The technician routing problem with experience-based service times

doi:10.1016/J.OMEGA.2015.07.006

Home
/
Papers
/
The technician routing problem with experience-based service times

Journal Article•DOI•

The technician routing problem with experience-based service times

Xi Chen¹, Barrett W. Thomas¹, Mike Hewitt²•Institutions (2)

University of Iowa¹, Loyola University Chicago²

01 Jun 2016-Omega-international Journal of Management Science (Pergamon)-Vol. 61, pp 49-61

TL;DR: A model of technician routing that explicitly models individualized, experience-based learning is introduced and demonstrates that explicit modeling and the resulting ability to capture changes in productivity over time due to learning lead to significantly better and different solutions than those found when learning and workforce heterogeneity is ignored.

read less

Abstract: While home services are a fast growing industry, little attention has been given to the management of its workforce. In particular, the productivity of home-service technicians depends not only on efficiently routing from customer-to-customer, but also the management of their skillsets. This paper introduces a model of technician routing that explicitly models individualized, experience-based learning. The results demonstrate that explicit modeling and the resulting ability to capture changes in productivity over time due to learning lead to significantly better and different solutions than those found when learning and workforce heterogeneity is ignored. We show that these differences result from the levels of specialization that occur in the workforce.

...read moreread less

Content maybe subject to copyright Report

Citations

PDF

Open Access

More filters

Journal Article•DOI•

Same-day delivery with heterogeneous fleets of drones and vehicles

[...]

Marlin W. Ulmer¹, Barrett W. Thomas²•Institutions (2)

Braunschweig University of Technology¹, University of Iowa²

01 Dec 2018-Networks

TL;DR: This paper analyze how drones can be combined with regular delivery vehicles to improve same-day delivery performance and reveals that geographical districting is highly effective increasing the expected number of sameday deliveries and a combination of drone and vehicle fleets may reduce routing costs significantly.

...read moreread less

Abstract: In this paper, we analyze how drones can be combined with regular delivery vehicles to improve same-day delivery performance. To this end, we present a dynamic vehicle routing problem with heterogeneous fleets. Customers order goods over the course of the day. These goods are delivered either by a drone or by a regular transportation vehicle within a delivery deadline. Drones are faster but have a limited capacity as well as charging times. Vehicles capacities are unlimited but vehicles are slow due to urban traffic. To decide whether an order is delivered by a drone or by a vehicle, we present a policy function approximation based on geographical districting. Our computational study reveals two major implications: First, geographical districting is highly effective increasing the expected number of sameday deliveries. Second, a combination of drone and vehicle fleets may reduce routing costs significantly.

...read moreread less

136 citations

Journal Article•DOI•

Resource constrained routing and scheduling: Review and research prospects

[...]

Dimitris C. Paraskevopoulos¹, Gilbert Laporte², Panagiotis P. Repoussis³, Panagiotis P. Repoussis⁴, Christos D. Tarantilis⁴ - Show less +1 more•Institutions (4)

University of Bath¹, HEC Montréal², Stevens Institute of Technology³, Athens University of Economics and Business⁴

16 Dec 2017-European Journal of Operational Research

TL;DR: This paper provides a comprehensive survey on resource constrained routing and scheduling that unveils the problem characteristics with respect to resource qualifications, service requirements and problem objectives and identifies the most effective exact and heuristic algorithms for this class of problems.

...read moreread less

80 citations

Cites background or methods from "The technician routing problem with..."

