Application of control charts in analytic hierarchy process

Vendor rating can be done using the Analytic Hierarchy Process (AHP) by a single decision maker or a group of decision makers. This approach may suffer from some drawbacks including bias in estimation process. The proposed methodology in this paper involves estimation by a group on an individual basis following the principle of anonymity. A control chart is constructed with an upper control limit and a lower control limit. Implementation ofthiscontrol chart will take into account the dynamic nature of vendor performance and also can be used for continuous monitoring of the vendor performance. This procedure can be used for a single vendor as well as for multiple vendor rating.     

Solving a process engineer's manpower-planning problem using analytic hierarchy process

The role of a process engineer (PE) is vital in a high-technology organization. Generic manpower-planning approaches, such as regression or mathematical programming methods, are difficult to apply to PE manpower-planning problem. A simple production-volume to staffing-level ratio did not properly reflect the required PE staffing needs. This research considered both production volume and complexity to plan the required PE staffing level. Production complexity is an indicator of the amount of PE attendance time, and this is innovatively evaluated in the present study using the analytic hierarchy process (AHP). A case study is used to illustrate the proposed methodology.     

The analytic hierarchy process with interval preference statements

In the analytic hierarchy process (AHP), interval judgments instead of precise ratios are widely accepted and can be practically used to solve decision-making problems when uncertainty exists because of scant information available or insufficient understanding of the problem. This paper presents a simple and effective method for finding the extreme points in a range of interval ratios (such as loose articulation, minimum number of interval ratios, and general interval ratios) and ultimately for establishing the dominance relations among alternatives using the identified extreme points. This is followed by an enumeration or simulation approach to manage situations in which the best or a full ranking of alternatives remains unidentified.     

A decision support system for the selection of a casting process using analytic hierarchy process

This paper presents an Analytic Hierarchy Process (AHP) based decision support system to select the most suitable casting process for a given product. The hierarchical structure of the proposed method allows the decision maker to compare the different casting processes using the material suitability and flexibility, geometrical complexity, dimensional tolerance and surface finish of the casting, and the cost as the criteria for selection. Judgemental inconsistency of the decision maker in selecting the casting process is taken care by ensuring that the value of consistency ratio is below (0.1). A numerical example is presented to illustrate the effectiveness of the proposed methodology for selecting the suitable casting process.     

一种新型不确定AHP的研究与应用

针对现有AHP方法在某些决策环境下不能准确反映决策者的偏好,以及群决策背景下专家偏好集结的信息丢失等问题,研究元素服从离散分布的一种新型不确定AHP,将未确知数概念引入到AHP之中,用局部一致性和整体一致性思想检验未确知数判断矩阵,并根据未确知数判断矩阵的特点,提出基于未确知数运算法则和蒙特卡罗模拟的两种权重求解方法,最后通过两个例子说明新方法的应用可行性.     

An analytic hierarchy process based simulation model for entry mode decision regarding foreign direct investment

A methodology is developed for ranking entry mode alternatives encountered by individual firms considering foreign direct investment (FDI). The methodology deals with the risks and uncertainties related to FDI. The analytic hierarchy process (AHP) is used to solve the multiple criteria decision-making problem using input from a firm's management. A simulation approach is incorporated into the AHP to handle the uncertainty considerations encountered in an FDI environment. The uncertainties include: (1) uncertainty regarding the future characteristics of the FDI decision making environment, (2) uncertainty associated with the decision maker's judgment regarding pairwise comparisons necessitated by the AHP.     

Green supply chain performance measurement using the analytic hierarchy process: a comparative analysis of manufacturing organisations

The main purpose of this research is to develop and deploy an analytical framework for measuring the environmental performance of manufacturing supply chains. This work's theoretical bases combine and reconcile three major areas: supply chain management, environmental management and performance measurement. Researchers have suggested many empirical criteria for green supply chain (GSC) performance measurement and proposed both qualitative and quantitative frameworks. However, these are mainly operational in nature and specific to the focal company. This research develops an innovative GSC performance measurement framework by integrating supply chain processes (supplier relationship management, internal supply chain management and customer relationship management) with organisational decision levels (both strategic and operational). Environmental planning, environmental auditing, management commitment, environmental performance, economic performance and operational performance are the key level constructs. The proposed framework is then applied to three selected manufacturing organisations in the UK. Their GSC performance is measured and benchmarked by using the analytic hierarchy process (AHP), a multiple-attribute decision-making technique. The AHP-based framework offers an effective way to measure and benchmark organisations’ GSC performance. This study has both theoretical and practical implications. Theoretically it contributes holistic constructs for designing a GSC and managing it for sustainability; and practically it helps industry practitioners to measure and improve the environmental performance of their supply chain.     

