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.
[1]
Gary R. Weckman,et al.
Applying a hybrid artificial immune systems to the job shop scheduling problem
,
2012,
Neural Computing and Applications.
[2]
Upendra Dave,et al.
Heuristic Scheduling Systems
,
1993
.
[3]
Nihar Shah,et al.
Using Distributed Computing To Improve The Performance Of Genetic Algorithms For Job Shop Scheduling Problems
,
2004
.
[4]
Jun Zhang,et al.
Implementation of an Ant Colony Optimization technique for job shop scheduling problem
,
2006
.
[5]
Serban Iordache,et al.
Consultant-guided search: a new metaheuristic for combinatorial optimization problems
,
2010,
GECCO '10.