...Chen et al. (2016) introduced an interesting extension of the personnel skills in which the technicians are scheduled throughout a time horizon (e.g., a week) and their skills proficiency improves over time as they learn how to perform the tasks, and thus the service times become smaller....
[...]
...Chen et al. (2016) introduced an interesting extension of the personnel skills in which the technicians are scheduled throughout a time horizon (e....
[...]
...(2013) ALNS Parallel algorithm, set covering, postoptimization Technician routing and scheduling Data based on Solomon (1987) instances, up to 100 service requests Chen et al. (2016) Markov decision process Record-to-record travel Technician routing and scheduling Randomly generated data...
[...]
...Chen et al. (2016) introduced an interesting extension of the personnel skills in which the technicians are scheduled throughout a time horizon (e.g., a week) and their skills proficiency improves over time as they learn how to perform the tasks, and thus the service times become smaller. The authors conducted extensive experiments and showed that explicitly considering experience-based learning significantly improves the routing solutions in terms of the total cost, compared with solutions obtained when learning is ignored. It is common to use a team of technicians to perform a task, especially when the service is delivered in a multi-stage fashion. Team building is appropriate in these cases, where individual skills matching or complementing takes place (Li et al., 2005; Kim et al., 2010; Kovacs et al., 2012). On the other hand, the paper by Goel and Meisel (2013) considers homogeneous workers, which means that all workers can perform all tasks....
[...]
...Chen et al. (2016) developed a rolling horizon procedure for the multiperiod technician routing and scheduling problem with experience-based service times....
[...]

Journal Article•DOI•

Multi-period technician scheduling with experience-based service times and stochastic customers

[...]

Xi Chen¹, Barrett W. Thomas², Mike Hewitt³•Institutions (3)

Beijing Foreign Studies University¹, University of Iowa², Loyola University Chicago³

01 Jun 2017-Computers & Operations Research

TL;DR: The multi-period technician scheduling problem with experience-based service times and stochastic customers is introduced and an approximate dynamic programming-based solution approach is introduced that is adapted to handle cases of worker attrition and new task types.

...read moreread less

33 citations

Cites background or result from "The technician routing problem with..."

...[18] consider a multi-period technician routing problem with experience-based learning and stochastic customers....
[...]
...[18] confirms the results of [17] for technician routing....
[...]
...Our choice of benchmark is motivated by the work in [18] that demonstrated that the difference between the quality of solutions from models that incorporate learning and those that do not are significant....
[...]
...A detailed review can be found in [18]....
[...]
...this work, [18] present a myopic solution approach that does not incorporate 7...
[...]

Journal Article•DOI•

A practical time slot management and routing problem for attended home services

[...]

Bruno Petrato Bruck¹, Jean-François Cordeau², Manuel Iori¹•Institutions (2)

University of Modena and Reggio Emilia¹, HEC Montréal²

01 Nov 2017-Omega-international Journal of Management Science

TL;DR: A large neighborhood search (LNS) heuristic is proposed to create time slot tables by relying on various simulation strategies to represent the behavior of customers and on an integer linear program to optimize the routing of technicians.

...read moreread less

Abstract: This paper describes the solution methodology developed to address an attended home delivery problem faced by an Italian provider of gas, electricity, and water services. This company operates in several regions and must dispatch technicians to customer locations where they carry out installation or maintenance activities within time intervals chosen by the customers. The problem consists of creating time slot tables specifying the amount of resources allocated to each region in each time slot, and of routing technicians in a cost-effective way. We propose a large neighborhood search (LNS) heuristic to create time slot tables by relying on various simulation strategies to represent the behavior of customers and on an integer linear program to optimize the routing of technicians. In addition, we also use a second integer program as a repair mechanism inside the LNS heuristic. Computational experiments carried out on data provided by the company confirm the efficiency of the proposed methodology.

...read moreread less

32 citations

Journal Article•DOI•

Iterated local search for workforce scheduling and routing problems

[...]

Fulin Xie¹, Chris N. Potts¹, Tolga Bektaş¹•Institutions (1)

University of Southampton¹

18 Jul 2017-Journal of Heuristics

TL;DR: The proposed iterated local search algorithm is applied to solve the skill vehicle routing problem, which can be viewed as a special case of the WSRP and computational results indicate that the proposed algorithm can produce high-quality solutions in short computation times.

