Generally in optimization problems, the values of all parameters are assumed to be exactly known beforehand and remain stationary throughout the optimization process. This might be valid for static environments; however, for most of the real life cases, those parameters as well as decision variables or constraints might change depending on the state or time. For example, through the ongoing optimization process, new arrivals or cancellations of decision variables (arrivals or cancellations of new jobs), revealing or cancellation of constraints or similarly the changes occurring on the system parameters are frequently encountered cases of real life manufacturing systems. For such cases, the aim is tracking of the moving optima rather than obtaining the optimum as in traditional static optimization problems. Such problems involving at least one of those mentioned features are called dynamic optimization problems in the literature.
Because dynamic optimization is still a developing research area and it is closer to real life cases as well as it is open for further developments, it has attracted a notable attention from researchers. However, in the current circumstance, it can be said that it's still at the theoretical stage and the reported studies focus on test problems provided within the scope of operations research literature. Those studies commonly include unconstrained problems where only continuous variables and changing parameters are handled as in moving peaks problem and time dependent function optimization problems. However, most of the real life problems can be represented by dynamic optimization problems where structural changes exist such as arrivals/cancellations of new decision variables or constraints including the changes in the domain set where the problem is defined. The significance of dynamic optimization for manufacturing systems has become a seriously studied research field and a notable increase in the number of the papers published on this field is observed particularly over the past decade. However, as a conflict, only few reported works are available yet with practical scope from diverse fields. As a result, this shows that practical studies in this field which will be introduced to the literature in the near feature, are demanded.
In accordance with these thoughts, meta-heuristic based dynamic optimization techniques, supported by representative problems which are thought to have real life reflections, are aimed to be developed within the scope of the project. Distinctly from the existing literature, these techniques will be provided to perform under both structural and parametric changes. In this regard, greedy randomized adaptive search procedure (GRASP) as a constructive technique is selected as the base methods. Our research has demonstrated that a constructive based approach might provide crucial advantages and they might be successful on dynamic optimization problems as well.
Papers produced from the project
Baykasoglu, A., Ozsoydan, F.B., Dinamik Optimizayon, "Cagdas Endustri Mühendisligi Yaklasimlari", (Editör: Prof. Dr. E. Oztemel), Papatya Yayincilik, pp. 485-518, ISBN 978-605-9594-56-1, 2019.
Ozsoydan, F.B., Baykasoglu, A., Quantum firefly swarms for multimodal dynamic optimization problems, Expert Systems with Applications, 115, 189-199, 2019.
Baykasoglu, A., Ozsoydan, F.B., Evolutionary and population-based methods versus constructive search strategies in dynamic combinatorial optimization, Information Sciences, 420, 159–183, 2017.
Baykasoglu, A., Ozsoydan, F.B., Minimisation of non-machining times in operating automatic tool changers of machine tools under dynamic operating conditions, International Journal of Production Research, DOI: 10.1080/00207543.2017.1357861
Baykasoglu, A., Ozsoydan, F.B., Minimizing tool switching and indexing times with tool duplications in automatic machines,International Journal of Advanced Manufacturing Technology, 89, 1775–1789, 2017.
Baykasoglu, A., Ozsoydan, F.B., An improved approach for determination of index positions on CNC magazines with cutting tool duplications by integrating shortest path algorithm, International Journal of Production Research, 54(3), 742-760, 2016.
Baykasoglu A., Ozsoydan F.B., A constructive search algorithm for combinatorial dynamic optimization problems, IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), December 1 – 3, 2015, Douai-France, pp. 34-40.
Ozsoydan F.B., Baykasoglu A., A multi-population firefly algorithm for dynamic optimization problems, IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), December 1 – 3, 2015, Douai-France, pp. 27-33.
Ozsoydan, F.B., Baykasoglu A., Minimizing non-machining times in automatic tool changers, EURO 2016: 28th European Conference on Operational Research, July 3-6, 2016, Poznan University of Technology, Poznan, Poland, p.297-298.
Ozsoydan, F.B., Baykasoglu, A., Solving dynamic optimization problems in real-life manufacturing systems, Uluslararasi Katilimli 16. Üretim Arastirmalari Sempozyumu, Istanbul Teknik Universitesi – Isletme Fakültesi, ISBN: 978-605-320-530-2, p. 910-916.