Suitable Decision Research Articles

Geometric Average Weakening Buffer GM(1,1) and its Application in Prediction of Employment Rate of College Graduates

Prediction of the employment rate of college graduates is very important not only for education department to make suitable decision but also for university to adjust its teaching plan. However, it is a difficult task to accurately predict the employment rate of college graduates because it is affected by many factors, making the original data fluctuant. In this paper, a simple and effective prediction approach is proposed to predict employment rate of college graduates using GM (1,1). To attenuate effect of data fluctuation and improve prediction accuracy of GM (1,1) model, the geometric average weakening buffer operator (GAWBO) is first applied to original employment rate data, and then GM (1,1) model is used to predict employment rate. The prediction experiment of Yanshan university employment rate from 2010 to 2019 is performed using the proposed method. Results show that the proposed method can obtain more accurate prediction result than GM (1,1). © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Determination of salt concentration in water using decision trees and electromagnetic waves.

Salt water adversely affects human health and plant growth. In parallel with the increasing interest in non-contact determination of salt concentration in water, a novel approach is proposed in this study. In the proposed approach, S parameter measurements, which show the scattering properties of electromagnetic waves, are used. First, the relationship between salt concentration in water and permittivity values, a distinguishing feature for liquids, is shown. Then, based on the derived correlations from a set of S parameter measurements, it is shown that the salt concentration in water can be predicted. Finally, after exactly determining the relations of permittivity, salt concentration and S parameter, a system that allows non-contact determination of salt concentration is proposed. Since the proposed system makes its prediction using a classifier, decision tree algorithms are employed for this purpose. In order to evaluate the appropriateness and success of the algorithms, a set of classification experiments were held using various water samples with different levels of salt concentration. The results of the classification experiments show that the Hoeffding tree algorithm achieved the best results and is the most suitable decision tree algorithm for determining the salt concentration of liquids. For this reason, the proposed non-contact approach can be used to determine the salt concentration in water reliably and quickly if its hardware and software components can be embedded into a prototype system.

Multidimensional risk assessment and categorization of hydrogen pipelines

The use of hydrogen has been shown to be an efficient form of producing energy and meeting society's demands for energy. In this perspective, pipelines are known as the safest and most economical transportation mode for hydrogen. However, accidents in this type of structure lead to multiple consequences of losses such as those related to people, the environment and properties. From this perspective, decision-makers are required to tackle the likelihood of these and to make suitable decisions that can prevent accidents from happening. Multicriteria methods have played an important role in addressing multiple risk problems when there is a need to assign priorities to risk-based decisions. This paper puts forward a multidimensional risk model to categorize hydrogen pipeline sections according to levels of risks based on Utility Theory and the ELECTRE TRI method. A probabilistic modeling is proposed so as to incorporate elements of risk analysis into the evaluation of situations such as accidental scenarios, their probabilities and consequences. An application of the model is presented for a hydrogen pipeline. The results demonstrate that measures to prevent and mitigate risks can be sharpened by taking into account the human, environmental and financial risks dimensions of impacts. Thus, the sections of the pipeline studied are sorted into categories of risks, where 4 out of 10 sections are assigned to the high-risk category, 1 to the medium-risk category, and 5 sections to the low-risk category. A local sensitivity analysis was conducted which varied the weights of the dimensions. Varying the weights by 20% showed that the risk of 3 sections increases to the medium-risk category and 1 section changes to the high-risk one. Moreover, this paper indicates visualization tools that are efficient at communicating information on the levels of risk and the categories of the sections, thus contributing to assessing models that categorize risk in hydrogen pipelines. By doing so this paper supports the process of prioritizing resources for pipeline sections, the aim being to minimize multiple losses.

Obesity and the Other Independent Predictors in Elective Endotracheal Tube Intubations: A Narrative Review.

