Please use this identifier to cite or link to this item: https://doi.org/10.1080/00207540903049381
Title: A gradient search and column generation approach for the build-pack planning problem with approved vendor matrices and stochastic demand
Authors: Sun, G.
Lee, L.H. 
Chew, E.P. 
Shao, J. 
Keywords: cycle time reduction
dispatching rules
evolutionary algorithms
genetic algorithms
inventory control
logistics
simulation
simulation theory
Issue Date: 1-Oct-2010
Source: Sun, G., Lee, L.H., Chew, E.P., Shao, J. (2010-10-01). A gradient search and column generation approach for the build-pack planning problem with approved vendor matrices and stochastic demand. International Journal of Production Research 48 (19) : 5783-5807. ScholarBank@NUS Repository. https://doi.org/10.1080/00207540903049381
Abstract: We study a production planning problem of product assembly with random demand, where the customers choose their preferred suppliers for pairs of inter-dependent components through the approved vendor matrix. The problem is to develop production plans that minimise the expected total shortage and holding costs while observing the matrix restrictions and limited component supplies. We provide a mathematical programming formulation of the problem with a large number of decision variables, whose cost function is the solution of a parametric stochastic transportation problem. We present a gradient-based interior-point approach to solve this problem where the gradient is estimated by the shadow price from the solution of such a transportation problem. A column generation scheme is integrated into the approach to handle the large problem issue. Computational results show that our algorithm significantly improves the computational time when compared with the approach without column generation. In addition, we also discuss some extensions of the basic problem to the multi-period rolling horizon case. © 2010 Taylor & Francis.
Source Title: International Journal of Production Research
URI: http://scholarbank.nus.edu.sg/handle/10635/54225
ISSN: 00207543
DOI: 10.1080/00207540903049381
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