By Geert-Jan van Houtum, Bram Kranenburg
This publication specializes in the tactical making plans point for spare components administration. It describes a sequence of multi-item stock versions and offers special and heuristic optimization equipment, together with grasping heuristics that paintings good for genuine, life-sized difficulties. The meant viewers includes graduate scholars, beginning students within the box of spare components stock keep watch over, and spare elements making plans experts within the industry.
In person chapters the authors think about subject matters together with: a easy single-location version; single-location versions with a number of computing device forms and/or desktop teams; the multi-location version with lateral transshipments; the classical METRIC version and its generalization to multi-indenture structures; and a single-location version with an specific modeling of the fix ability for failed elements and the priorities that you possibly can set there.
Various chapters of the ebook are utilized in a grasp path at Eindhoven college of expertise and in a PhD process the Graduate software Operations administration and Logistics (a Dutch community that organizes PhD classes within the box of OM&L). the necessary pre-knowledge contains chance thought and uncomplicated wisdom of Markov procedures and queuing idea. End-of-chapter difficulties look for all chapters, with a few solutions showing in an appendix.
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Extra resources for Spare Parts Inventory Control under System Availability Constraints
Sample text
If the average pipeline stock is larger than 1 (for slow moving SKU’s this will not happen in general), then max{ miti − 1 , 0} > 0 and we exclude solutions S with Si ≤ max{ miti −1 , 0} from the solution space. The excluded solutions have a very low value for βi (Si ) and they are generally not relevant for problem instances with a high target for the aggregate fill rate. We now reformulate Problem (Q) as defined in Sect. 4. First, we replace the minimization of EBO(S) by the maximization of β (S).
1 (continued). 1. 1. 031 and C(S) = 36,000 Euros. 031. 1, but apparently not optimal. 1. 5 %. The efficient solution S = (6, 2, 1) is not generated by the greedy to 32,000 algorithm. In general, the greedy algorithm generates only a subset of all efficient solutions. This follows clearly from Fig. 3, where both the efficient solutions from the enumeration and the efficient solutions from the greedy algorithm are displayed. In general, the greedy algorithm generates an ordered set E = {S0 , S1 , S2 , .
Notice that, in Eq. 9), we have |I| different Lagrangian functions, one for every SKU. Notice that we have only one λ because we have only one constraint in our problem. 5 Alternative Optimization Techniques 25 For any given value of λ , we can easily find a base stock level that minimizes the decentralized Lagrangian function Li (Si , λ ), i ∈ I. Since each decentralized Lagrangian function is a convex functions, we know that it has either one unique minimum or multiple minima in subsequent points.