Single Objective for Partial Flexible Open Shop Scheduling Problem using Hybrid Particle Swarm Optimization Algorithms

Objectives: Scheduling is an optimization problem in computer science and operation research in which ideal jobs are assigned to particular times. Nowadays, the problem is presented as an online problem, that is, each job is presented and the online algorithm needs to make a decision about the job before the next job is presented. Methods and Statstical Analysis: The proposed approach is used to solve the open job scheduling problem in which any job can be connected with the available machine. That was implemented in matlab to available best results with hybrid evolutionary algorithm. Findings: In this paper, a hybrid algorithm based on the particle swarm optimization is proposed, for flexible, open shop scheduling problem, to minimize the make-span. First an effective new approach using two decisions based on parallel priories dispatching rules is applied. Next we develop a hybridizing HPSO, that presents new components for updating velocity and position using evolutionary operators, with an adaptive neighbourhood procedure based on the insert-interchange fitness function, selection, mutation, crossover. Application/Improvements: The performance of the proposed a new hybrid algorithm is compared to other benchmark problems.