Nowadays, manufacturing control systems can respond more effectively to exigent market requirements and real-time demands. Indeed, they take advantage of changing their structural and behavioural arrangements to tailor the control solution from a diverse set of feasible configurations. However, the challenge of this approach is to determine efficient mechanisms that dynamically optimise the configuration between different architectures. This paper presents a dynamic hybrid control architecture that integrates a switching mechanism to control changes at both structural and behavioural level. The switching mechanism is based on a genetic algorithm and strives to find the most suitable operating mode of the architecture with regard to optimality and reactivity. The proposed approach was tested in a real flexible job shop to demonstrate the applicability and efficiency of including an optimisation algorithm in the switching process of a dynamic hybrid control architecture.

Nowadays, manufacturing systems are seeking control architectures that offer efficient production performance and reactivity to disruptive events. Dynamic hybrid control architectures are a promising approach as they are not only able to switch dynamically between hierarchical, heterarchical and semi-heterarchical structures, they can also switch the level of coupling between predictive scheduling and reactive control techniques. However, few approaches address an efficient switching process in terms of structure and coupling. This paper presents a switching mechanism framework in dynamic hybrid control architectures, which exploits the advantages of hierarchical manufacturing scheduling systems and heterarchical manufacturing execution systems, and also mitigates the respective reactivity and optimality drawbacks. The main feature in this framework is that it monitors the system dynamics online and shifts between different operating modes to attain the most suitable production control strategy. The experiments were carried out in an emulation of a real manufacturing system to illustrate the benefits of including a switching mechanism in simulated scenarios. The results show that the switching mechanism improves response to disruptions in a global performance indicator as it permits to select the best alternative from several operating modes.

This paper proposes a hybridized simheuristic approach that couples a greedy randomized adaptive search procedure (GRASP), a Monte Carlo simulation, a Pareto archived evolution strategy (PAES), and an analytic hierarchy process (AHP), in order to solve a multicriteria stochastic permutation flow shop problem with stochastic processing times and stochastic sequence-dependent setup times. For the decisional criteria, the proposed approach considers four objective functions, including two quantitative and two qualitative criteria. While the expected value and the standard deviation of the earliness/tardiness of jobs are included in the quantitative criteria to address a robust solution in a just-in-time environment, this approach also includes a qualitative assessment of the product and customer importance in order to appraise a weighted priority for each job. An experimental design was carried out in several study instances of the flow shop problem to test the effects of the processing times and sequence-dependent setup times, obtained through lognormal and uniform probability distributions with three levels of coefficients of variation, settled as 0.3, 0.4, and 0.5. The results show that both probability distributions and coefficients of variation have a significant effect on the four decision criteria selected. In addition, the analytical hierarchical process makes it possible to choose the best sequence exhibited by the Pareto frontier that adjusts more adequately to the decision-makers’ objectives.