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Optimal Inventory-Backorder Tradeoff in an Assemble-to-Order System with Random Leadtimes. Yingdong Lu – IBM T.J. Watson Research Center Jing-Sheng Song – University of California, Irvine David Yao – Columbia University. Outline. The Assemble-to-Order System Model formulation
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Optimal Inventory-Backorder Tradeoff in an Assemble-to-Order System with Random Leadtimes Yingdong Lu – IBM T.J. Watson Research Center Jing-Sheng Song – University of California, Irvine David Yao – Columbia University
Outline • The Assemble-to-Order System • Model formulation • Properties of optimal solution • Solution techniques • Numerical results • Conclusion
Problem Background • Assemble-to-order • Mass customization: Dell, Compaq, Ford • Only keep component inventory • Final product is assembled after an order is realized • Optimal tradeoff between inventory and service • Service measure • Average number of product backorders • E[B] = Average # of customer orders waiting • Proportion to average customer waiting time
The Assemble-to-Order System SuppliersComponents Products Backorders (Items) (Customer demands) L1 1 Q121 Q122 L2 l12 2 QK2 QK1 lK QKm Lm m
The Demand Model(multivariate compound Poisson process) • m different components • Overall demand: Poisson process with rate l • Type-K demand: requires only the components in K K = any subset of {1,…, m} QKi = required number of units of component i in K qK = probability a demand is of type-K SqK = 1 • Aggregate demand of component i: Compound Poisson process with rate li = lS{i: in K} qK
Other Modeling Assumptions • The leadtimes for each component are i.i.d. random variables • Li has distribution Gi • Base-stock policies (order-up-to policies) • si = base-stock level for item i • FCFS • An order is backlogged if it is not yet completely filled. • Committed inventory • If we have some items in stock but not others that are requested by an order, we put aside those available items as committed inventory for that order.
The Optimization Problem minimize E[B(s1, …, sm)] subject to c1s1+…+cmsm< C where B = total number of customer backorders For any demand type K, let BK = type-K backorders = number of type-K orders not yet completely satisfied Then, B = SK BK
Solution Properties Let si* be the optimal base-stock level for component i. If ci> cj, li E[Li] <lj E[Lj], and li<lj , then si* < sj*. Example: If i and j have the same cost, and are always requested together, then the one with longest leadtime has higher optimal base-stock level.
Solution Techniques • Surrogate the objective function by simple lower and upper bounds • Both the upper- and lower-bound problems share similar structures, which can be solved by • an exact network flow algorithm (but the number of arcs grows exponentially in the number of components) • faster greedy heuristic algorithms • Numerical results show that the heuristic algorithm is effective.
The Supply Subsystem Suppliers X1 Xm X2 QK2 Q121 Q122 QKm QK1 l12 lK Arrivals (Replenishment Orders)
The Lower Bound Xi = outstanding orders of component i in steady state, has a Poisson distribution with mean liE[Li] Bi = number of component i backorders = [Xi - si ]+ BK,i = number of type-K backorders that have component i backlogged E[BK,i] lK E[Bi]/li BK = number of type-K backorders = maxi e K {BK,i} E[BK] > maxi e K {E[BK,i]} maxi e K {lK E[Bi]/li} E[B] = average total number of backorders = S KE[BK] >S Kmaxi e K {lK E[Bi]/li}
Surrogate Problem I: Lower Bound • Using the lower bound to approximate the objective function, we obtain the following surrogate problem • After a change of variables, the problem becomes
Solving Surrogate Problem I • Hierarchy structure: • There exists a complete ordering of all the order types (the subsets). For any K and (i,j), such that i, j belong to K, but not any set lower than K, we have zi=zj • The hierarchy structure enables us to devise • an exact shortest-path algorithm (but still slow for large problems) • a much faster greedy-type heuristic K1 K2 K3
Surrogate Problem II: Upper Bound • Applying Lai-Robins inequality, we have the following surrogate problem:
6 differentiating items 1. built-in zip drive 2. standard hard drive 3. high-profile hard drive 4. DVD-Rom drive 5. standard processor 6. high-profile processor 6 major demand types {2,5} {3,5} {1,2,5} {1,3,6} {1,3,4,5} {1,3,4,6} A Personal Computer Example