Scheduling problems for the sustainability of manufacturing firms in the era of the fourth industrial revolution is addressed in this research. In terms of open innovation, innovative production scheduling can be defined as scheduling using big data, cyber-physical systems, internet of things, cloud computing, mobile network, and so on. In this environment, one of the most important things is to develop an innovative scheduling algorithm for the sustainability of manufacturing firms. In this research, a flexible flowshop scheduling problem is considered with the properties of sequence-dependent setup and different process plans for jobs. In a flexible flowshop, there are serial workstations with multiple pieces of equipment that are able to process multiple lots simultaneously. Since the scheduling in this workshop is known to be extremely difficult, it is important to devise an efficient and effective scheduling algorithm. In this research, a heuristic algorithm is proposed based on a few dispatching rules and economic lot size model with the objective of minimizing total tardiness of orders. For the purposes of performance evaluation, a simulation study is conducted on randomly generated problem instances. The results imply that our proposed method outperforms the existing ones, and greatly enhances the sustainability of manufacturing firms.

This research addresses a specific issue in the field of operation scheduling. Even though there are lots of researches on the field of planning and scheduling, a specific scheduling problem is introduced here. We focus on the operation scheduling requirements that the Fourth Industrial Revolution has brought currently. From the point of view of open innovation, operation scheduling is known as the one that is using the Internet of Things, Cloud Computing, Big Data, and Mobile technology. To build proper operation systems under the Fourth Industrial Revolution, it is very essential to devise effective and efficient scheduling methodology to improve product quality, customer delivery, manufacturing flexibility, cost saving, and market competence. A scheduling problem on designated parallel equipments, where some equipments are grouped according to the recipe of lots, is considered. This implies that a lot associated with a specific recipe is preferred to be processed on an equipment among predetermined (designated) ones regardless of parallel ones. A setup operation occurs between different recipes of lots. In order to minimize completion time of the last lot, a scheduling algorithm is proposed. We conducted a simulation study with randomly generated problems, and the proposed algorithm has shown desirable and better performance that can be applied in real-time scheduling.

In this paper, a technology for production scheduling is addressed for the sustainability and open innovation in a manufacturing business. Methodologies for scheduling jobs on parallel machines with the fixed processing property are devised. The fixed processing property, in which a group of specific jobs can be processed on the predetermined machine, can be found in most manufacturing systems due to the quality issues. Usually, even though parallel machines can process various types of jobs, the fixed processing is preferred as to not deteriorate products’ quality in real manufacturing systems. To minimize makespan of jobs, which is defined as the final completion time of all jobs, technology for production scheduling is developed. Several heuristic algorithms are devised for solving the problem and to evaluate performance of the suggested algorithms, a series of computational experiments is performed. Results show that better solutions are obtained by the suggested algorithms in a reasonable amount of computation time. That is, if the proposed technology is applied to the production scheduling system of a real manufacturing business, it can be expected that quantity and quality of the product will be enhanced since they are influenced by the production scheduling.

In the era of the fourth industrial revolution, the international community is striving to establish a coordinated system to prevent fatal climate change in a global sense. As a result of such changes in business environments, a new issue, sustainability, has recently presented a paradigm shift and new research opportunity in which the theories and practices in traditional production and operations management are being reinterpreted and reapplied in relation to this emerging issue. Under this research background, we consider an optimal emission-trading problem under a cap-and-trade (CAT) emission regulation when the customers’ demand is given as an arbitrary probability distribution. Such a CAT approach to reduce the amount of emissions is a normative system for the sustainable production of manufacturing firms, which is also closely related to a well-known open innovation in literature of inventory management. Then, we formulate two stochastic inventory optimization models, which can be applied immediately for two famous CAT policies that exist in reality. In particular, our objective is to draw theoretical and practical implications for baseline credit emission regulations, which are innovative and government-led emission regulation policies, with a well-known newsvendor analysis. For our analytical results, we first show that our objective functions are piecewise linear and (quasi)-concave. Thus, it is found that there exists a unique optimal solution to the problem. Second, we successfully obtain the closed-form optimal solutions for the two models considered. Finally, we conduct a sensitivity analysis through a comparative static analysis to examine how the model parameters can affect the optimal solution in each model. All these analytical results and implications are consistent with previous studies in the literature, as well as with our insights for the models.