A genetic algorithm for simultaneous optimisation of lot sizing and scheduling in a flow line assembly

2011 ◽  
Vol 49 (2) ◽  
pp. 375-400 ◽  
Author(s):  
PL.K. Palaniappan ◽  
N. Jawahar
Keyword(s):  
Genetic Algorithm ◽  
Lot Sizing ◽  
Flow Line ◽  
Lot Sizing And Scheduling

Solving Lot-Sizing and Sequencing Integrated Optimization Problems in Mixed-Model Production Systems

2012 ◽  
Vol 472-475 ◽  
pp. 3335-3338 ◽  
Author(s):  
Bing Gang Wang
Keyword(s):  
Genetic Algorithm ◽  
Mixed Model ◽  
Production Systems ◽  
Optimization Problems ◽  
Lot Sizing ◽  
Flow Line ◽  
Adaptive Genetic Algorithm ◽  
Integrated Optimization ◽  
Comparison Results ◽  
Mixed Model Assembly Line

This paper is concerned about the lot-sizing and sequencing integrated optimization problems in mixed-model production systems composed of one mixed-model assembly line and one fabrication flow line. The optimization objective is minimizing the total makespan cost in regular hour, the overtime makespan cost and the holding cost in the whole production system. The mathematic models are presented and an adaptive genetic algorithm is developed for solving this problem. A traditional genetic algorithm is also designed for testing the optimization performance of the adaptive genetic algorithm. Computational experiments are conducted and the optimization results are compared between the above two algorithms. The comparison results show that the adaptive genetic algorithm is a feasible and effective method for solving this problem.

Applications of JGA to Operations Management and Vehicle Routing

2007 ◽  
pp. 625-641 ◽  
Author(s):  
A. L. Medaglia
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Operations Management ◽  
Lot Sizing ◽  
Routing Problem ◽  
Operations Scheduling ◽  
Solution Methods ◽  
Capacitated Lot Sizing ◽  
Lot Sizing And Scheduling ◽  
Complex Activities

Two of the most complex activities in production and operations management (POM) are inventory planning and operations scheduling. This chapter presents two problems related to these activities, namely, the capacitated lot-sizing and scheduling problem and the capacitated vehicle routing problem. For each of these problems, the authors discuss several solution methods, present a competitive genetic algorithm, and describe its implementation in the Java Genetic Algorithm (JGA) framework. The purpose of this chapter is to illustrate how to use JGA to model and solve complex business problems arising in POM. The authors show that JGA-based solutions are quite competitive and easier to implement than widely used methods found in the literature.

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