Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Lot sizing in serial assembly systems with multiple constrained resources
Management Science
A heuristic method for lot-sizing in multi-stage systems
Computers and Operations Research
Integrating maintenance and production decisions in a hierarchical production planning environment
Computers and Operations Research - Special issue on aggregation and disaggregation in operations research
Memetic algorithms: a short introduction
New ideas in optimization
The number partitioning problem: an open challenge for evolutionary computation?
New ideas in optimization
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Questioning the relative virtues of dynamic lot sizing rules
Computers and Operations Research
Determining the adaptive decision zone of discrete lot sizing model with changes of total cost
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Application of genetic programming for modelling of material characteristics
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
In this paper a meta-heuristic approach for lot-size determination problems in a complex multi-stage production scheduling problems with production capacity constraint has been developed. This type of problem has multiple products with sequential production processes which are manufactured in different periods to meet customer's demand. By determining the decision variables, machinery production capacity and customer's demand, an integer linear program with the objective function of minimization of total costs of set-up, inventory and production is has been provided. In the first step, the original problem is converted to several individual problems using a heuristic approach based on the limited resource Lagrange multiplier. Thus, each individual problem can be solved using one of the easier methods. In the second step, through combining the genetic algorithm with one of the neighborhood search techniques, a new approach has been developed for the individual problems. In the third step, to obtain a better result, resource leveling is performed for the smaller problems using a heuristic algorithm. Using this method, each product's lot-size is determined through several steps. We have verified our results through several empirical experiments.