Dynamic domain transformation resource scheduling approach: water irrigation scheduling for urban farming

• Published in: IAES International Journal of Artificial Intelligence
• Main Author: Amerudin M.N.I.M.; Rahim S.K.N.A.; Omar N.; Sulaiman M.S.; Jaafar A.H.; Hamzah R.
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129099553&doi=10.11591%2fijai.v11.i2.pp624-631&partnerID=40&md5=100db58841fbb06baad11af73ce1106f

Scheduling resources under limited resources using tailored approaches can be done successfully. However, there are situations and problems that require a schedule to handle uncertainties dynamically. The changes in the environment could lead to a non-optimal schedule, which could lead to the wastage of resources. The infeasible schedule could also be an outcome of changes that would render the schedule obsolete, and a new schedule must be generated. The majority of the scheduling problems are solved by a heuristic approach that utilizes a random number generator, thus the outcome is not guaranteed to be optimal. Domain transformation approach (DTA) is a scheduling methodology that has confirmed its expressive power in producing feasible and good quality schedules through avoidance of randomness elements as highly used in heuristic approaches. DTA has been employed in this study to solve the water irrigation scheduling for urban farming. The proposed model was tested on three different datasets. It was observed that the costs obtained on all datasets without utilizing the dynamic DTA are higher in all instances, which indicates that the solution produced by DTA is of higher quality. Thus, dynamic DTA is a more effective way of scheduling resources with considering ad-hoc changes. © 2022, Institute of Advanced Engineering and Science. All rights reserved.