) that after a suitable transformation can satisfy demand. Stocking decisions are made before demand occurs. Unsatisfied demands are lost. The revenue, salvage value, ordering, transformation, and lost sales costs are proportional. There are fixed costs for utilizing stages for stock storage. The objective is to maximize the probability of achieving a given target profit level. We prove the existence of optimal stocking decisions where at most three stages receive nonzero stocks. We also characterize properties of the optimal stocking decisions and provide an O(N3) algorithm for their computation. For the special case where all fixed costs are zero, the stages utilized do not depend on the demand distribution or the target level, and one can find optimal stocking decisions by performing a simple O(N2) search and solving a single-variable optimization problem. "Counting Your Customers": When will they buy next? An empirical validation of probabilistic customer base analysis models based on purchase timing
http://repub.eur.nl/pub/38235/
Tue, 08 Jan 2013 00:00:01 GMT<div>Korkmaz, E.</div><div>Kuik, R.</div><div>Fok, D.</div>
This research provides a new way to validate and compare buy-till-you-defect [BTYD] models. These models specify a customer’s transaction and defection processes in a non-contractual setting. They are typically used to identify active customers in a com- pany’s customer base and to predict the number of purchases. Surprisingly, the literature shows that models with quite different assumptions tend to have a similar predictive performance.
We show that BTYD models can also be used to predict the timing of the next purchase. Such predictions are managerially relevant as they enable managers to choose appropriate promotion strategies to improve revenues. Moreover, the predictive performance on the purchase timing can be more informative on the relative quality of BTYD models.
For each of the established models, we discuss the prediction of the purchase timing. Next, we compare these models across three datasets on the predictive performance on the purchase timing as well as purchase frequency.
We show that while the Pareto/NBD and its Hierarchical Bayes extension [HB] models perform the best in predicting transaction frequency, the PDO and HB models predict transaction timing more accurately. Furthermore, we find that differences in a model’s predictive performance across datasets can be explained by the correlation between behavioral parameters and the proportion of customers without repeat purchases.Stochastic Inventory control for Product Recovery
http://repub.eur.nl/pub/2299/
Thu, 01 Jan 2004 00:00:01 GMT<div>Laan, E.A. van der</div><div>Kiesmueller, G.P.</div><div>Kuik, R.</div><div>Vlachos, D.</div><div>Dekker, R.</div>
On Optimal Inventory Control with Stochastic Item Returns
http://repub.eur.nl/pub/11393/
Sun, 16 Nov 2003 00:00:01 GMT<div>Fleischmann, M.</div><div>Kuik, R.</div>
To a growing extent companies take recovery of used products into account in their material management. One aspect distinguishing inventory control in this context from traditional settings is an exogenous inbound material flow. We analyze the impact of this inbound flow on inventory control. To this end, we consider a single inventory point facing independent stochastic demand and item returns. This comes down to a variant of a traditional stochastic single-item inventory model where demand may be both positive or negative. Using general results on Markov decision processes we show average cost optimality of an (s,S)-order policy in this model. The key result concerns a transformation of the model into an equivalent traditional (s,S)-model without return flows, using a decomposition of the inventory position. Traditional optimization algorithms can then be applied to determine control parameter values. We illustrate the impact of the return flow on system costs in a numerical example.Controlling inventories with stochastic item returns: a basic model
http://repub.eur.nl/pub/2287/
Mon, 01 Apr 2002 00:00:01 GMT<div>Fleischmann, M.</div><div>Kuik, R.</div><div>Dekker, R.</div>
Environmental legislation and customer expectations increasingly force manufacturers to take back their products after use. Returned products may enter the production process again as input resources. Material management has to be modified accordingly.Some extensions of the discrete lotsizing and scheduling problem
http://repub.eur.nl/pub/14359/
Mon, 01 Jul 1991 00:00:01 GMT<div>Salomon, M.</div><div>Kroon, L.G.</div><div>Kuik, R.</div><div>Wassenhove, L.N. van</div>
In this paper the Discrete Lotsizing and Scheduling Problem (DLSP) is considered. DLSP relates to capacitated lotsizing as well as to job scheduling problems and is concerned with determining a feasible production schedule with minimal total costs in a single-stage manufacturing process. This involves the sequencing and sizing of production lots for a number of different items over a discrete and finite planning horizon. Feasibility of production schedules is subject to production quantities being within bounds set by capacity. A problem classification for DLSP is introduced and results on computational complexity are derived for a number of single and parallel machine problems. Furthermore, efficient algorithms are discussed for solving special single and parallel machine variants of DLSP.