Analysis of Green Computing Strategy in University: Analytic Network Process (ANP) Approach

Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis do not provide an analytical means to determine the importance of the identified factors of green computing strategy and implementation. Although the SWOT analysis successfully explores the factors, individual factors are usually described very generally. For this reason, SWOT analysis possesses deficiencies in the measurement and evaluation of green computing steps. Even though the analytic hierarchy process (AHP) technique eliminates these deficiencies, it does not allow for measuring the possible dependencies among the individual factors. The AHP method assumes that the green computing factors presented in the hierarchical structure are independent; however, this assumption may be inappropriate in light of certain situation. Therefore, it is important to utilize a form of SWOT analysis that calculates and takes into account the possible dependency among the factors. This paper demonstrates a process for quantitative SWOT analysis of green computing implementation that can be performed even when there is dependence among strategic factors. The proposed algorithm uses the analytic network process (ANP), which allows measurement of the dependency among the green computing implementation factors, as well as AHP, which is based on the independence between the factors. There are four alternatives: campus awareness program, computer procurement, increase in heat removal requirement, and increase in equipment power density for improving the implementation of green computing in campus. Dependency among the SWOT factors is observed to effect the strategic and sub-factor weights, as well as to change the strategy priorities. Based on ANC method, the best alternative for this implementation is computer procurement

Multi criteria decision making methods for location selection of distribution centers

In recent years, major challenges such as, increase in inflexible consumer demands and to improve the competitive advantage, it has become necessary for various industrial organizations all over the world to focus on strategies that will help them achieve cost reduction, continual quality improvement, increased customer satisfaction and on time delivery performance. As a result, selection of the most suitable and optimal facility location for a new organization or expansion of an existing location is one of the most important strategic issues, required to fulfill all of these above mentioned objectives. In order to sustain in the global competitive market of 21st century, many industrial organizations have begun to concentrate on the proper selection of the plant site or best facility location. The best location is that which results in higher economic benefits through increased productivity and good distribution network. When a choice is to be made from among several alternative facility locations, it is necessary to compare their performance characteristics in a decisive way. As the facility location selection problem involves multiple conflicting criteria and a finite set of potential candidate alternatives, different multi-criteria decision-making (MCDM) methods can be effectively applied to solve such type of problem. In this paper, four well known MCDM methods have been applied on a facility location selection problem and their relative ranking performances are compared. Because of disagreement in the ranks obtained by the four different MCDM methods a final ranking method based on REGIME has been proposed by the authors to facilitate the decision making process

Discrete event simulation and data envelopment analysis models for selecting the best resource allocation alternative at an emergency department’s green zone

The Green Zone of Emergency Department Hospital Universiti Sains Malaysia (EDHUSM) which provides treatment for non-critical cases contributes partly to the hustle and bustle in the emergency department. The imbalance of doctors and nurses with the patient ratio which forms the resources’ bottleneck further results to the long patients’ waiting time especially after the office hours and during weekends and public holidays. Collectively, this disproportion and bottlenecks roots up the current problem faced by Green Zone EDHUSM which constantly fails to achieve the KPIs set by the hospital. Henceforth, this study focuses on the best resource allocation of doctors and nurses for shifts during the weekdays and for shifts during weekends and public holidays. The hybrid method of Discrete Event Simulation, and Data Envelopment Analysis models such as BCC-input oriented and Super-Efficiency, were deployed to obtain the best resource allocation for the two groups of shift. The method produced a series of resources allocation alternatives for doctors and nurses with a total of 64 alternatives for weekdays and 729 alternatives for weekends and public holidays. The results show that the best allocation for doctors and nurses during weekdays are three doctors and three nurses serving for every shift, while during weekends and public holidays, a combination of four doctors and four nurses for every shift are the best. The proposed combinations have reduced the average waiting time, optimized the utilization of doctors and nurses, and managed to increase the number of patients served during weekdays, weekends and public holidays

Integration of simulation and DEA to determine the most efficient patient appointment scheduling model for a specific healthcare setting

Purpose: This study is to develop a systematic approach for determining the most efficient patient appointment scheduling (PAS) model for a specific healthcare setting with its multiple appointments requests characteristics in order to increase patients’ accessibility and resource utilization, and reduce operation cost. In this study, three general appointment scheduling models, centralized scheduling model (CSM), decentralized scheduling model (DSM) and hybrid scheduling model (HSM), are considered. Design/methodology/approach: The integration of discrete event simulation and data envelopment analysis (DEA) is applied to determine the most efficient PAS model. Simulation analysis is used to obtain the outputs of different configurations of PAS, and the DEA based on the simulation outputs is applied to select the best configuration in the presence of multiple and contrary performance measures. The best PAS configuration provides an optimal balance between patient satisfaction, schedulers’ utilization and the cost of the scheduling system and schedulers’ training. Findings: In the presence of high proportion (more than 70%) of requests for multiple appointments, CSM is the best PAS model. If the proportion of requests for multiple appointments is medium (25%-50%), HSM is the best. Finally, if the proportion of requests for multiple appointments is low (less than 15%), DSM is the best. If the proportion is in the interval from 15% to 25% the selected PAS model could be either DSM or HSM based on expert idea. Similarly, if the proportion is in the interval from 50% to 70% the best PAS model could be either CSM or HSM. Originality/value: This is the first study that determines the best PAS model for a particular healthcare setting. The proposed approach can be used in a variety of the healthcare settings. Keywords: data envelopment analysis, discrete event simulation, patient appointment scheduling, multiple appointments, centralized scheduling model, decentralized scheduling model, hybrid scheduling modelPeer Reviewe