...read moreread less

Abstract: The integration of scheduling workers to perform tasks with the traditional vehicle routing problem gives rise to the workforce scheduling and routing problems (WSRP). In the WSRP, a number of service technicians with different skills, and tasks at different locations with pre-defined time windows and skill requirements are given. It is required to find an assignment and ordering of technicians to tasks, where each task is performed within its time window by a technician with the required skill, for which the total cost of the routing is minimized. This paper describes an iterated local search (ILS) algorithm for the WSRP. The performance of the proposed algorithm is evaluated on benchmark instances against an off-the-shelf optimizer and an existing adaptive large neighbourhood search algorithm. The proposed ILS algorithm is also applied to solve the skill vehicle routing problem, which can be viewed as a special case of the WSRP. The computational results indicate that the proposed algorithm can produce high-quality solutions in short computation times.

...read moreread less

26 citations

Cites background or methods from "The technician routing problem with..."

...The scheduling aspect of this problem is adapted from the study of Cordeau et al. (2010), which considers a technician and task scheduling problem arising in a large telecommunications company. Cordeau et al. (2010) focus on the construction of teams and the assignment of tasks to teams without considering routing costs between tasks. Their problem is solved by using a construction heuristic and an ALNS algorithm. Pillac et al. (2013) extend the study of Kovacs et al. (2012) by taking tools and spare parts into account,where each taskmust be carried out by a technicianwith the required skills, tools, and spare parts, and within the prescribed time window. The problem is solved by a matheuristic consisting of a parallel version of ALNS algorithm and a mathematical programming based post-optimization procedure. Xu and Chiu (2001) also consider a field technician scheduling problem arising in the telecommunications industry....
[...]
...The scheduling aspect of this problem is adapted from the study of Cordeau et al. (2010), which considers a technician and task scheduling problem arising in a large telecommunications company. Cordeau et al. (2010) focus on the construction of teams and the assignment of tasks to teams without considering routing costs between tasks. Their problem is solved by using a construction heuristic and an ALNS algorithm. Pillac et al. (2013) extend the study of Kovacs et al. (2012) by taking tools and spare parts into account,where each taskmust be carried out by a technicianwith the required skills, tools, and spare parts, and within the prescribed time window....
[...]
...Finally, Chen et al. (2015) describe a technician routing problem with experience-based service times, where technicians learn over time, which results in service times being reduced as experience increases....
[...]
...The scheduling aspect of this problem is adapted from the study of Cordeau et al. (2010), which considers a technician and task scheduling problem arising in a large telecommunications company. Cordeau et al. (2010) focus on the construction of teams and the assignment of tasks to teams without considering routing costs between tasks. Their problem is solved by using a construction heuristic and an ALNS algorithm. Pillac et al. (2013) extend the study of Kovacs et al....
[...]
...The scheduling aspect of this problem is adapted from the study of Cordeau et al. (2010), which considers a technician and task scheduling problem arising in a large telecommunications company. Cordeau et al. (2010) focus on the construction of teams and the assignment of tasks to teams without considering routing costs between tasks....
[...]

1
2
3
4
…
5
6
7
8
9
10

Collapse

References

PDF

Open Access

More filters

Journal Article•DOI•

Factors affecting the cost of airplanes

[...]

T. P. Wright¹•Institutions (1)

Curtiss-Wright¹

01 Feb 1936-Journal of the Aeronautical Sciences

TL;DR: The matter became of increasing interest and importance because of the program sponsored by the Bureau of Air Commerce for the development of a small two-place airplane which, it was hoped, could be marketed at $700 assuming a quantity of ten thousand units could be released for construction.

