A parallel machine batch scheduling problem in a brewing company

This work introduces a scheduling problem which is motivated from a real situation faced by one of the largest brewing companies in Mexico. Several products must follow a brewing process which is composed by three main stages: coction, fermentation, and conditioning. In the coction stage, there are...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology 2016-10, Vol.87 (1-4), p.65-75
Main Authors: Sáenz-Alanís, César A., V.D., Jobish, Salazar-Aguilar, M. Angélica, Boyer, Vincent
Format: Article
Language:English
Subjects:
Above ground tanks
Adaptive search techniques
Algorithms
Brewing
CAE) and Design
Computation
Computer-Aided Engineering (CAD
Computing time
Conditioning
Decision making
Engineering
Fermentation
Industrial and Production Engineering
Job shops
Mechanical Engineering
Media Management
Original Article
Production scheduling
Scheduling
Sequential scheduling
Setup times
Tanks
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Summary:This work introduces a scheduling problem which is motivated from a real situation faced by one of the largest brewing companies in Mexico. Several products must follow a brewing process which is composed by three main stages: coction, fermentation, and conditioning. In the coction stage, there are multiple unrelated parallel machines, and in the fermentation and conditioning stages, there are several heterogeneous tanks. Additionally, during the production process, some maintenance operations must be scheduled. The problem can be seen as a parallel machine batch scheduling problem with sequence-dependent setup times. Due to the complex structure of the problem, we propose a Greedy Randomized Adaptive Search Procedure to generate good quality solutions in a short computation time. Computational experiments are conducted with real and artificial instances. The solutions obtained for the real instances show that the proposed algorithm reaches better solutions than the current solutions generated by the decision maker at the brewery, and the computation time required by our algorithm is dramatically shorter than the one required by the company.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-016-8477-8