Special Issue: “The Planning and Scheduling of Manufacturing Systems”

The “Fourth Industrial Revolution” (alternatively known as “Industry 4.0”) is driving the technological and organizational transformation of the manufacturing environment. The classical production planning approaches cannot react to real-time data or use new paradigms due to the cloud manufacturing systems enabled by Industry 4.0. Therefore, new production planning and scheduling for the manufacturing systems will be developed to take advantage of Industry 4.0. This Special Issue collects a series of studies that summarize the latest trends in the field of the planning and scheduling of manufacturing systems to improve the responsiveness, efficiency, sustainability, and other methods used to support Industry 4.0 paradigms.

A total of nine papers (eight research papers and one review paper) addressing various fields of production planning, including subcontracting and maintenance, supply chain, flexible job-shop scheduling, agri-food, steel plant, the particle swarm optimization algorithm, quality control, flow-shop scheduling, and literature reviews on sustainability in manufacturing systems, are presented in this Special Issue.

Rivera-Gómez et al. [1] developed an original integrated model that includes coordinate production, subcontracting, and maintenance strategies in the context of stochastic uncertainty, quality deterioration, and random subcontracting availability. The numerical results highlight how the proposed model leads to relevant economic cost savings compared to other approaches.

Han et al. [2] studied a production planning problem with a two-level supply chain consisting of multiple suppliers and a manufacturing plant. They proposed a mixed-integer programming model to select the suppliers and their production lines for the production of semi-finished products for each period of a given planning horizon.

Wenzelburger and Allgöwer [3] proposed a scheduling control framework for flexible manufacturing systems in the context of Industry 4.0. The main objective was to provide the possibility to adapt the production process by reacting to changes and enabling the availability of customer-specific products. The proposed schemes are based on the Petri Net formulation.

Tangour et al. [4] studied a scheduling problem in the flow-shop agri-food production of perishable products under constrained resources. To solve the dynamic scheduling problem, a genetic algorithm and ant colony optimization algorithm under the perturbations of the expiration date of product components and production delays were proposed.

García-Menéndez et al. [5] investigated the different circumstances that can cause strand closures or sequence breaks, their consequences, and the different options available to minimize losses in steel plants. They proposed an algorithm capable of analyzing a workshop situation and making the most favorable decision to optimize production in a real steel plant.

Zheng et al. [6] proposed a framework for robust scheduling in an assembly job-shop context considering the uncertainty of process set-up times and processing time. The framework consisted of obtaining the distribution information of uncertain parameters based on historical data and using a particle swarm optimization algorithm to optimize the production schedule.

Hajej et al. [7] studied integrated maintenance, production, and product quality control policies for a supply chain consisting of a single machine producing only one type of product, a main storage warehouse, and multi-purchases warehouses. They proposed an optimal production strategy, including the use of a statistical process control chart.

Zou et al. [8] proposed a new algorithm for the permutation flow-shop scheduling problem. The proposed algorithm is based on the k-means clustering algorithm and a genetic algorithm. The numerical results highlight how the proposed algorithm is able to consistently converge with better optimal solutions than the other tested algorithms.

Renna and Materi [9] provided an overview of the literature to summarize the most important studies on energy efficiency and renewable energy sources in manufacturing systems published in the last fifteen years. The conclusion reports the main results of the review and suggests future directions for the researchers in the integration of renewable energy in manufacturing systems’ consumption models.

In combination, these complementary contributions provide a substantial body of knowledge in the context of production planning that is undergoing an epochal industrial transformation, at present.