...read moreread less

Abstract: TH I S subject is one which can always be relied upon to start a discussion whenever it is raised in aircraft circles. Great differences of opinion will be voiced as to the relative importance of various factors, depending somewhat on whether the discussion is between persons in the (industry who are engaged in sales, engineering, design or factory work. The attitude of those outside the industry is usually quite supercilious with the intimation present that everyone engaged in the design, development, or construction of airplanes is a sort of prima donna. Therefore, because of the rather hazy information which seems to surround the subject, it appears in order to discuss the problems from several points of view in an effort to arrive at logical conclusions. The effect of quantity production on cost, particularly, requires study as in this respect more than in others, there exists a lack of appreciation of the variation which occurs. Recently the matter became of increasing interest and importance because of the program sponsored by the Bureau of Air Commerce for the development of a small two-place airplane which, it was hoped, could be marketed at $700 assuming a quantity of ten thousand units could be released for construction.

...read moreread less

2,589 citations

"The technician routing problem with..." refers background in this paper

...Wright (1936) first quantified learning curves with the observation that the cost of assembling airplanes decreases as the number of airplanes manufactured increases....
[...]

Book•DOI•

Approximate dynamic programming : solving the curses of dimensionality

[...]

Warren B. Powell

04 Aug 2011

TL;DR: This book discusses the challenges of dynamic programming, the three curses of dimensionality, and some experimental comparisons of stepsize formulas that led to the creation of ADP for online applications.

...read moreread less

Abstract: Preface. Acknowledgments. 1. The challenges of dynamic programming. 1.1 A dynamic programming example: a shortest path problem. 1.2 The three curses of dimensionality. 1.3 Some real applications. 1.4 Problem classes. 1.5 The many dialects of dynamic programming. 1.6 What is new in this book? 1.7 Bibliographic notes. 2. Some illustrative models. 2.1 Deterministic problems. 2.2 Stochastic problems. 2.3 Information acquisition problems. 2.4 A simple modeling framework for dynamic programs. 2.5 Bibliographic notes. Problems. 3. Introduction to Markov decision processes. 3.1 The optimality equations. 3.2 Finite horizon problems. 3.3 Infinite horizon problems. 3.4 Value iteration. 3.5 Policy iteration. 3.6 Hybrid valuepolicy iteration. 3.7 The linear programming method for dynamic programs. 3.8 Monotone policies. 3.9 Why does it work? 3.10 Bibliographic notes. Problems 4. Introduction to approximate dynamic programming. 4.1 The three curses of dimensionality (revisited). 4.2 The basic idea. 4.3 Sampling random variables . 4.4 ADP using the postdecision state variable. 4.5 Lowdimensional representations of value functions. 4.6 So just what is approximate dynamic programming? 4.7 Experimental issues. 4.8 Dynamic programming with missing or incomplete models. 4.9 Relationship to reinforcement learning. 4.10 But does it work? 4.11 Bibliographic notes. Problems. 5. Modeling dynamic programs. 5.1 Notational style. 5.2 Modeling time. 5.3 Modeling resources. 5.4 The states of our system. 5.5 Modeling decisions. 5.6 The exogenous information process. 5.7 The transition function. 5.8 The contribution function. 5.9 The objective function. 5.10 A measuretheoretic view of information. 5.11 Bibliographic notes. Problems. 6. Stochastic approximation methods. 6.1 A stochastic gradient algorithm. 6.2 Some stepsize recipes. 6.3 Stochastic stepsizes. 6.4 Computing bias and variance. 6.5 Optimal stepsizes. 6.6 Some experimental comparisons of stepsize formulas. 6.7 Convergence. 6.8 Why does it work? 6.9 Bibliographic notes. Problems. 7. Approximating value functions. 7.1 Approximation using aggregation. 7.2 Approximation methods using regression models. 7.3 Recursive methods for regression models. 7.4 Neural networks. 7.5 Batch processes. 7.6 Why does it work? 7.7 Bibliographic notes. Problems. 8. ADP for finite horizon problems. 8.1 Strategies for finite horizon problems. 8.2 Qlearning. 8.3 Temporal difference learning. 8.4 Policy iteration. 8.5 Monte Carlo value and policy iteration. 8.6 The actorcritic paradigm. 8.7 Bias in value function estimation. 8.8 State sampling strategies. 8.9 Starting and stopping. 8.10 A taxonomy of approximate dynamic programming strategies. 8.11 Why does it work? 8.12 Bibliographic notes. Problems. 9. Infinite horizon problems. 9.1 From finite to infinite horizon. 9.2 Algorithmic strategies. 9.3 Stepsizes for infinite horizon problems. 9.4 Error measures. 9.5 Direct ADP for online applications. 9.6 Finite horizon models for steady state applications. 9.7 Why does it work? 9.8 Bibliographic notes. Problems. 10. Exploration vs. exploitation. 10.1 A learning exercise: the nomadic trucker. 10.2 Learning strategies. 10.3 A simple information acquisition problem. 10.4 Gittins indices and the information acquisition problem. 10.5 Variations. 10.6 The knowledge gradient algorithm. 10.7 Information acquisition in dynamic programming. 10.8 Bibliographic notes. Problems. 11. Value function approximations for special functions. 11.1 Value functions versus gradients. 11.2 Linear approximations. 11.3 Piecewise linear approximations. 11.4 The SHAPE algorithm. 11.5 Regression methods. 11.6 Cutting planes. 11.7 Why does it work? 11.8 Bibliographic notes. Problems. 12. Dynamic resource allocation. 12.1 An asset acquisition problem. 12.2 The blood management problem. 12.3 A portfolio optimization problem. 12.4 A general resource allocation problem. 12.5 A fleet management problem. 12.6 A driver management problem. 12.7 Bibliographic references. Problems. 13. Implementation challenges. 13.1 Will ADP work for your problem? 13.2 Designing an ADP algorithm for complex problems. 13.3 Debugging an ADP algorithm. 13.4 Convergence issues. 13.5 Modeling your problem. 13.6 Online vs. offline models. 13.7 If it works, patent it!

