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An improved Ant Colony Optimization (ACO) algorithm, named IACO, is proposed to address the inherent limitation of slow convergence, susceptibility to local optima and excessive number of inflection in traditional ACO when solving path planning problems. To this end, firstly, the search direction number is expanded from 4 or 8 into 32; Secondly, the distance heuristic information is replaced by an area heuristic function, which deviated from the traditional approach that only considers pheromone information between two points; Then, the influence of path angle and number of turns is taken into account in the local pheromone update. Additionally, a reward and punishment mechanism is employed in the global pheromone update to adjust the pheromone concentrations of different paths; Furthermore, an adaptive update strategy for pheromone volatility factor adaptive is proposed to expand the search range of the algorithm. Finally, simulation experiments are conducted under various scenarios to verify the superiority and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Hospital beds are one of the most critical resources in healthcare institutions. In practice, beds are usually allocated to different departments in advance to accommodate different kinds of patients. Inappropriate decisions in the allocation may lead to the idleness of beds or the high rejection ratio of patients. Hospital managers are under pressure to allocate beds to different departments. High variability in patient arrivals and service times make the allocation problem complex and challenging to solve. To address this problem, a mixed-integer non-linear programming model is formulated, with the objective of minimising the weighted cost of rejecting patients and holding them waiting. To solve this model, a data-driven metamodel simulation optimisation method is proposed, in which metamodels, based on an analytical queuing model and a general function, are proposed and embedded into a general-purpose algorithm Adaptive Hyperbox Algorithm. The metamodels designated for local and global approximation are separately fitted using different sets of simulation observations, which can combine structural information and simulation information, and can provide insightful guidance in solution improvements. A case study is conducted based on the real data collected from a public hospital in Shanghai. Numerical results demonstrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

The capacitated arc routing problem (CARP) is a difficult vehicle routing problem, where given an undirected graph, the objective is to minimize the total cost of all vehicle tours that serve all required edges under vehicle capacity constraints. In this paper, a memetic algorithm with iterated local search (MAILS) is proposed to solve this problem. The proposed MAILS incorporates a new crossover operator, i.e., the longest common substring crossover (LCSX), an iterated local search (ILS) and a perturbation mechanism into the framework of the memetic algorithm (MA). The proposed MAILS is evaluated on the CARP benchmark instances and computational results show that the MAILS is very competitive. [ABSTRACT FROM AUTHOR]

Bare-bones particle swarm optimization (BBPSO) was first proposed in 2003. Compared to the traditional particle swarm optimization, it is simpler and has only a few control parameters to be tuned by users. In this paper, an improved BBPSO algorithm with adaptive disturbance (ABPSO) is studied. By the proposed approaches, each particle has its own disturbance value, which is adaptively decided based on its convergence degree and the diversity of swarm. And an adaptive mutation operator is introduced to improve the global exploration of ABPSO. Moreover, the convergence of ABPSO is analyzed using stochastic process theory by regarding each particle's position as a stochastic vector. A series of experimental trials confirms that the proposed algorithm is highly competitive to other BBPSO-based algorithms, and its performance can be still further improved with the use of mutation. [ABSTRACT FROM AUTHOR]

This paper addresses the design of contract for reserving the capacity of a shared critical resource from the perspective of a given class of customers. The contract is composed of three parts: contracted time slots (CTSs) reserved by the service provider for the class of customers, advance cancellation of contracted time slots, and requests for regular time slots (RTSs). The problem of CTS cancellation and RTS assignment is formulated as an average cost Markov Decision Process in order to minimize the total cost including customer waiting times, unused CTS, and CTS cancellation. Structural properties of the optimal control policies are established via the discounted cost problem. A local optimization algorithm is proposed to improve a given initial contract. Numerical results show that advance CTS cancellation significantly reduces the ratio of unused CTS with slight increase of customer waiting time. [ABSTRACT FROM PUBLISHER]