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Journal of Applied Mathematics
Volume 2014, Article ID 964120, 14 pages
http://dx.doi.org/10.1155/2014/964120
Research Article

A Scheduling Problem in the Baking Industry

1Department of Applied Mathematics, Institute of Mathematics, Statistics and Scientific Computing, State University of Campinas, Campinas, SP, Brazil
2School of Applied Sciences, State University of Campinas, Limeira, SP, Brazil

Received 5 December 2013; Revised 29 April 2014; Accepted 29 April 2014; Published 19 June 2014

Academic Editor: Nachamada Blamah

Copyright © 2014 Felipe Augusto Moreira da Silva et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper addresses a scheduling problem in an actual industrial environment of a baking industry where production rates have been growing every year and the need for optimized planning becomes increasingly important in order to address all the features presented by the problem. This problem contains relevant aspects of production, such as parallel production, setup time, batch production, and delivery date. We will also consider several aspects pertaining to transportation, such as the transportation capacity with different vehicles and sales production with several customers. This approach studies an atypical problem compared to those that have already been studied in literature. In order to solve the problem, we suggest two approaches: using the greedy heuristic and the genetic algorithm, which will be compared to small problems with the optimal solution solved as an integer linear programming problem, and we will present results for a real example compared with its upper bounds. The work provides us with a new mathematical formulation of scheduling problem that is not based on traveling salesman problem. It considers delivery date and the profit maximization and not the makespan minimization. And it also provides an analysis of the algorithms runtime.