SCHEDULES: FUNDAMENTALS, EXAMPLES, AND IMPLEMENTATION
6. FINAL THOUGHTS
P(.) is the acceptance probability, AE is the objective function increase, T is the current temperature, and k is a system constant.
In the real metal annealing process, temperature must decrease gradually to avoid defects (cracks) in the metal surface. In simulated annealing, such defects will correspond to reaching a poor solution.
consume valuable resource time and do not add value to what is being manufactured. The right number of employees to be assigned to each work center (production cell or line) should also be wisely determined. Inventory levels should be kept minimal (certainly the minimum level should be appropriately estimated). It is in this restrained scenario that production needs to be scheduled, that is, the definition of which products to manufacture, when, where, and how many units of each, need to be appropriately established. On the top of all that, service level should be maximized since meeting customer demand is just what will bring the real money to the company.
Replanning or rescheduling is also an important issue. Frequent changes to schedules disrupt production on the shop, disturb orders placed to suppliers, generate stops to current jobs being executed, and complicate the financial aspect of the corporation (and supplier's operations, consequently) by increasing costs, and modifying purchases already made, and generating unexpected ones that need to be accomplished.
Because of the difficulty in creating a good master schedule in industrial environments, researchers and developers are implementing new computer algorithms for the MPS process, either with heuristics or optimization techniques. Some of the techniques being used or, better said, that can be used are based on linear programming, hill-climbing and branch-and-bound methods, and meta-heuristics with artificial intelligence, such as tabu search, genetic algorithms, and simulated annealing. AI techniques do not guarantee optimality but are usually efficient in terms of computer time and produce good results (maybe even optimal ones in some cases). This work described the use of two meta-heuristics, namely, genetic algorithms and simulated annealing, in the "optimization" of master production scheduling problems.
(In fact, the author could not find a single work on the literature considering these heuristics to the MPS problem.)
These techniques were implemented in CH-+ programming language.
Several examples of productive scenarios were used for illustration and analysis. For these techniques, the main characteristics of a real MPS process and production scenario were considered. Other examples were developed using the optimization techniques. Starting from the objective function, five performance measures were considered: service level, inventory level, overtime, chance of occurring stockouts, an setup times.
Results from some computer experiments were satisfactory, although no benchmarking was performed in this study. Computer time was also acceptable, ranging from seven to twenty minutes, depending on the AI technique used and the problem size.
As for future studies, there are still several questions to be answered, like:
• What is still missing to make the MPS process more easily solvable in today's marketplace?
• Considering the advance in computing speed, in which scenarios is the search for optimal MPS solution feasible?
• Based on the AI techniques implemented, which one can produce better results? In which scenarios?
• What is still missing to artificial intelligence approaches to be tested?
Can new techniques like, such as ant colonies, also be applied?
• In fact, are genetic algorithms and simulated annealing AI techniques that can always be applied to MPS problems? When is one approach better than the other?
• When will branch-and-bound and beam-search methods provide better results than AI methods? As a matter of fact, how can these techniques also be applied to the MPS problem?
• Both search heuristics presented in this study considered algorithm for MPS creation (or adaptation) based on some criteria - their algorithm however, did not started from the net requirements. Future research should consider net requirement as important information to the MPS creation.
ACKNOWLEDGEMENTS
The author would Uke to thank Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq), the Coordenagao de Aperfeigoamento de Pessoal de Nivel Superior (CAPES) and the Pontifical Catholic Univiersity of Parana (PUCPR) for funding this study. The author would also like to thank Marcio Morelli Soares and Paulo Cesar Ribas for helping in the development of computer experiments, and Dr. Jeffrey W. Herrmann for his contribution to the author's Doctorate in production scheduling and for his invitation to be part of this book.
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