ATHENS UNIVERSITY OF ECONOMICS & BUSINESS Department of Management Science & Technology. Ph.D. Thesis

Transcription

1 ATHENS UNIVERSITY OF ECONOMICS & BUSINESS Department of Management Science & Technology Ph.D. Thesis Design and evaluation of a real-time fleet management system for dynamic incident handling in urban freight distributions Vasileios S. Zeimpekis This thesis is submitted for the degree of Doctorate of Philosophy (Ph.D.)

2 Στους γονείς µου Σταύρο-Αγγελική & στον αδερφό µου Νίκο 2

3 Abstract In urban freight distribution, the use of an initial distribution plan, although necessary, is by no means sufficient to address unexpected events such as traffic congestion, adverse weather conditions and mechanical failures that are likely to occur during delivery execution and may have adverse effects on system performance. These events cause deviations between the actual and desired state during the execution of the schedule. Recent advances in mobile and positioning technologies allowed the development of fleet management systems that enable freight carriers to dynamically monitor their fleet and improve relevant delivery performance by intervening when such problems occur. Although the use of such technologies supports better utilization of the vehicles fleet, the systems based on these technologies are not typically designed to address unforeseen events in a systemic fashion. As a result, interventions are often performed manually and the resulting decisions are local with limited effectiveness. The aim of this thesis is to enhance urban delivery execution by modelling the process of dynamic incident handling through the design and implementation of a real-time fleet management system. The latter has three main functionalities: a) it monitors delivery vehicles using mobile and positioning technologies, b) it detects deviations from the distribution plan, and c) it adjusts the schedule accordingly, by suggesting rerouting strategies. The research methodology that was followed combines three basic steps: a) literature review and interviews for requirements elicitation and system design, b) theoretical system testing and evaluation via simulation and c) confirmatory study of the theoretical results through field experiments in two freight operators. Initially we present the results obtained from the requirements elicitation process which was based on a two-phase methodology: a) requirements based on the results from interviews with personnel from logistics departments and b) requirements defined from the real-time fleet management perspective, as presented in the literature review. The results show that there is need for real-time fleet management systems that deal with unexpected events such as travel time delays and vehicle breakdowns. Based on the synthesis of the aforementioned requirements, the design of the realtime fleet management system is then presented. It consists of three major subsystems: a) the Back-end that consists of a fleet management platform together with a novel mechanism, which detects incidents that occur during the delivery plan execution and facilitates vehicle re-routing when needed, b) the Wireless Communication that allows a two-way communication between the back-end and the front-end systems and c) the Front-end that enables a robust user interface, as well as interaction between the software platform that is installed in the on-board truck computer and the company s back-end system. 3

4 During the design process of the system we focus on two main performance aspects of the system. Firstly, such systems should have the ability to detect time deviations from the initial plan when they occur. We propose thus, a method for travel time estimation which is based on historical data from previous delivery deliveries. We demonstrate that this method provides very accurate results when traffic conditions are not exceptionally different from the historical ones. However, in urban settings there are cases in which travel times vary significantly during the day. For these cases we propose a second travel prediction method that uses real-time data to compute travel times in a dynamic manner. To enhance performance the system incorporates an intelligent mechanism that selects the method that gives the most accurate prediction based on traffic patterns and vehicle s state. It is worthwhile to mentioning that both methods have been evaluated by using an innovative testing framework that included the design of a series of experiments which demonstrate how certain variables affect the prediction accuracy of each method. A second critical issue for such systems is the decision process on whether a detected deviation between the scheduled delivery program and the current time prediction is significant or not. We propose and evaluate two alternative methods which can be used to guide the system decision for rerouting. The results show that both techniques can be used according to the traffic patterns of the road that a vehicle is traversing. After completing the design process, the system was implemented, by following well known implementation techniques such as functional hierarchical and entity relation diagrams. The system modules were developed in two distinctive layers: a) Application and user interface layer where the implementation of the system modules together with the control centre and vehicle user interfaces have taken place and b) Data layer that includes the development of the database module. The system is evaluated via simulation testing. Eight indicative test cases, generated by using the Design of Experiments (DoE) method, are used. In order to perform the aforementioned tests, a 7-step simulation process is followed. Various simulation tools have been built in order to simulate as closest as possible the real operating environment of such system. The results show that when the directions provided by the system are followed, delivery time delays are decreased and customer service is improved. We also test the system in two freight operators through field experiments so as to validate the theoretical results. Certain Key Performance Indicators (KPIs) were used to assess the process of distribution execution with and without system use. Results are derived from testing two incident handling scenarios: a) the case of incidents that cause time delays and result to single vehicle rerouting and b) vehicle breakdown where multiple vehicles are rerouted to handle the event efficiently. The results confirm the simulation results and show that the real-time fleet management system can significantly improve the execution of an urban delivery when an unforeseen event occurs. 4

