Search results for: leverage decision

Authors: Bello Abdullahi, Yahaya M. Ibrahim, Ahmed D. Ibrahim, Kabir Bala

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Public sector tendering has traditionally been conducted using manual paper-based processes which are known to be inefficient, less transparent and more prone to manipulations and errors. The advent of the Internet and the World Wide Web has led to the development of numerous e-Tendering systems that addressed some of the problems associated with the manual paper-based tendering system. However, most of these systems rarely support the evaluation of tenders and where they do it is mostly based on the single decision maker which is not suitable in public sector tendering, where for the sake of objectivity, transparency, and fairness, it is required that the evaluation is conducted through a tender evaluation committee. Currently, in Nigeria, the public tendering process in general and the evaluation of tenders, in particular, are largely conducted using manual paper-based processes. Automating these manual-based processes to digital-based processes can help in enhancing the proficiency of public sector tendering in Nigeria. This paper is part of a larger study to develop an electronic tendering system that supports the whole tendering lifecycle based on Nigerian procurement law. Specifically, this paper presents the design and implementation of part of the system that supports group evaluation of tenders based on a technique called fuzzy multi-attributes group decision making. The system was developed using Object-Oriented methodologies and Unified Modelling Language and hypothetically applied in the evaluation of technical and financial proposals submitted by bidders. The system was validated by professionals with extensive experiences in public sector procurement. The results of the validation showed that the system called NPS-eTender has an average rating of 74% with respect to correct and accurate modelling of the existing manual tendering domain and an average rating of 67.6% with respect to its potential to enhance the proficiency of public sector tendering in Nigeria. Thus, based on the results of the validation, the automation of the evaluation process to support tender evaluation committee is achievable and can lead to a more proficient public sector tendering system.

Keywords: e-Tendering, e-Procurement, group decision making, tender evaluation, tender evaluation committee, UML, object-oriented methodologies, system development

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Authors: Nutcha Taneepanichskul, Helen C. Hailes, Mark Miodownik

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Plastic waste has emerged as a critical global environmental challenge, primarily driven by the prevalent use of conventional plastics derived from petrochemical refining and manufacturing processes in modern packaging. While these plastics serve vital functions, their persistence in the environment post-disposal poses significant threats to ecosystems. Addressing this issue necessitates approaches, one of which involves the development of biodegradable plastics designed to degrade under controlled conditions, such as industrial composting facilities. It is imperative to note that compostable plastics are engineered for degradation within specific environments and are not suited for uncontrolled settings, including natural landscapes and aquatic ecosystems. The full benefits of compostable packaging are realized when subjected to industrial composting, preventing environmental contamination and waste stream pollution. Therefore, effective sorting technologies are essential to enhance composting rates for these materials and diminish the risk of contaminating recycling streams. In this study, it leverage hyperspectral imaging technology (HSI) coupled with advanced machine learning algorithms to accurately identify various types of plastics, encompassing conventional variants like Polyethylene terephthalate (PET), Polypropylene (PP), Low density polyethylene (LDPE), High density polyethylene (HDPE) and biodegradable alternatives such as Polybutylene adipate terephthalate (PBAT), Polylactic acid (PLA), and Polyhydroxyalkanoates (PHA). The dataset is partitioned into three subsets: a training dataset comprising uncontaminated conventional and biodegradable plastics, a validation dataset encompassing contaminated plastics of both types, and a testing dataset featuring real-world packaging items in both pristine and contaminated states. Five distinct machine learning algorithms, namely Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Logistic Regression, and Decision Tree Algorithm, were developed and evaluated for their classification performance. Remarkably, the Logistic Regression and CNN model exhibited the most promising outcomes, achieving a perfect accuracy rate of 100% for the training and validation datasets. Notably, the testing dataset yielded an accuracy exceeding 80%. The successful implementation of this sorting technology within recycling and composting facilities holds the potential to significantly elevate recycling and composting rates. As a result, the envisioned circular economy for plastics can be established, thereby offering a viable solution to mitigate plastic pollution.