Obesity is one of the challenging elements in health care. Studies have shown that as the body mass index (BMI) increases, the risk of chronic conditions tends to increase due to altered physiologic and metabolic demands. In addition to underlying physiological changes, anatomical changes can lead to common procedural challenges, such as difficult intravenous (IV) cannulation, difficult airway, and difficult intubation, which makes their preoperative and postoperative care challenging for the anesthesiologists. According to previous studies, there is no single best predictor for difficult airway or intubations and no designed protocol for choosing an intubation technique in obese patients. Some of the preoperative risk factors and techniques such as the modified Mallampati class, sternomental distance, thyromental distance, neck circumference, indirect mirror laryngoscopy, BMI, and intraoperative risk factors such as inappropriate positioning of the patient, suboptimal medication dosing, inappropriate laryngoscopy device acted as independent predictors for difficult airway and difficult intubation. Analyzing each element’s importance and making suitable decisions for the individual will reduce the complications and prepare for unplanned emergencies in the operating room. This review is convincing with previous studies that obesity itself is not an independent predictor. Instead, as a preoperative risk factor, and till date, sternomental distance and the number of intubation attempts were demonstrated as significant independent predictors for adverse events. All the other independent factors and considerations were discussed, which can help with further research.

An Integrated Investment Scheduling Model for New Job Shops: A Phase-In Approach

The establishment and sustainability of the small-scale manufacturing sector is a great contributor to any nation's economic and technological development. A job shop is a typical small-scale manufacturing system that produces simple equipment and machine parts as well as technical services in developing nations. This type of manufacturing system requires versatile production machines for its activities. This requires enormous capital that most interested investors could not afford, and a majority of the commercial banks are not willing to provide financial assistance. This study develops a model using the theory of internal funding by employing the little initial capital in phasing- in the acquisition of the needed machinery, construction of factory space in modular forms, and adequate consideration for recurrent expenditure (i.e. working capital). The initial capital is periodically augmented from the ploughed-back earnings for the gradual acquisition of other machinery until a fully functional job shop emanates within the shortest possible time. Job shop general scheduling methods available in the literature could not be employed to solve the model developed in this study due to its peculiarities. Hence a special heuristic that employs suitable decision rules was formulated and solved using software developed for the purpose. The model developed in this study will be helpful to the investors in job shops, especially in developing nations, to overcome the problem of financing the venture.

A hierarchical reference-based know-why model for design support of sustainable building envelopes

In current complex building designs, sustainability assessments are often performed after project completion, with limited impact on building performance which results in missed goals in terms of quality, cost, and time. We address this problem by proposing a hierarchical reference-based know-why model to answer the research question “what is a suitable decision support model to successfully integrate the sustainability requirements in the early design phase of buildings?”. The model presents a process that incorporates a life-cycle perspective and calculates design alternatives based on a defined reference and the DGNB building certification system. The results show that criteria synergies and trade-offs can be identified, leading to improved design by engineers and better building performance. Our findings pave the way for full integration of the model into building information modeling, combined with artificial intelligence. This can help manage the complexity of the sustainable design process on the path to carbon-neutral buildings.

Group Decision Analysis Based on Complex m-Polar Fuzzy N-Soft Environment

This research article presents a novel multicriteria group decision-making (MCGDM) technique, namely, complex m-polar fuzzy N-soft TOPSIS (CmPFNS-TOPSIS), that incorporates the remarkable features of manipulating the complex two-dimensional data and the multipolarity of the modern era with the help of CmPFNSS and the multicriteria group decision making potential of the TOPSIS technique. This newly proposed technique shares a very general parametric structure of the CmPFMSS and enables us to make the suitable decisions in this multipolar complex two-dimensional domain. The incredible CmPFNS-TOPSIS technique works on the principle of finding the optimal solution nearest to the positive ideal solution (PSS) and farthest from the negative ideal solution (NIS) by evaluating the Euclidean distance between the alternatives and the optimal solutions. The support of complex m-polar fuzzy N-soft weighted averaging operator (CmPFNSWA), Euclidean distance, score function, and the revised closeness index is utilized for uncovering our optimal solutions. The alternatives, with respect to the revised closeness index, are arranged in the descending order and the alternative with the least closeness index is preferred. The methodology of the CmPFNS-TOPSIS technique is illustrated with the help of the flow chart. The proficiency of this technique is proved by considering a case study of selection of the suitable surgical equipment in the oncology department of Shaukat Khanum Hospital, Lahore (Pakistan). To prove its validity and credibility, a comparative analysis between CmPFNS-TOPSIS and m-polar fuzzy N-soft TOPSIS (mFNS-TOPSIS) is pictured with the help of a bar chart displaying the same end results of the CmPFNS-TOPSIS and mFNS-TOPSIS.

