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After the occurrence of major sudden disasters, the dispatching and distribution of disaster relief materials are particularly important, but in the process of distribution, there may be excessive distribution of similar emergency materials, unbalanced distribution volume of relief materials in different disaster-affected points, high distribution cost, and low effective distribution rate. In order to solve the above problems, based on the application of big data, this paper proposes a three-level network postdisaster material scheduling and distribution model and an improved NSGA-II algorithm. The model takes the loss degree of the disaster area and the dynamic change rate of the demand for postdisaster relief materials as the constraints, takes the demand prediction of postdisaster relief materials, the optimization of distribution path, distribution nodes, and the satisfaction of victims as the objectives, and designs the sample average approximation method and the improved NSGA-II algorithm. In order to verify the effectiveness of the proposed model and strategy, through the comparative experiment of NSGA and PSO, it can be seen from the experimental results that the three-level network allocation model and the improved NSGA-II algorithm (nondominated sorting genetic algorithm II) proposed in this paper can not only solve the existing postdisaster relief material allocation and scheduling problem but also reduce the space-time complexity of the problem. [ABSTRACT FROM AUTHOR]

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Cloud providers deliver heterogeneous virtual machines to run complicated jobs submitted by users. The task scheduling issue is formulated to a discrete optimization problem which is well-known NP-Hard. This paper presents a hybrid meta-heuristic algorithm based on genetic and thermodynamic simulated annealing algorithms to solve this problem. In the proposed algorithm, the genetic and simulated annealing algorithms have respective global and local search inclinations covering each other's shortcomings. A novel theorem is presented and applied to produce a semi-conducted initial population. In a used genetic algorithm with a global trend, the crossover operator is performed to explore search space. The thermodynamic simulated annealing algorithm is utilized to improve the efficiency, which considers entropy and energy difference concepts in the cooling schedule process. After obtaining a suitable solution, one of the three novel neighbor operators is randomly called to enhance the given solution potentially. In this way, the efficient balance between exploration and exploitation in the search space is achieved. Simulation results prove that the proposed hybrid algorithm has 10.17%, 9.31%, 7.76%, and 8.21% dominance in terms of makespan, schedule length ratio, speedup, and efficiency against other comparative algorithms. [ABSTRACT FROM AUTHOR]

This paper introduces an extension to the well-established Resource-Constrained Project Scheduling Problem for the comprehensive description of disruption management problems. This conceptual framework employs the concept of alternative activities to consider both the temporal shift of activities or the reallocation of resources and switches from one valid process variant to another one. Activities can be serialized or parallelized, process steps can be inserted or removed and durations as well as resource requirements can be modified dynamically during optimization. Focusing on the domain of the aircraft turnaround as the most important airport ground process, we illustrate how the Extended RCPSP (x-RCPSP) can be applied for decision support. A specific evolutionary algorithm is presented that identifies good-quality solutions to relatively large disruption management problems within only a few seconds. The results of the evaluation illustrate fast convergence on good or optimal schedules and serve as a basis for the development of future problem solving algorithms. [ABSTRACT FROM AUTHOR]

This paper considers group scheduling problem in hybrid flexible flow shop with sequence-dependent setup times to minimize makespan. Group scheduling problem consists of two levels, namely scheduling of groups and jobs within each group. In order to solve problems with this context, two new metaheuristics based on simulated annealing (SA) and genetic algorithm (GA) are developed. A design procedure is developed to specify and adjust significant parameters for SA- and GA-based metaheuristics. The proposed procedure is based on the response surface methodology and two types of objective function are considered to develop multiple-objective decision making model. For comparing metaheuristics, makespan and elapsed time to obtain it are considered as two response variables representing effectiveness and efficiency of algorithms. Based on obtained results in the aspect of makespan, GA-based metaheuristic is recommended for solving group scheduling problems in hybrid flexible flow shop in all sizes and for elapsed time SA-based metaheuristic has better results. [ABSTRACT FROM AUTHOR]

The study of electromagnetic detection satellite scheduling problem (EDSSP) has attracted attention due to the detection requirements for a large number of targets. This paper proposes a mixed-integer programming model for the EDSSP problem and a genetic algorithm based on reinforcement learning (RL-GA). Numerous factors that affect electromagnetic detection are considered in the model, such as detection mode, bandwidth, and other factors. The RL-GA embeds a Q-learning method into an improved genetic algorithm, and the evolution of each individual depends on the decision of the agent. Q-learning is used to guide the population search process by choosing evolution operators. In this way, the search information can be effectively used by the reinforcement learning method. In the algorithm, we design a reward function to update the Q value. According to the problem characteristics, a new combination of < state, action > is proposed. The RL-GA also uses an elite individual retention strategy to improve search performance. After that, a task time window selection algorithm (TTWSA) is proposed to evaluate the performance of population evolution. Several experiments are used to examine the scheduling effect of the proposed algorithm. Through the experimental verification of multiple instances, it can be seen that the RL-GA can solve the EDSSP problem effectively. Compared with the state-of-the-art algorithms, the RL-GA performs better in several aspects. [Display omitted] [ABSTRACT FROM AUTHOR]