5 Περίληψη Κατά την εκτέλεση αστικών διανοµών προϊόντων, η χρήση µιας αρχικής δροµολόγησης, παρότι είναι σηµαντική, δεν είναι ικανή συνήθως να αντιµετωπίσει µε επιτυχία απρόοπτα γεγονότα όπως για παράδειγµα κυκλοφοριακά προβλήµατα, άσχηµες καιρικές συνθήκες και µηχανικές βλάβες του οχήµατος που µπορούν να συµβούν κατά την εκτέλεση ενός δροµολογίου. Τα συγκεκριµένα γεγονότα προξενούν αποκλίσεις µεταξύ της πραγµατικής και της αρχικά σχεδιαζόµενης εκτέλεσης ενός δροµολογίου µε αποτέλεσµα τη µη σωστή εξυπηρέτηση των πελατών. Οι εξελίξεις των τελευταίων ετών στο χώρο της τηλεµατικής, συνέβαλαν στην ανάπτυξη συστηµάτων διαχείρισης στόλου οχηµάτων τα οποία επέτρεψαν στις εταιρίες διανοµών να επιτηρούν το στόλο τους σε πραγµατικό χρόνο και να βελτιώσουν την εκτέλεση των δροµολογίων αντιµετωπίζοντας κάποιες από τις προαναφερθείσες περιπτώσεις. Όµως, το βασικό µειονέκτηµα αυτών των συστηµάτων έγκειται στην αδυναµία τους να αντιµετωπίζουν αναπάντεχα γεγονότα µε ένα αυτοµατοποιηµένο τρόπο. Αυτό έχει ως αποτέλεσµα, όταν χρειαστεί να παρθούν κρίσιµες αποφάσεις σε πραγµατικό χρόνο, αναφορικά µε την εξέλιξη του δροµολογίου, να µην λαµβάνονται υπόψη σηµαντικές παράµετροι (π.χ. παράθυρα διανοµής πελατών) και να µειώνεται µε αυτό τον τρόπο το επίπεδο εξυπηρέτησης των πελατών. Σκοπός της συγκεκριµένης διδακτορικής διατριβής είναι να βελτιώσει τον υφιστάµενο τρόπο εκτέλεσης αστικών διανοµών µοντελοποιώντας µε συστηµικό τρόπο τη διαχείριση απροόπτων γεγονότων µέσω του σχεδιασµού και υλοποίησης ενός συστήµατος διαχείρισης στόλου οχηµάτων σε πραγµατικό χρόνο. Το σύστηµα εµπεριέχει τρεις βασικές λειτουργίες: α) ελέγχει την εκτέλεση του δροµολογίου κάθε οχήµατος, β) ανιχνεύει χρονικές αποκλίσεις από το αρχικό πρόγραµµα µε τη χρήση µεθόδων πρόβλεψης χρόνου άφιξης και γ) επαναπροσδιορίζει τη σειρά επίσκεψης στους εναποµείναντες πελάτες χρησιµοποιώντας αλγορίθµους επαναδροµολόγησης. Η ερευνητική µεθοδολογία η οποία ακολουθείται για το σχεδιασµό, υλοποίηση και αξιολόγηση του εν λόγω συστήµατος περιλαµβάνει τρεις βασικές φάσεις (threephase triangulated research methodology): α) τη βιβλιογραφική επισκόπηση και µια σειρά από συνεντεύξεις µε στόχο τον προσδιορισµό των απαιτήσεων του συστήµατος και την σχεδίασή του, β) τον θεωρητικό έλεγχο του συστήµατος µέσω χρήσης προσοµοίωσης και γ) τον έλεγχο της ορθότητας των αποτελεσµάτων της προσοµοίωσης µέσω της πιλοτικής χρήσης του συστήµατος σε δύο εταιρίες διανοµής προϊόντων. Αρχικά παρουσιάζονται τα αποτελέσµατα της ανάλυσης των απαιτήσεων η οποία βασίστηκε σε δύο φάσεις: α) απαιτήσεις των χρηστών οι οποίες προέρχονται από συνεντεύξεις µε στελέχη τµηµάτων logistics και β) απαιτήσεις που αφορούν στη λειτουργία τέτοιων συστηµάτων και προέρχονται από τη βιβλιογραφική επισκόπηση. 5

6 Τα αποτελέσµατα καταδεικνύουν την ανάγκη ύπαρξης ενός συστήµατος διαχείρισης στόλου οχηµάτων το οποίο θα µπορεί να αντιµετωπίσει δυναµικά γεγονότα (π.χ. χρονικές καθυστερήσεις και βλάβες οχηµάτων) που συµβαίνουν κατά την εκτέλεση ενός δροµολογίου. Με βάση τη σύνθεση των απαιτήσεων, παρουσιάζεται στη συνέχεια ο σχεδιασµός του συστήµατος. Το τελευταίο αποτελείται από 3 βασικά υποσυστήµατα: α) το υποσύστηµα βάσης, το οποίο περιλαµβάνει την πλατφόρµα της διαχείρισης στόλου οχηµάτων καθώς επίσης και ένα καινοτόµο µηχανισµό ο οποίος ανιχνεύει χρονικές αποκλίσεις από το αρχικό πρόγραµµα και επιτρέπει την επαναδροµολόγηση ενός ή περισσοτέρων οχηµάτων, β) το τηλεπικοινωνιακό υποσύστηµα το οποίο επιτρέπει την επικοινωνία µεταξύ του υποσυστήµατος βάσης και οχήµατος και γ) το υποσύστηµα οχήµατος το οποίο περιλαµβάνει µια εύχρηστη διεπαφή η οποία επιτρέπει την αµφίδροµη επικοινωνία οδηγού και δροµολογητή. Κατά τον σχεδιασµό του συστήµατος δόθηκε έµφαση σε δύο βασικά θέµατα που αφορούν τις επιδόσεις του συστήµατος. Καταρχήν, τέτοιου είδους συστήµατα τα οποία διαχειρίζονται δυναµικές καταστάσεις θα πρέπει να έχουν τη δυνατότητα να ανιχνεύουν σε πραγµατικό χρόνο αποκλίσεις από το αρχικό πρόγραµµα διανοµής. Προτείνουµε λοιπόν µια µέθοδο πρόβλεψης χρόνου η οποία βασίζεται σε ιστορικά στοιχεία από παλαιότερα αντίστοιχα δροµολόγια. Τα αποτελέσµατα καταδεικνύουν πως η συγκεκριµένη µέθοδος παρέχει ιδιαίτερα ακριβείς προβλέψεις όταν οι κυκλοφοριακές συνθήκες δεν διαφέρουν κατά πολύ από τις αντίστοιχες των ιστορικών δροµολογίων. Παρόλα αυτά σε ένα αστικό περιβάλλον υπάρχουν περιπτώσεις όπου η µέση ταχύτητα κίνησης ενός οχήµατος µεταβάλλεται συνεχώς µέσα στην ηµέρα. Για αυτές τις περιπτώσεις προτείνουµε µια δεύτερη µέθοδο πρόβλεψης, η οποία χρησιµοποιεί δεδοµένα πραγµατικού χρόνου για να υπολογίσει το χρόνο αύξησης σε εναποµείναντες πελάτες. Για να πετύχουµε µεγαλύτερη απόδοση, το σύστηµα περιλαµβάνει έναν έξυπνο µηχανισµό ο οποίος επιλέγει την µέθοδο µε τα πιο ακριβή αποτελέσµατα, ανάλογα µε την κυκλοφορία του δρόµου που διασχίζει το όχηµα. Είναι σηµαντικό να σηµειωθεί πως και οι δυο µέθοδοι ελέγχθηκαν µε τη χρήση ενός καινοτόµου πλαισίου αξιολόγησης που περιλάµβανε το σχεδιασµό και υλοποίηση µιας σειράς πειραµάτων (design of experiments) τα οποία καταδεικνύουν πώς συγκεκριµένες παράµετροι επηρεάζουν την πρόβλεψη άφιξη σε επόµενο πελάτη. Το δεύτερο σηµαντικό θέµα το οποίο διερευνήθηκε αφορά στην ανάγκη ενός συστήµατος να αποφασίζει κατά πόσο µια απόκλιση η οποία παρατηρείται είναι σηµαντική και επιβάλει την επανδροµολόγηση ενός οχήµατος. Για αυτό το θέµα προτείνουµε και αξιολογούµε δύο µεθόδους οι οποίες µπορούν να χρησιµοποιηθούν για τον προαναφερθέντα σκοπό. Τα αποτελέσµατα καταδεικνύουν ότι κάθε µέθοδος προσφέρει σωστή καθοδήγηση όταν εφαρµόζεται σε διαφορετικές περιπτώσεις ανάλογα µε την πορεία ενός οχήµατος και την εξέλιξη της διανοµής. Μετά τον σχεδιασµό του συστήµατος προχωρήσαµε στην ανάπτυξή του µε τη χρήση ευρέως διαδεδοµένων τεχνικών, όπως δοµηµένη ανάλυση και σχεδίαση (structured analysis and design), διαγραµµατικές τεχνικές (π.χ. ιεραρχικό διάγραµµα λειτουργιών function hierarchy diagram, διάγραµµα οντοτήτων συσχετίσεων - 6