A risk-based decision-making approach for identifying natural-based tourism potential areas

Natural-based tourism (NBT) as a fast-growing and key part of the tourism industry, plays an important role in the economic development of countries. There has been always a degree of risk in making decisions with regard to selecting potential areas. Due to this challenge, the present study is aimed to propose a novel approach to identify NBT potential areas considering different risk degrees in decision-making. To this end, we used 12 geographical and environmental data layers. ordered weighted averaging (OWA) model was used to analyze the levels of risk in decision making, while the weights of criteria were calculated using analytical hierarchy process (AHP). In this study, for the first time, the OWA model is used to consider the concept of risk in decision making and prepare a fit map in different scenarios for decision making by NBT managers and planners. The results indicate that increasing the degree of the optimistic yields larger area of high potential class, while its decline results in smaller high potential class area. The proposed approach can be useful to minimize uncertainty when decision makers and planners are faced with obstacles in allocating resources. Integrating geographic information system (GIS) and ordered weighted averaging (OWA) contributes to more suitable decision making. Managers and decision makers can identify suitable areas e.g. by ranking various alternatives. Applying OWA model offers various strategies for decision making so that managers and planners can consider the risk parameter and related trade-offs in their decision making process to enhance a sustainable tourism development in natural areas and a wise resource management. In summary, the proposed approach can be useful to minimize uncertainty when decision makers and planners are faced with obstacles in allocating resources.

The Effect of Educational Program on Nurses Knowledge Regarding Physical Assessment of Cardiovascular System in Tertiary Hospital Lahore, Pakistan

Nurses play as frontline health care provider in clinical setting to assess patient illness, take timely and suitable decision to manage the patient health. In fact, critical care units and intensive care units are critical areas in which nurses frequently work alone, involved in decision making, and manage high risk patients. Literature findings that the physical examination is an important element of patient care in these settings, and nurses are generally well trained to perform a wide range of assessment skills to manage the critical care units. Nursing care has become more and more advance along with complexity of client treatment needed to improve their abilities in doing all physical assessment techniques, which is the first step to providing safe and effective care in clinical setting. Objective: Evaluate the effects of educational interventions on nurse’s knowledge regarding physical assessment of cardiovascular system. Methodology: Quasi experimental study was conducted. Setting: Research data was collected from critical care units in tertiary Hospital Lahore. Study Participants: 36 Nurses were participated in this study. Results: Before educational interventions Critical Care Nurses have minimum level of knowledge regarding physical assessment of cardiovascular system. Finding measured that before intervention less than 50% response of participants n=33 (91.7%) and after intervention above 50 % response of participants n=36 (100%). Study results indicated a significant mean difference between before intervention and after intervention scores of nurses knowledge, t = -29.663, p <0.000, Mean and SD (5.53±1.993 vs. 15.92±1.204). Conclusion: Educational session enable the Critical Care Nurses to incorporate knowledge regarding physical assessment of cardiovascular system into their daily clinical practices. Post-test finding shows highly significant results after workshop, lectures and hands on practice conducted by researcher. Keywords: Nurses knowledge, Cardiovascular System, Critical Care Units, Physical Assessment

Computer simulation modelling of the simultaneous adsorption of Cd, Cu and Cr from aqueous solutions by agricultural clay soil: A PHREEQC geochemical modelling code coupled to parameter estimation (PEST) study

