Journal
COMPUTERS & OPERATIONS RESEARCH
Volume 40, Issue 7, Pages 1804-1818Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cor.2013.01.015
Keywords
Multi-stage lotsizing and scheduling; Production rates; Pulp and paper industry; Mixed integer programming; Variable Neighbourhood Search; Hybrid methods
Categories
Funding
- FP7-PEOPLE-2009-IRSES project [246881]
- FAPESP
- ERDF through the Programme COMPETE
- Portuguese Government through FCT - Foundation for Science and Technology [PTDC/EGE-GES/117692/2010, SFRH/BD/80109/2011]
- Fundação para a Ciência e a Tecnologia [PTDC/EGE-GES/117692/2010, SFRH/BD/80109/2011] Funding Source: FCT
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Mathematical formulations for production planning are increasing complexity, in order to improve their realism. In short-term planning, the desirable level of detail is particularly high. Exact solvers fail to generate good quality solutions for those complex models on medium- and large-sized instances within feasible time. Motivated by a real-world case study in the pulp and paper industry, this paper provides an efficient solution method to tackle the short-term production planning and scheduling in an integrated mill. Decisions on the paper machine setup pattern and on the production rate of the pulp digester (which is constrained to a maximum variation) complicate the problem. The approach is built on top of a mixed integer programming (MIP) formulation derived from the multi-stage general lotsizing and scheduling problem. It combines a Variable Neighbourhood Search procedure which manages the setup-related variables, a specific heuristic to determine the digester's production speeds and an exact method to optimize the production and flow movement decisions. Different strategies are explored to speed-up the solution procedure and alternative variants of the algorithm are tested on instances based on real data from the case study. The algorithm is benchmarked against exact procedures. (C) 2013 Elsevier Ltd. All rights reserved.
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