Future Research

Given the novelty of the BCS problem and subsequent algorithm, there are many opportunities for future research to help expand the functionality and improve the performance of this algorithm. This section discusses some of these topics.

As previously stated, the manufacturing facility around which the BCS problem is modeled is in the process of implementing an active RFID system that should greatly increase the accuracy of the data collected. Once this system is installed, the performance of the BCS algorithm should be reevaluated in comparison to the facility’s current scheduling strategy using the improved data. Also, the parameters whose values were optimized in section 4.2.1 should then be adjusted to ensure the best values are used.

An interesting sub-problem arising out of BCS is calculating the value of an additional crane. That is, by how much may the potential production rate of the facility increase with each new crane added to the operation. Certainly each new crane’s value is different, leading to the conclusion that there exist an optimal number of cranes for a given set of jobs, in accordance with some overall objective function.

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Finally, the BCS algorithm should be evaluated against algorithms developed for the more basic OCS problem. To accomplish this, a set of standard data should first be developed for the OCS problem. Then the BCS algorithm can easily be used to schedule these jobs to cranes operating in a single bay (as is the case in the OCS problem). It would be interesting to see if the combination of a neighborhood based heuristic and continuous time simulation, as found in the BCS algorithm, outperforms the combination of solution-based heuristics with discrete event simulation.

As computers become more powerful, continuous time simulations can be completed in less time, making them useful tools for the evaluation of optimization techniques. Thus, the application of continuous time simulation should be expanded to other classical logistical problems. Some obvious candidate problems are the Meet/Pass Problem in the railroad industry, and the scheduling of Automated Guided Vehicles in large facilities. In both of these problems, knowing the exact location of the vehicles at all times is crucial to successfully optimizing the process.

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