Although minerals such as gold and platinum are important for improving life, their processing comes with social and environmental problems. During the processing of these minerals, toxic elements such as cadmium, copper and chromium are released along with wastewater into the environment including soils and surface water leading to plausible migration to groundwater. The removal of these elements from aqueous solutions is of utmost importance however, where no remediation is implemented, the ability of the soil to retain the elements and prevent migration is crucial. Moreover, there is a need for reliable and accurate computational modelling programs that requires minimum set of experiments for calibration in order to determine the success of the sorption of elements by natural and man-made adsorbents since laboratory and field experiments can be expensive, time consuming and often require repetition. In this study, the ability of an agricultural clay soil to adsorb and retain cadmium (Cd), copper (Cu) and chromium (Cr) was investigated by simulating different conditions using PHREEQC (which stands for, pH, redox, equilibrium written in the C language) geochemical modelling code coupled to parameter estimation (PEST). The role of pH (2–9) on the adsorption of Cd, Cu and Cr onto generalised weak, strong and super strong sites of the agricultural soil was determined and used to calibrate the model. The soil was found to be effective in preventing the mobility of Cd, Cu and Cr to groundwater however, the presence of competing ions and plant exudates increased their mobility. PHREEQC geochemical modelling code coupled with PEST was found to be a quick and a reliable tool to determine the performance of natural and man-made adsorbents in different conditions. Thus, PHREEQC coupled to PEST is a suitable decision making tool for environmental sustainability.

An economic fault-tolerant transportation strategy for multiple assembly station systems*

The paper deals with the development of a proof of concept pertaining to the application of Internet of Things tools to the multiple assembly station system. In particular, the problem is to provide efficient measures of an assembly efficiency, which are transformed into material demand predictions. As a result, such predictions are used to form suitable transportation events, which are realized through the shortest possible rout. Thus, instead of providing a periodical milk-run over all assembly stations economic routs are selected instead, which translates into significant savings. Since transportation delays are inevitable in practice, they can be perceived as faults acting onto the system. To overcome such unappealing effect a suitable fault-tolerant control algorithm is provided. Finally, the proposed approach is evaluated with a set of selected scenarios, which clearly illustrate the benefits of employing Internet of Things tools coupled with suitable decision and optimization strategies.

Reliability Assessment and Improvement of Electrical Distribution Systems by Using Multinomial Monte Carlo Simulations and a Component Risk Priority Index

In this paper, a method of assessing and improving the reliability of power distribution systems based on Monte Carlo simulation and a novel risk priority index is proposed. The initialization of the assessment process is carried out by using Multinomial Monte Carlo simulation with a nonsequential technique to assess system reliability in the form of SAIFI and SAIDI indices. Then, the novel per-time-based component reliability indices representing the insights obtained from root-cause analysis for each component in the system are evaluated to make suitable decisions on improvement measures. The proposed indices are derived as a component risk priority index based on the principle of the failure mode and an effect analysis to prioritize and select the implementation points by the Pareto principle. By applying the proposed method, a reliability improvement should be achieved at the correct point with minimal operations. In addition, the proposed method can be used to study the effect of uncertainty regarding some device operations on the system reliability. To verify the performance of the proposed method and demonstrate its application, three case studies were performed on the IEEE RBTS Bus-2 test system. From the first case study, the results of the proposed assessment process were validated by comparison with a standard benchmark. The second case study showed the performance of applying the entire process to improve system reliability, and the results showed that system reliability can be improved significantly. The third case study was performed to determine the effect of uncertainty in protective device operations. The results of the third case showed that there was a significant decrease in overall reliability in terms of a higher level of power outages, while the performance of the protective components was slightly reduced.

