Priority Weights Research Articles

A flexible formula for incorporating distributive concerns into cost-effectiveness analyses: priority weights

A flexible formula for incorporating distributive concerns into cost-effectiveness analyses: priority weights

Impact of Prioritization on the Outpatient Queuing System in the Emergency Department with Limited Medical Resources

The emergency department has an irreplaceable role in the hospital service system because of the characteristics of its emergency services. In this paper, a new patient queuing model with priority weight is proposed to optimize the management of emergency department services. Compared with classical queuing rules, the proposed model takes into consideration the key factors of service and the first-come-first-served queuing rule in emergency services. According to some related queuing indicators, the optimization of emergency services is discussed. Finally, a case study and some compared analysis are conducted to illustrate the practicability of the proposed model.

A consistency and consensus-based method for group decision making with hesitant fuzzy linguistic preference relations

Hesitant fuzzy linguistic preference relations (HFLPRs) can well denote the decision makers (DMs)’ hesitant qualitative comparison judgments. Considering that consistency and consensus are two key issues in group decisionmaking (GDM), this paper discusses GDM with HFLPRs within the context of multiplicative consistency and consensus. First, a multiplicative consistency concept for linguistic preference relations (LPRs) is presented, and a method for obtaining the priority weight vectors of LPRs is offered. Then, a method of deriving the priority weight vector of a HFLPR is proposed. Furthermore, the consistency index of a HFLPR is defined, an acceptable multiplicative consistency definition for HFLPRs is offered. Subsequently, for a HFLPR which has unacceptable consistency, an algorithm to improve its consistency level is developed. With respect to GDM with HFLPRs, a method to calculate the DMs’ weights is provided. Then, individual HFLPRs are integrated into a collective HFLPR. After that, a consensus index is defined, and an algorithm to improve the consensus is proposed that can guarantee the multiplicative consistency of individual HFLPRs. Moreover, an automatic algorithm to GDM with HFLPRs is developed. Lastly, we apply the new methods to two practical problems to verify their operability and applicability.

Optimal Operation of Building Microgrids with Rooftop Greenhouse Under Component Outages in Islanded Mode

An optimal operation scheme for a building microgrid with a rooftop greenhouse in islanded mode is proposed in this paper. In islanded mode, the fulfillment of entire demand is challenging due to the absence of connection with the utility grid and the scarcity of local resources. The situation becomes more challenging when one or more pieces of equipment fail during the islanded mode. Therefore, in addition to islanded mode operation, component outage and recovery are also considered in this paper. In order to use the available energy efficiently, prioritization of building loads and control parameters of the greenhouse are proposed. A priority weight matrix is adopted to decide the supply of energy to fulfill the requirements of control parameters in the case of insufficient energy. In addition to the normal operation bounds, new bounds are defined to operate the control parameters if the resources are not sufficient. Additional penalties are imposed if the new bounds are chosen, due to violation of the normal operation range. The microgrid system is rescheduled if any component outage or recovery is detected from the outage point to the end of the scheduling horizon. The performance of the proposed method is evaluated by carrying out several simulations including component outage, component recovery, and simultaneous outage of two or more types of equipment. Numerical simulation results have demonstrated the effectiveness of the proposed operation scheme for optimal operation of building microgrids with a rooftop greenhouse in islanded mode.

Size Versus Truthfulness in the House Allocation Problem

We study the House Allocation problem (also known as the Assignment problem), i.e., the problem of allocating a set of objects among a set of agents, where each agent has ordinal preferences (possibly involving ties) over a subset of the objects. We focus on truthful mechanisms without monetary transfers for finding large Pareto optimal matchings. It is straightforward to show that no deterministic truthful mechanism can approximate a maximum cardinality Pareto optimal matching with ratio better than 2. We thus consider randomised mechanisms. We give a natural and explicit extension of the classical Random Serial Dictatorship Mechanism (RSDM) specifically for the House Allocation problem where preference lists can include ties. We thus obtain a universally truthful randomised mechanism for finding a Pareto optimal matching and show that it achieves an approximation ratio of frac{e}{e-1}. The same bound holds even when agents have priorities (weights) and our goal is to find a maximum weight (as opposed to maximum cardinality) Pareto optimal matching. On the other hand we give a lower bound of frac{18}{13} on the approximation ratio of any universally truthful Pareto optimal mechanism in settings with strict preferences. By using a characterisation result of Bade, we show that any randomised mechanism that is a symmetrisation of a truthful, non-bossy and Pareto optimal mechanism has an improved lower bound of frac{e}{e-1}. Since our new mechanism is a symmetrisation of RSDM for strict preferences, it follows that this lower bound is tight. We moreover interpret our problem in terms of the classical secretary problem and prove that our mechanism provides the best randomised strategy of the administrator who interviews the applicants.

