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Regular version of the site
Of all publications in the section: 4
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Article
Gürbüz F., Pardalos P. M. Journal of Industrial and Management Optimization. 2012. Vol. 8. P. 285-297.
To increase productivity, companies are in search of techniques that enable them to make faster and more effective decisions. Data mining and fuzzy clustering algorithms can serve for this purpose. This paper models the decision making process of a ceramics production company using a fuzzy clustering algorithm and data mining. Factors that affect the quality of slurry are measured over time. Using this data, a fuzzy clustering algorithm assigns the degrees of memberships of the slurry for the different quality clusters. An expert can decide on acceptance or rejection of slurry based on calculated degrees of memberships. In addition, by using data mining techniques we generated some rules that provide the optimum conditions for acceptance of the slurry.
Added: Jan 9, 2013
Article
Cinar D., Oliveira J. A., Topcu Y. I. et al. Journal of Industrial and Management Optimization. 2016. Vol. 12. No. 4. P. 1391-1415.

In this study, a genetic algorithm (GA) with priority-based representation is proposed for a flexible job shop scheduling problem (FJSP) which is one of the hardest operations research problems. Investigating the effect of the proposed representation schema on FJSP is the main contribution to the literature. The priority of each operation is represented by a gene on the chromosome which is used by a constructive algorithm performed for decoding. All active schedules, which constitute a subset of feasible schedules including the optimal, can be generated by the constructive algorithm. To obtain improved solutions, iterated local search (ILS) is applied to the chromosomes at the end of each reproduction process. The most widely used FJSP data sets generated in the literature are used for benchmarking and evaluating the performance of the proposed GA methodology. The computational results show that the proposed GA performed at the same level or better with respect to the makespan for some data sets when compared to the results from the literature.

Added: Jan 16, 2016
Article
Pei J., Pardalos P. M., Liu X. et al. Journal of Industrial and Management Optimization. 2015. Vol. 11. No. 2. P. 399-419.

This paper investigates a three-stage supply chain scheduling problem in the application area of aluminium production. Particularly, the fi rst and the third stages involve two factories, i.e., the extrusion factory of the supplier and the aging factory of the manufacturer, where serial batching machine and parallel batching machine respectively process jobs in di erent ways. In the second stage, a single vehicle transports jobs between the two factories. In our research, both setup time and capacity constraints are explicitly considered. For the problem of minimizing the makespan, we formalize it as a mixed integer programming model and prove it to be strongly NP-hard. Considering the computational complexity, we develop two heuristic algorithms applied in two di fferent cases of this problem. Accordingly, two lower bounds are derived, based on which the worst case performance is analyzed. Finally, di erent scales of random instances are generated to test the performance of the proposed algorithms. The computational results show the e ffectiveness of the proposed algorithms, especially for large-scale instances.

Added: Sep 10, 2014
Article
Omelchenko A., Malozemov V. Journal of Industrial and Management Optimization. 2006. Vol. 2. No. 1. P. 55-62.

A two-dimensional discrete optimal control problem is considered. In this problem it is required that the first component admits the given value and the second component attains the largest value at the last step. The explicit solution of this problem is obtained under some assumptions.

Added: Sep 11, 2018