Along with the fourth industrial revolution, different tools coming from optimization, Internet of Things, data science, and artificial intelligence fields are creating new opportunities in production management. While manufacturing processes are stochastic and rescheduling decisions need to be made under uncertainty, it is still a complicated task to decide whether a rescheduling is worthwhile, which is often addressed in practice on a greedy basis. To find a tradeoff between rescheduling frequency and the growing accumulation of delays, we propose a rescheduling framework, which integrates machine learning (ML) techniques and optimization algorithms. To prove the effectiveness, we first model a flexible job-shop scheduling problem with sequence-dependent setup and limited dual resources (FJSP) inspired by an industrial application. Then, we solve the scheduling problem through a hybrid metaheuristic approach. We train the ML classification model for identifying rescheduling patterns. Finally, we compare its rescheduling performance with periodical rescheduling approaches. Through observing the simulation results, we find the integration of these techniques can provide a good compromise between rescheduling frequency and scheduling delays. The main contributions of the work are the formalization of the FJSP problem, the development of ad hoc solution methods, and the proposal/validation of an innovative ML and optimization-based framework for supporting rescheduling decisions.

The Optimization for Networked Data in Environmental Urban Waste Collection (ONDE-UWC) project is, to the best of our knowledge, the first attempt to apply the Internet of Things (IoT) paradigm to the field of waste collection. Sensors installed in dumpsters and garbage trucks share data, such as weight measurements and the number of user accesses. In this study, we schedule the weekly waste-collection activities for multiple types of waste without imposing periodic routes. An important characteristic of this project is that we consider heterogeneous stakeholders with different backgrounds and knowledge. In this context, we apply the GUEST OR methodology, highlighting how it can support the decision-making process to reduce the effects of these differences. As a result, we reduced the time required to implement the solution, increased operational efficiency, and achieved cost savings.