Job Shop Scheduling with the Best-so-far ABC

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.

► This paper proposes an effective scheduling method based on Best-so-far ABC.
► The solution direction is biased toward the best solution found so far.
► The set theory and a discrete job permutation are employed.
► The performance is empirically assessed using 62 benchmark problems.
► The experiment is benchmarked against six state-of-the-art methods.