Prioritization of sustainability indicators for promoting the circular economy: The case of developing countries

The concept of the circular economy has gained well-recognition across the world for the past decades. With the heightening risk of the impact of climate change, resource scarcity to meet the increasing world population, the need to transition to a more sustainable development model is urgent. The circular economy is often cited as one of the best solutions to support sustainable development. However, the diffusion of this concept in the industrial arena is still relatively slow, particularly in the developing country, which collectively exerts high potential to be the world's largest economies and workforce. It is crucial to make sure that the development of these nations is sustainable and not bearing on the cost of future generations. Thus, this work aims to provide a comprehensive review of the circular economy concept in developing country context. Furthermore, a novel model is proposed by adopting Fuzzy Analytics Network Process (FANP) to quantify the priority weights of the sustainability indicators to provide guidelines for the industry stakeholders at different stages of industry cycle to transition toward the circular economy. The results revealed that improvement in economic performance and public acceptance are the key triggers to encourage stakeholders for sustainable development. The outcomes serve as a reference to enhance the overall decision-making process of industry stakeholders. Local authorities can adopt the recommendations to design policy and incentive that encourage the adoption of circular economy in real industry operation to spur up economic development, without neglecting environmental well-being and jeopardizing social benefits.

ACCURACY OF A HEURISTIC FOR TOTAL WEIGHTED COMPLETION TIME MINIMIZATION IN PREEMPTIVE SINGLE MACHINE SCHEDULING PROBLEM BY NO IDLE TIME INTERVALS

Background. A special case of the job scheduling process is that when jobs are processed on a single machine, preemptions are allowed, and there are no idle time intervals. Despite the exact solution models are always much slower than the heuristics, laws of heuristic’s rapidness advantage and heuristic’s solution closeness to the exact solution are unknown. Such laws would be useful to estimate real benefits of solution approximation.Objective. Issuing from the lack of knowledge in relationship between heuristics and exact solution models, the goal is to study statistical difference between them for the preemptive single machine scheduling problem by no idle time intervals.Methods. The two well-known approaches are invoked – the rule of weighted shortest remaining processing period as a heuristic and the Boolean linear programming model as an exact model. The relative error of the heuristic is defined and then studied how it varies against increasing complexity of job scheduling problems. The heuristic’s rapidness gain is shown as well.Results. The main issue with the heuristic’s accuracy can arise at a few jobs to be scheduled. Additionally to this, if a sequence of jobs is divided into the fewest parts, the heuristic’s accuracy becomes the lowest. The exception exists for the shortest sequences – when only two jobs are to be scheduled. As the number of jobs increases up off 6, the relative error expectedly decreases along with the dramatically growing heuristic’s rapidness advantage. Therefore, scheduling a long sequence of jobs is preferable. The top relative error of the heuristic can exceed 6 % for three to five jobs to be scheduled, when they are divided into the fewest parts.Conclusions. Starting off six jobs, the heuristic’s accuracy averagely increases, by a fixed rate of randomness in processing periods, priority weights, and release dates, as the complexity of job scheduling problems increases. The rate of randomness influences inversely: if processing periods, priority weights, and release dates are more randomly scattered, the heuristic schedules more accurately. The exact approach is truly applicable for cases when three to five jobs are to be scheduled (in particular cases, when the number of job parts is constant and is 2, the upper number of jobs can be increased up to 10). For such cases, an approximate solution’s real loss (given by the heuristic) is the average relative error not exceeding 1.2 % for job scheduling problems with low rate of the randomness. If such a loss is not admissible, the exact approach will work instead.

Employee social sustainability: prioritizing dimensions in the UAE’s airlines industry

PurposeThis paper aims to identify and prioritize the dimensions that impact employee social sustainability in the airline industry in the United Arab Emirates (UAE).Design/methodology/approachThe five main criteria (employee well-being, communication, management support, reward and control system and training) and 18 sub-criteria were identified from the literature. The sample comprised four experts covering the HR, finance and training functions from a major UAE airline organization. Applying the analytical-hierarchy-process (AHP) methodology resulted in obtaining priority weights for the factors assigned to employee-social-sustainability implementation.FindingsManagement support was found to have the highest priority among the study dimensions impacting employee social sustainability. Surprisingly, reward system was found to be the least important dimension.Research limitations/implicationsThe study was carried out on a single airline organization, limiting the generalizability of the findings. Future studies should be extended to cater to different organizational contexts and varying operational conditions.Practical implicationsThe findings should be of value to human resource management and policymakers in developing countries, such as the UAE, where employee social sustainability should be sought as a means to develop an efficient and sustainable workforce in different industrial sectors.Originality/valueThis study is among the few pioneering studies that focus on employee social sustainability. The use of AHP to prioritize employee-social-sustainability dimensions is also considered pioneering within the field and is anticipated to support future studies, and a deeper understanding, of employee social sustainability.

