The “Dual-Ants Colony”: A novel hybrid approach for the flexible job shop scheduling problem with preventive maintenance

- We deal with the partial FJSSP with Preventives Maintenances to optimize the Makespan.
- We propose an adapted MIP model and a bi-level Disjunctive/Conjunctive graph.
- We develop a novel hybrid ACO approach with a dynamic history and dual tasks ants.
- Our approach provides an integration of a local search and a set of dispatching rules.
- Experiments carried out; we introduce new instances and a set of performance measures.

Due to their importance in the fields of both manufacturing industries and operations research, production scheduling and maintenance planning have received considerable attention both in academia and in industry. This paper investigates the Flexible Job Shop Scheduling Problem (FJSSP) with machine unavailability constraints due to Preventive Maintenance (PM) activities, under the objective of minimizing the makespan. We propose two new formulations: the first one in the form of a Mixed Integer Nonlinear Program (MINLP) and the second corresponding to a bi-level disjunctive/conjunctive graph. To deal with this variant FJSSP with PMs (FJSSP/PM), we develop the “Dual-Ants Colony” (DAC), a novel hybrid Ant Colony Optimization (ACO) approach with dynamic history, based on an ants system with dual activities. This optimization provides an effective integration of a local search and a set of dispatching rules. Three regular performance measures are also implemented. To show the efficiency of the DAC algorithm, computational experiments are carried out on a large range of well-known benchmarks from the literature and others newly generated. We address first the classical JSSP case, then the flexible FJSSP for partial flexibility. Finally, we study the case with preventive maintenance based on well-chosen PM periods. Obtained results demonstrate the viability and performance of the proposed approach, especially for the FJSSP/PM.