A cloud‐based and web‐based group decision support system in multilingual environment with hesitant fuzzy linguistic preference relations

Due to the growing needs in decision-making under uncertainty, existing studies introduced consistency and consensus-driven algorithms for group decision-making (GDM) problems with hesitant fuzzy linguistic preference relations (HFLPRs). A decision support system (DSS) that can host these GDM algorithms to provide decision-support services or tools for practical use is urgently needed. However, the state-of-the-art architectures cannot organize these algorithms and related data to run within one framework. This is mainly due to the running environments for these GDM algorithms are different since these algorithms were not originally designed to be compatible. Given the multilingual consistency and consensus-based decision support algorithms, how to design and implement a cloud-based DSS in a multilingual environment is still an open question. To fill this gap, this paper provides a web-based and cloud-based DSS with a novel architecture that utilizes the advantages of microservices. The proposed system implements a multilingual support framework to dynamically upload, manage and run multilingual consistency and consensus-based decision support algorithms. An algorithm recommendation module is developed to help users choose suitable decision support algorithms. Tokenization is applied to deal with regulatory issues of knowledge protection, data privacy, and security while storing, analyzing, and transforming data into different algorithms for effective decision-making. An expert feedback study verified that our web and cloud-based DSS is a right artifact to fulfill the objective claimed in this paper.

BRAF V600E Positive Hairy Plasma Cell Leukemia; Are the Cytoplasmic Projections in Plasma Cells Predictive of a Particular Molecular Characterization?

Introduction: Plasma cell leukemia (PCL) is a rare and clinically aggressive form of plasma cell dyscrasia. Despite the significant role of BRAF mutation in plasma cell neoplasms, this mutation has been rarely considered in these cases. Finding evidence guiding us toward assessing the BRAF mutation in patients with plasma cell neoplasms could help make the suitable decision for targeted therapy. Case Presentation: A 79-year-old man presented with leukocytosis. Peripheral blood smear exhibited marked lymphocytosis and infiltration of about 50% abnormal lymphoid cells with slender cell-surface projections and oval shape nucleus. These findings raised the provisional diagnosis of hairy cell leukemia (HCL) or HCL variants (HCL-v). Molecular analysis confirmed the presence of BRAFV600E mutation, which was in agreement with HCL diagnosis, albeit the flow cytometric assessment of abnormal lymphocytes corroborated PCL. Conclusions: Together with the previous comprehensive analysis regarding the association of cytoplasmic projections and BRAF mutations, our findings could suggest this morphological characteristic in plasma cells (PCs) as an indication for the assessment of BRAF V600E mutation in PC dyscrasias.

Body Weight Prediction of Thalli Sheep Reared in Southern Punjab Using Different Data Mining Algorithms

This study is conducted to predict the body weight (BW) for Thalli sheep of southern Punjab from different body measurements. In the BW prediction, several body measurements viz., withers height, body length, head length, head width, ear length, ear width, neck length, neck width, heart girth, rump length, rump width, tail length, barrel depth and sacral pelvic width are used as predictors. The data mining algorithms such as Chi-square Automatic Interaction Detector (CHAID), Exhaustive CHAID, Classification and Regression Tree (CART) and Artificial Neural Network (ANN) are used to predict the BW for a total of 85 female Thalli sheep. The data set is partitioned into training (80 %) and test (20 %) sets before the algorithms are used. The minimum number of parent (4) and child nodes (2) are set in order to ensure their predictive ability. The R2 % and RMSE values for CHAID, Exhaustive CHAID, ANN and CART algorithms are 67.38(1.003), 64.37(1.049), 61.45(1.093) and 59.02(1.125), respectively. The mostsignificant predictor is BL in the BW prediction of Thalli sheep. The heaviest BW average of 9.596 kg is obtained from the subgroup of those having BL > 25.000 inches. On behalf of the several goodness of fit criteria, we conclude that the CHAID algorithm performance is better in order to predict the BW of Thalli sheep and more suitable decision tree diagram visually. Also, the obtained CHAID results may help to determine body measurements positively associated with BW for developing better selection strategies with the scope of indirect selection criteria.

