Detailed research on the impact of longitudinal material transportation mode and facility direction on the layout based on the double-floor corridor allocation problem (DFCAP) is lacking. Hence, we proposed a mixed-in...
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Detailed research on the impact of longitudinal material transportation mode and facility direction on the layout based on the double-floor corridor allocation problem (DFCAP) is lacking. Hence, we proposed a mixed-integer nonlinear programming (MINLP) model of a multi-objective DFCAP (MODFCAP) for minimising the material handling cost (MHC), minimising total layout area, and optimising the equilibrium index of double elevators. Moreover, we proposed a multi-objective clonalselection algorithm with variable neighbourhood search (VNS) operations (ICSAVNS) for solving MODFCAP efficiently. ICSAVNS performs a deep search of the population using the Metropolis-based VNS operation and also performs a breadth search through the two-segment mutation simultaneously. The accuracy of the model and algorithm is validated experimentally using a 9-scale calculation instance. We designed the Taguchi experiment to explore reasonable algorithm parameters and analysed the advantages and disadvantages of the layout schemes under different target preferences based on the results of a set of 24-scale production examples. Finally, the simulation instances of MODFCAP and bi-objective CAP are tested and compared with a series of algorithms. The results show that ICSAVNS can achieve the solution per-formance of the current advanced multi-objective algorithm.
Based on the ergodicity of chaotic searching, the efficiency of quantum computing and antibody clonalselection theory of Artificial Immune System (AIS), Chaos Quantum clonal Multiobjective Evolutionary algorithm (CQC...
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Based on the ergodicity of chaotic searching, the efficiency of quantum computing and antibody clonalselection theory of Artificial Immune System (AIS), Chaos Quantum clonal Multiobjective Evolutionary algorithm (CQCMEA) is proposed in this paper. The algorithm encodes the initial population by qubit, updates individual by quantum rotated gates with chaotic variables and maintains the distribution and diversity of solutions by crowding distance. Theoretical analysis and experimental simulation proved the effectiveness of the algorithm.
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