This document presents a study that uses a multi-criteria decision analysis (MCDA) methodology to evaluate the performance and efficiency of different airline carriers. Specifically, it analyzes six European airlines, including both legacy carriers and low-cost carriers, across four key performance areas: transport performance, business performance, personnel performance, and environmental performance. Key performance indicators are identified for each area and weights are assigned through expert input. The MCDA tool MACBETH is then used to score the airlines based on their data for the indicators, allowing comparison of efficiency across carriers. The results show low-cost carriers generally achieving higher scores for transport performance due to higher passenger volumes, load factors, and available seat kilometers.
Benchmarking municipal public transport operators in the netherlandsEric Trel
Trel, E. and D.M. van de Velde (2008), “Benchmarking municipal public transport operators in the Netherlands”, European Transport Conference, October 2008. This paper presents the results of a study conducted to benchmark the performances of the municipally-owned public transport operators of Amsterdam, Rotterdam and The Hague in the Netherlands. The study, commissioned by the Dutch Ministry of Transport, compares the performances of the operators in terms of quality and efficiency and their steps towards more ‘market conformity’.
CONTAINER TRAFFIC PROJECTIONS USING AHP MODEL IN SELECTING REGIONAL TRANSHIPM...IAEME Publication
Shipping is a major link between the global economy and international trade. More than 90% of world merchandise trade is carried by sea and over 60% of that volume is containerized. The increasing number of container shipments causes higher demands on the seaport container terminals, container logistics and management as well as on technical equipment. In the Asian region, the existence of ports such as Singapore and trade evolving from developing countries makes it one of the busiest container sea routes in the world. The average vessel size registered in 2008 was approx 3400 TEU’s as against 2500 TEU’s in 2001.
HOPX Crossover Operator for the Fixed Charge Logistic Model with Priority Bas...IJECEIAES
In this paper, we are interested to an important Logistic problem modelised us optimization problem. It is the fixed charge transportation problem (FCTP) where the aim is to find the optimal solution which minimizes the objective function containig two costs, variable costs proportional to the amount shipped and fixed cost regardless of the quantity transported. To solve this kind of problem, metaheuristics and evolutionary methods should be applied. Genetic algorithms (GAs) seem to be one of such hopeful approaches which is based both on probability operators (Crossover and mutation) responsible for widen the solution space. The different characteristics of those operators influence on the performance and the quality of the genetic algorithm. In order to improve the performance of the GA to solve the FCTP, we propose a new adapted crossover operator called HOPX with the priority-based encoding by hybridizing the characteristics of the two most performent operators, the Order Crossover (OX) and Positionbased crossover (PX). Numerical results are presented and discussed for several instances showing the performance of the developed approach to obtain optimal solution in reduced time in comparison to GAs with other crossover operators.
An Enhanced Agglomerative Clustering Algorithm for Solving Vehicle Routing Pr...ijtsrd
An aggrandized solution is designed for the vehicles to reduce the total cost of distribution by which it can supply the goods to the customers with its known capacity can be named as a vehicle routing problem. In variable neighborhood search method, mainly an efficient vehicle routing can be achieved by calculating the distance matrix value based on the customers location or the path where the customers resides. The main objective of the paper is to reduce the total distance travelled to deliver the goods to the customers. The proposed algorithm is a hierarchy based enhanced agglomerative clustering algorithm technique which is used in the data mining scenario effectively. The proposed algorithm decreases the total distance assigning to each route and the important thing need to consider is that, this enhanced clustering algorithm can reduce the total distance when compared to the previously proposed variable neighborhood search method. V. Praveen | V. Hemalatha | M. Poovizhi"An Enhanced Agglomerative Clustering Algorithm for Solving Vehicle Routing Problem" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd4701.pdf http://www.ijtsrd.com/computer-science/other/4701/an-enhanced-agglomerative-clustering-algorithm-for-solving-vehicle-routing-problem/v-praveen
Bidding Strategies for Carrier in Combinatorial Transportation AuctionWaqas Tariq
In combinatorial auction for truckload transportation service procurement, we introduce the bidding strategy for carrier facing the hard valuation problem to all possible routes. The model uses a bid-to-cost ratio of carriers surveyed in Thailand to represent the bidding behavior in combinatorial freight procurement. This model facilitates carrier to value the bid price for interested packages that involve with pattern of transportation service under different competitive environment. The results of analysis with hypotheses in regression model reveal that pattern of transportation service, number of competitors, pattern of transportation service with number of competitors and a pre-empty backhaul to new lane distance ratio do impact significantly on bid price of carrier in combinatorial transportation auction. To find optimal bid price for interested packages in the incomplete information game, the empirical study in stochastic optimization problem with Monte Carlo method can present the best solution for bidder in order to acquire the maximum expected profit in the auction. The results obtained from optimal solution also show that they are more than the average benefit in the competition market considerably.
On The Use of Transportation Techniques to Determine the Cost of Transporting...IOSR Journals
This paper aims at identifying an effective and appropriate method of calculating the cost of transporting goods from several supply centers to several demand centers out of many available methods. Transportation algorithms of North-West corner method (NWCM), Least Cost Method (LCM), Vogel’s Approximation Method (VAM) and Optimality Test were carried out to estimate the cost of transporting produced newspaper from production center to ware-houses using Statistical software called TORA. The results revealed that: NWCM = 36,689,050.00, LCM = 55,250,034.00, VAM = 29,097,700.00 and Optimal solution = 19,566,332.00. It was discovered that Vogel’s Approximation method gives the transportation cost that closer to optimal solution. Also, the study revealed that a production center should be created at northern part of Nigeria to replace the dummy supply center used in the analysis, so as to make production capacity equal to requirement.