7 entity relationship diagram), κλπ. Τα επιµέρους τµήµατα του συστήµατος αναπτύχθηκαν σε δύο επίπεδα: α) το επίπεδο εφαρµογής και διεπαφών χρηστών και β) το επίπεδο δεδοµένων που περιλαµβάνει την ανάπτυξη της βάσης δεδοµένων. Το σύστηµα αξιολογήθηκε αρχικά µε τη χρήση προσοµοίωσης. Πιο συγκεκριµένα δηµιουργήθηκαν οκτώ τεστ µε τη χρήση της µεθόδου Σχεδιασµού Πειραµάτων (Design of Experiments). Για την εκτέλεση των παραπάνω πειραµάτων χρησιµοποιήθηκε µια διαδικασία προσοµοίωσης επτά βηµάτων. Μια σειρά από εργαλεία αναπτύχθηκαν για να µπορέσουµε να προσοµοιώσουµε το σύστηµα όσο το δυνατόν πιο κοντά στις αληθινές συνθήκες λειτουργίας του. Τα αποτελέσµατα δείχνουν πώς όταν χρησιµοποιείται το εν λόγω σύστηµα µειώνονται οι χρονικές καθυστερήσεις και βελτιώνεται η εξυπηρέτηση των πελατών. Το σύστηµα αξιολογήθηκε επίσης και σε δύο εταιρίες που εκτελούν αστικές διανοµές µε στόχο την επιβεβαίωση των δοκιµών µε τη χρήση προσοµοίωσης. Συγκεκριµένη δείκτες απόδοσης (Key Performance Indicators) χρησιµοποιούνται για να αποτιµήσουν τη διαδικασία εκτέλεσης ενός αστικού δροµολογίου πριν και µετά τη χρήση του συστήµατος. Συγκεκριµένα ελέγχονται 2 περιπτώσεις: α) η περίπτωση γεγονότων που προκαλούν χρονικές καθυστερήσεις και αντιµετωπίζονται µε την επαναδροµολόγηση ενός οχήµατος και β) η περίπτωση µηχανικής βλάβης σε όχηµα διανοµής η οποία αντιµετωπίζεται µε την επαναδροµολόγηση ενός ή περισσοτέρων οχηµάτων. Τα αποτελέσµατα από την πιλοτική χρήση είναι ενθαρρυντικά και επιβεβαιώνουν τα αποτελέσµατα της θεωρητικής αξιολόγησης του συστήµατος 7

8 Acknowledgments Conducting research in doctoral level involves a great deal of collaboration with different people in various ways. For that reason, there are certain persons that I would like to thank, for contributing to this dissertation, each of them with his/her own individual and unique way. First of all, I would like to thank my two supervisors Associate Professor George M. Giaglis and Professor Ioannis Minis for their invaluable advices and for working hard to make a fresh-faced student to become involved in the area of Information Systems and Supply Chain Management. Both of them provided me a great deal of academic stimulation during these years and encouraged me to become involved in academic research. In addition, I would like to thank Professor Georgios Doukidis for giving me the opportunity to study at the Department of Management Science and Technology and for being enthusiastic and supportive through these years. Special thanks go also to Lecturer Christos D. Tarantillis for our fruitful discussions during the writing-up period. This thesis would not have been possible without their endless and generous support. I am also indebted to all the people from Emphasis Telematics (especially Dimitris Venizelos and Stelios Vaiopoulos), Diakinis SA (especially George Samaridis and Yiannis Maris), P.G. NIKAS SA (especially Loukas Tsiaparas and Thanassis Varoutsos) who helped me by supporting the empirical part of this thesis. Special thanks go also to my colleagues in ELTRUN-WRC and to Kostis Mamassis and Antonis Tatarakis from the Department of Financial & Management Engineering at the University of the Aegean, for sharing my interests and helping me in various phases of this thesis through many discussions. I also acknowledge the Hellenic Ministry of Education (HRAKLEITOS PhD Fellowship), for financially supporting my doctoral studies. My final acknowledgement goes to my parents (Stavros and Angeliki), my brother Nikos and my cousin Kallis who during the years of my research gave me a great emotional support contributing in that way intellectually in my thesis. Thank you seems an inadequate word in this situation but it is the only one I can think of. Vasileios Zeimpekis 8