Hybrid Recommendation Using Temporal Data for Accuracy Improvement in Item Recommendation

Recommender systems have become a vital entity to the business world in form of software tools to make decisions. It estimates the overloaded information and provides the suitable decisions in any kind of business work through online. Especially in the area of e-commerce, recommender systems provide suggestions to users on the items that are likely based upon user’s true interest. Collaborative Filtering and Content Based Filtering are the main techniques of recommender systems. Collaborative Filtering is considered to be the best in all domains and always outperforms Content Based filtering. But, both the techniques have some limitations like data sparsity, cold start, gray sheep and scalability issues. To overcome these limitations, Hybrid Recommender Systems are used by combining Collaborative Filtering and Content Based Filtering. This paper proposes such kind of hybrid system by combining Collaborative Filtering and Content Based Filtering using time variance and machine learning algorithm.

Drones in Insect Pest Management

One of the major components in precision agriculture is crop health monitoring, which includes irrigation, fertilization, pesticide sprays, and timely harvest of the crop. Further, the progressive change in growth and development is critical in crop monitoring and taking suitable decisions to maintain health status. In order to accomplish the task, drones are highly useful for on site detection of problems so as to undertake corrective measures instantly. Although it is expensive to build algorithms and establish relationships between ground truth and spectral signatures, it is a user-friendly technique once the basics studies are done. As labor availability and technical manpower are extremely limited, particularly in India, drones are gaining popularity in the context of smart farming. Insect pests are known to cause catastrophe and drastic reduction in food grain production across the globe. The losses that have been predicted by FAO is over 37% due to pests and diseases. Recently, crops cultivated in India have been threatened by invasive pests like fall army worm (Spodoptera frugiperda) in corn and Rugose spiraling whitefly in coconut (Aleurodicus rugiperculatous Martin); these pests caused extensive damage during the years 2018 and 2019. The plant protection measures are to be taken on a community basis so as to ensure effective management of pests. In India, more than 80% of farmlands are in the category of small and marginal (<1 ha), so it is very difficult to manage the invasive pests. If one field is sprayed, the pests simply shift their feeding to the neighboring fields. To address this, drones become essential. Drones are unmanned aerial vehicles exploited in a wide array of disciplines such as defense, monitoring systems, and disaster management but are only beginning to be utilized in agricultural sciences. There are three major types of drones, namely fixed wing, multi-rotor, and hybrid type, and the usage depends on specific applications. The other types depend on degree of automation, size, weight, and power source. The set operational parameters such as flight speed, height, and endurance need to be optimized to use drones appropriately in agriculture and allied sectors. In addition, parameters related to drone-based spraying such as droplet size, spread, density, uniformity, deposition, and penetrability should also be factored in when implementing drone-based mitigation strategies. Despite the fact that drone technology is highly relevant and appropriate for pest management, the adoption of the technology is restricted. Regulatory guidelines have been set across the globe to perform site-specific farm management with higher precision at a very high resolution. Overall, drones can be employed in almost all agricultural field operations and are considered excellent tools for rapid, reliable, and non-destructive detection of field problems. This review provides panoramic views of drone technology and its application in the management of pests in a digital agriculture era.

763 Feasibility and reliability of a comprehensive three-dimensional transesophageal echocardiography screening process for transcatheter mitral valve replacement