An Integration of Rank Order Centroid, Modified Analytical Hierarchy Process and 0-1 Integer Programming in Solving A Facility Location Problem

Hadhramout province is the major producer of dates in The Republic of Yemen. Despite producing substantial quantity and quality of dates, the business losses are still high. The situation worsens with the widespread of the black market activities. Recently, the Yemeni government has issued an agreement stating the importance of building a date palm packaging factory as a resolution to the problems. Hence, this study aims to identify the best location for a date palm packaging factory among the seven districts which produce most of the date palm supplies in Hadhramout. The selection was based on eleven criteria identified by several representatives from the farmers and the local councils. These criteria were market growth, proximity to the markets, proximity to the raw materials, labor, labor climate, suppliers, community, transportation cost, environmental factors, production cost, and factory set up cost. The level of importance and the respective weight of each criterion were calculated using two different approaches, namely, Analytic Hierarchy Process (AHP) and Rank Order Centroid (ROC). In applying AHP, a slight modification was made in the pairwise comparison exercises that eliminated the inconsistency problem faced by the standard AHP pairwise comparison procedure. Likewise, in applying ROC, a normalization technique was proposed to tackle the problem of assigning weights to criteria having the same priority level, which was neither clarified nor available in the standard ROC. Both proposed techniques revealed that suppliers were the most important criterion, while community was regarded to be the least important criterion in deciding the final location for the date palm factory. Combining the criteria weights together with several hard and soft constraints that were required to be satisfied by the location, the final location was determined using three different mathematical models, namely, the ROC combined with 0-1 integer programming model, the AHP combined with 0-1 integer programming model, and the mean of ROC and AHP combined with 0-1 integer programming model. The three models produced the same result; Doean was the best location. The result of this study, if implemented, would hopefully help the Yemeni government in their effort to improve the production as well as the management of the date palm tree in Hadhramout

Enterprise Marketing Automation Software Selection

Enterprises devote a large amount of time and effort in selecting software products that are critical to their competitive advantage, and one such software is the marketing automation software. The paper discusses in detail the different criteria and methodology for selecting one marketing automation software product among the leading products available in the market. The selection process was identified as a multi-criteria decision-making (MCDM) problem and the methodology used for analysis was Hierarchical Decision Model (HDM), a variation of Analytic Hierarchy Process (AHP). Nine experts with different backgrounds in terms of job functions, functional experience, and geographies, who had expertise in marketing automation tools or related products were requested to give their inputs for the HDM analysis and a detailed analysis was done on the results. The results pointed to a single product as the winner of the analysis which was found to reflect the market share and research data and helped understand the decision-making process under different preferences and perspective differences. For future research and further analysis, the model could be made more robust with the inputs from more number of experts to eliminate any bias or outliers and corroborating the results by independently analyzing it with different decision-making methodologies such as TOPSIS or DEA

The state of the art development of AHP (1979-2017): A literature review with a social network analysis

Although many papers describe the evolution of the analytic hierarchy process (AHP), most adopt a subjective approach. This paper examines the pattern of development of the AHP research field using social network analysis and scientometrics, and identifies its intellectual structure. The objectives are: (i) to trace the pattern of development of AHP research; (ii) to identify the patterns of collaboration among authors; (iii) to identify the most important papers underpinning the development of AHP; and (iv) to discover recent areas of interest. We analyse two types of networks: social networks, that is, co-authorship networks, and cognitive mapping or the network of disciplines affected by AHP. Our analyses are based on 8441 papers published between 1979 and 2017, retrieved from the ISI Web of Science database. To provide a longitudinal perspective on the pattern of evolution of AHP, we analyse these two types of networks during the three periods 1979?1990, 1991?2001 and 2002?2017. We provide some basic statistics on AHP journals and researchers, review the main topics and applications of integrated AHPs and provide direction for future research by highlighting some open questions

The state of the art development of AHP (1979-2017): a literature review with a social network analysis

Although many papers describe the evolution of the analytic hierarchy process (AHP), most adopt a subjective approach. This paper examines the pattern of development of the AHP research field using social network analysis and scientometrics, and identifies its intellectual structure. The objectives are: (i) to trace the pattern of development of AHP research; (ii) to identify the patterns of collaboration among authors; (iii) to identify the most important papers underpinning the development of AHP; and (iv) to discover recent areas of interest. We analyse two types of networks: social networks, that is, co-authorship networks, and cognitive mapping or the network of disciplines affected by AHP. Our analyses are based on 8441 papers published between 1979 and 2017, retrieved from the ISI Web of Science database. To provide a longitudinal perspective on the pattern of evolution of AHP, we analyse these two types of networks during the three periods 1979–1990, 1991–2001 and 2002–2017. We provide some basic statistics on AHP journals and researchers, review the main topics and applications of integrated AHPs and provide direction for future research by highlighting some open questions

An integrated fuzzy AHP/DEA approach for performance evaluation of territorial units in Turkey

Due to the differences between regions and sub-regions in the countries, some problems come out especially in economic and social life. The issue of differences of regions has been widely implemented to evaluate the economic performance of Turkey in many disciplines. The objective of this paper is to evaluate the efficiency of 26 sub-regions of NUTS-2 classification using integration Fuzzy Analytic Hierarchy Process (FAHP) with Data Envelopment Analysis (DEA). The integrated FAHP/DEA method comprises two stages. In the first stage, linguistic terms are used to determine the decision makers’ opinion and are converted to quantitative forms by using FAHP methods. Subsequently, in the second stage, DEA method is applied to obtain relative efficiency of sub-regions in Turkey. The integrated FAHP/DEA method is illustrated with a real case study