Society is being reshaped by large volumes of data generated from various operations, and research in handling dynamic issues related to data-driven systems has greatly increased in the past decades, where most rely upon discrete optimization models for handling dynamic features contained in operations management (OM) activities. With the rapid growth in computational technologies, including data mining technologies, discrete event simulation techniques, and intelligence algorithms, OM relies more and more on optimal solutions (or their approximates) based on high-performance models and algorithms.

This special issue collects 32 original research contributions that present recent advances in models and algorithms concerning discrete optimizations on dynamic OM systems relevant to the data-driven society. In particular, the papers address the topics related to discrete optimization methodologies for stochastic OM problems, dynamic programming based exact methods for stochastic OM problems, system dynamics in behavior OM for E-commerce, data-driven risk analysis and modeling for OM decisions in dynamic contexts, data-driven models for dynamic supply chain management, etc. It is hoped that this special issue will help the integration of the latest research achievements in the relevant field.

The paper by S. Li et al. entitled “Probability Mechanism Based Particle Swarm Optimization Algorithm and Its Application in Resource-Constrained Project Scheduling Problems” proposes a new probability mechanism based particle swarm optimization (PMPSO) algorithm to solve combinatorial optimization problems by introducing new particles based on the optimal particles in the population and the historical optimal particles in the individual changes. This method is applied to solve resource-constrained project scheduling problems and the experimental results are quite encouraging.