Keywords: biodegradable plastics, sorting technology, hyperspectral imaging technology, machine learning algorithms

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Authors: Thewodros K. Geberemariam

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The rapid physical expansion of urbanization coupled with aging infrastructure presents a unique decision and management challenges for many big city municipalities. Cities must therefore upgrade and maintain the existing aging urban drainage infrastructure systems to keep up with the demands. Given the overall contribution of assets to municipal revenue and the importance of infrastructure to the success of a livable city, many municipalities are currently looking for a robust and smart urban drainage infrastructure asset management solution that combines management, financial, engineering and technical practices. This robust decision-making shall rely on sound, complete, current and relevant data that enables asset valuation, impairment testing, lifecycle modeling, and forecasting across the multiple asset portfolios. On this paper, predictive computational intelligence (CI) and multi-class machine learning (ML) coupled with online, offline, and historical record data that are collected from an array of multi-parameter sensors are used for the extraction of different operational and non-conforming patterns hidden in structured and unstructured data to determine and produce actionable insight on the current and future states of the network. This paper aims to improve the strategic decision-making process by identifying all possible alternatives; evaluate the risk of each alternative, and choose the alternative most likely to attain the required goal in a cost-effective manner using historical and near real-time urban drainage infrastructure data for urban drainage infrastructures assets that have previously not benefited from computational intelligence and machine learning advancements.

Keywords: computational intelligence, machine learning, urban drainage infrastructure, machine learning, classification, prediction, asset management space

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Authors: Thanh Nguyen, Andrei Doncescu, Pierre Siegel

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Classification is an important data mining technique and could be used as data filtering in artificial intelligence. The broad application of classification for all kind of data leads to be used in nearly every field of our modern life. Classification helps us to put together different items according to the feature items decided as interesting and useful. In this paper, we compare two classification methods Naïve Bayes and ADTree use to detect spam e-mail. This choice is motivated by the fact that Naive Bayes algorithm is based on probability calculus while ADTree algorithm is based on decision tree. The parameter settings of the above classifiers use the maximization of true positive rate and minimization of false positive rate. The experiment results present classification accuracy and cost analysis in view of optimal classifier choice for Spam Detection. It is point out the number of attributes to obtain a tradeoff between number of them and the classification accuracy.

Keywords: classification, data mining, spam filtering, naive bayes, decision tree

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Authors: Ritsuko Kawasaki (Aiba), Takeshi Hiromatsu

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Management is required to understand all information security risks within an organization, and to make decisions on which information security risks should be treated in what level by allocating how much amount of cost. However, such decision-making is not usually easy, because various measures for risk treatment must be selected with the suitable application levels. In addition, some measures may have objectives conflicting with each other. It also makes the selection difficult. Moreover, risks generally have trends and it also should be considered in risk treatment. Therefore, this paper provides the extension of the model proposed in the previous study. The original model supports the selection of measures by applying a combination of weighted average method and goal programming method for multi-objective analysis to find an optimal solution. The extended model includes the notion of weights to the risks, and the larger weight means the priority of the risk.

Keywords: information security risk treatment, selection of risk measures, risk acceptance, multi-objective optimization

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Authors: Pei-Ru Yang, Yu-Chen Lin, Jia-Min Wu

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Background: A 58-year-old man with a history of osteoporosis and diabetes presented with chronic pain in his left knee due to severe knee joint degeneration. The knee replacement surgery was recommended by the doctor. But the patient suffered from low pain tolerance and wondered if virtual reality could relieve acute postoperative wound pain. Methods: We used the PICO (patient, intervention, comparison, and outcome) approach to generate indexed keywords and searched systematic review articles from 2017 to 2021 on the Cochran Library, PubMed, and Clinical Key databases. Results: The initial literature results included 38 articles, including 12 Cochrane library articles and 26 PubMed articles. One article was selected for further analysis after removing duplicates and off-topic articles. The eight trials included in this article were published between 2013 and 2019 and recruited a total of 723 participants. The studies, conducted in India, Lebanon, Iran, South Korea, Spain, and China, included adults who underwent hemorrhoidectomy, dental surgery, craniotomy or spine surgery, episiotomy repair, and knee surgery, with a mean age (24.1 ± 4.1 to 73.3 ± 6.5). Virtual reality is an emerging non-drug postoperative analgesia method. The findings showed that pain control was reduced by a mean of 1.48 points (95% CI: -2.02 to -0.95, p-value < 0.0001) in minor surgery and 0.32 points in major surgery (95% CI: -0.53 to -0.11, p-value < 0.03), and the overall postoperative satisfaction has improved. Discussion: Postoperative pain is a common clinical problem in surgical patients. Research has confirmed that virtual reality can create an immersive interactive environment, communicate with patients, and effectively relieve postoperative pain. However, virtual reality requires the purchase of hardware and software and other related computer equipment, and its high cost is a disadvantage. We selected the best literature based on clinical questions to answer the patient's question and used share decision making (SDM) to help the patient make decisions based on the clinical situation after knee replacement surgery to improve the quality of patient-centered care.

