2022
DOI: 10.1109/jiot.2022.3176145
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A Meta-Learning Algorithm for Rebalancing the Bike-Sharing System in IoT Smart City

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Cited by 10 publications
(5 citation statements)
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“…To aggregate q-RFL assessment values @ ij of alternative x i on all criteria c j (j = 1, 2, 3, 4, 5) into the overall assessment value @ i of the alternative x i (i = 1, 2, 3, 4) the q-RFLWA operator Eq (12) is employed and the overall assessment values of alternatives x i (i = 1, 2, 3, 4) are obtained as: @ 1 = ((s 5 , −0.0063), h0.6003, 0.4698i), @ 2 = ((s 4 , 0.1969), h0.5066, 0.4408i), @ 3 = ((s 3 , −0.3348), h0.5100, 0.4381i), @ 4 = ((s 2 , −0.1469), h0.4954, 0.4383i).…”
Section: Q-rfl Aggregation-based Methodsmentioning
confidence: 99%
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“…To aggregate q-RFL assessment values @ ij of alternative x i on all criteria c j (j = 1, 2, 3, 4, 5) into the overall assessment value @ i of the alternative x i (i = 1, 2, 3, 4) the q-RFLWA operator Eq (12) is employed and the overall assessment values of alternatives x i (i = 1, 2, 3, 4) are obtained as: @ 1 = ((s 5 , −0.0063), h0.6003, 0.4698i), @ 2 = ((s 4 , 0.1969), h0.5066, 0.4408i), @ 3 = ((s 3 , −0.3348), h0.5100, 0.4381i), @ 4 = ((s 2 , −0.1469), h0.4954, 0.4383i).…”
Section: Q-rfl Aggregation-based Methodsmentioning
confidence: 99%
“…Furthermore, to the best of our knowledge, prior research on BS scarcely considers the choice of recycling supplier. After reviewing and summarising, the BS investigation concentrates mainly on site selection [5][6][7], demand prediction [8][9][10], and rebalancing approach [11,12]. Mete et al [5] conducted a case study focused on the Gaziantep University campus to identify potential station locations for students.…”
Section: Introductionmentioning
confidence: 99%
“…Heuristic algorithms are commonly used because they have a unique advantage in solving the problem of rebalancing large-scale stations. The main heuristic algorithms currently in use are the iterated tabu search heuristic [23], hybrid large neighborhood search [24], greedy-genetic heuristic [14], three-step math heuristic [22], cluster-first-route-second heuristic [29], artificial bee colony algorithm [8], PILOT/VNS/GRASP hybrid heuristic [19], and meta-learning algorithm [30]. Despite the advancement in heuristic algorithms, however, their solution speed is still not suitable for the design of static rebalancing problems in bike-sharing systems at medium-to large-scales.…”
Section: Related Workmentioning
confidence: 99%
“…Heuristic algorithms are used for their computational speed and ability to generate multiple sets of solutions in a short time. They include genetic algorithms [22], meta-learning algorithms [30], and ant colony algorithms [3]. This study chose a genetic iterative approach to compare it with the two-stage robust optimization algorithm.…”
Section: ) Comparison Of Algorithm Performancementioning
confidence: 99%
“…Data Pre-processing: The data pre-processing techniques [15] include removing the missing values using mean, median or most frequently values imputation; the next step is to identify the abnormal values that need to be removed from the dataset. Feature selection is a crucial step in the modelling process, as it involves identifying and removing duplicate, unnecessary, or noisy features.…”
Section: ) Data Collection and Data Pre-processingmentioning
confidence: 99%