{"title":"基于行为的通用目标编程方法,用于不精确条件下的决策","authors":"","doi":"10.1016/j.orp.2024.100316","DOIUrl":null,"url":null,"abstract":"<div><p>The body of literature on goal programming (GP) approaches in modeling preferences and the satisfaction philosophy in multi-objective programming (MOP) decision-making processes is extensive. However, there has been little focus on how preferences change in relation to the decision-maker's (DM) behavior within this satisfaction philosophy, particularly in situations involving risk. To address this challenge, we propose introducing a behavior-type utility function into the GP model using the concept of a behavior function. This idea offers an innovative perspective for modeling DM's behavioral preferences in the imprecise GP approach by integrating a risk-aversion parameter specific to each objective. We then formulate a generalized behavioral-based GP approach for decision-making based on this new behavior-type utility function. To validate our proposed approach, we present an illustrative example of project selection in health service institutions, followed by a sensitivity analysis and comparisons with other approaches. The results demonstrate that DM's behavioral preferences significantly impact the decision-making process, and the proposed model provides more reasonable and convenient decisions for DMs with varying degrees of risk aversion.</p></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214716024000204/pdfft?md5=b0a7089829d5bfb2f7e03c046a0abb6e&pid=1-s2.0-S2214716024000204-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A generalized behavioral-based goal programming approach for decision-making under imprecision\",\"authors\":\"\",\"doi\":\"10.1016/j.orp.2024.100316\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The body of literature on goal programming (GP) approaches in modeling preferences and the satisfaction philosophy in multi-objective programming (MOP) decision-making processes is extensive. However, there has been little focus on how preferences change in relation to the decision-maker's (DM) behavior within this satisfaction philosophy, particularly in situations involving risk. To address this challenge, we propose introducing a behavior-type utility function into the GP model using the concept of a behavior function. This idea offers an innovative perspective for modeling DM's behavioral preferences in the imprecise GP approach by integrating a risk-aversion parameter specific to each objective. We then formulate a generalized behavioral-based GP approach for decision-making based on this new behavior-type utility function. To validate our proposed approach, we present an illustrative example of project selection in health service institutions, followed by a sensitivity analysis and comparisons with other approaches. The results demonstrate that DM's behavioral preferences significantly impact the decision-making process, and the proposed model provides more reasonable and convenient decisions for DMs with varying degrees of risk aversion.</p></div>\",\"PeriodicalId\":38055,\"journal\":{\"name\":\"Operations Research Perspectives\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2214716024000204/pdfft?md5=b0a7089829d5bfb2f7e03c046a0abb6e&pid=1-s2.0-S2214716024000204-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Operations Research Perspectives\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214716024000204\",\"RegionNum\":4,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"OPERATIONS RESEARCH & MANAGEMENT SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Operations Research Perspectives","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214716024000204","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
引用次数: 0
摘要
在多目标程序设计(MOP)决策过程中,有关目标程序设计(GP)方法的偏好建模和满意理念的文献非常广泛。然而,人们很少关注在满意理念下,偏好如何随决策者(DM)行为的变化而变化,尤其是在涉及风险的情况下。为了应对这一挑战,我们建议使用行为函数的概念在 GP 模型中引入行为类型的效用函数。这一想法为在不精确的 GP 方法中模拟 DM 的行为偏好提供了一个创新的视角,即为每个目标整合一个特定的风险规避参数。然后,我们基于这种新的行为型效用函数,制定了一种基于行为的通用决策方法。为了验证我们提出的方法,我们以医疗服务机构的项目选择为例进行了说明,随后进行了敏感性分析,并与其他方法进行了比较。结果表明,管理者的行为偏好会对决策过程产生重大影响,而所提出的模型能为不同风险规避程度的管理者提供更合理、更便捷的决策。
A generalized behavioral-based goal programming approach for decision-making under imprecision
The body of literature on goal programming (GP) approaches in modeling preferences and the satisfaction philosophy in multi-objective programming (MOP) decision-making processes is extensive. However, there has been little focus on how preferences change in relation to the decision-maker's (DM) behavior within this satisfaction philosophy, particularly in situations involving risk. To address this challenge, we propose introducing a behavior-type utility function into the GP model using the concept of a behavior function. This idea offers an innovative perspective for modeling DM's behavioral preferences in the imprecise GP approach by integrating a risk-aversion parameter specific to each objective. We then formulate a generalized behavioral-based GP approach for decision-making based on this new behavior-type utility function. To validate our proposed approach, we present an illustrative example of project selection in health service institutions, followed by a sensitivity analysis and comparisons with other approaches. The results demonstrate that DM's behavioral preferences significantly impact the decision-making process, and the proposed model provides more reasonable and convenient decisions for DMs with varying degrees of risk aversion.