Capacity and Inventory Planning for Make-to-Order Production by Klaus Altendorfer

By Klaus Altendorfer

​The booklet provides diversified types for the simultaneous optimization challenge of means funding and paintings unlock rule parameterization. the final charges are minimized both together with backorder charges or contemplating a carrier point constraint. The to be had literature is prolonged with the mixing of a disbursed client required lead time as well as the particular call for distribution. in addition, an endogenous construction lead time is brought. diversified types for make-to-order creation platforms with one or a number of serial processing levels are built. potential funding is associated with the processing premiums of the machines or to the variety of the machines. effects are equations for carrier point, tardiness, and FGI lead time in any such construction process. For particular situations with M/M/1 and M/M/s queues particular suggestions of the optimization difficulties or optimality stipulations pertaining to potential funding and paintings unencumber rule parameterization are provided.

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Extra info for Capacity and Inventory Planning for Make-to-Order Production Systems: The Impact of a Customer Required Lead Time Distribution

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E. L with fL ðÁÞ denoting the probability density function (pdf), FL ðÁÞ the respective cumulated distribution function (cdf), and 1=β its mean. The customer required lead time cannot be influenced by the production system and all customer orders are accepted. Customer orders are processed by the single machine with exponential processing rate μ and utilization ρ following a first-in-first-served (FIFS) discipline. W denotes the random variable and 1=k the mean of production lead time needed for one order from release to the buffer in front of the machine until its completion and delivery to the FGI buffer.

Stidham (1970), Buzacott and Shanthikumar (1992), and Karaesmen et al. (2004)). Transformed to the manufacturing setting this model has exponentially distributed interarrival times between two consecutive orders and exponentially distributed processing times of the orders which are processed at one machine. 1) This model can be applied to show the nonlinear increase of WIP and production lead time with respect to the utilization. Furthermore, the M/M/1 model provides an exponentially distributed production lead time and a Poisson output stream.

5 reports on the results of a numerical study. Conclusions are provided in Sect. 6. Some additional proofs are added in the Appendix. K. 1007/978-3-319-00843-1_4, © Springer International Publishing Switzerland 2014 43 44 4 Simultaneous Capacity and Planned Lead Time Optimization Customer required lead time = L Buffer 2 Proces sing step 2 Tardiness C = max(0, W–L) Customer Buffer 1 WIP 2 = Y 2 Proces sing step 1 Buffer FGI = G WIP 1 = Y 1 Production lead time = W 2 Production lead time = W1 FGI lead time I = max(0, L–W ) Waiting time until release to stage 1 = max(0, X 2 –W 2) Fig.

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