Implementation of an Intelligent Model for Decision Making Based on CBR for Supply Chain Solution in Retail for a Cluster of Supermarkets

The latter includes customizing the user interface, as well as the way the system retrieves and processes cases afterward. The resulting cases may be shown to the user in different ways, and/or the retrieved cases may be adapted. This chapter is about an intelligent model for decision making based on case-based reasoning to solve the existing problem in the planning of distribution in the supply chain between a distribution center and a chain of supermarkets. First, the authors mentioned the need for intelligent systems in the decision-making processes, where they are necessary due to the limitations associated with conventional human decision-making processes. Among them, human experience is very scarce, and humans get tired of the burden of physical or mental work. In addition, human beings forget the crucial details of a problem, and many of the times are inconsistent in their daily decisions.

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Evaluation of Decision-Making Support Systems

Strategic Information Systems ◽

10.4018/978-1-60566-677-8.ch011 ◽

2011 ◽

pp. 131-140

Author(s):

Gloria E Phillips-Wren ◽

Manuel Mora ◽

Guisseppi Forgionne

Keyword(s):

Decision Making ◽

Intelligent Systems ◽

Support Systems ◽

The Internet ◽

Dynamic Data ◽

Human Decision ◽

Broad Classification ◽

Specific Contribution ◽

Decision Making Support

Decision support systems (DSSs) have been researched extensively over the years with the purpose of aiding the decision maker (DM) in an increasingly complex and rapidly changing environment (Sprague & Watson, 1996; Turban & Aronson, 1998). Newer intelligent systems, enabled by the advent of the Internet combined with artificial-intelligence (AI) techniques, have extended the reach of DSSs to assist with decisions in real time with multiple informaftion flows and dynamic data across geographical boundaries. All of these systems can be grouped under the broad classification of decision-making support systems (DMSS) and aim to improve human decision making. A DMSS in combination with the human DM can produce better decisions by, for example (Holsapple & Whinston, 1996), supplementing the DM’s abilities; aiding one or more of Simon’s (1997) phases of intelligence, design, and choice in decision making; facilitating problem solving; assisting with unstructured or semistructured problems (Keen & Scott Morton, 1978); providing expert guidance; and managing knowledge. Yet, the specific contribution of a DMSS toward improving decisions remains difficult to quantify.

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Evaluation of Decision-Making Support Systems

Encyclopedia of Decision Making and Decision Support Technologies ◽

10.4018/978-1-59904-843-7.ch037 ◽

2011 ◽

pp. 320-328 ◽

Cited By ~ 2

Author(s):

Gloria E. Phillips-Wren ◽

Manuel Mora ◽

Guisseppi Forgionne

Keyword(s):

Decision Making ◽

Intelligent Systems ◽

Support Systems ◽

The Internet ◽

Dynamic Data ◽

Human Decision ◽

Broad Classification ◽

Specific Contribution ◽

Decision Making Support

Decision support systems (DSSs) have been researched extensively over the years with the purpose of aiding the decision maker (DM) in an increasingly complex and rapidly changing environment (Sprague & Watson, 1996; Turban & Aronson, 1998). Newer intelligent systems, enabled by the advent of the Internet combined with artificial-intelligence (AI) techniques, have extended the reach of DSSs to assist with decisions in real time with multiple informaftion flows and dynamic data across geographical boundaries. All of these systems can be grouped under the broad classification of decision-making support systems (DMSS) and aim to improve human decision making. A DMSS in combination with the human DM can produce better decisions by, for example (Holsapple & Whinston, 1996), supplementing the DM’s abilities; aiding one or more of Simon’s (1997) phases of intelligence, design, and choice in decision making; facilitating problem solving; assisting with unstructured or semistructured problems (Keen & Scott Morton, 1978); providing expert guidance; and managing knowledge. Yet, the specific contribution of a DMSS toward improving decisions remains difficult to quantify.