9 PhD Thesis Publications 1. Journal Publications Zeimpekis, V., Tatarakis, A., Giaglis, G. M., Minis, I. (2007) Towards a Dynamic Real-Time Vehicle Management System for Urban Distribution, International Journal of Integrated Supply Management, Volume 3, Issue 3, pp Zeimpekis, V., Giaglis G. M. (2006) Urban dynamic real-time distribution services: Insights from SMEs, Journal of Enterprise Information Management, Volume 19, Issue 4, pp Giaglis, G. M., Minis, I., Tatarakis, A., Zeimpekis, V. (2004) Minimizing Logistics Risk through Real-Time Vehicle Routing and Mobile Technologies: Research To-Date and Future Trends, International Journal of Physical Distribution and Logistics Management, Vol. 34, No. 9, pp Zeimpekis, V., Giaglis, G. M., Lekakos, G. (2003) Towards a taxonomy of indoor and outdoor positioning techniques for mobile location based applications, Journal of ACM, SIGecom Exchanges, Vol. 3, No. 4, pp Conference Publications Zeimpekis, V., Giaglis, G., Minis, I. (2008) "Development and evaluation of an intelligent fleet management system for city logistics" In the Proceedings of the 41st Hawaii International Conference on System Sciences (41 st HICSS), January , Hawaii Zeimpekis, V., Giaglis, G., Minis, I. (2006) Dynamic vehicle dispatching with timedependent travel times in urban settings In the proceedings of the 21 th European Conference on Operational Research (EURO XXI), 2-5 July, Reykjavik, Iceland Zeimpekis, V., Giaglis, G., Minis, I. (2006) Dynamic Incident Handling in Urban Freight Distributions In the Proceeding of the 3 rd International Workshop on Freight Transportation and Logistics (ODYSSEUS 2006), May 23-26, Altea, Spain Zeimpekis, V., Giaglis, G., Minis, I. (2006) Dynamic Incident Handling in Urban Freight Distributions, In the proceedings of 3 rd Congress on Management Science & Technology, Athens University of Economics & Business, May, Athens, Greece Zeimpekis, V., Mamassis, K., Giaglis G.M., Minis, I. Mavros, A. (2005) Real-time fleet management for urban freight distributions In the Proceedings of the 9 th National Congress on Logistics, Logistics 2005, November, Thessaloniki, Greece Zeimpekis, V., Mamassis, K., Damianidis, T., Mavros, A. (2005) A Real-Time Vehicle Management System for Urban Distributions based on Time-Dependent Information, In the proceedings of 3rd International Workshop in Supply Chain 9

10 Management and Information Systems (SCMIS 2005), 6-8 July, Thessaloniki, Greece Zeimpekis, V., Giaglis, G. M., Minis I., (2005) A dynamic real-time fleet management system for incident handling in city logistics In the proceedings of 61 st IEEE Vehicular Technology Conference, (VTC2005 Spring), 30 May-1 June, Stockholm, Sweden Zeimpekis, V., Giaglis, G. (2005) Real-Time Fleet Management: The case of urban freight deliveries, In the proceedings of 2 nd Congress of Management Science & Technology, Athens University of Economics & Business, November Athens, Greece Zeimpekis, V., Giaglis, G. (2004) Mobile real-time services for city logistics: An empirical investigation into user perception and requirements In the proceedings of 3 rd International Conference on Mobile Business, (ICMB 2004), July, New York, USA Zeimpekis, V., Giaglis, G., Tatarakis, A. (2004) A systemic approach to real-time vehicle re-routing for urban distributions, In the proceedings of the 20 th European Conference on Operational Research, (EURO XX), 4-7 July, Rhodes, Greece Zeimpekis, V., Giaglis, G. (2004) The use of wireless systems for real-time vehicle re-routing, In the proceedings of 1 st Congress of Management Science & Technology, Athens University of Economics & Business, November Athens, Greece Giaglis, G. M., Minis, I., Tatarakis, A. Zeimepkis, V. (2003) Real-time Decision Support Systems in Urban Distributions: Opportunities afforded by mobile and wireless technologies In the proceedings of 3 rd International ECR Research Symposium, September, Athens Greece Zeimpekis, V., Giaglis, G. (2002) Investigation and assessment of Mobile Technology as a Supply Chain Management Facilitator, In the Proceedings of the 6 th National Congress on Logistics, Logistics 2002, November, Athens, Greece 3. Papers in Edited volumes Zeimpekis, V., Giaglis, G. M. Minis I. (2006), Real-time fleet management: The case of a Greek 3PL carrier, in Dynamic Fleet Management: Concepts, Systems, Algorithms & Case Studies, edited by Zeimpekis, V.S., Giaglis, G.M, Tarantilis, C.D., Minis, I., Management Science Series, Production & Logistics Section, Springer- Verlag, US (Forthcoming) Zeimpekis, V., Giaglis, G. M. (2004), A Dynamic Real-time Vehicle Routing System for Distribution Operations in Doukidis G. J., Vrechopoulos, A. (Eds), Consumer Driven Electronic Transformation: Apply New Technologies to Enthuse Consumers, Springer-Verlag, US 10

