Towongpaichayont, Witchaya
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Towongpaichayont, Witchaya
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witchaya.to@kmitl.ac.th
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Item type:Publication, Minimum cost of job assignment in polynomial time by adaptive unbiased filtering and branch-and-bound algorithm with the best predictor(2025-06-01); The minimum cost of job assignment (Min-JA) is one of the practical NP-hard problems to manage the optimization in science-and-engineering applications. Formally, the optimal solution of the Min-JA can be computed by the branch-and-bound (BnB) algorithm (with the efficient predictor) in O(n!), n = problem size, and O(n<sup>3</sup>) in the best case but that best case hardly occurs. Currently, metaheuristic algorithms, such as genetic algorithms (GA) and swarm-optimization algorithms, are extensively studied, for polynomial-time solutions. Recently, unbiased filtering (in search-space reduction) could solve some NP-hard problems, such as 0/1-knapsack and multiple 0/1-knapsacks with Latin square (LS) of m-capacity ranking, for the ideal solutions in polynomial time. To solve the Min-JA problem, we propose the adaptive unbiased-filtering (AU-filtering) in O(n<sup>3</sup>) with a new hybrid (search-space) reduction (of the indirect metaheuristic strategy and the exact BnB). Innovation-and-contribution of our AU-filtering is achieved through three main steps: 1. find 9 + n effective job-orders for the good initial solutions (by the indirect assignment with UP: unbiased predictor), 2. improve top 9-solutions by the indirect improvement of the significant job-orders (by Latin square of n permutations plus n complex mod-functions), and 3. classify objects (from three of the best solutions) for AU-filtering (on large n) with deep-reduction (on smaller n’) and repeat (1)-(3) until n’ < 6, the exact BnB is applied. In experiments, the proposed AU-filtering was evaluated by a simulation study, where its ideal results outperformed the best results of the hybrid swarm-GA algorithm on a variety of 2D datasets (n ≤ 1000). - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A GUIDELINE of DESIGNING GAMIFICATION in the CLASSROOM and ITS CASE STUDY(2021-06-01)This paper indicates some common mistakes in designing gamification in the classroom which some gamification designers fall to. The paper takes a step back and observes the actual purposes of gamification in the classroom. Some game design techniques are raised for proper designing process instead of just identifying game elements that can be included into the classroom. A guideline of designing classroom gamification is proposed which includes 1) identify the pillar roles of the classroom, 2) identify expected pain points in the classroom, 3) identify expected overall aesthetics and the purposes of including gamification into the classroom, 4) design mechanics in the class, 5) pick the right elements and tools for the classroom, and 6) iterative monitoring and adjustments. A case study of actual gamification implementation in classroom of undergraduate level is provided which has been conducted in four semesters. It received increasing assignment turn-in rate from 82.86 percent in the first semester to 92.86 percent in the latest semester. On the other hand, the satisfactory rate of students is steady with 8.80 out of ten in the first semester to 8.96 out of ten in the latest semester. These designing guideline and case study are expected to help understand the gamification design process for a classroom.