Keywords: knee replacement surgery, postoperative pain, share decision making, virtual reality

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Authors: Hecson Christian, Joel Macwan

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Landfill site selection in an urban area is a critical issue in the planning process. With the growth of the urbanization, it has a mammoth impact on the economy, ecology, and environmental health of the region. Outsized amount of wastes are produced and the problem gets soared every day. Hence, selection of ideal site for sanitary landfill is a challenge for urban planners and solid waste managers. Disposal site is a function of many parameters. Among all, Geotechnical parameters are very vital as the same is related to surrounding open land. Moreover, the accessible safe and acceptable land is also scarce. Therefore, in this paper geotechnical parameters are used to develop a GIS model to identify an ideal location for landfill purpose. Metropolitan city of Surat is highly populated and fastest growing urban area in India. The research objectives are to conduct field experiments to collect data and to transfer the facts in GIS platform to evolve a model, to find ideal location. Planners’ preferences were obtained to use analytical hierarchical process (AHP) to find weights of each parameter. Integration of GIS and Multi-Criteria Decision Analysis (MCDA) techniques are applied to improve decision-making. It augments an environment for transformation and combination of geographical data and planners’ preferences. GIS performs deterministic overlay and buffer operations. MCDA methods evaluate alternatives based on the decision makers’ subjective values and priorities. Research results have shown many alternative locations. Economic analysis of selected site from actual operations point of view is not included in this research.

Keywords: GIS, AHP, MCDA, Geo-technical

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Authors: Georges Bou Ghantous

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This research delves into the transformative impact of Artificial Intelligence (AI) on Infrastructure as Code (IaaC) practices, specifically focusing on the redefinition of infrastructure orchestration. By harnessing AI technologies such as machine learning algorithms and predictive analytics, organizations can achieve unprecedented levels of efficiency and optimization in managing their infrastructure resources. AI-driven IaaC introduces proactive decision-making through predictive insights, enabling organizations to anticipate and address potential issues before they arise. Dynamic resource scaling, facilitated by AI, ensures that infrastructure resources can seamlessly adapt to fluctuating workloads and changing business requirements. Through case studies and best practices, this paper sheds light on the tangible benefits and challenges associated with AI-driven IaaC transformation, providing valuable insights for organizations navigating the evolving landscape of digital infrastructure management.

Keywords: artificial intelligence, infrastructure as code, efficiency optimization, predictive insights, dynamic resource scaling, proactive decision-making

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Authors: Oyebade K. Oyedotun

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The cardiotocogram is a technical recording of the heartbeat rate and uterine contractions of a fetus during pregnancy. During pregnancy, several complications can occur to both the mother and the fetus; hence it is very crucial that medical experts are able to find technical means to check the healthiness of the mother and especially the fetus. It is very important that the fetus develops as expected in stages during the pregnancy period; however, the task of monitoring the health status of the fetus is not that which is easily achieved as the fetus is not wholly physically available to medical experts for inspection. Hence, doctors have to resort to some other tests that can give an indication of the status of the fetus. One of such diagnostic test is to obtain cardiotocograms of the fetus. From the analysis of the cardiotocograms, medical experts can determine the status of the fetus, and therefore necessary medical interventions. Generally, medical experts classify examined cardiotocograms into ‘normal’, ‘suspect’, or ‘pathological’. This work presents an artificial neural network based decision support system which can filter cardiotocograms data, producing the corresponding statuses of the fetuses. The capability of artificial neural network to explore the cardiotocogram data and learn features that distinguish one class from the others has been exploited in this research. In this research, feedforward and radial basis neural networks were trained on a publicly available database to classify the processed cardiotocogram data into one of the three classes: ‘normal’, ‘suspect’, or ‘pathological’. Classification accuracies of 87.8% and 89.2% were achieved during the test phase of the trained network for the feedforward and radial basis neural networks respectively. It is the hope that while the system described in this work may not be a complete replacement for a medical expert in fetus status evaluation, it can significantly reinforce the confidence in medical diagnosis reached by experts.

Keywords: decision support, cardiotocogram, classification, neural networks

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Authors: Evans Oteng, Clement Yeboah, Alexander Otechere-Fianko

