scholarly journals Multi-Criteria Decision Making for Efficient Tiling Path Planning in a Tetris-Inspired Self-Reconfigurable Cleaning Robot

2018 ◽  
Vol 9 (1) ◽  
pp. 63 ◽  
Author(s):  
Maryam Kouzehgar ◽  
Mohan Rajesh Elara ◽  
Mahima Ann Philip ◽  
Manimuthu Arunmozhi ◽  
Veerajagadheswar Prabakaran
Keyword(s):  
Decision Making ◽  
Energy Consumption ◽  
Path Planning ◽  
Robot Navigation ◽  
Current Approach ◽  
Area Coverage ◽  
Preference Order ◽  
Multi Criteria Decision Making ◽  
Cleaning Robot ◽  
Furniture Layout

In this study, we aim to optimize and improve the efficiency of a Tetris-inspired reconfigurable cleaning robot. Multi-criteria decision making (MCDM) is utilized as a powerful tool to target this aim by introducing the best solution among others in terms of lower energy consumption and greater area coverage. Regarding the Tetris-inspired structure, polyomino tiling theory is utilized to generate tiling path-planning maps which are evaluated via MCDM to seek a solution that can deliver the best balance between the two mentioned key issues; energy and area coverage. In order to obtain a tiling area that better meets the requirements of polyomino tiling theorems, first, the whole area is decomposed into five smaller sub-areas based on furniture layout. Afterward, four tetromino tiling theorems are applied to each sub-area to give the tiling sets that govern the robot navigation strategy in terms of shape-shifting tiles. Then, the area coverage and energy consumption are calculated and eventually, these key values are considered as the decision criteria in a MCDM process to select the best tiling set in each sub-area, and following the aggregation of best tiling path-plannings, the robot navigation is oriented towards efficiency and improved optimality. Also, for each sub-area, a preference order for the tiling sets is put forward. Based on simulation results, the tiling theorem that can best serve all sub-areas turns out to be the same. Moreover, a comparison between a fixed-morphology mechanism with the current approach further advocates the proposed technique.

Multi-Criteria Decision-Making for Heterogeneous Multiprocessor Scheduling

2018 ◽  
Vol 17 (05) ◽  
pp. 1399-1427 ◽  
Author(s):  
S. Saroja ◽  
T. Revathi ◽  
Nitin Auluck
Keyword(s):  
Decision Making ◽  
Energy Consumption ◽  
Energy Efficient ◽  
High Performance ◽  
Scheduling Algorithm ◽  
Multiprocessor Scheduling ◽  
Multi Criteria Decision Making ◽  
Link Failures ◽  
Order Preference ◽  
Simulation Results

This paper proposes a new tri-objective scheduling algorithm called Heterogeneous Reliability-Driven Energy-Efficient Duplication-based (HRDEED) algorithm for heterogeneous multiprocessors. The goal of the algorithm is to minimize the makespan (schedule length) and energy consumption, while maximizing the reliability of the generated schedule. Duplication has been employed in order to minimize the makespan. There is a strong interest among researchers to obtain high-performance schedules that consume less energy. To address this issue, the proposed algorithm incorporates energy consumption as an objective. Moreover, in order to deal with processor and link failures, a system reliability model is proposed. The three objectives, i.e., minimizing the makespan and energy, while maximizing the reliability, have been met by employing a method called Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). TOPSIS is a popular Multi-Criteria Decision-Making (MCDM) technique that has been employed to rank the generated Pareto optimal schedules. Simulation results demonstrate the capability of the proposed algorithm in generating short, energy-efficient and reliable schedules. Based on simulation results, we observe that HRDEED algorithm demonstrates an improvement in both the energy consumption and reliability, with a reduced makespan. Specifically, it has been shown that the energy consumption can be reduced by 5–47%, and reliability can be improved by 1–5% with a 1–3% increase in makespan.