Financial Benchmarking Of Transportation Companies In The New York Stock Exc...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/financial-benchmarking-of-transportation-companies-in-the-new-york-stock-exchange-nyse-through-data-envelopment-analysis-dea-and-visualization/
In this paper, we present a benchmarking study of industrial transportation companies traded in the New York Stock Exchange (NYSE). There are two distinguishing aspects of our study: First, instead of using operational data for the input and the output items of the developed Data Envelopment Analysis (DEA) model, we use financial data of the companies that are readily available on the Internet. Secondly, we visualize the efficiency scores of the companies in relation to the subsectors and the number of employees. These visualizations enable us to discover interesting insights about the companies within each subsector, and about subsectors in comparison to each other. The visualization approach that we employ can be used in any DEA study that contains subgroups within a group. Thus, our paper also contains a methodological contribution.
Benchmarking municipal public transport operators in the netherlandsEric Trel
Trel, E. and D.M. van de Velde (2008), “Benchmarking municipal public transport operators in the Netherlands”, European Transport Conference, October 2008. This paper presents the results of a study conducted to benchmark the performances of the municipally-owned public transport operators of Amsterdam, Rotterdam and The Hague in the Netherlands. The study, commissioned by the Dutch Ministry of Transport, compares the performances of the operators in terms of quality and efficiency and their steps towards more ‘market conformity’.
CONTAINER TRAFFIC PROJECTIONS USING AHP MODEL IN SELECTING REGIONAL TRANSHIPM...IAEME Publication
Shipping is a major link between the global economy and international trade. More than 90% of world merchandise trade is carried by sea and over 60% of that volume is containerized. The increasing number of container shipments causes higher demands on the seaport container terminals, container logistics and management as well as on technical equipment. In the Asian region, the existence of ports such as Singapore and trade evolving from developing countries makes it one of the busiest container sea routes in the world. The average vessel size registered in 2008 was approx 3400 TEU’s as against 2500 TEU’s in 2001.
HOPX Crossover Operator for the Fixed Charge Logistic Model with Priority Bas...IJECEIAES
In this paper, we are interested to an important Logistic problem modelised us optimization problem. It is the fixed charge transportation problem (FCTP) where the aim is to find the optimal solution which minimizes the objective function containig two costs, variable costs proportional to the amount shipped and fixed cost regardless of the quantity transported. To solve this kind of problem, metaheuristics and evolutionary methods should be applied. Genetic algorithms (GAs) seem to be one of such hopeful approaches which is based both on probability operators (Crossover and mutation) responsible for widen the solution space. The different characteristics of those operators influence on the performance and the quality of the genetic algorithm. In order to improve the performance of the GA to solve the FCTP, we propose a new adapted crossover operator called HOPX with the priority-based encoding by hybridizing the characteristics of the two most performent operators, the Order Crossover (OX) and Positionbased crossover (PX). Numerical results are presented and discussed for several instances showing the performance of the developed approach to obtain optimal solution in reduced time in comparison to GAs with other crossover operators.
An Enhanced Agglomerative Clustering Algorithm for Solving Vehicle Routing Pr...ijtsrd
An aggrandized solution is designed for the vehicles to reduce the total cost of distribution by which it can supply the goods to the customers with its known capacity can be named as a vehicle routing problem. In variable neighborhood search method, mainly an efficient vehicle routing can be achieved by calculating the distance matrix value based on the customers location or the path where the customers resides. The main objective of the paper is to reduce the total distance travelled to deliver the goods to the customers. The proposed algorithm is a hierarchy based enhanced agglomerative clustering algorithm technique which is used in the data mining scenario effectively. The proposed algorithm decreases the total distance assigning to each route and the important thing need to consider is that, this enhanced clustering algorithm can reduce the total distance when compared to the previously proposed variable neighborhood search method. V. Praveen | V. Hemalatha | M. Poovizhi"An Enhanced Agglomerative Clustering Algorithm for Solving Vehicle Routing Problem" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd4701.pdf http://www.ijtsrd.com/computer-science/other/4701/an-enhanced-agglomerative-clustering-algorithm-for-solving-vehicle-routing-problem/v-praveen
Bidding Strategies for Carrier in Combinatorial Transportation AuctionWaqas Tariq
In combinatorial auction for truckload transportation service procurement, we introduce the bidding strategy for carrier facing the hard valuation problem to all possible routes. The model uses a bid-to-cost ratio of carriers surveyed in Thailand to represent the bidding behavior in combinatorial freight procurement. This model facilitates carrier to value the bid price for interested packages that involve with pattern of transportation service under different competitive environment. The results of analysis with hypotheses in regression model reveal that pattern of transportation service, number of competitors, pattern of transportation service with number of competitors and a pre-empty backhaul to new lane distance ratio do impact significantly on bid price of carrier in combinatorial transportation auction. To find optimal bid price for interested packages in the incomplete information game, the empirical study in stochastic optimization problem with Monte Carlo method can present the best solution for bidder in order to acquire the maximum expected profit in the auction. The results obtained from optimal solution also show that they are more than the average benefit in the competition market considerably.
