Microchip Manufacturing Plant Production Floor Scheduling

Microchip manufacturing is a complex process that utilizes expensive machinery. To run process at maximum efficiency tight manufacturing schedules are used, to have minimum machinery down time and always have enough stock of demanded product. Occasionally microchip demand changes from existing or expected demand, and microchip production schedule has to be altered accordingly.
The aim of this research is to further investigate Ant Colony Optimization algorithm's feasibility and effectiveness of solving Dynamic Multi Objective Optimization Problems by solving real world Dynamic Microchip Manufacturing Plant Production Floor Scheduling Problem.
Investigate algorithm capability handle multiple aspects of dynamism, and varying degrees of dynamism. There has been no research done on solving optimization problems with more than one dynamic aspect of the problem yet.
Develop and investigate performance of multiple multi objective strategies for given problem
Pareto optimal frontier
Objective superposition
Objective tier based compromise
Dynamic optimization
Robust dynamic optimization approaches are in particular interest for real-world applications where problems at hand have a tendency to evolve over time and are not predictable accurately in advance. Often such problems must have some mechanisms to improve solutions in real time as new data is obtained about disturbances or incremental accuracy improvements of the optimization data.
Multi-objective optimization
In real world optimization problems often have several conflicting objectives to be considered. Multi-objective results can be expressed in series of solutions that each one of them cannot improve any individual objective without reducing other objectives scores, this is called pareto optimal frontier. However, pareto optimal frontier is computationally expensive to build and rarely useful to have all pareto optimal solutions.
Ant colony optimization
ACO is metaheuristic optimization algorithm that mimics ant behavior of searching shortest path connecting their colony to source of food. Ants visiting route to the source of food lays down pheromone that attracts more ants to visit this path. Once the food source is depleted and disperse and pheromone evaporates therefore no longer attracting ants to unattractive path.
Previous research has solved optimization problem for the pareto optimal frontier where decision maker has to decide which of the pareto optimal solutions to be used as a final solution, however knowing the domain of a problem, it is possible to enable algorithm find single best solution for a problem that can be automated and deployed in real time.