A multi-criteria decision-making in turning process using the MAIRCA, EAMR, MARCOS and TOPSIS methods: A comparative study

Multi-criteria decision-making is important and it affects the efficiency of a mechanical processing process as well as an operation in general. It is understood as determining the best alternative among many alternatives. In this study, the results of a multi-criteria decision-making study are presented. In which, sixteen experiments on turning process were carried out. The input parameters of the experiments are the cutting speed, the feed speed, and the depth of cut. After conducting the experiments, the surface roughness and the material removal rate (MRR) were determined. To determine which experiment guarantees the minimum surface roughness and maximum MRR simultaneously, four multi-criteria decision-making methods including the MAIRCA, the EAMR, the MARCOS, and the TOPSIS were used. Two methods the Entropy and the MEREC were used to determine the weights for the criteria. The combination of four multi-criteria making decision methods with two determination methods of the weights has created eight ranking solutions for the experiments, which is the novelty of this study. An amazing result was obtained that all eight solutions all determined the same best experiment. From the obtained results, a recommendation was proposed that the multi-criteria making decision methods and the weighting methods using in this study can also be used for multi-criteria making decision in other cases, other processes.

LB-DDQN for Handover Decision in Satellite-Terrestrial Integrated Networks

The frequent handover and handover failure problems obviously degrade the QoS of mobile users in the terrestrial segment (e.g., cellular networks) of satellite-terrestrial integrated networks (STINs). And the traditional handover decision methods rely on the historical data and produce the training cost. To solve these problems, the deep reinforcement learning- (DRL-) based handover decision methods are used in the handover management. In the existing DQN-based handover decision method, the overestimates of DQN method continue. Moreover, the current handover decision methods adopt the greedy strategy which lead to the load imbalance problem in base stations. Considering the handover decision and load imbalance problems, we proposed a load balancing-based double deep Q-network (LB-DDQN) method for handover decision. In the proposed load balancing strategy, we define a load coefficient to express the conditions of loading in each base station. The supplementary load balancing evaluation function evaluates the performance of this load balancing strategy. As the selected basic method, the DDQN method adopts the target Q-network and main Q-network to deal with the overestimate problem of the DQN method. Different from joint optimization, we input the load reward into the designed reward function. And the load coefficient becomes one handover decision factor. In our research, the handover decision and load imbalance problems are solved effectively and jointly. The experimental results show that the proposed LB-DDQN handover decision method obtains good performance in the handover decision. Moreover, the access of mobile users becomes more balancing and the throughput of network is also increased.

Use of Multivariable Decision Methods in Decision of Retail Organizations

The contribution is orientated to the extended well-known methods of multivariable decisions with a closer specification of Saaty and TOPSIS method. The goal of the contribution is to evaluate the performance of individual operations in the chosen company from eastern Slovakia. According to the results there is obvious several operations in smaller towns, and better results are achieved in comparing with bigger towns, since the bigger towns have higher density of population. Obtained facts can be useful during managerial decisions at the increasing of demands on several bigger operations, with a goal to intensify the performance of the operations in bigger towns.

Multipolicy Robot-Following Model Based on Reinforcement Learning

We propose in this paper a new approach to solve the decision problem of robot-following. Different from the existing single policy model, we propose a multipolicy model, which can change the following policy in time according to the scene. The value of this paper is to obtain a multipolicy robot-following model by the self-learning method, which is used to improve the safety, efficiency, and stability of robot-following in the complex environments. Empirical investigation on a number of datasets reveals that overall, the proposed approach tends to have superior out-of-sample performance when compared to alternative robot-following decision methods. The performance of the model has been improved by about 2 times in situations where there are few obstacles and about 6 times in situations where there are lots of obstacles.

A Bibliometric Review on Decision Approaches for Clean Energy Systems under Uncertainty

This paper aims to provide a bibliometric review on the diverse decision approaches in uncertain contexts for clean energy system (CES) assessment. A total of 126 publications are analyzed. Previous reviews on CES have discussed several research questions on the decision methods and the applicability of evaluating CES, along with the factors associated with CESs. In the present study, we focus on the bibliometric aspect that attempts to address questions related to the prominence of authors, countries/regions that focus on the current theme, impact of journals, importance of articles in the research community, and so on. The window considered for the study is from 2018 to 2021, with the motive to extend the review process from the preceding works. A review model is presented to address the questions based on the literature evidence. The results infer that CESs are the most viable mode for sustainable development, and the use of decision approaches is apt for the assessment of CESs.