On The Use of Transportation Techniques to Determine the Cost of Transporting...IOSR Journals
This paper aims at identifying an effective and appropriate method of calculating the cost of transporting goods from several supply centers to several demand centers out of many available methods. Transportation algorithms of North-West corner method (NWCM), Least Cost Method (LCM), Vogel’s Approximation Method (VAM) and Optimality Test were carried out to estimate the cost of transporting produced newspaper from production center to ware-houses using Statistical software called TORA. The results revealed that: NWCM = 36,689,050.00, LCM = 55,250,034.00, VAM = 29,097,700.00 and Optimal solution = 19,566,332.00. It was discovered that Vogel’s Approximation method gives the transportation cost that closer to optimal solution. Also, the study revealed that a production center should be created at northern part of Nigeria to replace the dummy supply center used in the analysis, so as to make production capacity equal to requirement.
Financial Benchmarking Of Transportation Companies In The New York Stock Exc...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/financial-benchmarking-of-transportation-companies-in-the-new-york-stock-exchange-nyse-through-data-envelopment-analysis-dea-and-visualization/
In this paper, we present a benchmarking study of industrial transportation companies traded in the New York Stock Exchange (NYSE). There are two distinguishing aspects of our study: First, instead of using operational data for the input and the output items of the developed Data Envelopment Analysis (DEA) model, we use financial data of the companies that are readily available on the Internet. Secondly, we visualize the efficiency scores of the companies in relation to the subsectors and the number of employees. These visualizations enable us to discover interesting insights about the companies within each subsector, and about subsectors in comparison to each other. The visualization approach that we employ can be used in any DEA study that contains subgroups within a group. Thus, our paper also contains a methodological contribution.
Optimizing Waste Collection In An Organized Industrial Region: A Case Studyertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/optimizing-waste-collection-in-an-organized-industrial-region-a-case-study/
In this paper we present a case study which involves the design of a supply chain network for industrial waste collection. The problem is to transport metal waste from 17 factories to containers and from containers to a disposal center (DC) at an organized region of automobile parts suppliers. We applied the classic mixed-integer programming (MIP) model for the two-stage supply chain to the solution of this problem. The visualization of the optimal solution provided us with several interesting insights that would not be easily discovered otherwise.
Route optimization algorithm are the mathematical formula that solve routing problems..
Some types of routing:
1) Vehicle Routing Problem (VRP)
2) Traveling Salesman Problem (TSP)
3) Ant Colony Optimization (ACO)
In this presentation, a new routing model was introduced in the form of integer linear programming by combining the concepts of time windows and multiple demands and by considering the two contradictory goals of minimizing travel: cost and maximizing demand coverage.
Making sense of value
The Value Management toolkit and transport in Leeds
by David Worsley
Wednesday 20 November 2019
Event evening write up newstory page:
https://www.apm.org.uk/news/making-sense-of-value/
Multi depot Time-dependent Vehicle Routing Problem with Heterogeneous FleetArian Razmi Farooji
This presentation compares two multi-objective metaheuristic algorithms, namely Simulated Annealing and Non-dominated Sorting Genetic Algorithm (NSGA II) for solving Multi depot Time-dependent Vehicle Routing Problem with Heterogeneous Fleet and was presented at Computational Multi Physics, Multi Scales
and Multi Big Data in Transport Modeling,
Simulation and Optimization (CM3) in Jyväskylä, Finland.
Formulation of a Combined Transportation and Inventory Optimization Model wit...IJERA Editor
Most distribution network design models existing in the literature have focused on minimizing the costs of
inventory and transportation. During the analysis of supply chain of currency management problem it is
observed that the transportation of currency from various sources to various destinations and the required
inventory to be maintained to meet the emerging demands requires formulation of a combined problem. This
framework aims to support the coordination of inventory and transportation activities to properly manage the
inventory profiles and currency flows between source locations and distribution centers. This paper considers a
multi-period inventory and transportation model for a single commodity. The key contribution of this paper is, a
mathematical programming formulation of transportation cum inventory problem is proposed and an algorithm
for this new formulation as a multi period decision process is intended. A numerical example of currency
transportation cum inventory is presented to illustrate the proposed algorithm.
UNDERSTANDING CUSTOMERS' EVALUATIONS THROUGH MINING AIRLINE REVIEWSIJDKP
Data mining can be evaluated as a strategic tool to determine the customer profiles in order to learn
customer expectations and requirements. Airline customers have different characteristics and if passenger
reviews about their trip experiences are correctly analyzed, companies can increase customer satisfaction
by improving provided services. In this study, we investigate customer review data for in-flight services of
airline companies and draw customer models with respect to such data. In this sense, we apply two
approaches as feature-based and clustering-based modelling. In feature-based modelling, customers are
grouped into categories based on features such as cabin flown types, experienced airline companies. In
clustering-based modelling, customers are first clustered via k-means clustering and then modeled. We
apply multivariate regression analysis to model customer groups in both cases. During this, we try to
understand how customers evaluate the given services and what dominant characteristics of in-flight
services can be from the customer viewpoint.
UNDERSTANDING CUSTOMERS' EVALUATIONS THROUGH MINING AIRLINE REVIEWSIJDKP
Data mining can be evaluated as a strategic tool to determine the customer profiles in order to learn
customer expectations and requirements. Airline customers have different characteristics and if passenger
reviews about their trip experiences are correctly analyzed, companies can increase customer satisfaction
by improving provided services. In this study, we investigate customer review data for in-flight services of
airline companies and draw customer models with respect to such data. In this sense, we apply two
approaches as feature-based and clustering-based modelling. In feature-based modelling, customers are
grouped into categories based on features such as cabin flown types, experienced airline companies. In
clustering-based modelling, customers are first clustered via k-means clustering and then modeled. We
apply multivariate regression analysis to model customer groups in both cases. During this, we try to
understand how customers evaluate the given services and what dominant characteristics of in-flight
services can be from the customer viewpoint.
