A Comparative Study of Outranking MADM Algorithms in Network Selection

Anupama, K. S. S.; Gowri, S.; Rao, B. Prabakara

doi:10.1109/iccmc.2018.8487931

Cited by 10 publications

(7 citation statements)

References 10 publications

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“…While preference value is determined based on the opinion of decision maker, in this study, is bus terminal authority. This is the benefit of Simple Additive Weighting Method that has capability to determine preference value from highest value to lowest value and make decision maker easy to choose alternatives with priority to be implemented [12][13][14].…”

Section: Methodsmentioning

confidence: 99%

ITS Design Priority at Large Bus Terminal in Indonesia in Supporting Sustainable Transportation

Sutandi¹,

Santosa²,

Hidayat³

et al. 2019

Proceedings of the 11th Asia Pacific Transportation and the Environment Conference (APTE 2018)

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Intelligent Transportation Systems (ITS) is an important part of sustainable transportation. Implementation of ITS at large bus terminal is needed in order to increase the service quality to the passenger and encourage people to use public transportation. Unfortunately, not all of large bus terminal in large cities in Indonesia have implemented ITS yet. The aim of this study is to identify availability of ITS implementation at the large bus terminal, to evaluate whether the ITS system has fulfil the ITS standard in Indonesia, and to recommend ITS design to be implemented based on the needs, priority, and local condition at the large bus terminal. Implementation of ITS is compulsory but priority is also important based on limited support of financial, officer's capability, and commitment of the authority are the challenges. Case study is carried out at Cicaheum large bus terminal type-A in Bandung, Indonesia. Standard used is regulation of Ministry of in Indonesia, and number 132 year 2015 regarding information technology standard, including safety aspect, security aspect, reliability aspect, convenience aspect, easily aspect, and equality aspect of ITS implementation. Data collection used is direct observation of ITS implementation at Cicaheum large bus terminal in Bandung, Indonesia, questionnaire and interview with the bus terminal authority regarding the ITS implementation. Analysis method is Simple Additive Weighting. Result indicated that ITS design priority is CCTV application, topping bus ticket, automatic door bar at terminal gate, availability of facility of disable person, digital facility sign, traffic sign development, on time bus schedule, passenger information in the terminal regarding bus delay, and online/ mobile ticketing. Since these part of ITS is implemented, then based on existing condition of the large bus terminal, other part of ITS can also be implemented in the future.

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Section: Methodsmentioning

confidence: 99%

ITS Design Priority at Large Bus Terminal in Indonesia in Supporting Sustainable Transportation

Sutandi¹,

Santosa²,

Hidayat³

et al. 2019

Proceedings of the 11th Asia Pacific Transportation and the Environment Conference (APTE 2018)

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“…Network selection plays a fundamental role in the provision of stable connections with an adequate level of QoS and hence network operators and providers commonly exploit several advanced techniques to select the best AP to allocate new connections. Among the various techniques proposed in the literature, Multiple Attribute Decision Making (MADM), proved to be one of the most flexible solutions to capture user preferences and QoE related aspects in the decision process [3]- [7]. In MADM solutions, the information characterizing the decision making is made by the so-called attribute values and attribute weights: the first ones describing characteristics, qualities and performances of different alternatives, whereas the latter ones are used to measure the relevance of attributes.…”

Section: State Of the Art Innovations And Limitations Of The Proposed...mentioning

confidence: 99%

Satellite Integration into 5G: Deep Reinforcement Learning for Network Selection

et al. 2022

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This paper proposes a Deep-Q-Network (DQN) controller for network selection and adaptive resource allocation in heterogeneous networks, developed on the ground of a Markov Decision Process (MDP) model of the problem. Network selection is an enabling technology for multi-connectivity, one of the core functionalities of 5G, and for this reason the present work considers a realistic network model that takes into account path-loss models and intra-RAT (Radio Access Technology) interference. Numerical simulations validate the proposed approach and show the improvements achieved thanks to the DQN algorithm with respect to a classic Reinforcement Learning algorithm and baseline approaches in terms of connection-flows' acceptance, resource allocation and load balancing.

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“…It is advised to utilize several simulations of the decision matrix's element estimates within a given error while determining the ranks of alternatives in order to acquire statistical estimates of the ranks. Anupamaet al [3] in 2015 compared three MADM approaches SAW (Simple Additive Weighting), WPM (Weighted Product Method), and PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) in this research for their effectiveness in network selection. Zavadskas and Turskis [4] in their work try to use ARAS method over microclimate in office room.…”

Section: Literature Reviewmentioning

confidence: 99%

A Sensitivity Analysis on a MCDM Problem Evaluated by ARAS Method

Roy¹,

Mitra²

2022

JMEP

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Multi-Criteria Decision Making, also known as MCDM, is a highly helpful technique for assessing numerous issues and conclusions as well as resolving conflicts in daily life. The choice of any technique, service, or product is fraught with difficulties. The choosing of any useful product, such as a scooter, is an important task that requires careful research into the selection criteria, where MCDM techniques play a significant role. But the results that are coming with different MCDM methods are has to be consistent enough. This particular study associated with the checking or verifying the results that are evaluated by the Additive Ratio Assessment (ARAS) method with AHP weights for each Criterion.

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A Comparative Study of Outranking MADM Algorithms in Network Selection

Cited by 10 publications

References 10 publications

ITS Design Priority at Large Bus Terminal in Indonesia in Supporting Sustainable Transportation

ITS Design Priority at Large Bus Terminal in Indonesia in Supporting Sustainable Transportation

Satellite Integration into 5G: Deep Reinforcement Learning for Network Selection

A Sensitivity Analysis on a MCDM Problem Evaluated by ARAS Method

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