11 Table of contents Table of Contents 1. URBAN FREIGHT DISTRIBUTIONS AND DYNAMIC INCIDENT HANDLING INTRODUCTION GOODS DISTRIBUTION IN AN URBAN ENVIRONMENT DYNAMIC INCIDENTS IN URBAN FREIGHT DISTRIBUTIONS CURRENT INCIDENT HANDLING PRACTICES EXPECTED CONTRIBUTION RESEARCH METHODOLOGY OUTLINE OF THE DISSERTATION BACKGROUND REVIEW IN FLEET MANAGEMENT SYSTEMS AND TRAVEL TIME PREDICTION METHODS INTRODUCTION FLEET MANAGEMENT SYSTEMS Main characteristics Functionalities of fleet management systems Review of current fleet management systems REAL-TIME FLEET MANAGEMENT SYSTEMS Main system components Review of current real-time fleet management systems The need for real-time incident handling systems TRAVEL TIME PREDICTION The concept of travel time Surveillance devices for travel time prediction Comparison of surveillance devices Travel time prediction & traffic forecasting methods Short term travel time prediction in urban networks Travel time prediction with non-parametric methods DYNAMIC VEHICLE ROUTING Main characteristics of Dynamic Vehicle Routing Problem Types of Dynamic Vehicle Routing Time Dependent Dynamic Vehicle Routing SUMMARY TRAVEL TIME PREDICTION METHODS FOR DYNAMIC INCIDENT HANDLING INTRODUCTION BACKGROUND THEORY ON NON-PARAMETRIC REGRESSION The k nearest neighbour (k-nn) model Implementation challenges TRAVEL TIME PREDICTION METHODS FOR DYNAMIC INCIDENT HANDLING Fundamentals of travel time prediction Travel time prediction using historical data Travel time prediction using real-time data Selecting the appropriate travel time prediction method EVALUATION OF TRAVEL TIME PREDICTION METHODS Data Collection Methodology Data Processing Filtering of Biased Data DESIGN OF EXPERIMENTS Recognition and statement of the problem Steps for designing an experiment Choice of factors and levels Choice of experimental design

12 Table of contents Relating the design factors with the scenarios Formulate Research Hypothesis Performing the experiment Statistical analysis of data Recommendations TESTING TRAVEL TIME PREDICTION METHODS IN REAL-LIFE CASES Travel time prediction with historical data Travel time prediction using real-time data SUMMARY DESIGN OF A REAL-TIME FLEET MANAGEMENT SYSTEM FOR DYNAMIC INCIDENT HANDLING INTRODUCTION REQUIREMENTS FOR REAL-TIME INCIDENT HANDLING SYSTEMS Research methods for requirements elicitation Requirements from the user perspective Requirements from the real-time fleet management perspective Synthesis of system requirements DESIGN OF THE REAL-TIME FLEET MANAGEMENT SYSTEM System architecture System modules Analysis DECISION SUPPORT MODULE FOR INCIDENT HANDLING Monitoring and Detection Trip Projection Decision Making and Rerouting DECISION PROCESS AT THE INCIDENT HANDLING MECHANISM The importance of the decision process Problem setting Decision process using the statistical detection Experimental results IMPLEMENTATION OF THE REAL-TIME FLEET MANAGEMENT SYSTEM Application and user interface layer Database layer SUMMARY EVALUATION OF THE REAL-TIME FLEET MANAGEMENT SYSTEM THROUGH SIMULATION TESTING INTRODUCTION SYSTEM EVALUATION PROCESS RESEARCH METHODOLOGY SIMULATION TESTING OBJECTIVES EXPERIMENTAL DESIGN Design Parameters Design of the test case scenarios SIMULATION PROCESS RESEARCH HYPOTHESIS EXPERIMENTAL RESULTS AND ANALYSIS DISCUSSION SUMMARY EMPIRICAL RESULTS FROM URBAN REAL-LIFE FREIGHT DISTRIBUTION SCENARIOS INTRODUCTION OBJECTIVES OF THE PILOT TESTS CHOICE OF KEY PERFORMANCE INDICATORS RESEARCH METHODOLOGY EMPIRICAL RESULTS FROM THE TIME DELAY SCENARIO Description of carrier s urban distribution operations Aims of the field experiment Design parameters of the field experiment

13 Table of contents Design of the test case scenarios Experimental Results and Analysis Discussion EMPIRICAL RESULTS FROM THE VEHICLE BREAKDOWN SCENARIO Description of carrier s urban distribution operations Aims of the field experiment Design parameters of the field experiment Design of the test case scenarios Experimental Results and Analysis Discussion SUMMARY CONCLUSIONS, LIMITATIONS AND FUTURE RESEARCH ISSUES INTRODUCTION THESIS OVERVIEW ACHIEVEMENTS LIMITATIONS FUTURE RESEARCH ISSUES SUMMARY REFERENCES APPENDIX A: POSITIONING TECHNOLOGIES APPENDIX B: SUPPORTIVE MATERIAL FOR SIMULATION TESTING

14 List of figures List of figures Figure 1.1 Design and evaluation process of the real-time fleet management system Figure 1.2 Schematic representation of the Thesis outline Figure 2.1 A typical Fleet Management System Architecture Figure 2.2 Main components of a real-time fleet management system Figure 2.3 Basic components for travel time estimation Figure 3.1 The use of the k-nn model in non parametric regression problems Figure 3.2 Travel time prediction in a certain link Figure 3.3 Travel time prediction using historical data Figure 3.4 Three different links of getting from Point A to Point B Figure 3.5 Travel time prediction with real-time data Figure 3.6 An example for Test C Figure 3.7 Vehicle track player of a vehicle Figure 3.8 Actual versus Predicted travel time Figure 3.9 Error of travel time prediction methods Figure 3.10 Error of travel time prediction methods (with low-pass filtering) Figure 3.11 Process for selecting the travel time prediction in each prediction step Figure 3.12 Model for travel-time prediction Figure 3.13 Geometric view Figure 3.14 The Normal Probability Plot Figure 3.15 Pareto Chart Figure 3.16 Residual Plots for Error Figure 3.17 Main Effects Plot Figure 3.18 Interaction Plot Figure 3.19 Travel time prediction accuracy results (historical data) Figure 3.20 Travel time prediction accuracy results (real-time data) Figure 4.1 Methodological framework for user requirements elicitation Figure 4.2 Dynamic real-time vehicle routing system architecture Figure 4.3 Trip projection mechanism Figure 4.4 Estimation error for different values of parameter α Figure 4.5 Results through statistical testing Figure 4.6 Control User Interface: Main screen for the time delay scenario Figure 4.7 Control User Interface: Deviation from the initial plan Figure 4.8 Control User Interface: Vehicle rerouting Figure 4.9 Control User Interface: Main screen for the vehicle breakdown scenario Figure 4.10 Control User Interface: Vehicle rerouting Figure 4.11 Vehicle User Interface Figure 4.12 Database diagram of the system Figure 5.1 Simulation process Figure 5.2 Route builder Figure 5.3 Delivery schedule simulator Figure 5.4Graphical route visualization (Real-time fleet management platform) Figure 5.5 Incident Handling Interface (Real-time fleet management platform) Figure 5.6 Proposed vehicle rerouting Figure 5.7 Performance results for the eight trials Figure 6.1 Truck monitoring and travel time prediction Figure 6.2 Delivery plan after two customer visits Figure 6.3 Time violation instant Figure 6.4 New delivery schedule for Truck B (after rerouting) 14