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In today’s economy, despite achievements and advances in the banking and financial institutions, there are challenges that will require intensive attempts on the portion of the banks in Ghana. The perceived decline in profitability of banks seems to have emanated from ineffective branding. Hence, the purpose of this quantitative descriptive-correlational study was to examine the effects of branding on the profitability of banks in Ghana. The researchers purposively sampled some 116 banks in Ghana. Self-developed Likert scale questionnaires were administered to the finance officers of the financial institutions. The results were found to be statistically significant, F (1, 114) = 4. 50, p = .036. This indicates that those banks in Ghana with good branding practices have strong marketing tools to identify and sell their products and services and, as such, have a big market share. The correlation coefficients indicate that branding has a positive correlation with profitability and are statistically significant (r=.207, p<0.05), which signifies that as branding increases, the return on equity’s profitability indicator improves and vice versa. Future researchers can consider other factors beyond branding, such as online banking. The study has significant implications for the success and competitive advantage of those banks that effective branding allows them to differentiate themselves from their competitors. A strong and unique brand identity can help a bank stand out in a crowded market, attract customers, and build customer loyalty. This can lead to increased market share and profitability. Branding influences customer perception and trust. A well-established and reputable brand can create a positive image in the minds of customers, enhancing their confidence in the bank's products and services. This can result in increased customer acquisition, customer retention and a positive impact on profitability. Banks with strong brands can leverage their reputation and customer trust to cross-sell additional products and services. When customers have confidence in the brand, they are more likely to explore and purchase other offerings from the same institution. Cross-selling can boost revenue streams and profitability. Successful branding can open up opportunities for brand extensions and diversification into new products or markets. Banks can leverage their trusted brand to introduce new financial products or expand their presence into related areas, such as insurance or investment services. This can lead to additional revenue streams and improved profitability. This study can have implications for education. Thus, increased profitability of banks due to effective branding can result in higher financial resources available for corporate social responsibility (CSR) activities. Banks may invest in educational initiatives, such as scholarships, grants, research projects, and sponsorships, to support the education sector in Ghana. Also, this study can have implications for logistics and supply chain management. Thus, strong branding can create trust and credibility among customers, leading to increased customer loyalty. This loyalty can positively impact the bank's relationships with its suppliers and logistics partners. It can result in better negotiation power, improved supplier relationships, and enhanced supply chain coordination, ultimately leading to more efficient and cost-effective logistics operations.

Keywords: branding, profitability, competitors, customer loyalty, customer retention, corporate social responsibility, cost-effective, logistics operations

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Authors: Ghada S. Moussa

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Vehicle classification is an important task in traffic surveillance and intelligent transportation systems. Classification of vehicle images is facing several problems such as: high intra-class vehicle variations, occlusion, shadow, illumination. These problems and others must be considered to develop a reliable vehicle classification system. In this study, a reliable multi-type vehicle classification system based on Bag-of-Words (BoW) paradigm is developed. Our proposed system used and compared four well-known classifiers; Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-Nearest Neighbour (KNN), and Decision Tree to classify vehicles into four categories: motorcycles, small, medium and large. Experiments on a large dataset show that our approach is efficient and reliable in classifying vehicles with accuracy of 95.7%. The SVM outperforms other classification algorithms in terms of both accuracy and robustness alongside considerable reduction in execution time. The innovativeness of developed system is it can serve as a framework for many vehicle classification systems.

Keywords: vehicle classification, bag-of-words technique, SVM classifier, LDA classifier, KNN classifier, decision tree classifier, SIFT algorithm

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Authors: Susanna Menis

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‘Unconscious’ bias is widespread, affecting society on all levels of decision-making and beyond. Placed in the law context, this study will explore the direct effect of the psycho-social and cultural evolution of unconscious bias on how a judicial decision was made. The aim of this study is to contribute to socio-legal scholarship by examining the formation of unconscious bias and its influence on the creation of legal rules that judges believe reflect social solidarity and protect against violence. The study seeks to understand how concepts like criminalization and unlawfulness are constructed by the common law. The study methodology follows two theoretical approaches: historical genealogy and emotions as sociocultural phenomena. Both methods have the ‘tracing back’ of the original formation of a social way of seeing and doing things in common. The significance of this study lies in the importance of reflecting on the ways unconscious bias may be formed; placing judges’ decisions under this spotlight forces us to challenge the status quo, interrogate justice, and seek refinement of the law.

Keywords: legal geneology, emotions, disgust, criminal law

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Authors: Anocha Rojanapanich

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The purpose of this research is to develop and design an accounting management information system for convenient shop in Bangkok Thailand. The study applied the System Development Life Cycle (SDLC) for development which began with study and analysis of current data, including the existing system. Then, the system was designed and developed to meet users’ requirements via the internet network by use of application software such as My SQL for database management, Product diversity, Apache HTTP Server for Web Server and PHP Hypertext Preprocessor for an interface between web server, database and users. The system was designed into two subsystems as the main system, or system for head office, and the branch system for branch shops. These consisted of three parts which are classified by user management as shop management, inventory management and Point of Sale (POS) management and importance of cost information for decision making also as well as.