UNDERSTANDING CUSTOMERS' EVALUATIONS THROUGH MINING AIRLINE REVIEWS IJDKP
Data mining can be evaluated as a strategic tool to determine the customer profiles in order to learn
customer expectations and requirements. Airline customers have different characteristics and if passenger
reviews about their trip experiences are correctly analyzed, companies can increase customer satisfaction
by improving provided services. In this study, we investigate customer review data for in-flight services of
airline companies and draw customer models with respect to such data. In this sense, we apply two
approaches as feature-based and clustering-based modelling. In feature-based modelling, customers are
grouped into categories based on features such as cabin flown types, experienced airline companies. In
clustering-based modelling, customers are first clustered via k-means clustering and then modeled. We
apply multivariate regression analysis to model customer groups in both cases. During this, we try to
understand how customers evaluate the given services and what dominant characteristics of in-flight
services can be from the customer viewpoint.
Optimizing Waste Collection In An Organized Industrial Region: A Case Studyertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/optimizing-waste-collection-in-an-organized-industrial-region-a-case-study/
In this paper we present a case study which involves the design of a supply chain network for industrial waste collection. The problem is to transport metal waste from 17 factories to containers and from containers to a disposal center (DC) at an organized region of automobile parts suppliers. We applied the classic mixed-integer programming (MIP) model for the two-stage supply chain to the solution of this problem. The visualization of the optimal solution provided us with several interesting insights that would not be easily discovered otherwise.
Route optimization algorithm are the mathematical formula that solve routing problems..
Some types of routing:
1) Vehicle Routing Problem (VRP)
2) Traveling Salesman Problem (TSP)
3) Ant Colony Optimization (ACO)
In this presentation, a new routing model was introduced in the form of integer linear programming by combining the concepts of time windows and multiple demands and by considering the two contradictory goals of minimizing travel: cost and maximizing demand coverage.
Making sense of value
The Value Management toolkit and transport in Leeds
by David Worsley
Wednesday 20 November 2019
Event evening write up newstory page:
https://www.apm.org.uk/news/making-sense-of-value/
Multi depot Time-dependent Vehicle Routing Problem with Heterogeneous FleetArian Razmi Farooji
This presentation compares two multi-objective metaheuristic algorithms, namely Simulated Annealing and Non-dominated Sorting Genetic Algorithm (NSGA II) for solving Multi depot Time-dependent Vehicle Routing Problem with Heterogeneous Fleet and was presented at Computational Multi Physics, Multi Scales
and Multi Big Data in Transport Modeling,
Simulation and Optimization (CM3) in Jyväskylä, Finland.
Formulation of a Combined Transportation and Inventory Optimization Model wit...IJERA Editor
Most distribution network design models existing in the literature have focused on minimizing the costs of
inventory and transportation. During the analysis of supply chain of currency management problem it is
observed that the transportation of currency from various sources to various destinations and the required
inventory to be maintained to meet the emerging demands requires formulation of a combined problem. This
framework aims to support the coordination of inventory and transportation activities to properly manage the
inventory profiles and currency flows between source locations and distribution centers. This paper considers a
multi-period inventory and transportation model for a single commodity. The key contribution of this paper is, a
mathematical programming formulation of transportation cum inventory problem is proposed and an algorithm
for this new formulation as a multi period decision process is intended. A numerical example of currency
transportation cum inventory is presented to illustrate the proposed algorithm.
Similar to [Open Engineering] Airlines Performance and Eflciency Evaluation%0Ausing a MCDA Methodology. The Case for Low Cost%0ACarriers vs Legacy Carriers
UNDERSTANDING CUSTOMERS' EVALUATIONS THROUGH MINING AIRLINE REVIEWSIJDKP
Data mining can be evaluated as a strategic tool to determine the customer profiles in order to learn
customer expectations and requirements. Airline customers have different characteristics and if passenger
reviews about their trip experiences are correctly analyzed, companies can increase customer satisfaction
by improving provided services. In this study, we investigate customer review data for in-flight services of
airline companies and draw customer models with respect to such data. In this sense, we apply two
approaches as feature-based and clustering-based modelling. In feature-based modelling, customers are
grouped into categories based on features such as cabin flown types, experienced airline companies. In
clustering-based modelling, customers are first clustered via k-means clustering and then modeled. We
apply multivariate regression analysis to model customer groups in both cases. During this, we try to
understand how customers evaluate the given services and what dominant characteristics of in-flight
services can be from the customer viewpoint.
UNDERSTANDING CUSTOMERS' EVALUATIONS THROUGH MINING AIRLINE REVIEWSIJDKP
Data mining can be evaluated as a strategic tool to determine the customer profiles in order to learn
customer expectations and requirements. Airline customers have different characteristics and if passenger
reviews about their trip experiences are correctly analyzed, companies can increase customer satisfaction
by improving provided services. In this study, we investigate customer review data for in-flight services of
airline companies and draw customer models with respect to such data. In this sense, we apply two
approaches as feature-based and clustering-based modelling. In feature-based modelling, customers are
grouped into categories based on features such as cabin flown types, experienced airline companies. In
clustering-based modelling, customers are first clustered via k-means clustering and then modeled. We
apply multivariate regression analysis to model customer groups in both cases. During this, we try to
understand how customers evaluate the given services and what dominant characteristics of in-flight
services can be from the customer viewpoint.