15 List of figures Figure 6.5 Final delivery schedule for Truck B (after rerouting) Figure 6.6 Route followed by Truck B (after rerouting) Figure 6.7 Final delivery schedule for Truck A Figure 6.6 Route followed by Truck A Figure 6.7 Customer service (%) for test case 3 Figure 6.8 Customer service (%) for test case 1 Figure 6.9 Customer service (%) for test case 2 Figure 6.10 Customer service (%) for test case 4 Figure 6.11 Customer service (%) for test case 5 Figure 6.12 Customer service (%) for test case 6 Figure 6.13 Customer service (%) for test case 7 Figure 6.14 Customer service (%) for test case 8 Figure 6.15 Customer Service for all test cases Figure 6.16 Monitoring and travel time prediction for Truck A Figure 6.17 Total customers assigned in Truck B Figure 6.18 Meeting point of Trucks A & B Figure 6.19 Graphical representation of customer service (%) for Schedule A Figure 6.20 Graphical representation of customer service (%) for Schedule B Figure 6.21 Graphical representation of customer service (%) for Schedules A & B Figure 6.22 Graphical representation of customer service (%) for Schedule A Figure 6.22 Graphical representation of customer service (%) for Schedule B Figure 6.23 Graphical representation of customer service (%) for Schedule C Figure B.1 Performance results for Test case 1 Figure B.2 Performance results for Test Case 2 Figure B.3 Performance results for Test case 3 Figure B.4 Performance results for Test Case 4 Figure B.5 Performance results for Test Case 5 Figure B.6 Performance results for Test Case 6 Figure B.7 Performance results for Test Case 7 Figure B.8 Performance results for Test Case 8 15

16 List of tables List of tables Table 1.1 Types of urban deliveries Table 1.2 Dynamic incidents in urban freight distributions Table 1.3 The three-phased triangulated research methodology Table 2.1 Monitoring Systems in the literature Table 2.2 Real-time Fleet Management Systems in the literature Table 2.3 Comparison of advanced travel time collection devices Table 2.4 Applications of advanced travel time collection devices Table 2.5 Characteristics of travel time prediction and traffic forecasting methods Table 2.6 Literature review for travel time prediction methods in urban networks Table 2.7 Types of Dynamic Vehicle Routing Table 2.8 DVRP Classification Table 2.9 TDDVR Classification Table 3.1 Initial route identification Table 3.2 Calculation of the distance Table 3.3 Sort the distance & determine nearest neighbours Table 3.4 Identification of routes to be included Table 3.5 Paired t-test results Table 3.6 Paired t-test analysis for Test A Table 3.7 Paired t-test analysis for test B Table 3.8 Paired t-test analysis for test C Table 3.9 Format of collected data for vehicle s trip towards customer A2 Table 3.10 Processing of data Table 3.11 Processing of data (with low-pass filtering) Table 3.12 The design matrix Table 3.13 Definition of Low & High levels Table 3.14 Scenarios Table 3.15 Experimental scenarios Table 3.16 Relating scenarios with factors Table 3.17 Paired t-test analysis for Case A ( V ) Table 3.18 Paired t-test analysis for Case A (σ 2 V ) Table 3.19 Paired t-test analysis for Case B ( V ) Table 3.20 Paired t-test analysis for Case B (σ 2 V ) Table 3.21 Paired t-test analysis for Case C ( V ) Table 3.22 Paired t-test analysis for Case C (σ 2 V ) Table 3.23 Data collection Table 3.24 Estimated Effects Table 3.25 Analysis of Variance Table 3.26 Analysis of Variance (ANOVA) test Table 3.27 Prediction Errors for the eight scenarios (in seconds) Table 3.28 Proposed method for each scenario Table 3.29 Details of the delivery schedule Table 3.30 Travel time prediction accuracy results Table 3.31 Results from 30 cases Table 3.32 Details of the delivery schedule Table 3.33 Results from 30 cases Table 4.1 Mobile network access technologies Table 4.2 Accuracy of positioning methods Table 4.3 Data Management Module data Table 4.4 Client cost matrix with mean travel and service times 16

17 List of tables Table 4.5 Client cost matrix with variances of travel and service times Table 4.6 Performance tests with generated data Table 4.7 Detection of time delay through instance testing Table 4.8 Detection of time delay through statistical testing Table 4.9 Results with 5% standard deviation (σ) generated data Table 4.10 Results with 10% standard deviation (σ) generated data Table 4.11 Results with 25% standard deviation (σ) generated data Table 4.12 Performance tests with real-life data Table 4.13 Results with 5% standard deviation (σ) real-life data Table 4.14 Results with 10% standard deviation (σ) - real-life data Table 4.15 Results with 25% standard deviation (σ) - real-life data Table 5.1 Research Methods for Technical theories evaluation Table 5.2 Design parameters for the simulation process Table 5.3 Test case scenarios Table 5.4 Test Case 1 Table 5.5 Paired t-test analysis for Scenario 1 Table 5.6 Test Case 2 Table 5.7 Paired t-test analysis for Scenario 1 Table 5.8 Test Case 3 Table 5.9 Paired t-test analysis for Scenario 3 Table 5.10 Test Case 4 Table 5.11 Paired t-test analysis for Scenario 4 Table 5.12 Test Case 5 Table 5.13 Paired t-test analysis for Scenario 5 Table 5.14 Test Case 6 Table 5.15 Paired t-test analysis for Scenario 6 Table 5.16 Test Case 7 Table 5.17 Paired t-test analysis for Scenario 7 Table 5.18 Test Case 8 Table 5.19 Paired t-test analysis for Scenario 8 Table 5.20 Summary of the statistical results from the simulation results Table 6.1 Key Performance Indicators for the Pilot testing Table 6.2 Design parameters of the field experiment Table 6.3 Test case scenarios Table 6.4 Customers list for Test Case 2 Table 6.5 New delivery schedule (after applying the rerouting algorithms) Table 6.6 Customers visited by both trucks Table 6.7 Time plan of Truck B (rerouted) Table 6.7 Time plan of Truck A Table 6.8 Key Performance Indicators results for Test Case 3 Table 6.9 Key Performance Indicators results for Test Case 1 Table 6.10 Key Performance Indicators results for Test Case 2 Table 6.11 Key Performance Indicators results for Test Case 4 Table 6.12 Key Performance Indicators results for Test Case 5 Table 6.13 Key Performance Indicators results for Test Case 6 Table 6.14 Key Performance Indicators results for Test Case 7 Table 6.15 Key Performance Indicators results for Test Case 8 Table 6.16 Results from the Time delay scenario Table 6.17 Design parameters of the field experiment Table 6.18 Test case scenarios Table 6.19 Customers initially assigned in Truck A Table 6.20 Customers initially assigned in Truck B Table 6.21 Customers visited by Truck A (until the moment of breakdown) Table 6.22 Customers assignment Table 6.23 Final delivery schedule of Truck B 17

