Modeling Gender Based Customer Preferences of Information Search Channels

2018 ◽  
pp. 622-638
Author(s):  
Gaurav Khatwani ◽  
Praveen Ranjan Srivastava

The disparity in consumer and organization preferences of information channels is a major concern. Further, making decisions in the presence of a wide range of conflicting criteria through the use of a multiple criteria decision-making (MCDM) approach has gained increased prominence in recent years and research in this area has become an important consideration for business operations that involve dealing with complex decision problems. This paper describes how an integrated approach can be applied to a decision-making problem that combines a fuzzy analytical hierarchy process (AHP) and TOPSIS for identifying preferences consumers of information search channels according to demographic factors such as gender.

2017 ◽  
Vol 25 (2) ◽  
pp. 52-67 ◽  
Author(s):  
Gaurav Khatwani ◽  
Praveen Ranjan Srivastava

The disparity in consumer and organization preferences of information channels is a major concern. Further, making decisions in the presence of a wide range of conflicting criteria through the use of a multiple criteria decision-making (MCDM) approach has gained increased prominence in recent years and research in this area has become an important consideration for business operations that involve dealing with complex decision problems. This paper describes how an integrated approach can be applied to a decision-making problem that combines a fuzzy analytical hierarchy process (AHP) and TOPSIS for identifying preferences consumers of information search channels according to demographic factors such as gender.


Author(s):  
Sang Song ◽  
Li-Hua Jun

A new method of interactive Multiple Criteria Decision Making (MCDM) is presented. In order to settle the problem that in the cases for the ship designers sometimes it is difficult to make a decision when facing so complex ship form schemes. The conventional AHP (Analytical Hierarchy Process) is adopted, but it mostly depends on the designer’s subjective and leads to the systematic error. The new method can obtain the accurate result with the rigid least square method as a tool, making full use of the AHP and the objective information entropy, which reflects the inherent attribute. When applied in the practice, it is proved to be effective, practical and dependable for future ships’ complex Decision Systems (DS).


2017 ◽  
Vol 24 (1) ◽  
pp. 71-86
Author(s):  
Amin Wibowo

Up to now, organizational buying is still interesting topic discussed. There are divergences among the findings in organizational buying researches. Different perspectives, fenomena observed, research domains and methods caused the divergences. This paper will discusse organizational buying behavior based on literature review, focused on behavior of decision making unit mainly on equipment buying. From this review literatures, it would be theoritical foundation that is valid and reliable to develop propositions in organizational buying behavior. Based on review literature refferences, variables are classified into: purchase situation, member of decision making unit perception, conflict among the members, information search, influences among members of decision making unit. Integrated approach is used to develop propositions relating to: purchasing complexity, sharing responsibility among the members, conflict in decision making unit, information search, time pressure as moderating variable between sharing responsibility and conflict in decision making unit, the influence among the members inside decision making unit and decision making outcome


2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.


2019 ◽  
pp. 135481661988520
Author(s):  
Joseph Andria ◽  
Giacomo di Tollo ◽  
Raffaele Pesenti

In this article, we propose a method for ranking tourist destinations and evaluating their performances under a sustainability perspective: a fuzzy multiple criteria decision-making method is applied for determining sustainability performance values and ranking destinations accordingly. We select a set of sustainability evaluation criteria and use a fuzzy analytic hierarchy process to weight the selected criteria. We also optimize each evaluator’s membership function support by means of a fuzzy entropy maximization criteria. A case study is illustrated and results are compared with two data envelopment analysis–based models. The simplicity of the proposed approach along with the easy readability of the results allow its direct applicability for all involved stakeholders.


2015 ◽  
Vol 32 (7) ◽  
pp. 763-782 ◽  
Author(s):  
Hu-Chen Liu ◽  
Jian-Xin You ◽  
Xue-Feng Ding ◽  
Qiang Su

Purpose – The purpose of this paper is to develop a new failure mode and effect analysis (FMEA) framework for evaluation, prioritization and improvement of failure modes. Design/methodology/approach – A hybrid multiple criteria decision-making method combining VIKOR, decision-making trial and evaluation laboratory (DEMATEL) and analytic hierarchy process (AHP) is used to rank the risk of the failure modes identified in FMEA. The modified VIKOR method is employed to determine the effects of failure modes on together. Then the DEMATEL technique is used to construct the influential relation map among the failure modes and causes of failures. Finally, the AHP approach based on the DEMATEL is utilized to obtain the influential weights and give the prioritization levels for the failure modes. Findings – A case study of diesel engine’s turbocharger system is provided to illustrate the potential application and benefits of the proposed FMEA approach. Results show that the new risk priority model can be effective in helping analysts find the high risky failure modes and create suitable maintenance strategies. Practical implications – The proposed FMEA can overcome the shortcomings and improve the effectiveness of the traditional FMEA. Particularly, the dependence and interactions between different failure modes and effects have been addressed by the new failure analysis method. Originality/value – This paper presents a systemic analytical model for FMEA. It is able to capture the complex interrelationships among various failure modes and effects and provide guidance to analysts by setting the suitable maintenance strategies to improve the safety and reliability of complex systems.


Author(s):  
Andrejs Radionovs ◽  
Oleg Uzhga-Rebrov

Being able to evaluate risks is an important task in many areas of human activity: economics, ecology, etc. Usually, environmental risk assessment is carried out on the basis of multiple and sometimes conflicting factors. Using multiple criteria decision-making (MCDM) methodology is one of the possible ways to solve the problem. Methodologies of analytic hierarchy process (AHP) are the most commonly used MCDM methods, which combine subjective and personal preferences in risk assessment process. However, AHP involves human subjectivity, which introduces vagueness type of uncertainty and requires the usage of decision making under those uncertainties. In this paper it was considered to deal with uncertainty by using the fuzzy-based techniques. However, nowadays there exist multiple Fuzzy AHP methodologies developed by different authors. In this paper, these Fuzzy AHP methodologies will be compared, and the most appropriate Fuzzy AHP methodology for the application in case of environmental risks assessment will be offered on the basis of this comparison.


2011 ◽  
Vol 5 (9) ◽  
pp. 27 ◽  
Author(s):  
Carlos Parra López ◽  
Javier Calatrava Requena ◽  
Tomás De Haro Giménez

Even though multifunctionality concept is reflected, implicit or explicitly, in the design of actual agrarian policies, its consideration when analysing and assessing farming systems is relatively limited in the scientific literature. Analytic Hierarchy Process (AHP) is proposed with this aim. AHP is a multicriteria discrete decision support technique that is used in complex decision making. This methodology is stated jointly with a proposed procedure to measure relative agreement among decision makers and uniformity of alternatives’ performances in group decision making. Finally AHP is implemented in the assessment of organic, integrated and conventional olive groves in Andalusia considering criteria of a different nature – economic, technical, sociocultural and environmental –. The final purpose is determining the more interesting growing techniques from a holistic point of view for all the society in the medium/long-term on the basis of knowledge of experts on olive.


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