An Additive-Consistency- and Consensus-Based Approach for Uncertain Group Decision Making With Linguistic Preference Relations

Linguistic preference relations (LPRs) can indicate the decision makers (DMs)’ qualitative pairwise judgments regarding a set of alternatives in uncertain multicriteria decision-making problems. This paper examines several goal programming models for managing the additive consistency and consensus of LPRs and then develops an additive-consistency- and consensus-based method for group decision making (GDM) with LPRs. First, this paper offers a consistency index to quantify the consistency level for LPRs and define acceptable consistent LPRs. For an LPR that is unacceptably additive consistent, several additive-consistency-based programming models are developed to address the inconsistency and to establish an acceptably consistent LPR. Then, an additive-consistency-based procedure to generate the priority weight vector from the LPR is offered. An additive-consistency-based algorithm for decision making with an LPR is presented. Subsequently, considering the consensus in GDM, a consensus index is proposed for gauging the agreement degree among individual LPRs. Regarding individual LPRs that do not exhibit acceptably additive consistency or acceptable consensus, several goal programming models to derive new LPRs with acceptable consistency and consensus are provided. Afterward, the DMs’ weights are determined objectively, and individual LPRs are integrated into a collective LPR. An additive-consistency- and consensus-based GDM method with a group of LPRs is developed. Finally, two practical numerical examples are offered, and a comparative analysis is presented.

Assessing the surface rainwater harvesting potential for Abuja, Nigeria: a short-term projection

This study aimed at assessing the surface rainwater harvesting potential in Abuja as a means of mitigating the problem of water scarcity. Surface rainwater harvesting potential for the year 2046 was assessed using Geographical Information System (GIS) and Spatial Multi-criteria Evaluation (MCE). The criteria considered were annual rainfall, land use/land cover (LULC), slope and soil. The spatial MCE was used to estimate the extent of surface rainwater harvesting and rank the potential. Analytical Hierarchy Process (AHP) was employed to determine the priority weights of the criteria which gave; rainfall 55.9%, LULC 26.3%, slope 12.2% and soil 5.7%. A potential map for surface rainwater harvesting was produced showing moderate, good and excellent with the percentages of Abuja 10.7, 34.4 and 54.9% respectively. After considering an increased rainfall from (1170 mm-1470 mm) in 2016 to (1230 mm-1910 mm) in 2046, expansion in built-up areas, bare surfaces due to urbanisation and population growth, the result showed that Abuja will have a minimum of 5.8 billion litres of water harvestable from rainfall per year which is about 14.8% increase compared to the estimated harvestable quantity for 2016. Therefore, the potential for surface rainwater harvesting in 2046 is significantly greater than it was in 2016. Keywords: Surface Rainwater Harvesting Potential, Water Scarcity, Analytical Hierarchy Process, Multi-Criteria Evaluation

Spatial targeting of payments for ecosystem services to achieve conservation goals and promote social equity and economic impact

AbstractThe objective of this research was to identify optimal spatial targeting and distributions of a fixed PES (payment systems for ecosystem services) budget that achieves the three objectives of maximizing forest‐based carbon storage to improve cost efficiency, maximizing poverty alleviation to promote equity, and maximizing economic impact to encourage economic development. We used the Central and Southern Appalachian Region as a case study to develop a county‐level framework for identifying optimal county targets and optimal budget allocations for forest‐based carbon sequestration that addresses the three objectives. The results show that optimal budgets are more geographically widespread under the multiple‐objective priority scenarios than under the single‐objective of maximizing carbon cost efficiency, and the optimal spatial distributions of the four priority scenarios do not change appreciably across priority scenarios. The quantified relationships reveal that different priority weights among the priority scenarios yield both competitive trade‐offs and synergistic relationships between the objectives.

Estimation of priority weights based on a resampling technique and a ranking method in analytic hierarchy process

AbstractIndividual judgments are sought in order to elicit values of the entries of pairwise comparison matrices in analytic hierarchy process. Some of these matrices are more consistent than others. But throwing out inconsistent matrices reduces the number of matrices. In this article, we propose a resampling technique to generate sets of pairwise comparison matrices, which pass the consistency check. The advantage of the resampling technique is that one can generate as many sets of pairwise comparison matrices as needed to select the ones that satisfy the consistency requirement. Based on these selected matrices, the priority weights of the alternatives are then estimated. We propose rank‐based statistical procedures to check the significance in the difference between estimated priorities of the alternatives to establish their most significant rank order.

