This work presents a novel hybrid algorithm called GA-RRHC based on genetic algorithms (GAs) and a random-restart hill-climbing (RRHC) algorithm for the optimization of the flexible job shop scheduling problem (FJSSP) with high flexibility (where every operation can be completed by a high number of machines). In particular, different GA crossover and simple mutation operators are used with a cellular automata (CA)-inspired neighborhood to perform global search. This method is refined with a local search based on RRHC, making computational implementation easy. The novel point is obtained by applying the CA-type neighborhood and hybridizing the aforementioned two techniques in the GA-RRHC, which is simple to understand and implement. The GA-RRHC is tested by taking four banks of experiments widely used in the literature and comparing their results with six recent algorithms using relative percentage deviation (RPD) and Friedman tests. The experiments demonstrate that the GA-RRHC is a competitive method compared with other recent algorithms for instances of the FJSSP with high flexibility. The GA-RRHC was implemented in Matlab and is available on Github.

This paper provides new insights to the area of sustainable manufacturing systems at analyzing the novel paradigm of integrated production logistics, quality, and maintenance design. For this purpose, we investigate the optimal production and repair/major maintenance switching strategy of an unreliable deteriorating manufacturing system. The effects of the deterioration process are mainly observed on the failure intensity and on the quality of the parts produced, where the rate of defectives depends on the production rate. When unplanned failures occur, either a minimal repair or a major maintenance could be conducted. The integration of availability and quality deterioration led us to propose a new stochastic dynamic programming model where optimality conditions are derived through the Hamilton-Jacobi-Bellman equations. The model defined the joint production and repair/major maintenance switching strategies minimizing the total cost over an infinite planning horizon. In the results, the influence of the deterioration process were evident in both the production and maintenances control parameters. A numerical example and an extensive sensitivity analysis were conducted to illustrate the usefulness of the results. Finally, the proposed control policy was compared with alternative strategies based on common assumptions of the literature in order to illustrate its efficiency.

The development of quadrotor unmanned aerial vehicles (QUAVs) is a growing field due to their wide range of applications. QUAVs are complex nonlinear systems with a chaotic nature that require a controller with extended dynamics. PD and PID controllers can be successfully applied when the parameters are accurate. However, this parameterization process is complicated and time-consuming; most of the time, parameters are chosen by trial and error without guaranteeing good performance. The originality of this work is to present a novel nonlinear mathematical model with aerodynamic moments and forces in the Newton–Euler formulation, and identify metaheuristic algorithms applied to parameter optimization of compensated PD and PID controls for tracking the trajectories of a QUAV. Eight metaheuristic algorithms (PSO, GWO, HGS, LSHADE, LSPACMA, MPA, SMA and WOA) are reported, and RMSE is used to measure each dynamic performance of the simulations. For the PD control, the best performance is obtained with the HGS algorithm with an RMSE = 0.037247252379126. For the PID control, the best performance is obtained with the HGS algorithm with an RMSE = 0.032594309723623. Trajectory tracking was successful for the QUAV by minimizing the error between the desired and actual dynamics.

Manufacturing systems face several disturbances during production, such as sudden failures, defects, and unreliable subcontractors that reduce their production capacity. Currently, subcontracting represents an efficient alternative to support production decisions. The novelty of the study was the development of a new integrated model that properly coordinates production, subcontracting, and maintenances strategies in the context of stochastic uncertainty, quality deterioration, and random subcontracting availability. Such a set of characteristics has not been addressed before in the literature. A simulation–optimization approach was proposed to address such a stochastic model. A numerical case study was performed as an illustration of the approach and a comprehensive sensitivity analysis was performed to analyze the impact of several costs. Furthermore, the effect of the availability of the subcontractor and the producer was analyzed. The main finding of the study showed that the integrated model led to significant economic cost savings compared to other approaches that address such policies in isolation. The results also indicated that quality deterioration had a strong impact on the subcontracting rate and that the proposed joint control policy adequately coordinated these three key functions. The level of subcontracting participation was directly defined by its availability and the subcontracting cost.

The Flexible Job Shop Scheduling Problem (FJSSP) continues to be studied extensively to test new metaheuristics and because of its closeness to current production systems. A variant of the FJSSP uses fuzzy processing times instead of fixed times. This paper proposes a new algorithm for FJSSP with fuzzy processing times called the global neighborhood with hill-climbing algorithm (GN-HC). This algorithm performs solution exploration using simple operators concurrently for global search neighborhood handling. For local search, random restart hill-climbing is applied at each solution to find the best machine for each operation. For the selection of operations in hill climbing, a record of the operations defining the fuzzy makespan is employed to use them as a critical path. Finally, an estimation of the crisp makespan with the longest processing times in hill climbing is made to improve the speed of the GN-HC. The GN-HC is compared with other recently proposed methods recognized for their excellent performance, using 6 FJSSP instances with fuzzy times. The obtained results show satisfactory competitiveness for GN-HC compared to state-of-the-art algorithms. The GN-HC implementation was performed in Matlab and can be found on GitHub (check Data Availability Statement at the end of the paper).

We present a distributed control modeling approach for an automated manufacturing system based on the dynamics of one‐dimensional cellular automata. This is inspired by the fact that both cellular automata and manufacturing systems are discrete dynamical systems where local interactions given among their elements (resources) can lead to complex dynamics, despite the simple rules governing such interactions. The cellular automaton model developed in this study focuses on two states of the resources of a manufacturing system, namely, busy or idle. However, the interaction among the resources such as whether they are shared at different stages of the manufacturing process determines the global dynamics of the system. A procedure is shown to obtain the local evolution rule of the automaton based on the relationships among the resources and the material flow through the manufacturing process. The resulting distributed control of the manufacturing system appears to be heterarchical, and the evolution of the cellular automaton exhibits a Class II behavior for some given disordered initial conditions.