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Wang, Daqin
Publications (3 of 3) Show all publications
Wang, D., Tang, O. & Zhang, L. (2014). A periodic review lot sizing problem with random yields, disruptions and inventory capacity. International Journal of Production Economics, 155, 330-339
Open this publication in new window or tab >>A periodic review lot sizing problem with random yields, disruptions and inventory capacity
2014 (English)In: International Journal of Production Economics, ISSN 0925-5273, E-ISSN 1873-7579, Vol. 155, p. 330-339Article, review/survey (Refereed) Published
Abstract [en]

This paper examines a periodic-reviewed lot sizing problem with random yields, disruptions and limited inventory capacity. To characterise the continuous production, an additive random yield model is considered rather than a multiplicative one. Disruptions cause breakdowns to production. Inventory capacity is included since the production has to be shut down when the inventory buffer is full. Both disruptions and shutdowns lead to a start-up cost and a stochastic lead time to recover the production. These compound factors of uncertainty are encountered in practical planning decisions in process industries. We review the existing random yield models, which are then compared with the additive model. With a linear production cost, the additive model has an order-up-to policy to be optimal. Disruptions deteriorate the expected actual production quantity and the fill-rate dramatically, even though the optimal order-up-to level increases compared with the cases of no disruption. Considering inventory capacity makes the problem to be a non-convex dynamic programming problem. Numerical analysis shows that the performance is dramatically deteriorated when the inventory capacity is rather tight, which indicates the importance of selecting a proper inventory capacity to reduce the negative impacts and avoid redundant investment on capacity. Moreover, the start-up cost plays an important role in determining the level of inventory capacity.

Place, publisher, year, edition, pages
Elsevier, 2014
Keywords
Lot sizing; Production planning; Random yield; Disruption; Inventory capacity
National Category
Economics and Business
Identifiers
urn:nbn:se:liu:diva-111267 (URN)10.1016/j.ijpe.2014.02.007 (DOI)000341466000035 ()
Note

Funding Agencies|Swedish Foundation for Strategic Research (SSF)

Available from: 2014-10-15 Created: 2014-10-14 Last updated: 2019-06-27
Wang, D. & Tang, O. (2014). Dynamic inventory rationing with mixed backorders and lost sales. International Journal of Production Economics, 149, 56-67
Open this publication in new window or tab >>Dynamic inventory rationing with mixed backorders and lost sales
2014 (English)In: International Journal of Production Economics, ISSN 0925-5273, E-ISSN 1873-7579, Vol. 149, p. 56-67Article in journal (Refereed) Published
Abstract [en]

Customers may react differently when stockouts occur. In this paper we investigate the rationing policy for an inventory system with a mixture of demand classes of backorder type and lost sales type. Since the penalty cost of backorders varies with time, the priorities of demand classes also alter with time. This totally changes the problem structure compared with the classic rationing models. A dynamic rationing policy is studied in this paper by considering the dynamics of demand priorities. A Markov decision model is developed to obtain the optimal dynamic rationing levels for multiple demand classes. The results indicate that between the priority switching points, rationing levels often exhibit different patterns. For lost sales demand classes, the rationing levels always decrease as the remaining time approaches to zero. For backorder demand classes, the rationing levels increase in some parts due to declining of the priorities. The rationing levels of all demand classes finally decline to zero to reduce the inventory holding cost. The application of dynamic rationing is further extended from a single period model to a multi-period (S,T) model where unit cost has to be included. The optimal ordering policy is proved to be a myopic base stock policy and the dynamic rationing policy in the single period model can still be applied with modified time-independent penalty costs for lost sales classes. Too vercome the computational complexity, a heuristic dynamic rationing policy is introduced. Due to its good outcome, implementing such a heuristic dynamic rationing policy can be a practical solution for inventory system with mixed backorders and lost sales, in order to enhance the system performance.

Place, publisher, year, edition, pages
Elsevier, 2014
Keywords
Multiple demand classes; Inventory rationing; Dynamic rationing; Backorders; Lost sales
National Category
Other Natural Sciences
Identifiers
urn:nbn:se:liu:diva-102699 (URN)10.1016/j.ijpe.2013.10.004 (DOI)000332439600007 ()
Funder
Swedish Foundation for Strategic Research
Available from: 2013-12-19 Created: 2013-12-19 Last updated: 2019-06-27
Wang, D., Tang, O. & Huo, J. (2013). A heuristic for rationing inventory in two demand classes with backlog costs and a service constraint. Computers & Operations Research, 40(12), 2826-2835
Open this publication in new window or tab >>A heuristic for rationing inventory in two demand classes with backlog costs and a service constraint
2013 (English)In: Computers & Operations Research, ISSN 0305-0548, E-ISSN 1873-765X, Vol. 40, no 12, p. 2826-2835Article in journal (Refereed) Published
Abstract [en]

We study the rationing policy in an inventory system with two demand classes and different service criteria for backorders. Due to the difference of customer values, system performance sometimes has to be measured with a mixture of penalty cost and service level in managing inventory. With a continuous review (r,Q) system, we develop a critical level rationing policy in which a threshold mechanism is adopted to allocate backorders when multiple outstanding orders exist. Due to the complexity of the problem, a heuristic is developed based on the principle that both demand classes are served with respective target service levels. We also introduce bounds so that the search ranges of decision variables become restrictive. The numerical examples indicate an excellent performance of our heuristic. In addition, when ordering cost is medium or high, the threshold clearing mechanism has the same results as the optimal one. When ordering cost is small (set to zero), different clearing mechanisms should be used depending on the priorities of demand classes. Further analysis indicates that transforming the service constraint into a cost parameter and then applying the existing algorithm will not be a good approach for this problem with mixed performance criteria. It either increases the costs or violates the service constraint. This study also shows the importance of applying rationing policy when high priority class has a low demand volume, target service levels between two classes have a large gap, or replenishment lead time is long. The results of this study should enhance our understanding of how to implement rationing policies in practice.

Place, publisher, year, edition, pages
Elsevier, 2013
Keywords
Multiple demand classes; Rationing policy; Mixed service criteria; Service constraint
National Category
Social Sciences
Identifiers
urn:nbn:se:liu:diva-100967 (URN)10.1016/j.cor.2013.06.001 (DOI)000326610000002 ()
Available from: 2013-11-15 Created: 2013-11-15 Last updated: 2019-06-27Bibliographically approved
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