OPEN SHOP SCHEDULING PROBLEM WITH INTELLIGENT ALGORITHMS

The Open Shop Scheduling Problem (OSSP) is a notable NP-hard combinatorial optimization challenge with significant implications for resource allocation and efficiency in various manufacturing and service industries. Due to the limitations of exact methods like Mixed Integer Linear Programming (MILP) in solving large-scale instances, intelligent algorithms offer promising alternatives. This paper presents a survey of prominent intelligent algorithms applied to the OSSP, specifically focusing on Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Cuckoo Search (CS), and Ant Colony Optimization (ACO). The review details the fundamental principles of these algorithms and discusses their adaptation, including the common use of sequence-based solution representations, for addressing the unique structure of the OSSP. A key insight from this survey highlights the varying efficacy of these algorithms based on problem scale. For instance, Ant Colony Optimization demonstrates robust performance on smaller OSSP instances but faces challenges with larger, more complex problems. Conversely, Cuckoo Search, leveraging mechanisms such as Lévy flights, emerges as a particularly effective approach for large-scale OSSP scenarios, often yielding superior solutions among the surveyed methods. This survey concludes that intelligent algorithms are indispensable tools for OSSP, with the optimal choice contingent on specific problem characteristics, underscoring the continued importance of research in this domain.