An Empirical Framework for Understanding Human-Technology Interaction Optimisation for Route Planning

A number of interactive systems have been developed in the past to simulate or improve optimised route planning as part of problem solving (e.g. Vehicle Routing Problems (VRPs)) focussing mainly in the utilisation of computational algorithms. Main reasons for developing such interactive systems is that they combine the strengths both computerised systems and humans have, to aid the generation of optimal solutions and promote green logistics. Under a joint-cognitive perspective, the system and the human operator (user) become parts of a single ecosystem, co-operating to complete a task and in which cognitive technologies aid them to reach a decision. This paper reports the performance-based design of such an interactive tool that supports optimisation in route planning. It aims to identify human performance, behaviour and opportunities for designing innovative user-centred interactive optimisation tools for route planning. Twenty-six users evaluated the interactive route planner. Results suggest that switching strategies while planning routes lead to increase in route optimality while providing different levels of control for the user. Results lead to the extension of a joint-cognitive approach framework for optimisation routing problems that takes into account both performance metrics and contextual factors such as changes within the task environment. Related implications to optimisation systems’ design and evaluation are also discussed with a particular focus on how new ubiquitous navigation technologies can be improved to promote co-operation and more optimal route planning.

Content

Author and article information

Contributors

Genovefa Kefalidou

Conference

Publication date: July 2018

Publication date (Print): July 2018

Pages: 1-9

Affiliations

[1 ] Department of Informatics

College of Science and Engineering

University of Leicester

University Road

Leicester, LE1 7RH

UK

Article

DOI: 10.14236/ewic/HCI2018.62

SO-VID: 6943bc3f-68e5-4fe8-94e0-645a695e5095

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This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: Proceedings of the 32nd International BCS Human Computer Interaction Conference

Conference acronym: HCI

Conference number: 32

Conference location: Belfast, UK

Conference date: 4 - 6 July 2018

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Human Computer Interaction Conference

History

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1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2018.62

Self URI (journal page): https://ewic.bcs.org/

REFERENCES

J. Annett 1996 Recent developments in hierarchical task analysis, Contemporary Ergonomics 263 268
A. Bodhani 2012 Smart Transport Engineering & Technology 70 73
S. BoydL. Vandenberghe 2004 Convex optimization Cambridge university press
E. K. BurkeG. Kendall 2005 “Search Methodologies – Introductory Tutorials in Optimisation and Decision Support Techniques” Springer, USA
T. G. CrainicM. GendreauJ. Y. Potvin 2009 Intelligent freight-transportation systems: Assessment and the contribution of operations research Transportation Research Part C: Emerging Technologies 17 6 541 557
G.B. DanzigJ. M. Ramser 1959 The truck dispatching problem, Management Science 6 81 91
A. S. FukunagaA. D. Stechert 1997 An evolutionary optimization system for spacecraft design In Industrial and Engineering Applications of Artificial Intelligence and Expert Systems: Proceedings of the Tenth International Conference 1 CRC Press
E. Hollnagel 1993 Human reliability analysis: Context and control Academic press
E. HollnagelD.D. Woods 2005 Joint cognitive systems: Foundations of cognitive systems engineering CRC Press
T. C. Hu 1982 Combinatorial Algorithms Addison-Wesley
Y. E. Kalay 1999 Performance-based design Automation in construction 8 4 395 409
G. KefalidouT.C. Ormerod 2014 The Fast and the Not-So-Frugal: Human Heuristics for Optimization Problem Solving. In Proceedings of the 36th Annual Meeting of the Cognitive Science Society Quebec, Canada Cognitive Science Society
G. Kefalidou 2017 When immediate interactive feedback boosts optimization problem solving: A „human-in-the-loop‟ approach for solving Capacitated Vehicle Routing Problems Computers in Human Behavior 73 110 124
M. D. LeeD. Vickers 2000 The importance of the Convex-Hull for human performance on the Traveling Salesman Problem: A comment on MacGregor and Ormerod (1996) Perception and Psychophysics 62 226 228
J.N. MacGregorT.C. Ormerod 1996 Human performance on the traveling salesman problem, Perception and Psychophysics 58 527 539
F. Maffioli 2003 Subgradient optimization methods in integer programming with an application to a radiation therapy problem (Unpublished Dissertation) Kaiserlautern University, Kaiserlautern, Germany
D. G. Maringer 2006 Portfolio management with heuristic optimization 8 Springer Science & Business Media
D. Marr 1982 Vision. San Francisco: Freeman
Z. MichalewiczD. Fogel 2000 How to Solve it: Modern Heuristics, Springer Verlag
J. Sreevalsan-NairM. VerhoevenD. L. WoodruffI. HotzB. Hamann 2007 Human-guided enhancement of a stochastic local search: Visualization and adjustment of 3d pheromone In Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics 182 186 Springer Berlin Heidelberg
P. TothD. Vigo P. TothD. Vigo 2002 The Vehicle Routing Problem, SIAM Society for Industrial and Applied Mathematics Philadelphia USA
C.D.J. Waters 1984 Interactive vehicle routeing J. of the Operational Research Society 35 9 821 826