Sustainability, Big Data and Mathematical Techniques: A Bibliometric Review

This article has reviewed international research, up to the first half of 2021, focused on sustainability, big data and the mathematical techniques used for its analysis. In addition, a study of the spatial component (city, region, nation and beyond) of the works has been carried out and an analysis has been made of which Sustainable Development Goals (SDGs) have received the most attention. A bibliometric analysis and a fractal cluster analysis were performed on the papers published in the Web of Science. The results show a continuous increase in the number of published articles and citations over the whole period, demonstrating a growing interest in this topic. China, the United States and India are the most productive countries and there are more papers at the regional level. It has been found that the environmental dimension is the most studied and the least studied is the social dimension. The mathematical techniques used in the empirical work are mainly regression analysis, neural networks and multi-criteria decision methods. SDG9 and SDG11 are the most worked on. The trend shows a convergence in recent years towards big data applied to supply chains, Industry 4.0 and the achievement of sustainable cities.

SOLVING DYNAMIC PLANNING PROBLEMS ON THE BASIS OF GROUP DECISION METHODS

This article discusses problems based on group methods of decision-making based on a multimodal approach to solving the problems of dynamic planning of Smart City development. In particular, the main tool for decision-making in the process of dynamic planning is a system of models and methods, which are the basis for creating the structure of the dynamic plan, optimizing the plan and finding the desired solution. Collective decision-making methods are used in the collective development of planning decisions, including in the context of solving the problems of dynamic planning of Smart City development and group decision-making, which requires the agreed decisions of several specialists.

An integrated spherical fuzzy AHP multi-criteria method for Covid-19 crisis management in regarding lean six sigma

Purpose The Covid-19 pandemic has caused disruptions in many businesses. A difficult process has been experienced for businesses caught unprepared for this situation. The purpose of the study, the difficulties experienced in the business have been mapped in terms of crisis management. Design/methodology/approach For this purpose, crisis management strategies have been discussed in terms of lean six sigma (LSS) strategies. Afterward, LSS strategies have been weighted with the spherical fuzzy analytical hierarchy process (AHP) method, which has been recently introduced to the literature (Gündogdu and Kahraman, 2020). While weighting has been done with the spherical fuzzy AHP method, three experts in the field of LSS at the master blackbelt level have been studied. Findings The Covid-19 process has caused the usual processes of businesses to change. Businesses do not have enough time to adapt their business processes in the Covid-19 process. To eliminate waste and time in the Covid-19 process, the lean methodology was used. At the same time, quality deficiencies have been tried to be eliminated with the six sigma methodology. Therefore, the six sigma methodology and lean techniques should be considered together. With LSS, the adaptation of this process has been wanted to be accelerated. In this context, the criteria in the Covid-19 process were prioritized and losses were reduced in accordance with the LSS methodology. In this study, the most important criterion during the covid-19 pandemic has been determined as “responding to the changing needs of customers” and the least important criterion has been determined as “communication with stakeholders in enterprises.” Research limitations/implications This study has been applied the Covid-19 pandemics. So, it has been evaluated related to these specific criteria. Practical implications Covid-19 crisis management was examined for the first time within the scope of the LSS methodology. The criteria in the Covid-19 process were determined by six sigma expert master black belts. For the first time, spherical fuzzy AHP, one of the multi-criteria decision methods, has been applied to the Covid-19 process. Originality/value This study has been aimed to create a roadmap to apply LSS steps in businesses against a possible second wave of the Covid-19 pandemic. In this context, it is thought that the study will fill the following gaps in the literature: Covid-19 crisis management was examined for the first time within the scope of LSS methodology. The criteria in the Covid-19 process were determined by six sigma expert master black belts. For the first time, spherical fuzzy AHP, one of the multi-criteria decision methods, has been applied to the Covid-19 process.

Model Supporting Development Decisions by Considering Qualitative–Environmental Aspects

Dynamic changes in customers’ expectations and unfavorable climate changes have generated the need to consider such aspects in the process of creating new products and the modernization of existing products. Simultaneously including customers’ expectations and environmental impact is a key element of the sustainable development of products. Enterprises attempt, within their awareness and possibilities, to apply the idea of sustainability; they do this more or less methodically. As such, an instrument to support decision-making in the area of product development is still needed because it would both be desirable for customers and have less of a negative effect on the natural environment. The purpose of this study was to develop a model that supports decision-making in the development of products while considering sustainability. The model determines the key criteria of the product, criteria states (current and future), and their positive correlations (e.g., achieving high levels of product quality and no (or a reduction in) destructive impact on the environment). To reduce the fuzzy decision-making environment, multiplicative decision methods with the fuzzy Saaty scale were implemented. These methods were the fuzzy analytic hierarchy process (FAHP) and the fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS). The model is able to support qualitative–environment decisions in the development of any product.