Flood risk mapping and analysis using an integrated framework of machine learning models and analytic hierarchy process

In this study, a new approach of machine learning (ML) models integrated with the analytic hierarchy process (AHP) method was proposed to develop a holistic flood risk assessment map. Flood susceptibility maps were created using ML techniques. AHP was utilized to combine flood vulnerability and exposure criteria. We selected Quang Binh province of Vietnam as a case study and collected available data, including 696 flooding locations of historical flooding events in 2007, 2010, 2016, and 2020; and flood influencing factors of elevation, slope, curvature, flow direction, flow accumulation, distance from river, river density, land cover, geology, and rainfall. These data were used to construct training and testing datasets. The susceptibility models were validated and compared using statistical techniques. An integrated flood risk assessment framework was proposed to incorporate flood hazard (flood susceptibility), flood exposure (distance from river, land use, population density, and rainfall), and flood vulnerability (poverty rate, number of freshwater stations, road density, number of schools, and healthcare facilities). Model validation suggested that deep learning has the best performance of AUC = 0.984 compared with other ensemble models of MultiBoostAB Ensemble (0.958), Random SubSpace Ensemble (0.962), and credal decision tree (AUC = 0.918). The final flood risk map shows 5075 ha (0.63%) in extremely high risk, 47,955 ha (5.95%) in high-risk, 40,460 ha (5.02%) in medium risk, 431,908 ha (53.55%) in low risk areas, and 281,127 ha (34.86%) in very low risk. The present study highlights that the integration of ML models and AHP is a promising framework for mapping flood risks in flood-prone areas.     

Modelling decentralized decision making in the energy sector

Large linear programming models, which have been widely used to determine the optimal structure of national energy systems, are based on the assumption that there is an absolutely centralized decision making process within the energy system. In this paper an attempt is made to match the real decision structure of a given energy system, by decomposing an LP energy model into smaller models, with the corresponding system decision centres. This is done by applying the ‘transfer price’ algorithm of Dantzing and Wolfe. The ‘master’ problem corresponds to the central planning unit, i.e. a Ministry of Energy, whereas the subproblems correspond to peripheral operating units, i.e. enterprises, usually state owned, which produce and distribute the energy carriers. The optimal plans of the peripheral units are submitted to the central unit, which through the mechanism of pricing of both common resources, inputs and energy services outputs, co-ordinates the overall planning of the energy system. An illustrative example is given referring to the Hellenic national energy system. The research reported is placed within a wider research endeavour, whose objectives and main line of work are also given.     

包含Jump-Arch过程的利率模型及其应用

从4个方面对比分析了 Jump-Arch 扩散模型、跳跃扩散模型、Arch 扩散模型和扩散模型,发现 Jump-Arch 扩散模型是研究中的最优模型,它在解释和预测利率的波动方面表现出很强的能力,跳跃不仅是利率均值回复的来源,也是利率波动的最主要来源,因此对利率动态行为的描述必须考虑跳跃过程.同时研究发现,R091国债回购市场波动存在明显的"周一"和"周五效应",并利用 Jump-Arch 扩散模型解释了出现这种异象的原因.     

Revitalising energy capital project development and execution strategies: lessons from the energy sector

This study sought to examine project delivery challenges currently being faced by energy clients and to determine how they could achieve value creation through better alignment of project delivery processes. There are important lessons to be learned from the energy sector on how to deal with the unique operational and project challenges. Four exploratory focus groups were held with twenty senior project management practitioners, to better understand the greatest needs and project management processes in the energy sector. A formal deductive approach was used to examine and evaluate existing and future energy project delivery processes. From the qualitative data, participants recognised the need to introduce science-based project techniques such as system dynamics and project predictive analytics in project management processes. Participants further noted that comprehensive innovative project delivery processes and analytical approaches are required to cope with the increasing scale and complexity of energy capital projects.     

The combination of forecasts in the trading of electricity from renewable energy sources

The legal support provided by the ‘Act on Granting Priority to Renewable Energy Sources’ (German Erneuerbare Energien Gesetz, EEG) and its precursor has in the last 20 years led to a marked growth in Germany in the use of renewable energies to generate electricity. As a result of the EEG amendment adopted in the summer of 2011 and in force since 1 January 2012, the market integration of electricity generated from renewable energy sources (RES-E) has become more important. Consequently, the economic importance of trading RES-E has also increased. A major role in determining costs in trading electricity from wind and solar energy on the wholesale markets plays the forecasting method used. If a forecast inaccurately predicts the amount of electricity actually generated, one result could be elevated costs in the trading process. In the beginning of this article we introduce the legal framework governing the trading of RES-E. Subsequently, we present a method for combining several individual forecasting methods. Finally, using empirical data, we show that in comparison to the best available individual forecast, the proposed combined forecast results in a clear improvement of forecasting quality as well as in a reduction in trading costs.     

On the strategic project management process in the UK upstream oil and gas sector

This paper reports on an investigation into strategic project management in the UK upstream oil and gas sector. The management process is represented by a set of elements which covers context, content and output and are balanced across financial, internal business, external environment, and learning and innovation perspectives. The paper uncovers elements that appear to explain successful project management and compares these with the elements to which managers pay greatest attention. There appears to be a mismatch between those elements which are associated with success and those receiving significant management attention.     

