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All Journal Jurnal Rekayasa Sistem Industri
Yogi Purnawan
Fakultas Teknologi Industri, Jurusan Teknik Industri, Universitas Katolik Parahyangan
Industrial & Manufacturing Engineering

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Penerapan Algoritma Consultant-Guided Search dalam Masalah Penjadwalan Job Shop untuk Meminimasi Makespan Sitorus, Hotna Marina; Juwono, Cynthia P.; Purnawan, Yogi
Jurnal Rekayasa Sistem Industri Vol 4, No 1 (2015)
Publisher : Jurnal Rekayasa Sistem Industri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (177.97 KB)

Abstract

This research uses the Consultant-Guided Search (CGS) algorithm to solve job shop schedulingproblems minimizing makespan. CGS is a metaheuristics inspired by people making decisionsbased on consultant’s recommendations. A number of cases from literatures is developed to evaluatethe optimality of this algorithm. CGS is also tested against other metaheuristics, namely GeneticAlgorithms (GA) and Artificial Immune Systems (AIS) for the same cases. Performance evaluationsare conducted using the best makespan obtained by these algorithms. From computational results,it is shown that CGS is able to find 3 optimal solutions out of 10 cases. Overall, CGS performs bettercompared to the other algorithms where its solution lies within 0 - 6,77% from the optimal solution,averaging only 2,15%. Futhermore, CGS outperforms GA in 7 cases and performs equally well inthe other 3 cases. CGS is also better than AIS in 8 cases and is equally well in only 2 cases.
Penerapan Algoritma Consultant-Guided Search dalam Masalah Penjadwalan Job Shop untuk Meminimasi Makespan Sitorus, Hotna Marina; Juwono, Cynthia P.; Purnawan, Yogi
Jurnal Rekayasa Sistem Industri Vol 4, No 1 (2015): Jurnal Rekayasa Sistem Industri
Publisher : Universitas Katolik Parahyangan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (177.97 KB) | DOI: 10.26593/jrsi.v4i1.1390.55-63

Abstract

This research uses the Consultant-Guided Search (CGS) algorithm to solve job shop schedulingproblems minimizing makespan. CGS is a metaheuristics inspired by people making decisionsbased on consultant’s recommendations. A number of cases from literatures is developed to evaluatethe optimality of this algorithm. CGS is also tested against other metaheuristics, namely GeneticAlgorithms (GA) and Artificial Immune Systems (AIS) for the same cases. Performance evaluationsare conducted using the best makespan obtained by these algorithms. From computational results,it is shown that CGS is able to find 3 optimal solutions out of 10 cases. Overall, CGS performs bettercompared to the other algorithms where its solution lies within 0 - 6,77% from the optimal solution,averaging only 2,15%. Futhermore, CGS outperforms GA in 7 cases and performs equally well inthe other 3 cases. CGS is also better than AIS in 8 cases and is equally well in only 2 cases.
Co-Authors Ali Djamhuri Cynthia P. Juwono Hotna Marina Sitorus