A Particle Swarm Optimization Algorithm on Job-Shop Scheduling Problems With Multi-Purpose Machines

Abstract

This paper is a contribution to the research which aims to provide an efficient optimization algorithm for job-shop scheduling problems with multi-purpose machines or MPMJSP. To meet its objective, this paper proposes a new variant of particle swarm optimization algorithm, called GLN-PSOc, which is an extension of the standard particle swarm optimization algorithm that uses multiple social learning topologies in its evolutionary process. GLN-PSOc is a metaheuristic that can be applied to many types of optimization problems, where MPMJSP is one of these types. To apply GLN-PSOc in MPMJSP, a procedure to map the position of particle into the solution of MPMJSP is proposed. Throughout this paper, GLN-PSOc combined with this procedure is named MPMJSP-PSO. The performance of MPMJSP-PSO is evaluated on well-known benchmark instances, and the numerical results show that MPMJSP-PSO performs well in terms of solution quality and that new best known solutions were found in some instances of the test problems.

Penyusunan Jadwal Asisten Praktikum Menggunakan Algoritma Genetika

“…Setiap partikel solusi berisi urutan susunan operasi yang harus dilakukan menggunakan mesin berdasarkan prioritasnya. Solusi optimal dihitung dari banyaknya waktu yang diperlukan untuk menyelesaikan seluruh pekerjaan yang ada [5]. Kemudian penelitian yang lain menggunakan Simulated Annealing untuk melakukan optimasi pada penjadwalan panen tebu.…”

Optimisation of Dynamic Heterogeneous Rainfall Sensor Networks in the Context of Citizen Observatories

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Biofuel supply chain optimal design considering economic, environmental, and societal aspects towards sustainability

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