Keywords: accounting management information system, convenient shop, cost information for decision making system, development life cycle

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Authors: Soheil Ashrafi

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In this paper, it is attempted to chart and exposit terra incognito of the transcendental intuition of ‘non-being’, a peculiar species of négatité and a form of consciousness which underpins the phenomenal capacity for design thinking, and which serves as the ground of the ‘designing being-relation to the world’. The paper’s contention is that the transcendental intuition of the non-being indwells the agent’s being-relation to the world as a continual tension in that neither does the agent relinquish its ontological leverage and submit altogether to the world’s curbs and dictates, nor is it able to subdue satisfactorily or settle into the world once and for all. By way of phenomenological-hermeneutic analysis, it is endeavoured to argue that design thinking occurs by virtue of a phenomenal transition between the a priori ‘not-being’, the basis of ‘that-which-is’, and the transcendental intuition of non-being through which that-which-is-not-yet announces itself. Along with this, the other two species of négatité as ‘absence’ and ‘non-existence’ are clarified and contrasted with not-being and non-being, which have widely been used in the literature interchangeably as identical terms. In conclusion, it is argued that not only has design thinking in its unadulterated, originary mode historically preceded scientific thinking, but it also has served as the foundation of its emergence. In short, scientific thinking is a derivative, reformed application of design thinking; it indeed supervenes upon it.

Keywords: design thinking, designing being-relation to the world, négatité, not-being, non-being

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Authors: M. Shadmand

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The curing process of insulation system for electrical machines plays a determinative role for its durability and reliability. Polar structure of insulating resin molecules and used filler of insulation system can be taken as an occasion to leverage it to enhance overall characteristics of insulation system, mechanically and electrically. The curing process regime for insulating system plays an important role for its mechanical and electrical characteristics by arranging the polymerization of chain structure for resin. In this research, the effect of electrical field application on in-curing insulating system for Global Vacuum Pressurized Impregnation (GVPI) system for traction motor was considered by performing the dissipation factor, polarization and de-polarization current (PDC) and voltage endurance (aging) measurements on sample test objects. Outcome results depicted obvious improvement in mechanical strength of the insulation system as well as higher electrical characteristics with routing and long-time (aging) electrical tests. Coming together, polarization of insulation system during curing process would enhance the machine life time. 

Keywords: insulation system, GVPI, PDC, aging

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Authors: Zahra Abdolkarimi, Naser Zouri

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The actual purpose of RoboCup is creating independent team of robots in 2050 based of FiFa roles to bring the victory in compare of world star team. There is unbelievable growing of Robots created a collection of complex and motivate subject in robotic and intellectual ornate, also it made a mechatronics style base of theoretical and technical way in Robocop. Decision making of robots depends to environment reaction, self-player and rival player with using inductive Fuzzy system valuation subsidiary to solve issue of robots in land game. The measure of selection in compare with other methods depends to amount of victories percentage in the same team that plays accidentally.

Keywords: particle swarm optimization, chaos theory, inference fuzzy system, simulation environment rational fuzzy system, mamdani and assilian, deffuzify

Procedia PDF Downloads 379

Authors: Nitin Singh, Meng Ling, Talha Ahmed, Tianxia Zhao, Reinier van de Pol

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We propose the use of reinforcement learning (RL) as a viable alternative for optimizing supply chain management, particularly in scenarios with stochasticity in product demands. RL’s adaptability to changing conditions and its demonstrated success in diverse fields of sequential decision-making makes it a promising candidate for addressing supply chain problems. We investigate the impact of demand fluctuations in a multi-product supply chain system and develop RL agents with learned generalizable policies. We provide experimentation details for training RL agents and statistical analysis of the results. We study the generalization ability of RL agents for different demand uncertainty scenarios and observe superior performance compared to the agents trained with fixed demand curves. The proposed methodology has the potential to lead to cost reduction and increased profit for companies dealing with frequent inventory movement between supply and demand nodes.

Keywords: inventory management, reinforcement learning, supply chain optimization, uncertainty

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Authors: Alaa Abdullah Altaee

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This paper presents an adaptive decision feedback equalizer (ADFE) for multi-Gbps serial links utilizing a fix-step error signal extracted from cross-points of received data symbols. The extracted signal is generated based on violation of received data symbols with minimum detection requirements at the clock and data recovery (CDR) stage. The iterations of the adaptation process search for the optimum feedback tap coefficients to maximize the data eye-opening and minimize the adaptation convergence time. The effectiveness of the proposed architecture is validated using the simulation results of a serial link designed in an IBM 130 nm 1.2V CMOS technology. The data link with variable channel lengths is analyzed using Spectre from Cadence Design Systems with BSIM4 device models.