UNDERSTANDING CUSTOMERS' EVALUATIONS THROUGH MINING AIRLINE REVIEWS IJDKP
Data mining can be evaluated as a strategic tool to determine the customer profiles in order to learn
customer expectations and requirements. Airline customers have different characteristics and if passenger
reviews about their trip experiences are correctly analyzed, companies can increase customer satisfaction
by improving provided services. In this study, we investigate customer review data for in-flight services of
airline companies and draw customer models with respect to such data. In this sense, we apply two
approaches as feature-based and clustering-based modelling. In feature-based modelling, customers are
grouped into categories based on features such as cabin flown types, experienced airline companies. In
clustering-based modelling, customers are first clustered via k-means clustering and then modeled. We
apply multivariate regression analysis to model customer groups in both cases. During this, we try to
understand how customers evaluate the given services and what dominant characteristics of in-flight
services can be from the customer viewpoint.
Measuring the performance of your fleet by clearly defining strategy and tacticsTristan Wiggill
A presentation by Christopher Hill, fleet operations specialist, Altech Netstar, at the 1st annual Fleet Management Conference held at the Indaba Hotel in Johannesburg, South Africa.
Decision Support System Using FUCOM-MARCOS for Airline Selection in IndonesiaGede Surya Mahendra
Abstract— Since deregulation in 1999, the development of the Indonesian aviation industry has continued to develop. However, many airlines still face various problems before and during flights. Problems in the plane, ranging from engine problems, technical problems, tire damage, cockpit problems to air pressure problems. Airlines customers have personal considerations and preferences when choosing an airline. The many choices and many considerations of airlines often confuse customers. To solve this problem, a decision support system (DSS) can be used to provide advice in selecting airlines based on customer preferences. This study uses the FUCOM-MARCOS method, using 8 criteria and 6 testing alternatives. When using FUCOM to calculate criterion weights, it appears that the factor price (C5) is the factor that counts most. Calculations using FUCOM-MARCOS show that Garuda Indonesia is the favorite airline in Indonesia with a preference value of 0.7390, followed by Citilink in second place, and Batik Air in third. Testing using consistency analysis shows that Garuda Indonesia remains stable and is the first choice by being ranked first 15 times out of 17 tests, with an average ranking distribution reaching 1.23466.
Intisari— Sejak deregulasi pada 1999, perkembangan industri penerbangan Indonesia semakin berkembang. Namun, banyak maskapai penerbangan yang masih menghadapi berbagai masalah sebelum dan selama penerbangan. Terdapat masalah di pesawat, mulai dari masalah mesin, masalah teknis, kerusakan ban, masalah kokpit hingga masalah tekanan udara. Pelanggan maskapai penerbangan memiliki pertimbangan dan preferensi pribadi saat memilih maskapai. Banyaknya pilihan dan banyaknya pertimbangan maskapai seringkali membingungkan pelanggan. Untuk mengatasi masalah tersebut, dapat digunakan sistem pendukung keputusan (SPK) untuk memberikan saran dalam memilih maskapai penerbangan berdasarkan preferensi pelanggan. Penelitian ini menggunakan metode FUCOM-MARCOS, menggunakan 8 kriteria dan 6 alternatif untuk dilakukan pengujian. Ketika menggunakan FUCOM untuk menghitung bobot kriteria, terlihat bahwa faktor harga (C5) adalah faktor yang paling diperhitungkan. Perhitungan menggunakan FUCOM- MARCOS menunjukkan bahwa Garuda Indonesia merupakan maskapai terfavorit di Indonesia dengan nilai preferensi 0,7390, disusul Citilink, sebagai peringkat kedua, dan Batik Air menduduki peringkat ketiga. Pengujian menggunakan analisis konsistensi menunjukkan bahwa Garuda Indonesia tetap stabil dan menjadi pilihan pertama, menduduki peringkat pertama sebanyak 15 kali dari 17 pengujian, dengan rata-rata sebaran peringkat mencapai 1.23466.
PROPOSAL OF AN APPROACH TO IMPROVE BUSINESS PROCESSES OF A SERVICE SUPPLY CHAINIAEME Publication
The purpose of this paper is to develop an approach to analyze and improve business
processes of a service supply chain (SSC), through a real case study. Within this
framework, the paper suggests an approach based on the DMAIC (Define-MeasureAnalyze-Improve-Control) method of Six Sigma - that combined Business Process
Management (BPM), Supply Chain Operations Reference (SCOR), root cause analysis
tree diagram, and Characteristics of Smart Supply chain - to improve one chosen
business process of the Moroccan retirement supply chain. Based on this case study, the
paper shows that the suggested approach identifies the malfunctioning causes for the
studied business process, improves its behavior and manages its control. The approach
is detailed, and it combines methods which are not complicated, so it can be used by
academics and organization's managers. More case studies can be used to more
thoroughly evaluate the presented approach.
Case study A fresh approach of theBalanced Scorecard in the.docxtidwellveronique
Case study: A fresh approach of the
Balanced Scorecard in the Heathrow
Terminal 5 project
Ron Basu, Chris Little and Chris Millard
Summary
Purpose – The purpose of this paper is to present a case study of the Heathrow Terminal 5 project and
to illustrate a customised application of the Balanced Scorecard in a major infrastructure project with
multiple stakeholders.
Design/methodology/approach – The research methodology applied in this work was based on the
case study methodology. The focus was on ‘‘how’’ questions and exploratory analysis of primary and
secondary data supported in-depth interviews with members from both the project team and suppliers.
