Abstract
The higher-level control layer presented in Chap. 4 can be used to improve the operation of a food processing plant from two different perspectives: On the one hand, it can be used to decide which product is most profitable to produce given the current features of the raw inputs; on the other hand, it can provide which values of the process variables can be used to produce the target product so that a certain objective function is optimized.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andersen MS, Dahl J, Vandenberghe L (2013) CVXOPT: a python package for convex optimization, version 1.1. 6. Available at cvxopt. org, 54
Belotti P, Lee J, Liberti L, Margot F, Wächter A (2009) Branching and bounds tightening techniques for non-convex MINLP. Optim Methods Softw 24(4–5):597–634
Boyd SP, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge
Diamond S, Boyd S (2016) CVXPY: a python-embedded modeling language for convex optimization. J Mach Learn Res 17(83):1–5
Domahidi A, Chu E, Boyd S (2013) ECOS: an SOCP solver for embedded systems. In: European control conference (ECC), pp 3071–3076
Nocedal J, Wright S (2006) Numerical optimization, 2nd edn. Springer, Berlin
O’Donoghue B, Chu E, Parikh N, Boyd S (2017) SCS: splitting conic solver. version 2.0.2. https://github.com/cvxgrp/scs
Pochet Y, Wolsey LA (2006) Production planning by mixed integer programming. Springer series in operations research and financial engineering. Springer, Berlin
Tawarmalani M, Sahinidis NV (2005) A polyhedral branch-and-cut approach to global optimization. Math Program 103:225–249
Vanderbei RJ (2007) Linear programming: foundations and extensions. Springer Science and Business Media, Berlin
Vaz AIF, Vicente LN (2007) A particle swarm pattern search method for bound constrained global optimization. J Glob Optim 39(2):197–219
Voß S, Woodruff DL (2010) Introduction to computational optimization models for production planning in a supply chain. Springer, Berlin (edición: softcover reprint of hardcover 2nd ed. 2006 edition)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Cano Marchal, P., Gómez Ortega, J., Gámez García, J. (2019). Production Planning for Food Transformation Processes. In: Production Planning, Modeling and Control of Food Industry Processes. Advances in Industrial Control. Springer, Cham. https://doi.org/10.1007/978-3-030-01373-8_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-01373-8_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01372-1
Online ISBN: 978-3-030-01373-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)