Performance measurement of supply chain management using the analytical hierarchy process

Performance measurement of supply chain management (SCM) is a rapidly growing multi-criteria decision-making problem owing to the large number of factors affecting decision-making. The right choice of performance metrics and measures is critical to the success and competitiveness of the firms in the era of globalisation. Recognising the multiple objective nature of the problem, this paper proposes the use of the analytical hierarchy process (AHP) methodology as aid in making SCM evaluation decisions. For pair-wise comparison in AHP, a survey methodology is used. The methodology presented can help firms to prioritise and formulate viable performance measurement strategies in the volatile and complex global decision environment from different balanced scorecard (BSC) perspectives. A demonstration of the application of this methodology in a real life problem is presented.     

Combining analytical hierarchy process and Choquet integral within non-additive robust ordinal regression

We consider multiple criteria decision aiding in the case of interaction between criteria. In this case the usual weighted sum cannot be used to aggregate evaluations on different criteria and other value functions with a more complex formulation have to be considered. The Choquet integral is the most used technique and also the most widespread in the literature. However, the application of the Choquet integral presents two main problems being the necessity to determine the capacity, which is the function that assigns a weight not only to all single criteria but also to all subset of criteria, and the necessity to express on the same scale evaluations on different criteria. While with respect to the first problem we adopt the recently introduced Non-Additive Robust Ordinal Regression (NAROR) taking into account all the capacities compatible with the preference information provided by the DM, with respect to the second one we build the common scale for the considered criteria using the Analytic Hierarchy Process (AHP). We propose to use AHP on a set of reference points in the scale of each criterion and to use an interpolation to obtain the other values. This permits to reduce considerably the number of pairwise comparisons usually required by the DM when applying AHP. An illustrative example details the application of the proposed methodology.     

Robust Ordinal Regression and Stochastic Multiobjective Acceptability Analysis in multiple criteria hierarchy process for the Choquet integral preference model

The paper deals with two important issues of Multiple Criteria Decision Aiding: interaction between criteria and hierarchical structure of criteria. To handle interactions, we apply the Choquet integral as a preference model, and to handle the hierarchy of criteria, we apply the recently proposed methodology called Multiple Criteria Hierarchy Process. In addition to dealing with the above issues, we suppose that the preference information provided by the Decision Maker is indirect and has the form of pairwise comparisons of criteria with respect to their importance and pairwise preference comparisons of some pairs of alternatives with respect to some criteria. In consequence, many instances of the Choquet integral are usually compatible with this preference information. These instances are identified and exploited by Robust Ordinal Regression and Stochastic Multiobjective Acceptability Analysis. To illustrate the whole approach, we show its application to a real world decision problem concerning the ranking of universities for a hypothetical Decision Maker.     

Chemical and biological agent incident response and decision process for civilian and public sector facilities.

In the event of a terrorist attack or catastrophic release involving potential chemical and/or biological warfare agents, decisionmakers will need to make timely and informed choices about whether, or how, to respond. The objective of this article is to provide a decision framework to specify initial and follow-up actions, including possible decontamination, and to address long-term health and environmental issues. This decision framework consists of four phases, beginning with the identification of an incident and ending with verification that cleanup and remediation criteria have been met. The flowchart takes into account both differences and similarities among potential agents or toxins at key points in the decision-making process. Risk evaluation and communication of information to the public must be done throughout the process to ensure a successful effort.     

Using Lean Six Sigma to improve mobile order fulfilment process in a telecom service sector

This study proposes and follows a specific and systematic framework for implementing Lean Six Sigma (LSS) methodology in a telecom company in order to improve customer satisfaction by minimizing the company’s response time to customer requirements. The goal of this study was achieved by utilizing several LSS tools under five phases of the DMAIC methodology. Unlike previous studies in the telecom sector that used only qualitative method, in this study, both qualitative and quantitative methods were utilized to draw meaningful conclusions. As a result of the implementation of the LSS methodology, the average order fulfilment lead time for sales orders (SO) and value-added service (VAS) orders was reduced from 10.3 to 5.9 days and from 1.5 to 0.5 days, respectively. The reduction in lead time resulted in an increase in the sigma level for SO and VAS orders from 0.44 to 1.26 and from 0.73 to 2.66, respectively. These improvements were expected to lead to a financial benefit in savings of over $600,000 per year in operational costs, enhancements to customer experience and an increase in revenue generating opportunities. Moreover, this article enriches the existing literature on the application of LSS concept in the service industry, and helps the company to speed up the response to customer requirements.     

Strategy mapping in the public sector

The Balanced Scorecard links strategic objectives and performance measures across a number of perspectives. A development of the Balanced Scorecard—strategy mapping—provides a powerful way of demonstrating the links between each of the perspectives. This paper explores the use of strategy mapping as a tool to develop strategy in a public sector agency and suggests a simplified version which can communicate that strategy effectively, both inside and outside the organisation.