18 List of tables Table 6.24 Time plan of Truck B (rerouted truck) Table 6.25 Customer Service for Schedule A Table 6.26 Customer Service for Schedule B Table 6.27 Customers assignment Table 6.28 Customer Service for Schedule A Table 6.29 Customer Service for Schedule B Table 6.30 Customer Service for Schedule C Table 6.31 Results from the Vehicle Breakdown Scenario (Test Case 1) Table 6.32 Results from the Vehicle Breakdown Scenario (Test Case 2) Table A1 Accuracy of positioning methods Table B1. Performance results per trial (replicate) Case 1 Table B2. Performance results per trial (replicate) Case 2 Table B3. Performance results per trial (replicate) Case 3 Table B4. Performance results per trial (replicate) Case 4 Table B5. Performance results per trial (replicate) Case 5 Table B6. Performance results per trial (replicate) Case 6 Table B7. Performance results per trial (replicate) Case 7 Table B8. Performance results per trial (replicate) Case 8 18

19 Urban freight distributions and dynamic incident handling Chapter 1 1. Urban freight distributions and dynamic incident handling 1.1 Introduction Supply Chain Management processes can be classified in two major categories: Planning and Execution. While Supply Chain Planning (SCP) embraces the processes related to demand forecasting, materials requirements, and planning for production and distribution, Supply Chain Execution (SCE) focuses on the actual implementation of the supply chain plan, comprising processes such as production and stock control, warehouse management, transportation, and product delivery (Lambert et al., 1998). Supply Chain Planning has attracted significant attention, due to its critical impact on customer service, cost effectiveness, and, thus, competitiveness in increasingly demanding global markets. As an outgrowth of the research advances in this area, a number of technology-enabled systems have also emerged to assist in supply chain planning operations including MRP, MRP-II, and ERP applications, as well as integrated SCP information systems (Ballou, 2004). Supply Chain Execution has, conversely, received less attention at least as far as real-time decision-making and risk management are concerned (Gendreau and Potvin, 1998b). While processes such as stock control and warehouse management have been thoroughly investigated and supported by applications such as Warehouse Management Systems (WMS), improvement opportunities still lie in the area of goods distribution especially in an urban environment (Taniguchi et al., 2001). In this area, most existing work has focused on optimally allocating vehicles to known delivery demand under a priori assumed conditions. Conversely, Ghiani et al., (2003) argue that limited research has to date been devoted to the real-time management of vehicles during the actual execution of the distribution schedule in order to respond to unforeseen events (such as traffic congestion and vehicle breakdown) that often occur and may deteriorate the effectiveness of the predefined static routing decisions. Drawing on the need for research in the execution of urban freight distributions, the main aim of this thesis is to model the process of dynamic incident handling through the design and implementation of a real-time fleet management system. The 19

20 Urban freight distributions and dynamic incident handling following sections present the concept of goods distribution in an urban environment, focusing on the dynamic events that take place during delivery execution. Current methods and techniques for incident handling are presented and the need for a realtime fleet management system in urban environments is identified. Then, the research contribution of this thesis is presented and the research methodology that has been followed for the design, testing and evaluation of the system is analyzed. The chapter concludes with the thesis outline. 1.2 Goods distribution in an urban environment Goods distribution is a key logistics activity and contributes, on average, the highest portion to the total logistics-related costs (Ballou, 2004). Indeed, urban freight carriers face complex problems as they are expected to provide higher levels of service to their customers with lower costs. In order to do that, they should be able to determine the optimal number, capacity, and location of their warehouse facilities and find the optimal set of vehicle schedules and routes to serve as many customers as possible with the minimum operational cost (Ioannou et al., 2003). One may distinguish at least two ways for distributing goods in an urban distribution scenario: pre-placed orders delivery or on-site orders delivery. While both cases use a typical delivery network with N warehouses that deliver to M customers through a fleet of K vehicles, they differ in the way they handle demand. Deliveries of preplaced orders are based on a known demand (orders are placed by customers some time before delivery), while deliveries of on-site orders operate in a stochastic demand environment where orders are being placed during the vehicle s visit to the customer site. Table 1.1 summarizes the main attributes of the two modes of urban deliveries. Table 1.1 Types of urban deliveries Pre-placed orders delivery On-site orders delivery Known demand per sales point Unknown demand per sales point Fleet delivers based on orders Fixed schedules and delivery time windows Truck routes determined a priori based on demand, truck capacity, time constraints, and other parameters in a near-optimal way Orders are not known in advance (only sales area is) More relaxed schedules and delivery time windows Distribution of work per truck is based on past area sales (historical data) The performance of either urban distribution model may deteriorate significantly due to a number of factors (Min et al., 1998). No matter how well the initial delivery plan has been designed, a number of unforeseen events inevitably occur during the distribution execution stage, thereby resulting in a need to make real-time adjustments (Brown et al., 1987; Rego and Roucairol, 1995; Savelsbergh and Sol, 20

