Performance Evaluation of Recently Proposed Metaheuristics Algorithms on Solving Job Shop Scheduling Problem

Metaheuristic optimization approaches can be effectively used to model and solve real-world scheduling problems that include making decisions about how to allocate a certain amount of capacity or resources (machinery, workers, and workspace) to tasks or customers over time. Many metaheuristic techniques are introduced yearly, and their correct assessment and improvement can aid in solving different real-life optimization problems. In this paper, the performance comparison study of the three recently proposed metaheuristic techniques, namely the African Vultures Optimization Algorithm (AVOA), the Coot Bird Optimization Algorithm (COOT), and the Mayfly Optimization Algorithm (MA), on a real-life scheduling problem of combinatorial complexity is done. The current scheduling problem considered for this article consists of a set of dissimilar parallel machines. Jobs with different release and due dates need to be assigned to the machines. The problem aims to minimize the processing cost of jobs assigned. Based on 750 unique trials (5 problems x 2 datasets x 25 runs x 3 algorithms), it was observed that COOT outperformed the other two algorithms for scheduling problems.