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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.

We study the optimal production planning and major maintenance scheduling for an unreliable manufacturing system. We assume that the production unit experiences progressive deterioration that negatively influences product quality. For the production policy, we extend improve traditional threshold policies with a superior alternative, based on a just-in-time (JIT) strategy. The paper brings a new vision on the importance of implementing more effective production strategies based on JIT methods, instead of traditional threshold policies. When a failure occurs, the production unit is minimally repaired, and when the major maintenance is selected, the machine is restored to brand-new conditions. The objective of the model is to determine the simultaneous JIT production and major maintenance strategy that minimizes the total cost. Due to the stochastic features of the system, a simulation-based optimization approach is proposed, which combines the descriptive capabilities of simulation modeling with analytical models, statistical analysis, and optimization techniques. The results verify that the proposed simulation–optimization approach provides new and coherent results that highlight the strong influence of quality deterioration on the determination of the control parameters. A sensitivity analysis and a comparative study are conducted to illustrate that significant cost savings could be obtained with the proposed approach.

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.

The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that continues to be studied extensively due to its practical implications in manufacturing systems and emerging new variants, in order to model and optimize more complex situations that reflect the current needs of the industry better. This work presents a new metaheuristic algorithm called the global-local neighborhood search algorithm (GLNSA), in which the neighborhood concepts of a cellular automaton are used, so that a set of leading solutions called smart-cells generates and shares information that helps to optimize instances of the FJSP. The GLNSA algorithm is accompanied by a tabu search that implements a simplified version of the Nopt1 neighborhood defined in Mastrolilli & Gambardella (2000) to complement the optimization task. The experiments carried out show a satisfactory performance of the proposed algorithm, compared with other results published in recent algorithms, using four benchmark sets and 101 test problems.