From the perspective of the global value chain of the cruise industry, the output value of cruise operation accounts for 50%, with the highest added value, and cruise ship supply is the crucial link in cruise operation management, with substantial economic benefits. erefore, optimizing the purchasing process can not only save costs for cruise companies but also improve cruise level service. Aiming at the purchasing characteristics and modes of cruise ship supply, an optimization model of multi-item joint ordering is constructed under global cruise ship supply chain, considering different order cycles, integrated operation of purchase, delivery, and inventory based on a cruise distribution center. And an improved swarm intelligence algorithm, called fireworks algorithm with inertia weight (WFWA), is proposed for global optimization of the objective function. By comparing the optimization results with fireworks algorithm (FWA), genetic algorithm (GA), and particle swarm optimization (PSO) through experimental tests, it demonstrates that WFWA has higher optimization accuracy and better global convergence.
According to the characteristics of cruise ship supply logistics, using a mixed-integer linear programming model (MILP), we describe the location-distribution and time utility requirements, in which the capacity constraints of the distribution centres and time window are considered, in order to minimize the sum of fixed cost, variable cost, distribution cost between nodes and time penalty cost. Further, a 'two-stage' method is suggested to solve the MILP problem. In the first stage, the Benders decomposition algorithm is adopted to determine the location of the distribution centres, flow allocation and shipment sizes on each link. Then IBM ILOG CPLEX optimization studio software is used to get the optimal value of the distribution schedule of ship suppliers in the second stage. Finally, the proposed method is verified by a study simulation.
To overcome challenges like market dynamic configuration, information integration, and quick response, it is necessary to build an efficient, stable, and well-coordinated supply chain relationship for cruise ship supply. This requires building of a solid evaluation index system of logistics service providers (LSPs) in the cruise ship supply chain. In this paper, we introduce an evaluation index system that consists of four dimensions, based on the characteristics of cruise ship supply and the connotation and type of cruise ship supply LSPs. The four dimensions are business level, collaborative capacity, service price, and information level, including ten subcriteria. We first establish an evaluation decision model for the interdependence and feedback relationship between the criteria by using analytic network process (ANP) for weight definition of each index; then, we use Super Decisions software to simulate the results, combine RBF neural network training and validation, and extract implicit knowledge and laws. We propose an incremental algorithm that can effectively avoid the influence of subjective factors and increase the dynamic nature of evaluation. The results show that the ANP-RBF method has strong practicability in the evaluation of cruise ship supply LSPs.
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<p>For the rapid development of the cruise industry, the cruise disaster relief supply chain has attracted extensive attention, especially because COVID-19 cases on international cruise ships occurred. In this paper, we propose an idea of coordination layout for cruise ship emergency supplies, the problem optimized two objective functions of maximizing coverage satisfaction and minimizing the total cost, addressing the low efficiency of resource utilization at the same. By applying to cruise ship emergency supplies layout of Northeast Asia cruise port group system, using expert scoring method and AHP to evaluate cruise port security vulnerability. The NSGA-II algorithm is used to solve the multi-objective programming model. A numerical example shows that the optimization design model and method are valid and feasible, and the algorithm is efficient for solving the above collaborative location and allocation problem of sectional reserves, which can also offer a variety of decision-making options.</p>
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