Fuzzy AHP-based multi-criteria decision-making analysis for route alignment planning using geographic information system (GIS)

An approach for identifying and selecting the route alignment using GIS and fuzzy AHP is developed in the present study. Spatial multi-criteria decision analysis is applied to identify optimal route alignment with the consideration of criteria such as environmental, social, economic and technical spatial datasets. These criteria play a significant role in the identification and selection of optimal route alignment. Fuzzy set theory is used to handle vagueness-type uncertainty in the multi-criteria decision analysis. Fuzzy AHP is introduced to compare the fuzzy criteria for ranking purpose, and fuzzy criteria weights are used to develop relative surface cost maps. The least-cost path (LCP) method is applied to find alternate four route alignments from source to destination by considering several criteria in GIS. The criteria values of each four alignment and weight of each criterion on the basis of maximum priority weight are calculated. The designed four routes are evaluated using priority weight method and fuzzy-AHP method. The selection of optimum route is considered by maximum weight of the alignment. The technical route alignment (alignment 4) is considered as optimum route with weight value 80.38%. The proposed methodology is implemented for a study area, outer region of Allahabad city, India.

Deriving priority weights from hesitant fuzzy preference relations in view of additive consistency and consensus

Given that deriving priority weights is essential in group decision making, this study focuses on deriving priority weights from hesitant fuzzy preference relation (HFPR) in view of additive consistency and consensus. To achieve this goal, first, a new additive consistency concept of the HFPR is proposed. The main feature of the proposed additive consistency concept is that it considers all evaluation information provided by decision makers, that is, neither add values into nor remove values from hesitant fuzzy elements. Second, a programming model is constructed to verify the complete additive consistency of HFPR, an additive consistency index is suggested to validate its consistency degree, and then, a programming model is established to improve its consistency degree. Third, an algorithm is designed to derive a priority weight vector from the HFPR, and the proposed algorithm not only addresses the situation in which the HFPR is a complete and acceptable additive consistency. Fourth, a programming model is presented to determine the decision makers’ weights, and then, a consensus measure index based on extraction priority weight vectors is introduced. Moreover, a programming model is constructed to derive the priority weights that correspond to expected consensus levels. Finally, the most cost-effective car selection problems are provided to illustrate the effectiveness of the proposed method. Comparative studies with several existing methods are also provided.

Measuring Social Sustainable Practices in Middle Eastern Airlines: The Employees’ Perspective

Measuring social sustainability across any organisation is a complex phenomenon involving many criteria and sub-criteria. To address this level of complexity, a multi criteria decision-making process called the 'analytical hierarchy process (AHP)' has been used in this research. Data have been collected from 15 experts of two full service carriers (FSCs) from the Middle East. The findings of this research showed that ten main criteria and 32 sub-criteria are important measures of social sustainability from the employees' perspective. It has been found that employees' well-being, learning, development and engagement are the most important criteria for infusing social sustainability into the airline industry. However, community and organisational culture have the lowest priority weights. Measuring social sustainability from the employees' perspective is a multifaceted issue. The proposed framework can help policy makers to enhance the social sustainability standards of their employees and to gain a sustainable advantage.

Sustainable organizational performance

Purpose The purpose of this paper is to identify factors affecting sustainable organizational performance, to build a framework for the United Arab Emirates (UAE) public health-care sector to facilitate sustainable organizational performance and to prioritize the factors for sustainable organizational performance. Design/method/approach The method used in this research is the quantitative method called the analytical hierarchal process (AHP) to help the decision makers in the public health-care sector to prioritize the factors that are affecting sustainable organizational performance. The method will also help to deal with the complexity of the sustainable organizational performance issue by interviewing nine experts in the field. Findings The findings of this research showed 21 sub-factors for sustainable organizational performance in the public health-care sector in UAE. It emphasizes that patient safety and quality of care are the most important factors for sustainable organizational performance. Research limitations/implications This study can be repeated by targeting other private hospitals in UAE. The novelty of this research means that it is the first study done in sustainable organizational performance in the health-care sector in UAE. Practical implications Health-care management can benefit from this research in many ways: Medical errors have a high impact on the hospital’s reputation and these determine the customer demand. Thus, the hospital’s management should give more attention to minimize the medical errors in order to have a sustainable organizational performance. This can be accomplished through clear protocols and procedures that may affect patients’ lives, the hospital’s reputation and organizational performance. Nevertheless, the policymakers should focus on society engagement; focus on social sustainability should be an integral part of their operational and business strategy. According to Abu Dhabi Health Authority (HAAD), the UAE has a highest rate of chronic diseases, such as diabetes, obesity and cardiovascular disease. Cardiovascular disease itself accounted for 36.7% of all 2013 deaths. The health-care sector should focus more on educating the community by conducting workshops, seminars and awareness campaigns across the UAE. In addition, decision makers in the health-care sector should spend more on continued improvement by focusing on lean activities that focus on waste minimization and linking the service quality to the hospital outcomes and patient satisfaction. The fourth highest overall priority weight for both transformational leadership and for the waiting time sub-criteria should also be considered by top management to focus more on hiring, retaining, and developing their transformational leaders, and to keep an eye on the waiting time and improving customer service. This will result in the sustainable organizational performance. Interestingly, all of the HR processes showed the lowest overall weights at 1%, which is a bit strange. HR should play more of a role in sustainable organizational performance, equal to the other sub-factors. Originality/value Originality of this research stems from a reliable and valid framework that can be subsequently used for measuring the organizational performance of health care organizations.