Findings – The application of the concept of the Balanced Scorecard by Kaplan and Norton in project
management is less frequent in comparison with operations management. The study has established a
proven application of the Balanced Scorecard in managing quality in a major infrastructure project.
Practical implications – For practitioners of major projects the paper gives implications for
implementing the theoretical and customising requirements of the Balanced Scorecard involving key
stakeholders.
Originality/value – The paper illustrates that metrics can be customised for major projects within the
framework of the Kaplan and Norton Balanced Scorecard and that suppliers should be empowered to
own the monitoring and improvement process using their performance data.
Keywords Balanced scorecard, Stakeholders, Suppliers, Partnership,
Performance measurement (quality)
Paper type Case study
1. Introduction
Heathrow Terminal 5 opened on 27 March 2008 with high expectations. It represents a major
step in the transformation of Heathrow and it is now amajor gateway to the UK. From the start
T5 was different and it needed to be due to its size, complexity and proximity. Despite some
teething problems on opening, T5 was a catalyst for new and improved ways of working.
One such initiative is the application of a Balanced Scorecard approach in managing quality
in major projects.
For nearly two decades organisations in both the manufacturing and service industries have
been working arduously at trying to bring the power, discipline and rigour of performance
measurement into their organisations based on the Balanced Scorecard. The concept of a
Balanced Scorecard by Kaplan and Norton (1996) is a strategic measurement system
organised in four perspectives (financial, customer, internal processes, and learning and
growth) that aims to establish tangible performance indicators in all functions of the
business. One of the proven virtues of this system is that it proposes a balance between
concepts that could be contradictory to managers. For example, it aims to balance between
short-term and longer-term objectives, financial measures versus operational measures,
internal performance versus external performance, enabling indicators versus results
indicators and between leading an ...
Similar to [Open Engineering] Airlines Performance and Eflciency Evaluation%0Ausing a MCDA Methodology. The Case for Low Cost%0ACarriers vs Legacy Carriers (20)
2. 390 | M. Miranda et al.
Figure 1: Main drivers of cost differences between LCCs and LCs [3].
Figure 2: Methodology.
ing to the most referred indicators in the airlines annual
reports, and also accordingly with references [5, 6] and [7].
Subsequently the assignment of weights for each in-
dicator were obtained throughout a negotiation (survey
and meetings) with experts, all professionals involved in
Aircraft Operations, Flight Safety and Air Transport Eco-
nomics and Management.
Finally, a Multi Criteria Decision Analysis (MCDA) plat-
form called MACBETH (Measuring Attractiveness by a Cat-
egorical Based Evaluation technique) was used to obtain
the desired outputs (Differences Profile, Thermometer –
for each KPI, Classification, Overall Ranking, and Sensi-
bility Analysis), (Figure 2).
The core of this study lies on a MCDA. MACBETH is a
MCDA approach that only requires qualitative judgements
about differences of value to help an individual or a group
of individuals to quantify the relative attractiveness of op-
tions [8].
Figure 3: Decision Tree.
3 Key Performance Areas and Key
Performance Indicators
Four main areas were chosen: Transport Performance,
Business Performance, Personnel Performance and Envi-
ronmental Performance (Figure 3).
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Transport Performance
Passengers are the annual number of passengers. Aircrafts
are the number of aircrafts of the fleet. The available seat
kilometre, ASK, is a measure of an airline flight’s pas-
senger carrying capacity. It corresponds to the number of
seats available multiplied by the number of miles or kilo-
metres flown. The Load Factor, LF, is a measure of how
much of an airline passenger carrying capacity is used. It
means passenger-kilometres flown as a percentage of seat-
kilometres available.
Business Performance
This KPA groups eight key performance indicators,
namely: Operating Revenue per Passenger; Operating
Costs per Passenger; Operating Result (income) per Pas-
senger - where Operating Result is the difference between
the Revenues and Operating Costs; Operating Margin -
which is the percentage of Operating Result concerning
Revenues; EBITDA per Passenger - where EBITDA means
earnings before interest, taxes, depreciation, and amor-
tization; EBITDA Margin - which corresponds to the ra-
tio of EBITDA by Revenues; Revenue per Available Seat-
Kilometre (RASK); and Operating Cost per Available Seat-
Kilometre (CASK).
Personnel
This area, which is related to the sustainability indicators,
consists of four Key Performance Indicators: Number of
employees, Staff per Passengers - where the Staff refers to
the Number of employees, Staff per Aircraft and Revenue
per Employee.
Figure 4: Example of MACBETH Judgements.
Environmental Performance
Finally, this area is composed of two Key Performance Indi-
cators, namely: Fuel Consumption - in litres per Passenger,
and Carbon Dioxide Emissions - in kilograms per Passen-
ger.
4 Multi Criteria Decision Analysis
(MCDA)
MCDA, or Multi Criteria Decision Making (MCDM), is a
decision-making tool aimed to support decision makers
who are faced with numerous and conflicting evalua-
tions [9]. An advantage of MCDA approach is that, it helps
decision makers to organise and synthesize such informa-
tion in a way which leads them to feel comfortable and
confident to make a decision, minimizing post-decision
regrets by assuring that all criteria or factors have prop-
erly been taken into account. Thus, we use the expression
MCDA as an umbrella term to describe a collection of for-
mal approaches which seek to take explicit account of mul-
tiple criteria in helping individuals or groups of individu-
als explore decisions that are really important [10].
