Summary
This chapter addresses integrated production and delivery scheduling of several items in a two-echelon supply chain. A single supplier produces the items on a flexible flow line (FFL) under a cyclic policy and delivers them directly to an assembly facility over a finite planning horizon. A new mixed zero-one nonlinear programming model is developed, based on the basic period (BP) policy to minimize average setup, inventory-holding and delivery costs per unit time where stock-out is prohibited. This problem has not yet been addressed in literature. It is computationally complex and has not been solved optimally especially in real-sized problems. Two efficient hybrid genetic algorithms (HGA) are proposed using the power-of-two (PT-HGA) and non-power-of-two (NPT-HGA) policies. The solution’s quality of the proposed algorithms is evaluated and compared with the common cycle approach in a number of randomly generated problem instances. Numerical experiments demonstrate the merit of the NPT-HGA and indicate that it constitutes a very promising solution method for the problem.
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Torabi, S.A., Jenabi, M., Mansouri, S.A. (2008). Hybrid Genetic Algorithms for the Lot Production and Delivery Scheduling Problem in a Two-Echelon Supply Chain. In: Fink, A., Rothlauf, F. (eds) Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management. Studies in Computational Intelligence, vol 144. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69390-1_13
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DOI: https://doi.org/10.1007/978-3-540-69390-1_13
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