A CRITICAL EVALUATION AND COMPARISON OF KEY APPROACHES TO ARTIFICIAL INTELLIGENCE IN PRODUCTION SCHEDULING
| Main Author: | Brahma Datta Shukla* & Pragya Singh Tomar |
|---|---|
| Format: | Article Journal |
| Terbitan: |
, 2021
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/5221770 |
Daftar Isi:
- Production scheduling is a branch of operational research that uses discrete approaches to address a combinational optimization problem. This broad category includes a wide range of issues such as truck routing, bin packing, and work prioritization. Operational research uses two primary ideas to address these issues: precise techniques, which offer the absolute best answer but only solve minor problems, and approximate approaches, which provide just a decent solution but solve problems that are close to real life scale. The second group of approaches includes heuristics, which are problem-specific procedures, and met heuristics, which are more general methods. Many of these met heuristic approaches, such as Genetic Algorithm, Neural Network, and Fuzzy Logic, have dominated the literature on production scheduling over the past two decades. This study reveals that only a few studies have compared heuristic methods for scheduling problems. Scholars must concentrate on evolutionary manufacturing systems and hybrid models in order to solve the scheduling challenge.