4.1 Measuring Attractiveness by a
Categorical Based Evaluation Technique
(MACBETH)
MACBETH is a decision-aid approach to multi criteria val-
ues measurement. The goal behind its conceptualization is
to allow measurement of the attractiveness or value of op-
tions through a non-numerical pairwise comparison ques-
tioning mode, which is based on seven qualitative cate-
gories of difference in attractiveness: is there no differ-
ence (indifference), or is the difference very weak, weak,
moderate, strong, very strong, or extreme (Figure 4). The
key distinction from numerical value-measurement proce-
dures, such as the simple multi-attribute rating technique,
or SMART approach, is that MACBETH uses only such qual-
itative judgements of difference in attractiveness in order
to generate, by mathematical programming, value scores
for options and weights for criteria [11]. According to previ-
ous studies, preliminary results evidenced how MACBETH
approach seems to be very promising when compared with
those (DEA based) traditionally in use [12]. Mainly because
not only MACBETH seems to be more user-friendly than
DEA but also it can be applied easily in managerial prac-
tice involving the stakeholders in the process.
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The mathematical foundations of MACBETH are ex-
plained in several publications as in [8] and [11].
Figure 4 is an example of a generic MACBETH Judge-
ments Matrix, where a, b, c, d and e could represent any
set of indicators, and below are the differences of attrac-
tiveness between them. Thus, the difference of attractive-
ness between a and b is very weak, between a and c is
moderate, and between c and d is weak. Between equal de-
scriptors there are no difference in attractiveness, and for
the cases where there is no difference of attractiveness as-
signed, MACBETH assigns a positive difference that guar-
antees the consistence of judgements, as it can be seen be-
tween a and e.
4.2 MACBETH and Airlines Performance and
Eflciency Evaluation
A set of six European airlines was chosen among LCs and
LCCs, respectively: Aer Lingus, Aeroflot and Turkish Air-
lines; and Ryanair, EasyJet and Air Berlin (EasyJet and
Ryanair are the largest LCCs in Europe followed not far be-
hind by Air Berlin [13]).
In this study two of the four key performance areas
mentioned above were used, as well as the related key per-
formance indicators: Transport Performance and Business
Performance. All data refers to the year 2013.
The study uses the efficiency of each area separately.
Transport Performance Area
Figure 5 is the decision tree of transport performance area,
an extract of the global decision tree as in Figure 3.
Figure 6 shows data available for each KPI of the KPA
of Transport Performance.
As stated in section 2, assignment of weights for each
indicator were given upon negotiation with experts, all
Figure 5: Decision Tree of Transport Performance Area.
professionals involved in Aircraft Operations, Flight Safety
and Air Transport Economics and Management.
Figure 7 is the Ponderation Table, and depicts the
weights that were assigned to each indicator as well as the
relevance of the relationships among them. The most rele-
vant is PAX indicator and least one is AIRCRAFTS. The col-
umn of Current Scale shows the weights assigned for each
KPI.
Based on the information of Figures 6 and 7, M-
MACBETH software attributed the scores of efficiency re-
lating to the transport performance of each carrier as in
Figure 8. It can be seen that the best results are obtained
by low cost carriers: RYR (79.03 points) and EZY (78.16
points). This is mainly due to the higher flow of passengers
(PAX), greater load factor (LF) and in some cases higher of-
fer (ASK). This is a table of scores, which have by reference
the Good value (sup.) with 100 points assigned and Neu-
Figure 6: Transport Performance Data.
Figure 7: Ponderation (Weighting) Table.
Figure 8: Table of Scores: Transportation.
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5. Airlines Performance and Eflciency evaluation using a MCDA Methodology | 393
Figure 9: Global Thermometer: Trans-
portation.
Figure 10: Sensitivity Analysis on Weight: Load-Factor.
tral value (inf.) with 0 (zero) points assigned. Once the two
performance references are introduced into the MACBETH
model, the Criteria value scales automatically.
A negative score of “−3.49” attributed to the LF of
Aeroflot means a worse value than the neutral one. In that
dimension is a necessary and sufficient condition for a pro-
posal to be considered negative (worse than neutral) in the
set of all dimensions, this means that a determinant di-
mension has a non-compensatory nature [11].
Further down on the scoreboard is the first LC, the
Turkish Airlines, with a score of 48.66 points, followed by
the third LCC, the Russian Aeroflot, with 41.88 points. In
the last place is the Irish Aer Lingus, with 35.27 points. Irish
Aer Lingus position is an obvious result for this KPA as this
carrier is the smaller one, with less than half of the number
of aircrafts, and therefore less ASK.
Also the M-MACBETH platform allows to obtain a
graph with the results of global efficiency similar to that
of a thermometer (Figure 9) which shows the ranking po-
sition of the 6 carriers.
Figure 10 is the sensitivity analysis on weight of the LF.
The left vertical axis represents the overall score, and the
right vertical axis represents the LF scores for each carrier.
The red line represents the weight (31.32%) assigned to this
indicator as explained in Figure 6 above. Air Berlin (BER),
for example, has a better score than Aeroflot (AFL). How-
ever, if the weight of this indicator was below 20.00%, the
score of Aeroflot would be better than that of Air Berlin.
Figure 11: Decision Tree of Business Performance Area.
Otherwise, Air Berlin has a worse score than that of Turk-
ish Airlines (THY), but if the weight of this indicator was
above 50.00%, this situation would be inverted. Also it is
important to underline that if the LF weight was slightly
increased from 31.32% to 33.00% the relative position be-
tween RYR and EZY would change.
