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Abstract Local delivery networks expect drivers to make deliveries to and/or pickups from customers using the shortest routes in order to minimize costs, delivery time, and environmental impact. However, in real‐world applications, it is often the case that not all customers are known when planning the initial delivery route. Instead, additional customers become known while the driver is making deliveries or pickups. Before serving the new demand requests, the vehicle will return to the depot for restocking. In other words, there exists a precedence relation in the delivery route to visit the depot before delivering new orders. The uncertainty in new customer locations can lead to expensive rerouting of the tour, as drivers revisit previous neighborhoods to serve the new customers. We address this issue by constructing the delivery route with the knowledge that additional customers will appear, using historical demand patterns to guide our predictions for the uncertainty. We model this network delivery problem as a precedence‐constrained asymmetric traveling salesman problem using mixed‐integer optimization. Experimental results show that the proposed robust optimization approach provides an effective delivery route under the uncertainty of customer demands. 

The emergence of nonlinear and nonstationary dynamics is common when multiple entities collaborate, compete, or interfere in manufacturing and service operations. Operational management calls on effective monitoring, modeling, and control of in-process nonlinear dynamics. This, in turn, can result in significant economic and societal benefits. Nevertheless, traditional reductionist approaches often fall short in comprehending nonlinear dynamical systems. Also, the theory of nonlinear dynamics is mainly studied in mathematics and physics. A critical gap remains in the knowledge base that pertains to integrating nonlinear dynamics research with operations engineering. The need to leverage nonlinear dynamics has become increasingly urgent for the development of high-quality products and services. This tutorial presents a review of nonlinear dynamics methods and tools for real-time system informatics, monitoring and control. Specifically, we discuss the characterization and modeling of recurrence dynamics, network dynamics, and self-organizing dynamics hidden in operational data for process improvement. Furthermore, we contextualize the theory of nonlinear dynamics with real-world case studies and discuss future opportunities to improve the monitoring and control of manufacturing and service operations. We posit this work will help catalyze more in-depth investigations and multidisciplinary research efforts at the intersection of nonlinear dynamics and data mining for operational excellence.