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Algorithms as discrimination detectors

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1912790117 ◽

2020 ◽

Vol 117 (48) ◽

pp. 30096-30100 ◽

Cited By ~ 1

Author(s):

Jon Kleinberg ◽

Jens Ludwig ◽

Sendhil Mullainathan ◽

Cass R. Sunstein

Keyword(s):

Decision Making ◽

Human Beings ◽

Regulatory Systems ◽

Human Decision ◽

The Right

Preventing discrimination requires that we have means of detecting it, and this can be enormously difficult when human beings are making the underlying decisions. As applied today, algorithms can increase the risk of discrimination. But as we argue here, algorithms by their nature require a far greater level of specificity than is usually possible with human decision making, and this specificity makes it possible to probe aspects of the decision in additional ways. With the right changes to legal and regulatory systems, algorithms can thus potentially make it easier to detect—and hence to help prevent—discrimination.

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Similarity-matching in decision-making processes of Supply Chain Analytics: a systematic literature review

Supply Chain Forum an International Journal ◽

10.1080/16258312.2017.1278670 ◽

2017 ◽

Vol 18 (1) ◽

pp. 13-20 ◽

Cited By ~ 2

Author(s):

Tino T. Herden

Keyword(s):

Decision Making ◽

Supply Chain ◽

Literature Review ◽

Systematic Literature Review ◽

Similarity Matching ◽

Decision Making Processes

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Using a coupled agent-based modeling approach to analyze the role of risk perception in water management decisions

Hydrology and Earth System Sciences ◽

10.5194/hess-23-2261-2019 ◽

2019 ◽

Vol 23 (5) ◽

pp. 2261-2278 ◽

Cited By ~ 5

Author(s):

Jin-Young Hyun ◽

Shih-Yu Huang ◽

Yi-Chen Ethan Yang ◽

Vincent Tidwell ◽

Jordan Macknick

Keyword(s):

Decision Making ◽

Risk Perception ◽

Regional Scale ◽

Agent Based ◽

Natural Processes ◽

Complex Adaptive ◽

San Juan River ◽

Human Decision ◽

Decision Making Processes ◽

Engineered Systems

Abstract. Managing water resources in a complex adaptive natural–human system is a challenge due to the difficulty of modeling human behavior under uncertain risk perception. The interaction between human-engineered systems and natural processes needs to be modeled explicitly with an approach that can quantify the influence of incomplete/ambiguous information on decision-making processes. In this study, we two-way coupled an agent-based model (ABM) with a river-routing and reservoir management model (RiverWare) to address this challenge. The human decision-making processes is described in the ABM using Bayesian inference (BI) mapping joined with a cost–loss (CL) model (BC-ABM). Incorporating BI mapping into an ABM allows an agent's psychological thinking process to be specified by a cognitive map between decisions and relevant preceding factors that could affect decision-making. A risk perception parameter is used in the BI mapping to represent an agent's belief on the preceding factors. Integration of the CL model addresses an agent's behavior caused by changing socioeconomic conditions. We use the San Juan River basin in New Mexico, USA, to demonstrate the utility of this method. The calibrated BC-ABM–RiverWare model is shown to capture the dynamics of historical irrigated area and streamflow changes. The results suggest that the proposed BC-ABM framework provides an improved representation of human decision-making processes compared to conventional rule-based ABMs that do not take risk perception into account. Future studies will focus on modifying the BI mapping to consider direct agents' interactions, up-front cost of agent's decision, and upscaling the watershed ABM to the regional scale.

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Simulation and Modeling Efforts to Support Decision Making in Healthcare Supply Chain Management

The Scientific World JOURNAL ◽

10.1155/2014/354246 ◽

2014 ◽

Vol 2014 ◽

pp. 1-16 ◽

Cited By ~ 14

Author(s):

Eman AbuKhousa ◽

Jameela Al-Jaroodi ◽

Sanja Lazarova-Molnar ◽

Nader Mohamed

Keyword(s):

Decision Making ◽

Supply Chain ◽

Supply Chain Management ◽

Simulation And Modeling ◽

Healthcare Organizations ◽

Chain Management ◽

Decision Making Processes ◽

Environment Simulation ◽

The Cost ◽

Support Decision Making

Recently, most healthcare organizations focus their attention on reducing the cost of their supply chain management (SCM) by improving the decision making pertaining processes’ efficiencies. The availability of products through healthcare SCM is often a matter of life or death to the patient; therefore, trial and error approaches are not an option in this environment. Simulation and modeling (SM) has been presented as an alternative approach for supply chain managers in healthcare organizations to test solutions and to support decision making processes associated with various SCM problems. This paper presents and analyzes past SM efforts to support decision making in healthcare SCM and identifies the key challenges associated with healthcare SCM modeling. We also present and discuss emerging technologies to meet these challenges.

