Applying neural network to move operations in exchange heuristic in solving resource constrained scheduling

Abstract

The Exchange Heuristic (EH) is a tool that solves Resource Constrained Scheduling (RCS) problems with general assumptions. EH attempts to balance resource utilization throughout the scheduling period. EH does this by shifting some activities later in the schedule to make enough space to assign a promising activity earlier in the schedule. This reassignment frequently leads to an improvement in the schedule. The promising activity is termed as target activity. Selecting the most promising target as well as the order of activities to be shifted constitutes the success of EH. The current version of EH highly depends on an expert's intuition in these operations. The possibility of improvement by introducing intelligence in these operations has been conjectured. Due to the capability of learning as well as dealing with fuzzy data, neural networks (NN) have been considered as alternatives for human experts. The Enhanced Exchange Heuristic (EEH) is applied to turnaround scheduling in Bayer Co. in Baytown, TX.