─ 5 ─To solve the problem of “time to wait for cargo (cargo waiting time),” which is a major factor for drivers’ long working hours, the number of delivery trucks in each time zone is scheduled and equalized by using the model for an SSP and a solution method. By applying a method of allocating shifts according to time zone, which was used in a workplace consisting mainly of part-time workers, 6. Conclusion and the future directionThis study investigated previous studies on hybrid methods based on swarm intelligence algorithms as methods for the model of an SSP in a workplace consisting mainly of part-time workers in the method using swarm intelligence and GA, into which the optimistic consultation of computer shogi contemporary business environment. To solve an SSP consisting mainly of part-time workers, in which the size of the complicated problem and the rotation of workers differ according to the workplace, the solution programs is incorporated, is useful. In the future, we will embed the proposed solution method in a hardware and the validity of the proposed method will be veried using multiple models.7. References1)A.Ikegami, “Nurse Scheduling: Research, Model, Algorithm”, Suri Tokei, Vol.53, No.2, (2005), pp.231-2592)D. Karaboga and B. Basturk, “A powerful and efcient algorithm for numerical function optimization: Articial bee colony (ABC) algorithm”, J. Global Optimization, Vol.39, (2007), pp.459-4713)X.-S. Yang, “Firefly algorithms for multimodal optimization”, Stochastic Algorithms: Foundations and Applications, Vol.5792, (2009), pp.169-1784)Y. Murayama, K. Suzuki, K. Wakabayashi, J. Toyotani, “Application of swarm intelligence optimization to integer programming problems”, The 22nd Japan society of Directories National Convention, (2018), pp.611-645)T.Kato, Y. Maeda, Y. Takahashi., “Modied Articial Bee Colony Algorithm Applied Genetic Operation”, 28th Fuzzy System Symposium, (2012), pp.430-4346)Y.Maeda,“Hybrid Search Method for Artificial Bee Colony Algorithm, Japan Society for Fuzzy Theory and Intelligent Informatics,Chino to Joho, vol.30,(2018), pp.556-563 pp.2048-20547)Sandeep K, V. K.Sha, R. Kumari, “A Novel Hybrid Crossover based Articial Bee Colony Algorithm for Optimization Problem”, International Journal of Computer Applications, Vol.82, (2013) , pp.18-258)R. Storn, K. Price, “Differential evolution: A simple and efcient heuristic for global optimization over continuous spaces”, Journal of Global Optimi-zation, Vol.11, (1997), pp.341-3569)A. Abraham, R. K. Jatoth, A. Rajasekhar,“Hybrid Differential Artificial Bee Colony Algorithm”, Computational and Theoretical Nano-science,Vol.9 (2), (2012), pp.249-25710)A. Panniem, P.Puphasuk, “A Modified Artificial Bee Colony Algorithm with Firey Algorithm Strategy for Continuous Optimization Problems”, Journal of Applied Mathematics, Volume 2018, (2018), Article ID 1237823, Research Article (9 pages)11)Ito, T, “New Trends in Computer Shogi Research : “MONJU”, Consultation Algorithm - A New Technology by Using Simple Majority System -”, Journal of the Information Processing Society of Japan, Vol.50, No.9, (2009), pp.887-89412)Sugiyama, T et al., “Consultation Algorithm in Shogi ─A Move Decision Based on the Positional Evaluation Value”, Journal of the Information Processing Society of Japan, Vol.51, No.11, (2010), pp.2048-205413)K.Suzuki, Y.Murayama, “Vehicle allocation device, vehicle allocation method, and program”, Patent Application 2020-024630, (2020)
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