The Value of Component Commonality in a Dynamic Inventory System with Lead Times

Song, Jing-Sheng; Zhao, Yue

doi:10.1287/msom.1080.0235

Cited by 38 publications

(38 citation statements)

References 43 publications

(40 reference statements)

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“…First, Song and Zhao (2009) considered FRFS and the general NHB rules but focused on a model with only one common component (including system-W), which is a special case of the model studied here. Also, these authors only studied performance evaluation of the fill rates and did not consider backorders and optimality conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

No-Holdback Allocation Rules for Continuous-Time Assemble-to-Order Systems

Song

Zhao

2010

Operations Research

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This paper analyzes a class of common-component allocation rules, termed no-holdback (NHB) rules, in continuous-review assemble-to-order (ATO) systems with positive lead times. The inventory of each component is replenished following an independent base-stock policy. In contrast to the usually assumed first-come-first-served (FCFS) component allocation rule in the literature, an NHB rule allocates a component to a product demand only if it will yield immediate fulfillment of that demand. We identify metrics as well as cost and product structures under which NHB rules outperform all other component allocation rules. For systems with certain product structures, we obtain key performance expressions and compare them to those under FCFS. For general product structures, we present performance bounds and approximations. Finally, we discuss the applicability of these results to more general ATO systems.Subject classifications: stochastic multi-item inventory system; assemble-to-order; base-stock policy; common-component allocation rule; non-FCFS; sample-path analysis.

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Section: Literature Reviewmentioning

confidence: 99%

No-Holdback Allocation Rules for Continuous-Time Assemble-to-Order Systems

Song

Zhao

2010

Operations Research

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“…By the definition ofG(·) given by (35) and noting that D 2 (t − L) has the same distribution as D 2 , taking further expectation yields…”

Section: By (E-15)-(e-16) Taking a Further Expectation Yieldsmentioning

confidence: 99%

Optimal and asymptotically optimal policies for assemble-to-order n- and W-systems

Song

Zhang

2015

Naval Research Logistics

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Abstract:We consider two specially structured assemble-to-order (ATO) systems-the N-and W-systems-under continuous review, stochastic demand, and nonidentical component replenishment leadtimes. Using a hybrid approach that combines samplepath analysis, linear programming, and the tower property of conditional expectation, we characterize the optimal component replenishment policy and common-component allocation rule, present comparative statics of the optimal policy parameters, and show that some commonly used heuristic policies can lead to significant optimality loss. The optimality results require certain symmetry in the cost parameters. In the absence of this symmetry, we show that, for systems with high demand volume, the asymptotically optimal policy has essentially the same structure; otherwise, the optimal policies have no clear structure. For these latter systems, we develop heuristic policies and show their effectiveness.

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“…a general paper by Sobel about pooling [71] and the paper by Benjaafar et al about the utilisation of pooling in a production-inventory system [72]. Pooling, flexibility [73], and component commonality [74] are related terms and they basically mean the same thing, but from different perspectives: from the management, resource, or product perspectives.…”

Section: Flexibility Of Assemble-to-order Production (Publication Iv)mentioning

confidence: 99%

Block Erection Sequencing in Shipbuilding With General Lifting and Joining Times

Tokola¹,

Niemi²,

Remes³

2013

j ship prod des

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Manufacturing companies can use it as a part of their information systems (including e.g. material requirements planning (MRP), enterprise resource planning (ERP), and manufacturing execution systems (MES)) for controlling the production system, i.e. for the adjustment of their inventory level, defining the capacity, and even scheduling the starting times of the jobs. A method that can be used to improve the decisions dynamically is online optimisation. Online optimisation repeatedly optimises and adjusts the decisions when there are changes or disturbances in the system. Online optimisation differs from traditional optimisation. First, the data is uncertain but, luckily, the uncertainty might decrease as time goes by. Second, quick decisions are needed but they should be made carefully as the decisions will also affect future decisions. This thesis studies online optimisation from the production planning perspective. In practice, many real factors, such as rescheduling intervals and material deliveries, affect online optimisation. The six publications of the thesis focus, first, on finding different short-term planning problems in manufacturing companies, second, on worker reactive coordination in parallel stations, third, on periodical rescheduling, fourth, on the effect of disturbances on assemble-to-order systems, fifth, on production line rescheduling, and, sixth, on the erection of the hull in shipbuilding in the event of material delays. The methods that are used in the publications of the thesis include survey, Markov models, simulation, mixed-integer-linear programming, and stochastic modelling.The modelling of online solutions to practical short-term planning problems is complex because of the large number of variables, most of which have to be considered. As the variables cannot be aggregated in the short term, a single variation in a variable, even a small one, can have significant consequences for the system. The thesis shows that online optimisation gives an advantage in certain short-term situations, such as in the cases of rush jobs or delays of components.Keywords Optimisation, Production planning Valmistava teollisuus voisi käyttää reaaliaikaista tietoa tietojärjestelmissään (joita ovat mm. materiaalinhallintajärjestelmät, toiminnanohjausjärjestelmät ja tuotannonohjausohjelmistot) ohjaamaan tuotantoa esimerkiksi säätämällä varastotasoja, määrittelemällä kapasiteetit tai jopa aikatauluttamalla töiden aloitusajat. Yksi menetelmä, jolla reaaliaikaista tietoa voi käyttää dynaamisesti päätöksenteossa, on online-optimointi. Onlineoptimoinnissa toistuvasti optimoidaan ja säädetään päätöksiä silloin kun järjestelmään tulee muutoksia. Online-optimointi eroaa perinteisestä optimoinnista monin tavoin. Onlineoptimoinnissa tieto on epävarmaa, mutta epävarmuus voi vähetä kun aika menee eteenpäin. Lisäksi online-optimoinnissa tarvitaan nopeita päätöksiä, jotka täytyy tehdä huolellisesti, koska ne vaikuttavat usein tulevaisuuden päätöksiin. Väitöskirja tutkii online-optimointia tuotannonohjauksen näkökulmasta....

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The Value of Component Commonality in a Dynamic Inventory System with Lead Times

Cited by 38 publications

References 43 publications

No-Holdback Allocation Rules for Continuous-Time Assemble-to-Order Systems

No-Holdback Allocation Rules for Continuous-Time Assemble-to-Order Systems

Optimal and asymptotically optimal policies for assemble-to-order n- and W-systems

Block Erection Sequencing in Shipbuilding With General Lifting and Joining Times

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