scholarly journals Multi-Criteria Decision-Making Method for Sustainable Energy-Saving Retrofit Façade Solutions

2021 ◽  
Vol 13 (23) ◽  
pp. 13168
Author(s):  
Ziortza Egiluz ◽  
Jesús Cuadrado ◽  
Andoni Kortazar ◽  
Ignacio Marcos
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Savings ◽  
Multi Criteria Decision Making ◽  
Effective Energy ◽  
Economic Basis ◽  
Selection Of

The increasing energy consumption levels of buildings within Europe call for controlled consumption and improvements to energy savings and efficiency and effective energy efficiency regulations. However, many aging and energy-inefficient buildings require energetic retrofitting that can employ various façades solutions and insulation materials. The selection of the most sustainable options in each situation therefore requires a decision-making methodology that can be used to prioritize available retrofit solutions based on economic, functional, environmental and social criteria. In this paper, both the methodology and the economic basis of the retrofitting process are presented. The methodology was validated in a case study, and a sensitivity analysis also demonstrated its validity, robustness and stability

scholarly journals Robust Aggregation Operators for Intuitionistic Fuzzy Hypersoft Set With Their Application to Solve MCDM Problem

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 688
Author(s):  
Rana Muhammad Zulqarnain ◽  
Imran Siddique ◽  
Rifaqat Ali ◽  
Dragan Pamucar ◽  
Dragan Marinkovic ◽  
...  
Keyword(s):  
Decision Making ◽  
Weighted Average ◽  
Current Approach ◽  
Aggregation Operators ◽  
Multi Criteria Decision Making ◽  
Sustainable Supply Chain ◽  
Fuzzy Soft Set ◽  
Fundamental Properties ◽  
Intuitionistic Fuzzy ◽  
Operational Laws

In this paper, we investigate the multi-criteria decision-making complications under intuitionistic fuzzy hypersoft set (IFHSS) information. The IFHSS is a proper extension of the intuitionistic fuzzy soft set (IFSS) which discusses the parametrization of multi-sub attributes of considered parameters, and accommodates more hesitation comparative to IFSS utilizing the multi sub-attributes of the considered parameters. The main objective of this research is to introduce operational laws for intuitionistic fuzzy hypersoft numbers (IFHSNs). Additionally, based on developed operational laws two aggregation operators (AOs), i.e., intuitionistic fuzzy hypersoft weighted average (IFHSWA) and intuitionistic fuzzy hypersoft weighted geometric (IFHSWG), operators have been presented with their fundamental properties. Furthermore, a decision-making approach has been established utilizing our developed aggregation operators (AOs). Through the established approach, a technique for solving decision-making (DM) complications is proposed to select sustainable suppliers in sustainable supply chain management (SSCM). Moreover, a numerical description is presented to ensure the validity and usability of the proposed technique in the DM process. The practicality, effectivity, and flexibility of the current approach are demonstrated through comparative analysis with the assistance of some prevailing studies.

scholarly journals Applied Robot Coverage Path Planning with Multiple Decision Making Capability under Uncertainty using Knowledge Inference with Hedge Algebras

2018 ◽  
Author(s):  
Hai Van Pham ◽  
Philip Moore
Keyword(s):  
Decision Making ◽  
Path Planning ◽  
Optimal Path ◽  
Self Awareness ◽  
Conceptual Approach ◽  
High Coverage ◽  
Coverage Path Planning ◽  
Knowledge Reasoning ◽  
Cleaning Robot ◽  
Context Of Use

