Defense National Stockpile Holding Costs

1990 ◽  
Author(s):  
James W. Anderson
Keyword(s):  
Author(s):  
N. Knofius ◽  
M. C. van der Heijden ◽  
A. Sleptchenko ◽  
W. H. M. Zijm

Abstract The low-volume spare parts business is often identified as a potential beneficiary of additive manufacturing (AM) technologies. Currently, high AM unit costs or low AM part reliabilities deem the application of AM economical inferior to conventional manufacturing (CM) methods in most cases. In this paper, we investigate the potential to overcome these deficiencies by combining AM and CM methods. For that purpose, we develop an approach that is tailored toward the unique characteristics of dual sourcing with two production methods. Opposed to the traditional dual sourcing literature, we consider the different failure behavior of parts produced by AM and CM methods. Using numerical experiments and a case study in the aviation industry, we explore under which conditions dual sourcing with AM performs best. Single sourcing with AM methods typically leads to higher purchasing and maintenance costs while single sourcing with CM methods increases backorder and holding costs. Savings of more than 30% compared to the best single sourcing option are possible even if the reliability or unit costs of a part sourced with AM are three times worse than for a CM part. In conclusion, dual sourcing methods may play an important role to exploit the benefits of AM methods while avoiding its drawbacks in the low-volume spare parts business.


2007 ◽  
Vol 22 (1) ◽  
pp. 107-131 ◽  
Author(s):  
Dimitrios G. Pandelis

We consider two-stage tandem queuing systems with dedicated servers in each station and flexible servers that can serve in both stations. We assume exponential service times, linear holding costs, and operating costs incurred by the servers at rates proportional to their speeds. Under conditions that ensure the optimality of nonidling policies, we show that the optimal allocation of flexible servers is determined by a transition-monotone policy. Moreover, we present conditions under which the optimal policy can be explicitly determined.


Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Maedeh Bank ◽  
Mohammad Mahdavi Mazdeh ◽  
Mahdi Heydari ◽  
Ebrahim Teimoury

PurposeThe aim of this paper is to present a method for finding the optimum balance between sequence-dependent setup costs, holding costs, delivery costs and delay penalties in an integrated production–distribution system with lot sizing decisions.Design/methodology/approachTwo mixed integer linear programming models and an optimality property are proposed for the problem. Since the problem is NP-hard, a genetic algorithm reinforced with a heuristic is developed for solving the model in large-scale settings. The algorithm parameters are tuned using the Taguchi method.FindingsThe results obtained on randomly generated instances reveal a performance advantage for the proposed algorithm; it is shown that lot sizing can reduce the average cost of the supply chain up to 11.8%. Furthermore, the effects of different parameters and factors of the proposed model on supply chain costs are examined through a sensitivity analysis.Originality/valueAlthough integrated production and distribution scheduling in make-to-order industries has received a great deal of attention from researchers, most researchers in this area have treated each order as a job processed in an uninterrupted time interval, and no temporary holding costs are assumed. Even among the few studies where temporary holding costs are taken into consideration, none has examined the effect of splitting an order at the production stage (lot sizing) and the possibility of reducing costs through splitting. The present study is the first to take holding costs into consideration while incorporating lot sizing decisions in the operational production and distribution problem.


Drug Research ◽  
2017 ◽  
Vol 68 (02) ◽  
pp. 89-99 ◽  
Author(s):  
Alexis Rump ◽  
Daniela Stricklin ◽  
Andreas Lamkowski ◽  
Stefan Eder ◽  
Matthias Port

AbstractIn the case of an attack by a “dirty bomb” with cesium-137 there is a risk of internal contamination. The excretion of cesium-137 can be enhanced by Prussian Blue (PB), and thus the committed effective dose be reduced. We analyzed the benefit and costs of PB decorporation treatment. We simulated the reduction of the radiological dose by PB treatment after cesium-137 incorporation by inhalation. The saving of life time was quantified using the monetary “value of a statistical life” (VSL). Treatment costs were based on the market price of PB in Germany. Moreover we considered the holding costs of stockpiling. The benefit of PB treatment increases with its duration up to about 90 days. If treatment initiation is delayed, the maximum achievable benefit is decreased. For a VSL of 1.646 million €, the net benefit of a 90-days treatment started 1 day after the incorporation remains positive up to a stockpiling duration of 10 years. If starting PB treatment as late as the 180th day after incorporation, the costs will surpass the benefit. We conclude that a prompt decision making and early treatment initiation greatly impacts on the medical but also economic efficiency of a PB treatment.


2015 ◽  
Vol 52 (02) ◽  
pp. 473-489
Author(s):  
Yonit Barron

We consider a production-inventory model operating in a stochastic environment that is modulated by a finite state continuous-time Markov chain. When the inventory level reaches zero, an order is placed from an external supplier. The costs (purchasing and holding costs) are modulated by the state at the order epoch time. Applying a matrix analytic approach, fluid flow techniques, and martingales, we develop methods to obtain explicit equations for these cost functionals in the discounted case and under the long-run average criterion. Finally, we extend the model to allow backlogging.


Author(s):  
Ming Hu ◽  
Yun Zhou

Problem definition: We consider an intermediary’s problem of dynamically matching demand and supply of heterogeneous types in a periodic-review fashion. Specifically, there are two disjoint sets of demand and supply types, and a reward for each possible matching of a demand type and a supply type. In each period, demand and supply of various types arrive in random quantities. The platform decides on the optimal matching policy to maximize the expected total discounted rewards, given that unmatched demand and supply may incur waiting or holding costs, and will be fully or partially carried over to the next period. Academic/practical relevance: The problem is crucial to many intermediaries who manage matchings centrally in a sharing economy. Methodology: We formulate the problem as a dynamic program. We explore the structural properties of the optimal policy and propose heuristic policies. Results: We provide sufficient conditions on matching rewards such that the optimal matching policy follows a priority hierarchy among possible matching pairs. We show that those conditions are satisfied by vertically and unidirectionally horizontally differentiated types, for which quality and distance determine priority, respectively. Managerial implications: The priority property simplifies the matching decision within a period, and the trade-off reduces to a choice between matching in the current period and that in the future. Then the optimal matching policy has a match-down-to structure when considering a specific pair of demand and supply types in the priority hierarchy.


2020 ◽  
Vol 19 (03) ◽  
pp. 567-587
Author(s):  
Seyedeh Sanaz Mirkhorsandi ◽  
Seyed Hamid Reza Pasandideh

One of the classical models for inventory control is economic production quantity (EPQ), which is widely used in industry. In this paper, an EPQ model with partial shortage is developed by considering the real world conditions, and costs related to the backorder demand are taken as fixed and time-dependent. In the proposed model, determination of the inventory cycle length, the length of positive inventory cycle and backordered demand rate are considered in shortage period. The aim of the presented research is to minimize the total inventory costs and the space required for storage products so that the stochastic and classic constraints including holding costs, lost sales, backorder, budget, total number of productions and average shortage times should be satisfied while optimizing the multi-objective problem. Presented model is a bi-objective nonlinear programming model. Then, to solve the proposed model, three multi-objective decision-making methods including Lp-metric, goal programming and goal attainment are used. Besides, numerical examples are executed in small, medium and large scales by use of GAMS software, and the performance of the methods is compared in terms of objective functions and required CPU time. Finally, sensitivity analysis is done to determine the effect of change in the main parameters of the model on the objective function value.


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