THE PERFORMANCE IMPROVEMENT OF OCBC NISP BANK'S TRADE OPERATIONS DIVISION EMPLOYEES : APPLICATION OF ANALYTIC NETWORK PROCESS METHODS

This study discusses the system design for improving the performance of OCBC NISP Bank's Trade Operations division employees using the Analytic Network Process (ANP) method. Performance measurement and scope of analysis is carried out on the activities of employees of the Trade Operations section in the process of issuing LC documents. This research determines what policy strategies can help improve the performance of OCBC NISP Bank's Trade Operations employees. The results of the study using the ANP method show that by implementing a productivity-based compensation system is a top priority with a priority weight of 42.69%. Whereas work process improvement is the second priority with 36.72% priority weight. For the last priority, the sequence is the preparation of the right target and key performance indicator (KPI) with a priority weight of 20.57%. Based on the results of ANP analysis, implementing a productivity-based compensation system provides the highest benefits and opportunities compared to the risks that may arise. Productivity-based compensation will motivate employees to compete to increase productivity to get better compensation compared to employees with lower productivity.

Quality evaluation for multimedia contents of e-learning systems using the ANP approach on high speed network

E-learning systems have played an important role in the education field and have been widely employed in many educational institutions. Although the need to evaluate the quality of e-learning systems is emerging, there is currently no appropriate evaluation method due to the complicated correlations between quality attributes. This study develops a quality evaluation model that calculates the priority weights of each quality attribute while accounting for their correlations and evaluates the overall quality of a learning system with numerical results. First, the study constructs a quality attribute network that reflects the correlations between 4 main quality clusters and 19 sub-attributes. Second, it calculates the priority weights of the attributes using the Analytic Network Process (ANP). Finally, using the quality network and weights, this study evaluates three types of e-learning systems employed by Kyunghee Cyber University. The results indicate that the proposed evaluation method provides a mechanism for objectively analyzing and comparing the qualities of various kinds of learning systems and suggests guidelines for constructors and managers of learning systems.

Service-Differentiation-Based Limited Feedback Scheme for Interference Alignment

In this letter, we explore a limited feedback scheme for meeting the requirements that different users demand different communication rates. The main idea of this letter is to prioritize users by assigning different priority weights to the residual interference of each user. Then, by minimizing the weighted sum of residual interference, the optimal scheme for the allocation of feedback bits to support service differentiation is designed. Simulation results show that the proposed scheme can support service differentiation regarding communication rates and the degree of difference in communication rate between users can be flexibly adjusted by changing the weights.

A hybrid approach based on the fuzzy AHP and HFACS framework for identifying and analyzing gross navigation errors during transatlantic flights

Despite the considerable efforts to alleviate problems of North Atlantic (NAT) air traffic and implementation of novel surveillance systems, oceanic errors still remain stubbornly high. The purpose of this study is to identify and examine the factors that contribute to navigation errors in the NAT Region by integrating a Fuzzy Analytic Hierarchy Process (FAHP) into the Human Factors Analysis and Classifying System (HFACS) framework for the first time to the best of our knowledge. According to the priority weights, the most important factors contributing to oceanic errors are lack of training provided by airlines, use of inappropriate routes for oceanic crossing by dispatchers, and failure to take preventive measures against oceanic errors. To mitigate the possibility of the aforementioned errors, pilots as individuals must increase their knowledge on cockpit procedures and airlines as organizations have to pay particular attention to training programs to implement appropriate preventive actions.