Technical Sessions – Monday July 11

� TB-09
Tuesday, 11:00-12:30
Meeting Room 108
Meta-heuristic Approaches to Vehicle
Routing
Stream: Vehicle Routing
Invited session
Chair: Bülent Çatay, Faculty of Eng. & Natural Sciences, Sabanci
University, Tuzla, 34956, Istanbul, Turkey, catay@sabanciuniv.edu
Chair: Louis-Martin Rousseau, Mathematics and Industrial
Engineering, École Polytechnique de Montréal, CP6079 succ
centre-ville, H3C 3A7, Montreal, QC, Canada,
louism@crt.umontreal.ca
1 - Optimizing Cash Delivery Operations for Bantas Using
Tabu Search: A Case Study
Burcin Bozkaya, Faculty of Management, Sabanci University,
Orhanli, Tuzla, 34956, Istanbul, Turkey,
bbozkaya@sabanciuniv.edu, Alper Erdemir
Bantas A.S., a company formed as a joint venture of 3 major banks in Turkey,
must deliver cash everyday to more than 1200 branches and ATMs in Turkey,
using a fleet of 120+ vehicles. The solution we implemented at Bantas utilizes
ArcLogistics, a commercial route optimization software built on Tabu Search
heuristic optimization principles. We describe details of this implementation
along with an overview of the meta-heuristic used and some numerical results.
We also describe the integration of this system with a vehicle tracking application
that allows comparison of planned vs. actual routes.
2 - Solving Bakery Distribution Planning Problem Using a
Genetic Algorithm
Timur Keskinturk, School of Business, University of Istanbul,
Turkey, tkturk2010@gmail.com, Mehmet Bayram Yildirim
In this paper, we analyze a distribution problem at a bakery company. This
bakery utilizes its own truck fleet to deliver different types of breads to serve
grocery stores in a large metropolitan area. Problem is modeled as a variation
of a capacitated vehicle routing problem with time windows. To obtain good
quality solutions in a reasonable amount of time, a genetic algorithm with a
two exchange improvement local search heuristic is proposed. The proposed
algorithm which improves the bakery’s distribution significantly is also tested
on several problems.
3 - Route Optimization for Mobile Phone Distribution and
Repair Service
Vincent F. Yu, Department of Industrial Management, National
Taiwan University of Science and Technology, 43, Sec. 4,
Keelung Rd., 106, Taipei, Taiwan, vincent@mail.ntust.edu.tw,
Shan-Huen Huang, Tirza Naftali, Hsiu-I Ting
Mobile phone distributors dispatch vehicles from several distribution centers
(DCs) to service points to deliver new phones, parts, and repaired products, and
pick up recycled phones and repair orders. Under such distribution mode, it is
important to appropriately select the location of DCs and plan vehicle routes to
minimize the total distribution cost. We formulate the problem as the location
routing problem with simultaneous pickup and delivery problem (LRPSPD). A
mathematical programming model and a heuristic based on genetic algorithm
are developed for solving the LRPSPD.
4 - Metaheuristics for an Oil Delivery Vehicle Routing Problem
Louis-Martin Rousseau, Mathematics and Industrial
Engineering, École Polytechnique de Montréal, CP6079 succ
centre-ville, H3C 3A7, Montreal, QC, Canada,
louism@crt.umontreal.ca, Guy Desaulniers, Eric
Prescott-Gagnon
IFORS 2011 - Melbourne TB-10
Companies distributing heating oil typically solve vehicle routing problems on
a daily basis. Their problems may involve various features such as a heterogeneous
vehicle fleet, multiple depots, intra-route replenishments, time windows,
driver shifts and optional customers. In this paper, we consider such a rich
vehicle routing problem that arises in practice and develop three metaheuristics
to address it, namely, a tabu search (ts) algorithm, a large neighborhood
search (lns) heuristic based on this ts heuristic and another lns heuristic based
on a column generation (cg) heuristic. Computational results obtained on instances
derived from a real dataset indicate that the lns methods outperform
the ts heuristic. Furthermore, the lns method based on cg tends to produce
better quality results than the ts-based lns heuristic, especially when sufficient
computational time is available.
� TB-10
Tuesday, 11:00-12:30
Meeting Room 111
Railway Applications I
Stream: Public Transit
Invited session
Chair: Dennis Huisman, Econometric Institute, Erasmus University,
Rotterdam, Netherlands, huisman@ese.eur.nl
1 - Passenger Oriented Disruption Management: the Value
of Smart Card Data
Evelien Van der Hurk, Erasmus University, NL-3000 DR,
Rotterdam, Netherlands, EHurk@rsm.nl
In disruption management the emphasis is shifting from rescheduling resources
(rolling stock and crew) to passenger service. For passenger oriented disruption
management, information is needed on passengers’ movements, destination
and behavior during a disruption. Our case study shows that smart card
data provides this information. The value of smart card data for disruption
management is twofold. We can better predict passenger flows and thus the
seat demand during a disruption. Also, it can be used for estimating passenger
delays and for evaluating the disruption management strategy.
2 - Passenger Oriented Disruption Management by Adapting
Stopping Patterns & Rolling Stock Schedules
Lucas Veelenturf, Department of Decision and Information
Sciences, Rotterdam School of Management, Erasmus
University, Postbus 1738, NL-3000 DR, Rotterdam, Netherlands,
LVeelenturf@rsm.nl, Gabor Maroti, Leo Kroon
Often unforeseen disruptions require railway operators to quickly adjust the
timetable and resource schedules, and passengers to adapt their routes to their
destinations. The railway operator has to take the changed passenger behavior
into account. Next to increasing capacities of trains for which the operator
expects more demand it may be useful to operate additional trains or to adapt
stopping patterns, e.g. intercity trains also dwell at regional stations. In this
research we construct a passenger oriented rolling stock rescheduling approach
which integrates such stopping pattern decisions.
3 - Minimization of Delay Propagation in Multi-area Railway
Traffic Control
Dario Pacciarelli, Dipartimento di Informatica e Automazione,
Università Roma Tre, via della vasca navale, 79, 00146, Roma,
Italy, pacciarelli@dia.uniroma3.it, Francesco Corman, Andrea
D’Ariano, Marco Pranzo
Optimization of multi-area railway traffic control is a bilevel programming
problem in which a coordinator imposes constraints at the border of several
dispatching areas. Each area is controlled by a dispatcher which reschedules
trains by minimizing delays in its area. The coordinator objective is to attain
global feasibility and optimality of the decisions taken by all dispatchers. We
present a branch and bound procedure for the coordinator problem that has
been tested on a large real railway network in the Netherlands with busy traffic
conditions. Experimental results are very promising.
4 - Mathematical Model Analyses for the Optimal Railway
Track Maintenance Strategy with Consideration on Risk
for Derailment Accident
Masashi Miwa, Track Management, Railway Technical Research
Institute, 2-8-38 Hikari-cho Kokubunji-shi, 185-8540,
Kokubunji, Tokyo, Japan, miwa@rtri.or.jp, Tatsuo Oyama
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