Abstract Aims The planning of transcatheter mitral valve replacement (TMVR) requires cardiac imaging assessment to establish patient eligibility according to mitral valve anatomy, device characteristics and risk of left ventricular outflow tract (LVOT) obstruction. In this setting, computed tomography (CT) is considered the reference method. Although recent studies demonstrated that three-dimensional transesophageal echocardiographic (3D-TEE) assessment of mitral anatomy presents a good agreement compared to CT, its potential role in the TMVR planning has never been fully evaluated. To test feasibility and reliability of a comprehensive 3D-TEE screening in candidates for TMVR. Methods We performed a single-centre retrospective observational study including 59 consecutive patients referred to our centre for TMVR due to high surgical risk, who performed a pre procedural CT and 3D-TEE screening. The measurements of mitral annulus (MA), native LVOT and predicted Neo-LVOT, assessed with CT and 3D-TEE were collected in order to evaluate their concordance in the assessment of TMVR eligibility. The final suitability decision was given by the valve manufacturer based on CT measurements and then compared with the screening results obtained with 3D-TEE evaluation. 3D-TEE measurements were obtained with a post processing analysis using a novel automated software platform (3mensio Structural Heart 10.1 - 3mSH, Pie Medical Imaging, Bilthoven, Netherlands). Results Excellent correlation was found between 3D-TEE and CT measurements for MA area (r =0.85), antero-posterior diameters (r= 0.81), native LVOT (r=0.82) and Neo-LVOT areas (r=0.95) (all P-values <0.0001). Among 59 patients screened, 17 did not undergo the intervention because were found ineligible due to small predicted neo-LVOT, too small or too large MA area. Among the 42 patients with a successful screening, 32 underwent TMVR and all of them showed a correct implantation and no LVOT obstruction at post procedural echocardiographic evaluation. An almost perfect agreement among CT and 3D-TEE was found in assessing the eligibility for TMVR implantation (Cohen kappa 0.82, P<0.001). Interobserver and intraobserver agreements were found excellent for the parameters appraised with ICCs >0.80. Conclusions 3D-TEE appraisements of MA dimensions, native LVOT and Neo-LVOT are feasible in patients candidate for TMVR, showing good correlations with CT measurements and high accuracy to predict TMVR screening success.

Accelerating the Search for Optimal Solution of Integrated Process-Network Synthesis and Scheduling

Scheduling is crucial for effective implementations of production, manufacturing, and logistics. With rare exceptions of extremely simple cases, only mathematical programming, e.g., Mixed Integer Linear Programming (MILP), can guarantee the optimal answer to practical scheduling problems. Unfortunately, the applicability of general-purpose MILP solvers is limited by the required computational time. To achieve a sufficiently fast answer in practice by mathematical programming, a highly flexible solution procedure is needed that can be tailored to the problem under investigation. Solution methods utilizing graph theory in parallel with algebraic operations give more room for customization. Process Network Synthesis (PNS) and the P-graph framework were originally developed to design and optimize chemical engineering process structures with continuous operation. Time Constrained Process Network Synthesis (TCPNS) has made it capable to handle batch processes with time constraints and storage strategies. P-graph algorithms extended to TCPNS can solve the precedence-based MILP model formulation of scheduling problems and are highly customizable as well. The aim of the current research is to examine and find the most suitable decision variable selection strategy for P-graph framework’s optimization method to gain possible accelerations for different classes of scheduling problems.

A cost effective approach to design of energy efficient residential buildings

The objective of this study is to identify cost-optimal efficiency packages at several levels of building energy savings. A two-story residential building located in Jordan is selected as a case study. DesignBuilder software is used to predict the annual energy usage of a two-story residence in Irbid, Jordan. Real-time experimental data from a single isolated controlled room was used to verify the proposed model. In addition to energy analysis, the economic, environmental, and social benefits of the proposed design have been investigated. The sequential search optimization approach is used to estimate the minimum cost of the building while considering various design scenarios. In addition, the impact of various energy conservation techniques on residential buildings is assessed, and the payback period for each program is calculated. Ultimately, the optimal combination of design to achieve energy efficiency measures has been identified in several climate regions. The simulations results predict that the annual electricity consumption can be reduced up to 50% if the proper combinations of energy conservation measures are selected at the lowest cost. The payback period is 9.3 years. Finally, energy efficiency measures can lead to a total of 9470 jobs/year job opportunities.The study provide practical framework to link between energy performance criteria and economic goals of building. Linking the energy performance requirements to economic targets provides guidelines for homeowners, contractors, and policymakers for making a suitable decision regarding the retrofitting of existing residential buildings. The study focuses on developing new methodologies that support minimizing costs during a building's lifecycle while maximizing environmental benefits which can not be identified by a series of parametric analyses using individual energy-efficient measures.