Implementation of MCDM Approaches for a Real-Life Location Selection Problem: A Case Study of Consumer Goods Sector

2021 ◽  
pp. 77-88
Author(s):  
Oğuz Emir
Keyword(s):  
Consumer Goods ◽  
Real Life ◽  
Selection Problem ◽  
Location Selection

scholarly journals A STOCHASTIC MULTI-CRITERIA DECISION-MAKING ANALYSIS FOR A WAREHOUSE LOCATION SELECTION PROBLEM: A CASE STUDY

2020 ◽  
Vol 7 (12) ◽  
pp. 133-143
Author(s):  
Şeyma Emeç ◽  
Gökay Akkaya
Keyword(s):  
Decision Making ◽  
Real Life ◽  
Multiple Criteria Decision Making ◽  
Selection Problem ◽  
Multi Criteria Decision Making ◽  
Analytic Hierarchy ◽  
Location Selection ◽  
Solution Approach ◽  
Life Problems ◽  
Warehouse Location

The problem of a warehouse location selecting which has a significant impact on logistics costs is an important decision problem based on the best choice of alternatives under multiple conflicting criteria. Multiple-criteria decision-making (MCDM) methods are used as a solution approach for the decision problems including several criteria. In this study, a new stochastic multi-criteria decision-making approach has been developed to solve the warehouse location selection problem (WLSP) in the stochastic environment which contains uncertain situations. In the proposed approach, the SAHP (Stochastic Analytic Hierarchy Process) method was used to calculate the weight of criteria, and the alternatives were ranked and evaluated by fuzzy MOORA (Multi-Objective Optimization by Ratio Analysis). The proposed approach is applied to warehouse selection problem of a supermarket chain located in Turkey. The results of the research indicated that A2 is the best alternative. It can be said that the proposed method can be applied to the real life problems because it found a suitable solution to the problem.

A novel intuitionistic fuzzy MCDM-based CODAS approach for locating an authorized dismantling center: a case study of Istanbul

2020 ◽  
Vol 38 (6) ◽  
pp. 660-672 ◽  
Author(s):  
Selman Karagoz ◽  
Muhammet Deveci ◽  
Vladimir Simic ◽  
Nezir Aydin ◽  
Ufuk Bolukbas
Keyword(s):  
Decision Making ◽  
Intuitionistic Fuzzy Set ◽  
Real Life ◽  
Weighted Averaging ◽  
Location Selection ◽  
Intuitionistic Fuzzy ◽  
Effective Decision ◽  
Fuzzy Mcdm ◽  
Effective Decision Making

As the number of end-of-life vehicles (ELVs) increases rapidly, their management has become one of the most important environmental topics worldwide. This study is conducted to evaluate various alternatives for location selection of an authorized dismantling center (ADC) for ELVs using a multi-criteria decision-making (MCDM) approach. An intuitionistic fuzzy MCDM-based combinative distance-based assessment (CODAS) approach is proposed to aid waste managers and solve their problem. The intuitionistic fuzzy weighted averaging operator is utilized to aggregate individual opinions of decision-makers into a group opinion. The intuitionistic fuzzy Euclidean and Hamming distances are used to calculate the assessment score of alternatives. A real-life case study of Istanbul is provided to illustrate how this novel intuitionistic fuzzy MCDM-based CODAS approach can be used for alternative selection in real-world applications. The comparison with the available state-of-the-art intuitionistic fuzzy set-based MCDM approaches approves the validity and consistency of the proposed intuitionistic fuzzy CODAS approach. The intuitionistic fuzzy CODAS, WASPAS, and TOPSIS approaches generate exactly the same ordering of alternatives for the new ADC in Istanbul. The results show that the intuitionistic fuzzy CODAS approach indicates valid results and is an effective decision-making technique for vagueness and uncertainty nature of linguistic assessments.

Contamination Lab of Turin (CLabTo)

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Eleonora FIORE ◽  
Giuliano SANSONE ◽  
Chiara Lorenza REMONDINO ◽  
Paolo Marco TAMBORRINI
Keyword(s):  
University Students ◽  
Single Case ◽  
Real Life ◽  
Learning By Doing ◽  
Assessment Data ◽  
Entrepreneurial Skills ◽  
Fields Of Study ◽  
Education Levels ◽  
Positive Effect

Interest in offering Entrepreneurship Education (EE) to all kinds of university students is increasing. Therefore, universities are increasing the number of entrepreneurship courses intended for students from different fields of study and with different education levels. Through a single case study of the Contamination Lab of Turin (CLabTo), we suggest how EE may be taught to all kinds of university students. We have combined design methods with EE to create a practical-oriented entrepreneurship course which allows students to work in transdisciplinary teams through a learning-by-doing approach on real-life projects. Professors from different departments have been included to create a multidisciplinary environment. We have drawn on programme assessment data, including pre- and post-surveys. Overall, we have found a positive effect of the programme on the students’ entrepreneurial skills. However, when the data was broken down according to the students’ fields of study and education levels, mixed results emerged.