Business Performance Area
Figure 11 is the decision tree of business performance area,
an extract of the global decision tree as in Figure 3.
Figure 12 shows data available for each KPI of the KPA
of Business Performance.
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Figure 12: Business Performance Data.
Figure 13: Ponderation (Weighting) Table.
Figure 14: Table of Scores: Business.
It is evident how LCCs tend to have a smaller amount
of Revenue per Passenger (REV/PAX) mainly because the
lower fares they practice. Also it may be concluded that
these carriers have lower Costs per Passenger (COST/PAX).
To get a clear picture of these evidences let’s look at
the Income per Passenger (INC/PAX) and the Income Mar-
gin (INC MARG): the LCCs have lower values of Income
per Passenger than the Legacy ones; however, concern-
ing the Income Margin - which represents the ratio of In-
come per Revenues, LCCs clearly have better results than
the Legacy ones. The same must be concluded for the Rev-
enue per Available Seat Kilometre (RASK), Cost per Avail-
able Seat Kilometre (CASK) and EBITDA per passenger
(EBITDA/PAX), which are clearly lower for Low Cost Car-
riers than for Legacy ones.
As stated in section 2, upon negotiation with special-
ists it was decided to assign equal weights (12.50) to all KPI
of this KPA. Figure 13 is the Ponderation Table, and depicts
precisely those weights as well as the relevance of the rela-
tionships among them. The most relevant is REV/PAX in-
dicator and less one is CASK. The column of Current Scale
shows the weights assigned for each KPI.
Based on the information of Figures 12 and 13 M-
MACBETH software attributed the scores of efficiency re-
lating to the business performance of each carrier as in
Figure 14. It can be seen that the best overall result is ob-
tained by THY (67.73 points) followed by EZY (60.91 points).
The last position belongs to BER (25.28 points). As stated
above this is a table of scores, which has by reference the
Good value (sup.) with 100 points assigned and Neutral
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7. Airlines Performance and Eflciency evaluation using a MCDA Methodology | 395
Figure 15: Global Thermometer: Busi-
ness.
Figure 16: Sensitivity Analysis on Weight: Revenue per Passenger.
value (inf.) with 0 (zero) points assigned. Once the two
performance references are introduced into the MACBETH
model, the Criteria value scales automatically.
Negative scores of “−0.20” and “−0.07” (BER for
INC/PAX and EBITDA Margin, respectively) as well as
“−0.04” (RYR for EBITDA per Passenger) mean a worse
value than the neutral one. The scores over 100.00 points
– the cases like AFL – REV/PAX (100.03 points), RYR –
INC MARG (100.02 points), and THY – EBIDTA/PAX (100.08
points) – mean better values than the Good one.
Figure 15 shows the ranking position of the 6 carriers
based on this KPA of Business. There is a quite uniform dis-
tribution of scores among 5 carriers, with the exception of
Air Berlin.
Figure 16 is the sensitivity analysis on weight of the
Revenue per Passenger. The red line represents the weight
(12.50%) assigned to this indicator as explained in Fig-
ure 12 above. Thus, Turkish Airlines (THY) has a better
score than Aeroflot (AFL), (see left vertical axis). However,
if the weight of this indicator is changed from 12.50% to a
value above 40.00% the score of AFL would be better than
that of THY. Otherwise, actually Ryanair has a worse score
than Aeroflot, but if the weight of this indicator is changed
to below 5.00% the situation would reverse.
5 Conclusions and Future Work
Performance of the LCCs and LCs changes depending on
the area upon which they are compared: LCCs have higher
efficiencies based on Transport Performance KPA while
LCs have higher performance efficiencies based on Busi-
ness Performance KPA. LCCs low prices implies lower rev-
enue per passenger, which necessarily does not mean they
have a lower income margin because the cost per passen-
ger is lower too. Still, LCCs need higher flow of passengers
as well as greater offer than the LCs to obtain better results.
The main idea of this study was to test this model for
the carriers’ efficiency, both legacy and low cost. When we
simulate different scenarios with two KPAs the results vary
dramatically, so that in the future it will be interesting to
include all KPAs to understand how these areas may influ-
ence the overall performance of a carrier too. Also data col-
lected must be extended to several years in order to eval-
uate the performance of a single carrier over a set of years
(self-benchmarking) or the performance of several carriers
with each other (benchmarking).
There is an ongoing survey sent to a wider range
of Air Transport experts in order to obtain more robust
weights thus to mitigate the subjectivity of the assignment
of weights.
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Acronyms
AFL Aeroflot
ASK Available Seat kilometres
BER Air Berlin
CASK Revenue per Available Seat kilometre
COST/PAX Cost per Passenger
DEA Data Envelopment Analysis
EBITDA MARG EBITDA Margin
EBITDA Earnings Before Interest, Taxes, Depre-
ciation, and Amortization
EIN Aer Lingus
EZY EasyJet
IATA International Air Transport Association
ICAO International Civil Aviation Organiza-
tion
INC MARG Income Margin
INC/PAX Income per Passenger
KPA Key Performance Area
KPI Key Performance Indicator
LCC Low Cost Carrier
LC Legacy (Flag) Carrier
LF Load Factor
MACBETH Measuring Attractiveness by a Category
Based Evaluation Technique
MCDA Multi Criteria Decision Analysis
MCDM Multi Criteria Decision Making
PAX Passengers
RASK Revenue per Available Seat kilometre
REV/PAX Revenue per Passenger
RYR Ryanair
THY Turkish Airlines
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