{"title":"轨迹决策框架","authors":"M. Levin","doi":"10.53921/18195822_2021_21_4_265","DOIUrl":null,"url":null,"abstract":"The paper addresses a general view to trajectory (route) decision making framework (i.e., designing a trajectory). The view is based on four-part morphological scheme: (a) routing combinatorial problems (e.g., shortest path problem, minimum spanning tree problems, TSP), (b) assessment scales (i.e., quantitative, ordinal, poset-like), (c) graph/network based models as “solving space” (e.g., k-partite graph), and (d) node/vertex models/types. The following issues are considered: (i) structuring the “design/solving space”, (ii) problem statement, (iii) heuristics. A realistic university student trajectory problem (route from BS degree to PostDoc position) is examined.","PeriodicalId":254179,"journal":{"name":"Information Processes","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Trajectory Decision Making Framework\",\"authors\":\"M. Levin\",\"doi\":\"10.53921/18195822_2021_21_4_265\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper addresses a general view to trajectory (route) decision making framework (i.e., designing a trajectory). The view is based on four-part morphological scheme: (a) routing combinatorial problems (e.g., shortest path problem, minimum spanning tree problems, TSP), (b) assessment scales (i.e., quantitative, ordinal, poset-like), (c) graph/network based models as “solving space” (e.g., k-partite graph), and (d) node/vertex models/types. The following issues are considered: (i) structuring the “design/solving space”, (ii) problem statement, (iii) heuristics. A realistic university student trajectory problem (route from BS degree to PostDoc position) is examined.\",\"PeriodicalId\":254179,\"journal\":{\"name\":\"Information Processes\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Processes\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.53921/18195822_2021_21_4_265\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processes","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.53921/18195822_2021_21_4_265","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The paper addresses a general view to trajectory (route) decision making framework (i.e., designing a trajectory). The view is based on four-part morphological scheme: (a) routing combinatorial problems (e.g., shortest path problem, minimum spanning tree problems, TSP), (b) assessment scales (i.e., quantitative, ordinal, poset-like), (c) graph/network based models as “solving space” (e.g., k-partite graph), and (d) node/vertex models/types. The following issues are considered: (i) structuring the “design/solving space”, (ii) problem statement, (iii) heuristics. A realistic university student trajectory problem (route from BS degree to PostDoc position) is examined.