Participation and spatial transformation in large housing estates in Poland. Case study of Ursynów Północny

2018 ◽  
Vol 60 (1) ◽  
pp. 55-65
Author(s):  
Krystyna Ilmurzyńska
Keyword(s):  
Basic Assumption ◽  
Real Life ◽  
Business Environment ◽  
Spatial Transformation ◽  
Reciprocal Relationships ◽  
Spatial Transformations ◽  
Large Housing Estates ◽  
Existing Housing ◽  
Housing Estates

Abstract This article investigates the suitability of traditional and participatory planning approaches in managing the process of spatial development of existing housing estates, based on the case study of Warsaw’s Ursynów Północny district. The basic assumption of the article is that due to lack of government schemes targeted at the restructuring of large housing estates, it is the business environment that drives spatial transformations and through that shapes the development of participation. Consequently the article focuses on the reciprocal relationships between spatial transformations and participatory practices. Analysis of Ursynów Północny against the background of other estates indicates that it presents more endangered qualities than issues to be tackled. Therefore the article focuses on the potential of the housing estate and good practices which can be tracked throughout its lifetime. The paper focuses furthermore on real-life processes, addressing the issue of privatisation, development pressure, formal planning procedures and participatory budgeting. In the conclusion it attempts to interpret the existing spatial structure of the estate as a potential framework for a participatory approach.

Bleak Weather for Sun-Shine AG: A Case Study of Impairment of Assets

2014 ◽  
Vol 30 (2) ◽  
pp. 113-126 ◽  
Author(s):  
Dominic Detzen ◽  
Tobias Stork genannt Wersborg ◽  
Henning Zülch
Keyword(s):  
Real Life ◽  
Accounting Standards ◽  
Point Of View ◽  
Professional Judgment ◽  
Economic Point ◽  
Instructional Resource

ABSTRACT This case originates from a real-life business situation and illustrates the application of impairment tests in accordance with IFRS and U.S. GAAP. In the first part of the case study, students examine conceptual questions of impairment tests under IFRS and U.S. GAAP with respect to applicable accounting standards, definitions, value concepts, and frequency of application. In addition, the case encourages students to discuss the impairment regime from an economic point of view. The second part of the instructional resource continues to provide instructors with the flexibility of applying U.S. GAAP and/or IFRS when students are asked to test a long-lived asset for impairment and, if necessary, allocate any potential impairment. This latter part demonstrates that impairment tests require professional judgment that students are to exercise in the case.

Design and Real-life Deployment of a Smart Nanogrid: A Greek Case Study

2020 ◽  
Author(s):  
Apostolos C. Tsolakis ◽  
Angelina D. Bintoudi ◽  
Lampros Zyglakis ◽  
Stylianos Zikos ◽  
Christos Timplalexis ◽  
...  
Keyword(s):  
Real Life

scholarly journals Investigating the Potential of Network Optimization for a Constrained Object Detection Problem

2021 ◽  
Vol 7 (4) ◽  
pp. 64
Author(s):  
Tanguy Ophoff ◽  
Cédric Gullentops ◽  
Kristof Van Beeck ◽  
Toon Goedemé
Keyword(s):  
Computational Complexity ◽  
Object Detection ◽  
Network Optimization ◽  
Real Life ◽  
Optimization Techniques ◽  
Training Data ◽  
Single Shot ◽  
Standard Object ◽  
Number Of Classes

Object detection models are usually trained and evaluated on highly complicated, challenging academic datasets, which results in deep networks requiring lots of computations. However, a lot of operational use-cases consist of more constrained situations: they have a limited number of classes to be detected, less intra-class variance, less lighting and background variance, constrained or even fixed camera viewpoints, etc. In these cases, we hypothesize that smaller networks could be used without deteriorating the accuracy. However, there are multiple reasons why this does not happen in practice. Firstly, overparameterized networks tend to learn better, and secondly, transfer learning is usually used to reduce the necessary amount of training data. In this paper, we investigate how much we can reduce the computational complexity of a standard object detection network in such constrained object detection problems. As a case study, we focus on a well-known single-shot object detector, YoloV2, and combine three different techniques to reduce the computational complexity of the model without reducing its accuracy on our target dataset. To investigate the influence of the problem complexity, we compare two datasets: a prototypical academic (Pascal VOC) and a real-life operational (LWIR person detection) dataset. The three optimization steps we exploited are: swapping all the convolutions for depth-wise separable convolutions, perform pruning and use weight quantization. The results of our case study indeed substantiate our hypothesis that the more constrained a problem is, the more the network can be optimized. On the constrained operational dataset, combining these optimization techniques allowed us to reduce the computational complexity with a factor of 349, as compared to only a factor 9.8 on the academic dataset. When running a benchmark on an Nvidia Jetson AGX Xavier, our fastest model runs more than 15 times faster than the original YoloV2 model, whilst increasing the accuracy by 5% Average Precision (AP).

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

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