Robotic decision-support systems must facilitate a robots interactions with their environment, this demands adaptability. Adaptability relates to awareness of the environment and `self-awareness', human behaviour exemplifies the concept of awareness to arrive at an optimal choice of action or decision based on reasoning and inference with learned preferences. A similar conceptual approach is required to implement awareness in autonomous robotic systems which must adapt to the current dynamic environment (the context of use). By incorporating `self-awareness' with knowledge of a Robot's preferences (in decision making) the decision maker interface should adapt to the current context of use. This paper proposes a novel approach to enable an autonomous robotics which implements path planning combining adaptation with knowledge reasoning techniques and hedge algebra to enable an autonomous robot to realise optimal coverage path planning under dynamic uncertainty. The results for a cleaning robot show that using our proposed approach demonstrated the capability to avoid both static and dynamic obstacles while achieving optimal path planning with increased efficiency. The proposed approach achieves the multiple decision-making objectives (path planning) with a high-coverage and low repetition rates. Compared to other current approaches, the proposed approach has demonstrated improved performance over the conventional robot control algorithms.

scholarly journals Recommended System for Cluster Head Selection in a Remote Sensor Cloud Environment Using the Fuzzy-Based Multi-Criteria Decision-Making Technique

2021 ◽  
Vol 13 (19) ◽  
pp. 10579
Author(s):  
Proshikshya Mukherjee ◽  
Prasant Kumar Pattnaik ◽  
Ahmed Abdulhakim Al-Absi ◽  
Dae-Ki Kang
Keyword(s):  
Decision Making ◽  
Energy Consumption ◽  
Energy Efficient ◽  
Fuzzy Ahp ◽  
Cluster Head ◽  
Base Station ◽  
Multi Criteria Decision Making ◽  
Cloud Environment ◽  
Sensor Cloud ◽  
Ch Selection

Clustering is an energy-efficient routing algorithm in a sensor cloud environment (SCE). The clustering sensor nodes communicate with the base station via a cluster head (CH), which can be selected based on the remaining energy, the base station distance, or the distance from the neighboring nodes. If the CH is selected based on the remaining energy and the base station is far away from the cluster head, then it is not an energy-efficient selection technique. The same applies to other criteria. For CH selection, a single criterion is not sufficient. Moreover, the traditional clustering algorithm head nodes keep changing in every round. Therefore, the traditional algorithm energy consumption is less, and nodes die faster. In this paper, the fuzzy multi-criteria decision-making (F-MCDM) technique is used for CH selection and a threshold value is fixed for the CH selection. The fuzzy analytical hierarchy process (AHP) and the fuzzy analytical network process (ANP) are used for CH selection. The performance evaluation results exhibit a 5% improvement compared to the fuzzy AHP clustering method and 10% improvement compared to the traditional method in terms of stability, energy consumption, throughput, and control overhead.

scholarly journals Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment with Multi-Criteria Decision Making using Full Consistency Method

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1241 ◽  
Author(s):  
Zagradjanin ◽  
Pamucar ◽  
Jovanovic
Keyword(s):  
Decision Making ◽  
Path Planning ◽  
Multi Criteria Decision Making ◽  
Robot Path Planning ◽  
Robot System ◽  
Global Cost ◽  
Consistency Method ◽  
Crowded Environment ◽  
Robot Path ◽  
Multi Robot

The progress in the research of various areas of robotics, artificial intelligence, and other similar scientific disciplines enabled the application of multi-robot systems in different complex environments and situations. It is necessary to elaborate the strategies regarding the path planning and paths coordination well in order to efficiently execute a global mission in common environment, prior to everything. This paper considers the multi-robot system based on the cloud technology with a high level of autonomy, which is intended for execution of tasks in a complex and crowded environment. Cloud approach shifts computation load from agents to the cloud and provides powerful processing capabilities to the multi-robot system. The proposed concept uses a multi-robot path planning algorithm that can operate in an environment that is unknown in advance. With the aim of improving the efficiency of path planning, the implementation of Multi-criteria decision making (MCDM) while using Full consistency method (FUCOM) is proposed. FUCOM guarantees the consistent determination of the weights of factors affecting the robots motion to be symmetric or asymmetric, with respect to the mission specificity that requires the management of multiple risks arising from different sources, optimizing the global cost map in that way.

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