Emerging technology and management research in the container terminals: Trends and the COVID-19 pandemic impacts.

Despite the significant fluctuations in global trend due to the rising trade friction and the COVID19 pandemic, the container terminals are continuously working in three technology areas including automation, electrification and digitalization. This study reviewed recent technology trends as well as relevant research topics related to the container terminals, and investigated how the trends and topics would facilitate the terminals to achieve their strategic objectives. We also studied the trends in the container terminal industry before and after the pandemic outbreak.

Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Optimization.

Particle Swarm Optimization (PSO) is a popular metaheuristic for deterministic optimization. Originated in the interpretations of the movement of individuals in a bird flock or fish school, PSO introduces the concept of personal best and global best to simulate the pattern of searching for food by flocking and successfully translate the natural phenomena to the optimization of complex functions. Many real-life applications of PSO cope with stochastic problems.

An Efficient Simulation Budget Allocation Method Incorporating Regression for Partitioned Domains.

Simulation can be a very powerful tool to help decision making in many applications but exploring multiple courses of actions can be time consuming. Numerous ranking & selection (R&S) procedures have been developed to enhance the simulation efficiency of finding the best design. To further improve efficiency, one approach is to incorporate information from across the domain into a regression equation.

Memetic algorithm for real-time combinatorial stochastic simulation optimization problems with performance analysis.

A three-phase memetic algorithm (MA) is proposed to find a suboptimal solution for real-time combinatorial stochastic simulation optimization (CSSO) problems with large discrete solution space. In phase 1, a genetic algorithm assisted by an offline global surrogate model is applied to find N good diversified solutions. In phase 2, a probabilistic local search method integrated with an online surrogate model is used to search for the approximate corresponding local optimum of each of the N solutions resulted from phase 1.