Comments

Comment on this article

[1] J. Annett 1996 Recent developments in hierarchical task analysis, Contemporary Ergonomics 263 268

[2] A. Bodhani 2012 Smart Transport Engineering & Technology 70 73

[3] S. BoydL. Vandenberghe 2004 Convex optimization Cambridge university press

[4] E. K. BurkeG. Kendall 2005 “Search Methodologies – Introductory Tutorials in Optimisation and Decision Support Techniques” Springer, USA

[5] T. G. CrainicM. GendreauJ. Y. Potvin 2009 Intelligent freight-transportation systems: Assessment and the contribution of operations research Transportation Research Part C: Emerging Technologies 17 6 541 557

[6] G.B. DanzigJ. M. Ramser 1959 The truck dispatching problem, Management Science 6 81 91

[7] A. S. FukunagaA. D. Stechert 1997 An evolutionary optimization system for spacecraft design In Industrial and Engineering Applications of Artificial Intelligence and Expert Systems: Proceedings of the Tenth International Conference 1 CRC Press

[8] E. Hollnagel 1993 Human reliability analysis: Context and control Academic press

[9] E. HollnagelD.D. Woods 2005 Joint cognitive systems: Foundations of cognitive systems engineering CRC Press

[10] T. C. Hu 1982 Combinatorial Algorithms Addison-Wesley

[11] Y. E. Kalay 1999 Performance-based design Automation in construction 8 4 395 409

[12] G. KefalidouT.C. Ormerod 2014 The Fast and the Not-So-Frugal: Human Heuristics for Optimization Problem Solving. In Proceedings of the 36th Annual Meeting of the Cognitive Science Society Quebec, Canada Cognitive Science Society

[13] G. Kefalidou 2017 When immediate interactive feedback boosts optimization problem solving: A „human-in-the-loop‟ approach for solving Capacitated Vehicle Routing Problems Computers in Human Behavior 73 110 124

[14] M. D. LeeD. Vickers 2000 The importance of the Convex-Hull for human performance on the Traveling Salesman Problem: A comment on MacGregor and Ormerod (1996) Perception and Psychophysics 62 226 228

[15] J.N. MacGregorT.C. Ormerod 1996 Human performance on the traveling salesman problem, Perception and Psychophysics 58 527 539

[16] F. Maffioli 2003 Subgradient optimization methods in integer programming with an application to a radiation therapy problem (Unpublished Dissertation) Kaiserlautern University, Kaiserlautern, Germany

[17] D. G. Maringer 2006 Portfolio management with heuristic optimization 8 Springer Science & Business Media

[18] D. Marr 1982 Vision. San Francisco: Freeman

[19] Z. MichalewiczD. Fogel 2000 How to Solve it: Modern Heuristics, Springer Verlag

[20] J. Sreevalsan-NairM. VerhoevenD. L. WoodruffI. HotzB. Hamann 2007 Human-guided enhancement of a stochastic local search: Visualization and adjustment of 3d pheromone In Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics 182 186 Springer Berlin Heidelberg

[21] P. TothD. Vigo P. TothD. Vigo 2002 The Vehicle Routing Problem, SIAM Society for Industrial and Applied Mathematics Philadelphia USA

[22] C.D.J. Waters 1984 Interactive vehicle routeing J. of the Operational Research Society 35 9 821 826

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