Keywords: adaptive DFE, CMOS equalizer, error detection, serial links, timing jitter, wire-line communication

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Authors: Sanjiti Sharma, Carol Seger

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Pavlovian biases are crucial to the decision-making processes, however, if left unchecked can extend to maladaptive behavior such as Substance Use Disorders (SUDs), anxiety, and much more. This study explores the interaction between Pavlovian biases and goal-directed instrumental learning by examining how each adapts to task reversal. it hypothesized that Pavlovian biases would be slow to adjust after reversal due to their reliance on inflexible learning, whereas the more flexible goal-directed instrumental learning system would adapt more quickly. The experiment utilized a modified Go No-Go task with two phases: replication of existing findings and a task reversal paradigm. Results showed instrumental learning's flexibility, with participants adapting after reversal. However, Pavlovian biases led to decreased accuracy post-reversal, with slow adaptation, especially when conflicting with instrumental objectives. These findings emphasize the inflexible nature of Pavlovian biases and their role in decision-making and cognitive rigidity.

Keywords: pavlovian bias, goal-directed learning, cognitive flexibility, learning bias

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Authors: M. A. T. AlSudairi, T. G. K. Vasista

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The concept of social media is becoming the top of the agenda for many business executives and public sector executives today. Decision makers as well as consultants, try to identify ways in which firms and enterprises can make profitable use of social media and network related applications such as Wikipedia, Face book, YouTube, Google+, Twitter. While it is fun and useful to participating in this media and network for achieving the communication effectively and efficiently, semantic and sentiment analysis and interpretation becomes a crucial issue. So, the objective of this paper is to provide literature review on social media, network and related technology related to semantics and sentiment or opinion analysis covering business and governance perspectives. In this regard, a case study on the use and adoption of Social media in Saudi Arabia has been discussed. It is concluded that semantic web technology play a significant role in analyzing the social networks and social media content for extracting the interpretational knowledge towards strategic decision support.

Keywords: CRASP methodology, formative assessment, literature review, semantic web services, social media, social networks

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Authors: D. Tedesco, G. Feletti, P. Trucco

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The present study aims to develop a Decision Support System (DSS) to support the operational decision of the Emergency Medical Service (EMS) regarding the assignment of medical emergency requests to Emergency Departments (ED). In the literature, this problem is also known as “hospital selection” and concerns the definition of policies for the selection of the ED to which patients who require further treatment are transported by ambulance. The employed research methodology consists of the first phase of revision of the technical-scientific literature concerning DSSs to support the EMS management and, in particular, the hospital selection decision. From the literature analysis, it emerged that current studies are mainly focused on the EMS phases related to the ambulance service and consider a process that ends when the ambulance is available after completing a request. Therefore, all the ED-related issues are excluded and considered as part of a separate process. Indeed, the most studied hospital selection policy turned out to be proximity, thus allowing to minimize the transport time and release the ambulance in the shortest possible time. The purpose of the present study consists in developing an optimization model for assigning medical emergency requests to the EDs, considering information relating to the subsequent phases of the process, such as the case-mix, the expected service throughput times, and the operational capacity of different EDs in hospitals. To this end, a Discrete Event Simulation (DES) model was created to evaluate different hospital selection policies. Therefore, the next steps of the research consisted of the development of a general simulation architecture, its implementation in the AnyLogic software and its validation on a realistic dataset. The hospital selection policy that produced the best results was the minimization of the Time To Provider (TTP), considered as the time from the beginning of the ambulance journey to the ED at the beginning of the clinical evaluation by the doctor. Finally, two approaches were further compared: a static approach, which is based on a retrospective estimate of the TTP, and a dynamic approach, which is based on a predictive estimate of the TTP determined with a constantly updated Winters model. Findings reveal that considering the minimization of TTP as a hospital selection policy raises several benefits. It allows to significantly reduce service throughput times in the ED with a minimum increase in travel time. Furthermore, an immediate view of the saturation state of the ED is produced and the case-mix present in the ED structures (i.e., the different triage codes) is considered, as different severity codes correspond to different service throughput times. Besides, the use of a predictive approach is certainly more reliable in terms of TTP estimation than a retrospective approach but entails a more difficult application. These considerations can support decision-makers in introducing different hospital selection policies to enhance EMSs performance.

Keywords: discrete event simulation, emergency medical services, forecast model, hospital selection

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Authors: Miran Ismail

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COVID-19 has caused widespread disruption in all economical sectors and industries around the world. The COVID-19 lockdown measures have resulted in production halts, restrictions on persons and goods movement, border closures, logistical constraints, and a slowdown in trade and economic activity. The main subject of this paper is to leverage technology to manage the supply chain effectively and efficiently through the usage of artificial intelligence. The research methodology is based on empirical data collected through a questionnaire survey. One of the approaches utilized is a case study of industrial organizations that face obstacles such as high operational costs, large inventory levels, a lack of well-established supplier relationships, human behavior, and system issues. The main contribution of this research to the body of knowledge is the empirical insights and on supply chain sustainability performance measurement. The results provide guidelines for the selection of advanced technologies to support supply chain processes and for the design of sustainable performance measurement systems.

