TRIBES Optimization Algorithm Applied to the Flexible Job Shop Scheduling Problem

To avoid the user to tune the parameters, a lot of effort in metaheuristics have been put to propose “adaptive” algorithms. For such algorithms, we have only to define the problem and the stopping criterion. This paper presents a new attempt involving the use of TRIBES, which is an autonomous adaptive search heuristic, for the well-known flexible job shop scheduling problem with the objective of minimizing the makespan or the total duration of schedule. TRIBES, a metaphor for different sized groups of people moving in an unknown area/region, looking for a “good” place, is a parameter-free Particle Swarm Optimization (PSO) algorithm that does not need any parameter tuning (swarm size, sociometry, ...). Numerical results highlight the effectiveness of the proposed algorithm and show that TRIBES in which a local search engine has been included is promising for solving combinatorial optimization problems, which are of significant importance in the manufacturing sector.