21 Urban freight distributions and dynamic incident handling 1998) to adapt to the new conditions and achieve the objectives of the initial plan as closely as possible. The following section analyzes such dynamic incidents (factors). 1.3 Dynamic incidents in urban freight distributions In general, an incident is any event that occurs during delivery execution and cannot be anticipated with certainty (Aronson et al., 2002). In case of urban freight deliveries, one can distinguish three sources of incidents: Incidents originating from the clients served: Typical examples include cancellation, time window changes, new customer request, amount of request, no available unloading area and changes of source and/or destination. Incidents from the road infrastructure and environment: Road blocks, traffic congestion, road constructions, flea markets, protests, rain. Incidents that arise from delivery vehicles: Typical examples include car accident and/or mechanical failure. Table 1.2 shows the classification of dynamic incidents in urban freight distributions. Each category of dynamic incidents has a direct effect on delivery execution. Incidents that arise from road infrastructure and environmental sources usually result in increased vehicle travel times, whereas client incidents result either to increased service times, vehicle re-routing or no service at all. Finally, for the case where the source of the incident arises from the vehicle itself, the effect is usually delayed or on service at all. The current methods used by freight carriers to tackle these incidents are presented in the following section. Cause of incidents Road Infrastructure & Environment Table 1.2 Dynamic incidents in urban freight distributions Incident Effect on delivery Traffic congestion, adverse weather conditions, road constructions, flea markets, protests Increased vehicle travel time Clients Delivery Vehicle No available unloading area, problems with the delivered products (e.g. wrong order) New customer request (delivery or pickup), amount of request Car accident, mechanical failure Increased customer service time Vehicle re-routing / no service No service/ Delayed service 1.4 Current incident handling practices Current practices for incident handling include mainly the use of fleet management systems which provide real-time information about the execution of a delivery schedule such as vehicle location, served customers, load temperature and so on. 21

22 Urban freight distributions and dynamic incident handling The rapidly growing interest for such systems stems mainly from the advances in information and communication technology (ICT), which has provided all the technical prerequisites for both the control of a vehicle fleet and the management of customer orders in real time. Such system makes use of satellite location identification systems such as the Global Positioning System and terrestrial mobile communication systems, such as General Packet Radio Service (GPRS) or Terrestrial Trunked Radio (TETRA). These technologies enable freight carriers to dynamically monitor their fleet through vector maps and to improve relevant delivery network performance by using a posteriori reports that include various details concerning the execution of the distribution plan by every vehicle. Although the use of such technologies supports improved utilization and management of the delivery fleet, current fleet management systems are not typically designed to address unforeseen events in a systemic fashion. When there is need for real-time intervention (i.e. in case of an unexpected event), it may be necessary to re-compute the plan using new input data. If a typical vehicle routing approach is used for re-planning (i.e. re-planning the whole schedule from scratch), many vehicle schedules may be affected, and thus cause significant performance inefficiencies (high overhead and high costs) (Psaraftis, 1995). Thus, re-planning based on classical vehicle routing solution methods may not be a realistic option. In the absence of algorithms capable of minimizing the disturbance to the overall schedule, interventions are typically performed manually (for example, through voice communication between drivers and the control centre) and the resulting decisions are local with sometimes limited effectiveness.thus, the need for enhancing existing methods or developing novel systems for managing unexpected events becomes clearer. The main aim of this thesis is the modelling of dynamic incident handling in urban freight distribution through the design, testing and evaluation of such a real-time fleet management system. The system will be able to monitor delivery vehicles, detect deviations from the distribution plan, and adjust the schedule accordingly, by suggesting optimal rerouting strategies. Using such a system, information about unforeseen events is transmitted when they occur directly from the affected truck(s) through a mobile network to headquarters and/or other parts of the fleet. Given an efficient re-routing algorithm, appropriate and feasible plan modifications are transmitted back to the fleet in a timely fashion to respond effectively to the new system state. The following section analyses the expected research contribution of this thesis. 22

23 Urban freight distributions and dynamic incident handling 1.5 Expected Contribution The expected contribution of this thesis can be analysed in the following issues: System design: Current fleet management systems are used mainly for monitoring purposes and are unable to handle in a systemic fashion various unexpected events that occur during delivery execution. Current research in the area of dynamic incident handling focuses mainly on the creation and testing of efficient algorithms that are able to handle dynamic events usually in an optimal or near optimal manner. However, such algorithms give a partial solution to the problem as in order to be effective they must be implemented in a fleet management system. The latter is able to provide real-time information about traffic or vehicle s status which acts as input data to the rerouting algorithms. There is thus a need for a holistic approach in the problem of dynamic incident handling, through the design of a real-time fleet management system that would be able not only to monitor certain vehicles but also detect possible deviations from the initial plan, and suggest new routes by using well known rerouting algorithms from the literature. Travel time prediction methods and evaluation framework: One of the basic prerequisites for detection of possible deviation from the initial plan is to be able to predict the arrival time in the remaining customers. This can be achieved by using a travel prediction method during delivery execution. We propose a method for travel time estimation which is based on historical data from previous delivery schedules. Such methods can give very accurate results when traffic patterns at the moment of travel time prediction are similar to the historical ones retrieved from the database. However, as in urban settings there are cases where travelling times vary over time and depend on when a vehicle is traversing a particular segment we propose a second travel prediction method that uses real-time data to compute the network travel time in a dynamic manner. As the vehicle is travelling towards its destination, travel time is predicted sequentially by summing the travel time derived from speed measurements at different sections of the road. The system has an intelligent mechanism that monitors the traffic situation in consecutive time steps and decides which method gives the most accurate results. It is worthwhile to mentioning that both methods have been evaluated by using an innovative testing framework that included the design of a series of experiments which demonstrate how certain variables affect the prediction accuracy of each method. Decision process for vehicle rerouting: Even if a real-time fleet management system uses accurate travel time prediction techniques, there should be a mechanism that would be able to decide whether a detected deviation is significant or not. For that reason we propose and evaluate two methods that can be used to assure that vehicle rerouting will be 23