The shifting bottleneck procedure for job shop scheduling
Management Science
An algorithm for solving the job-shop problem
Management Science
A bee colony optimization algorithm to job shop scheduling
Proceedings of the 38th conference on Winter simulation
Journal of Global Optimization
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem
Applied Soft Computing
Computers and Industrial Engineering - Special issue: Selected papers from the 30th international conference on computers; industrial engineering
An efficient job-shop scheduling algorithm based on particle swarm optimization
Expert Systems with Applications: An International Journal
A survey: algorithms simulating bee swarm intelligence
Artificial Intelligence Review
Artificial bee colony algorithm for small signal model parameter extraction of MESFET
Engineering Applications of Artificial Intelligence
The best-so-far selection in Artificial Bee Colony algorithm
Applied Soft Computing
A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem
Information Sciences: an International Journal
An Effective PSO and AIS-Based Hybrid Intelligent Algorithm for Job-Shop Scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Pilot, rollout and monte carlo tree search methods for job shop scheduling
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
The performance and sensitivity of the parameters setting on the best-so-far ABC
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Self-Optimization module for Scheduling using Case-based Reasoning
Applied Soft Computing
Artificial bee colony algorithm: a survey
International Journal of Advanced Intelligence Paradigms
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The Job Shop Scheduling Problem (JSSP) is known as one of the most difficult scheduling problems. It is an important practical problem in the fields of production management and combinatorial optimization. Since JSSP is NP-complete, meaning that the selection of the best scheduling solution is not polynomially bounded, heuristic approaches are often considered. Inspired by the decision making capability of bee swarms in the nature, this paper proposes an effective scheduling method based on Best-so-far Artificial Bee Colony (Best-so-far ABC) for solving the JSSP. In this method, we bias the solution direction toward the Best-so-far solution rather a neighboring solution as proposed in the original ABC method. We also use the set theory to describe the mapping of our proposed method to the problem in the combinatorial optimization domain. The performance of the proposed method is then empirically assessed using 62 benchmark problems taken from the Operations Research Library (OR-Library). The solution quality is measured based on ''Best'', ''Average'', ''Standard Deviation (S.D.)'', and ''Relative Percent Error (RPE)'' of the objective value. The results demonstrate that the proposed method is able to produce higher quality solutions than the current state-of-the-art heuristic-based algorithms.