...read moreread less

2,300 citations

"The technician routing problem with..." refers background in this paper

...In the language of MDPs, we are seeking the optimal myopic policy (Powell 2011)....
[...]

Journal Article•DOI•

A state-of-the-art review on scheduling with learning effects

[...]

Dirk Biskup

16 Jul 2008-European Journal of Operational Research

TL;DR: The questions why and when learning effects in scheduling environments might occur and should be regarded from a planning perspective are discussed.

...read moreread less

614 citations

Book•

Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)

[...]

Warren B. Powell

26 Sep 2007

574 citations

"The technician routing problem with..." refers background in this paper

...In the language of MDPs, we are seeking the optimal myopic policy (Powell 2011)....
[...]

Journal Article•DOI•

PREDICTING TRAINING SUCCESS: NOT MUCH MORE THAN g

[...]

Malcolm James Ree¹, James A. Earles¹•Institutions (1)

Armstrong Laboratory¹

07 Dec 2006-Personnel Psychology

TL;DR: In this paper, the roles of general ability (g) and specific abilities (s1,sg) were investigated in prediction of job-training-school grades of 78,041 Air Force enlistees in 82 jobs.

...read moreread less

Abstract: The roles of general ability (g) and specific abilities (s1…sg) were investigated in prediction of job-training-school grades. Subjects were 78,041 Air Force enlistees in 82 jobs. General ability and specific abilities were defined by scores on the first and subsequent unrotated principal components of the enlistment selection and classification test, the Armed Services Vocational Aptitude Battery. Linear models analyses revealed that s1…s9 added little to the prediction afforded by g. It was also determined that a common prediction equation for all jobs was almost as predictive as an equation for each job.

...read moreread less

376 citations

"The technician routing problem with..." refers background in this paper

...This choice reflects the literature on human learning that suggests that individuals tend to learn different skills at the same rate (Ree and Earles 1991)....
[...]