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Cognitive Model of Team Collaboration: Macro-Cognitive Focus

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/154193120504900312 ◽

2005 ◽

Vol 49 (3) ◽

pp. 269-273 ◽

Cited By ~ 18

Author(s):

Norman Warner ◽

Michael Letsky ◽

Michael Cowen

Keyword(s):

Decision Making ◽

Cognitive Processes ◽

Empirical Data ◽

Cognitive Model ◽

Distributed Teams ◽

Descriptive Model ◽

Team Collaboration ◽

Face To Face ◽

Human Decision ◽

Decision Making Processes

The purpose of this paper is to describe a cognitive model of team collaboration emphasizing the human decision-making processes used during team collaboration. The descriptive model includes the domain characteristics, collaboration stages, meta- and macro cognitive processes and the mechanisms for achieving the stages and cognitive processes. Two experiments were designed to provide empirical data on the validity of the collaboration stages and cognitive processes of the model. Both face-to-face and asynchronous, distributed teams demonstrated behavior that supports the existence of the collaboration stages along with seven cognitive processes.

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Every Adolescent Deserves a Parent

10.31228/osf.io/pxe47 ◽

2018 ◽

Author(s):

Dale Margolin Cecka

Keyword(s):

Decision Making ◽

Foster Care ◽

Child Welfare ◽

Young Person ◽

Human Beings ◽

Foster Child ◽

Foster Care System ◽

The Past ◽

Child Welfare Professionals ◽

Decision Making Processes

This article argues that all adolescents, indeed all human beings, deserve at least one parent�one person who takes the good with the bad because that person�s life is intertwined with the child�s. The child matters to the parent in a way that a friend, nephew, or foster child may not. Child welfare professionals must never lose sight of this principle when they recruit, train, and maintain parents for adolescents. The parent can be someone who is already in the young person�s life or someone who has been unable to parent in the past, but is now ready to secure that bond. True parents are attainable for teenagers in foster care as long as child welfare professionals remember what they are looking for and are steadfast and creative in their efforts to find and nurture these relationships. Section Two of this article details the issues that adolescents face when they age out 5 of the foster care system. Next, Section Three discusses the obstacles adolescents face in attaining familial permanency. Section Four examines the aspects of successful adoptions, including the recruitment and decision making processes, in an effort to apply those principals to developing and maintaining adolescent permanency. Finally, Section Five concludes with the keys to successful adolescent permanency.

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Human behavior inside and outside bureaucracy: Lessons from psychology

Journal of Behavioral Public Administration ◽

10.30636/jbpa.11.13 ◽

2018 ◽

Vol 1 (1) ◽

Author(s):

Asbjørn Sonne Nørgaard

Keyword(s):

Decision Making ◽

Psychological Research ◽

Psychological Needs ◽

Human Beings ◽

Public Officials ◽

Personality Psychology ◽

Intuitive Thinking ◽

Human Decision ◽

Key Issues ◽

And Behavior

Both Herbert A. Simon and Anthony Downs borrowed heavily from psychology to develop more accurate theories of Administrative Behavior outside and Inside Bureaucracy: Simon, to explicate the cognitive shortcomings in human rationality and its implications; and Downs, to argue that public officials, like other human beings, vary in their psychological needs and motivations and, therefore, behave differently in similar situations. I examine how recent psychological research adds important nuances to the psychology of human decision-making and behavior and points in somewhat other directions than those taken by Simon and Downs. Cue-taking, fast and intuitive thinking, and emotions play a larger role in human judgment and decision-making than what Simon suggested with his notion of bounded rationality. Personality trait theory provides a more general and solid underpinning for understanding individual differences in behavior, both inside and outside bureaucracy, than the 'types of officials' that Downs discussed. I present an agenda for a behavioral public administration that takes key issues in cognitive psychology and personality psychology into account.

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