Keywords: information technology, artificial intelligence, supply chain management, industrial organizations

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Authors: Mahmoud Almasri, Xavier Marjou, Fanny Parzysz

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The demand for services provided by Unmanned Aerial Vehicles (UAVs) is increasing pervasively across several sectors including potential public safety, economic, and delivery services. As the number of applications using UAVs grows rapidly, more and more powerful, quality of service, and power efficient computing units are necessary. Recently, cellular technology draws more attention to connectivity that can ensure reliable and flexible communications services for UAVs. In cellular technology, flying with a high speed and altitude is subject to several key challenges, such as frequent handovers (HOs), high interference levels, connectivity coverage holes, etc. Additional HOs may lead to “ping-pong” between the UAVs and the serving cells resulting in a decrease of the quality of service and energy consumption. In order to optimize the number of HOs, we develop in this paper a Q-learning-based algorithm. While existing works focus on adjusting the number of HOs in a static network topology, we take into account the impact of cells deployment for three different simulation scenarios (Rural, Semi-rural and Urban areas). We also consider the impact of the decision distance, where the drone has the choice to make a switching decision on the number of HOs. Our results show that a Q-learning-based algorithm allows to significantly reduce the average number of HOs compared to a baseline case where the drone always selects the cell with the highest received signal. Moreover, we also propose which hyper-parameters have the largest impact on the number of HOs in the three tested environments, i.e. Rural, Semi-rural, or Urban.

Keywords: drones connectivity, reinforcement learning, handovers optimization, decision distance

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Authors: Fatih Iscan, Ceren Yagci

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Rapid population growth, urbanization and industrialization are known as the most important factors of environment problems. Elimination and management of solid wastes are also within the most important environment problems. One of the main problems in solid waste management is the selection of the best site for elimination of solid wastes. Lately, Geographical Information System (GIS) has been used for easing selection of landfill area. GIS has the ability of imitating necessary economical, environmental and political limitations. They play an important role for the site selection of landfill area as a decision support tool. In this study; map layers will be studied for minimum effect of environmental, social and cultural factors and maximum effect for engineering/economical factors for site selection of landfill areas and using GIS for an decision support mechanism in solid waste landfill areas site selection will be presented in Aksaray/TURKEY city, Güzelyurt district practice.

Keywords: GIS, landfill, solid waste, spatial analysis

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Authors: Chen Chih-Cheng, Chen I-Cheng, Lee Yung-Tan, Kuo Yen-Whea, Yu Chin-Hung

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The purpose of this study is to construct a model for best spike player selection in a top volleyball tournament of the world. Data consisted of the records of 2013 World Grand Prix declared by International Volleyball Federation (FIVB). Simple Multiple-Attribute Rating Technique (SMART) was used for optimal decision-making model on the best spike player selection. The research results showed that the best spike player ranking by SMART is different than the ranking by FIVB. The results demonstrated the effectiveness and feasibility of the proposed model.

Keywords: simple multiple-attribute rating technique, World Grand Prix, best spike player, International Volleyball Federation

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Authors: Chih-Hsiang Chang, Fang-Jyun Su

Abstract:

This paper explores whether stock characteristics influence the herding formation among investors in the US equity market. To extend the research scope of the existing literature, this paper further examines the role that stock risk characteristics play in the US equity market, and the way they influence investors’ decision-making. First, empirical results show that whether general stocks or high-risk stocks, there are no herding behaviors among the investors in the US equity market during the whole research period or during four great events. Moreover, stock characteristics have great influence on investors’ trading decisions. Finally, there is a bidirectional lead-lag relationship of the herding formation between high-risk stocks and low-risk stocks, but the influence of high-risk stocks on the low-risk stocks is stronger than that of low-risk stocks on the high-risk stocks.

Keywords: stock characteristics, herding formation, investment decision, US equity market, lead-lag relationship

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Authors: Eva Queheille, Franck Taillandier, Nadia Saiyouri

Abstract:

Waste management has become a major issue in demolition works, because of its environmental impact (energy consumption, resource consumption, pollution…). However, improving waste management requires to take also into account the overall demolition process and to consider demolition main objectives (e.g. cost, delay). Establishing a strategy with these conflicting objectives (economic and environment) remains complex. In order to provide a decision-support for demolition companies, a multi-objective optimization model was developed. In this model, a demolition strategy is computed from a set of 80 decision variables (worker team composition, machines, treatment for each type of waste, choice of treatment platform…), which impacts the demolition objectives. The model has experimented on a real-case study (demolition of several buildings in France). To process the optimization, different optimization algorithms (NSGA2, MOPSO, DBEA…) were tested. Results allow the engineer in charge of this case, to build a sustainable demolition strategy without affecting cost or delay.

