Penerapan Algoritma Consultant-Guided Search dalam Masalah Penjadwalan Job Shop untuk Meminimasi Makespan

Abstract

This research uses the Consultant-Guided Search (CGS) algorithm to solve job shop scheduling
problems minimizing makespan. CGS is a metaheuristics inspired by people making decisions
based on consultant’s recommendations. A number of cases from literatures is developed to evaluate
the optimality of this algorithm. CGS is also tested against other metaheuristics, namely Genetic
Algorithms (GA) and Artificial Immune Systems (AIS) for the same cases. Performance evaluations
are 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 better
compared 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 in
the other 3 cases. CGS is also better than AIS in 8 cases and is equally well in only 2 cases.