Mixed-Integer Nonlinear Programming for Energy-Efficient Container Handling: Formulation and Customized Genetic Algorithm

Energy consumption is expected to be reduced while maintaining high productivity for container handling. This paper investigates a new energy-efficient scheduling problem of automated terminals, in which quay cranes (QCs) and lift guided vehicles (AGVs) cooperate handle inbound outbound containers. In our problem, operation times task sequences are both determined. The underlying optimization mixed-integer nonlinear programming (MINLP). To deal with its computational intractability, customized efficient genetic algorithm (GA) developed solve the studied MINLP lexicographic weighted-sum strategies further considered. An $\epsilon $ -constraint also analyze Pareto frontiers. Comprehensive experiments tested on handling benchmark system, results show effectiveness proposed GA, compared obtained two commonly-used metaheuristics, commercial solver, state-of-the-art methods.