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Bed allocation is a crucial issue in hospital management. This paper proposes a multi-objective comprehensive learning particle swarm optimization with a representation scheme based on binary search (BS-MOCLPSO) to deal with this problem in general hospital. The bed allocation problem (BAP) is first modeled as an M/PH/c queue. Based on the queuing theory, the mathematical forms of admission rates...
Real coded genetic algorithms (RCGAs) have been widely studied and applied to deal with continuous optimization problems for years. However, how to improve the degree of accuracy so as to produce high quality solutions is still one of the main difficulties that RCGAs face with. This paper proposes a novel mutation scheme for RCGAs. The mutation operator is defined as a linear map in the space of chromosomes...
Ant colony optimization (ACO) has been widely and successfully applied to NP-hard combinatorial optimization problems for its strong searching ability and robustness. Recently, several extended ACO algorithms have also been proposed to deal with continuous optimization problems. However, the ACO algorithms always have slow convergence speed and encounter premature convergence in engineering applications...
This paper proposes a PSO-FFPP algorithm based on the particle swarm optimization (PSO) framework to solve the flight frequency programming problem (FFPP). The FFPP is to determine the flight frequency for each type of aircraft on each flight route. This problem is fundamental to an airline's operational planning because it affects the airline's profit and market share greatly. The FFPP can be formulated...
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