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In this paper, we examine crane scheduling for ports. This important component of port operations management is studied when the non-crossing spatial constraint, which is common to crane operations, are considered. Our objective is to minimize the latest completion time for all jobs, which is a widely used criteria in practice. We provide the proof that this problem is NP-complete and design a branch-and-bound algorithm to obtain optimal solutions. A simulated annealing meta-heuristic with effective neighborhood search is designed to find good solutions in larger size instances. The elaborate experimental results show that the branch-and-bound algorithm runs much faster than CPLEX and the simulated annealing approach can obtain near optimal solutions for instances of various sizes.

Scheduling problems in manufacturing, logistics and project management have frequently been modeled using the framework of Resource Constrained Project Scheduling Problems with minimum and maximum time lags (RCPSP/max). Due to the importance of these problems, providing scalable solution schedules for RCPSP/max problems is a topic of extensive research. However, all existing methods for solving RCPSP/max assume that durations of activities are known with certainty, an assumption that does not hold in real world scheduling problems where unexpected external events such as manpower availability, weather changes, etc. lead to delays or advances in completion of activities. Thus, in this paper, our focus is on providing a scalable method for solving RCPSP/max problems with durational uncertainty. To that end, we introduce the robust local search method consisting of three key ideas: (a) Introducing and studying the properties of two decision rule approximations used to compute start times of activities with respect to dynamic realizations of the durational uncertainty; (b) Deriving the expression for robust makespan of an execution strategy based on decision rule approximations; and (c) A robust local search mechanism to efficiently compute activity execution strategies that are robust against durational uncertainty. Furthermore, we also provide enhancements to local search that exploit temporal dependencies between activities. Our experimental results illustrate that robust local search is able to provide robust execution strategies efficiently

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State of charge (SOC) represents the amount of electricity stored and is calculated and used by battery management systems (BMSs). However, SOC cannot be observed directly, and SOC estimation is a challenging task due to the battery’s nonlinear characteristics when operating in complex conditions. In this paper, based on the new advanced deep learning techniques, a SOC estimation approach for Lithium-ion batteries using a recurrent neural network with gated recurrent unit (GRU-RNN) is introduced where observable variables such as voltage, current, and temperature are directly mapped to SOC estimation. The proposed technique requires no model or knowledge of the battery’s internal parameters and is able to estimate SOC at various temperatures by using a single set of self-learned network parameters. The proposed method is evaluated on two public datasets of vehicle drive cycles and another high rate pulse discharge condition dataset with mean absolute errors (MAEs) of 0.86%, 1.75%, and 1.05%. Experiment results show that the proposed method is accurate and robust.

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