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

This article investigates a new collision-free routing problem of a multirobot system. The objective is to minimize the cycle time of operation tasks for each robot while avoiding collisions. The focus is set on the operation of the end-effector and its connected joint, and the operation is projected onto a circular area on the plane. We propose to employ a time-space network (TSN) model that maps the robot location constraints into the route planning framework, leading to a mixed integer programming (MIP) problem. A dedicated genetic algorithm is proposed for solving this MIP problem and a new encoding scheme is designed to fit the TSN formulation. Simulation experiments indicate that the proposed model can obtain the collision-free route of the considered multirobot system. Simulation results also show that the proposed genetic algorithm can provide fast and high-quality solutions, compared to two state-of-the-art commercial solvers and a practical approach.

In this paper, we study the collision-free routing of a multi-robot system to complete given tasks in the shortest time. In a robotic assembly unit, several stations work serially and in parallel. In a station, multiple robots share the same workspace and face the challenge of minimizing the cycle time and avoiding collisions at the same time. For this problem, we propose a new mathematical model that is the so-called time-space network (TSN) model. The TSN model can map the robot location constraints into the routing planning framework, leading to a mixed integer programming problem. By solving this mixed integer programming problem, the collision-free path of multiple robots can be determined. Finally, simulation results illustrate the proposed TSN model can obtain the collision-free route of the multi-robot system.

This paper proposes a new methodology for modeling and controlling quality degradation of perishable foods when zero-order kinetics are considered. This methodology approximates the nonlinear model of the zero-order quality kinetics using the piecewise affine (PWA) modeling representation. For obtaining a proper PWA model, two state-of-the-art methods are discussed, and eventually, a hybrid identification-based PWA model is considered after the comparison. This PWA model is then transformed into a computational mixed logical dynamical model, based on which an optimal control strategy is proposed that balances food quality and associated energy consumption. Furthermore, a model predictive control is proposed for improving energy efficiency when a dynamical weather environment is considered. Simulation experiments illustrate the potentials of the proposed optimal controller and the model predictive controller in a case study involving the bighead carp.

This paper proposes a new modeling and control methodology for allocating materials in a dry bulk terminal with a finite storage capacity. The dynamical process of material storage allocation in the terminal is modeled using a hybrid system perspective that combines both discrete-event and continuous-time dynamics. The stockyard space is partitioned into a number of slots for exchanging incoming and outgoing material flows in the terminal, leading to a so-called mixed logical dynamical (MLD) model with the maximal storage capacity. Based on the MLD model, a model predictive controller is then proposed for maximizing the economic profit in a rolling horizon manner. A number of Monte Carlo simulations have been performed involving a real case study for analyzing the effects of different slot volumes on the economic performance and the computational performance of the controller. Simulations also demonstrate the potential of the proposed methodology.