Keywords: deconstruction, life cycle assessment, multi-objective optimization, waste management

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Authors: Asma Shaheen Hashmi, Omer Riaz, Khalid Mahmood, Fahad Ullah, Tanveer Ahmad

Abstract:

The urban landscapes are being change with the population growth and advancements in new technologies. The urban sprawl pattern and utilizes are related to the local socioeconomic and physical condition. Urban policy decisions are executed mostly through spatial planning. A decision support system (DSS) is very powerful tool which provides flexible knowledge base method for urban planning. An application was developed using geographical information system (GIS) for urban planning. A scenario based DSS was developed to integrate the hierarchical muti-criteria data of different aspects of urban landscape. These were physical environment, the dumping site, spatial distribution of road network, gas and water supply lines, and urban watershed management, selection criteria for new residential, recreational, commercial and industrial sites. The model provided a framework to incorporate the sustainable future development. The data can be entered dynamically by planners according to the appropriate criteria for the management of urban landscapes.

Keywords: urban, GIS, spatial, criteria

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Authors: Medani Prasad Rijal

Abstract:

The study outlines the impacts of decentralized forest policy on natural Sal (shorea robusta) forests in the Terai region of Nepal. The government has implemented community forestry program to manage the forest resources and improve the livelihood of local people collectively. The forest management authorities such as conserve, manage, develop and use of forest resources were shifted to the local communities, however, the ownership right of the forestland retained by the government. Local communities took the decision on harvesting, distribution, and sell of forest products by fixing the prices independently. The local communities were putting the low value of forest products and distributed among the user households on the name of collective decision. The decision of low valuation is devaluating the worth of forest products. Therefore, the study hypothesized that decision-making capacities are equally prominent next to the decentralized policy and program formulation. To accomplish the study, individual to group level discussions and questionnaire survey methods were applied with executive committee members and user households. The study revealed that the local intuition called Community Forest User Group (CFUG) committee normally took the decisions on consensus basis. Considering to the access and affording capacity of user households having poor economic backgrounds, low pricing mechanism of forest products has been practiced, even though the Sal timber is far expensive in the local market. The local communities thought that low pricing mechanism is accessible to all user households from poor to better off households. However, the analysis of forest products distribution opposed the assumption as most of the Sal timber, which is the most valuable forest product of community forest only purchased by the limited households of better economic conditions. Since the Terai region is heterogeneous by socio-economic conditions, better off households always have higher affording capacity and possibility of taking higher timber benefits because of low price mechanism. On the other hand, the minimum price rate of forest products has poor contribution in community fund collection. Consequently, it has poor support to carry out poverty alleviation activities to poor people. The local communities have been fixed Sal timber price rate around three times cheaper than normal market price, which is a strong evidence of forest product devaluation itself. Finally, the study concluded that the capacity building of local executives as the decision-makers of natural Sal forests is equally indispensable next to the policy and program formulation for effective decentralized forest management. Unilateral decentralized forest policy may devaluate the forest products rather than devolve of power to the local communities and empower to them.

Keywords: community forestry program, decentralized forest policy, Nepal, Sal forests, Terai

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Authors: A. Abbou, V. Makis, N. Salari

Abstract:

We study the condition-based maintenance (CBM) problem of a system subject to stochastic deterioration. The system is composed of three units (or modules): (i) Module 1 deterioration follows a Markov process with two operational states and one failure state. The operational states are partially observable through periodic condition monitoring. (ii) Module 2 deterioration follows a Gamma process with a known failure threshold. The deterioration level of this module is fully observable through periodic inspections. (iii) Only the operating age information is available of Module 3. The lifetime of this module has a general distribution. A CBM policy prescribes when to initiate a maintenance intervention and which modules to repair during intervention. Our objective is to determine the optimal CBM policy minimizing the long-run expected average cost of operating the system. This is achieved by formulating a Markov decision process (MDP) and developing the value iteration algorithm for solving the MDP. We provide numerical examples illustrating the cost-effectiveness of the optimal CBM policy through a comparison with heuristic policies commonly found in the literature.

Keywords: reliability, maintenance optimization, Markov decision process, heuristics

Procedia PDF Downloads 215