Technical Sessions – Monday July 11
Technical Sessions – Monday July 11
Technical Sessions – Monday July 11
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MC-09 IFORS 20<strong>11</strong> - Melbourne<br />
pwan9882@mail.usyd.edu.au, Pengyi Yang, Jonathan Arthur,<br />
Jean Yang<br />
iTRAQ(TM) for protein quantisation using mass spectrometry is a powerful<br />
means of determining relative protein levels. A comprehensive iTRAQ data<br />
analysis comprises of several components including spectrum pre-processing,<br />
protein identification and protein quantisation. Each of these components involves<br />
its own statistical challenges. We are developing a comprehensive statistical<br />
analysis system with novel spectrum preprocessing and quantification<br />
algorithms. Our initial assessments demonstrate that our pipeline can produce<br />
comparable results to TransProteomicPipeline.<br />
� MC-09<br />
<strong>Monday</strong>, 16:00-17:30<br />
Meeting Room 108<br />
Pick-up and Delivery<br />
Stream: Vehicle Routing<br />
Invited session<br />
Chair: Ronald Askin, Industrial Engineering, Arizona State<br />
University, Computing, Informatics and Dec. Systems Engineering,<br />
PO Box 8809, 85287-8809, Tempe, AZ, United States,<br />
ron.askin@asu.edu<br />
1 - Methods for Dynamic Vehicle Routing Problems with<br />
Pickups, Deliveries and Time Windows<br />
Penny Holborn, School of Mathematics, Cardiff University,<br />
Senghennydd Road, CF24 4AG, Cardiff, United Kingdom,<br />
holbornpl@cf.ac.uk, Jonathan Thompson, Rhyd Lewis<br />
To solve the dynamic pickup and delivery problem with time windows we are<br />
investigating methods embedded in a rolling horizon framework. The problem<br />
is thus viewed as a series of static problems. Our initial research has focused<br />
on this static variant, and after producing an initial feasible solution we use various<br />
neighbourhood operators in conjunction with tabu search and branch and<br />
bound to make further improvements. Our current algorithm gives results that<br />
are competitive with the state of the art, and we will discuss how these methods<br />
can be applied to the dynamic variant of the problem.<br />
2 - Saving Based Algorithm for Multi-depot Version of Vehicle<br />
Routing Problem with Simultaneous Pickup and<br />
Delivery<br />
Yuvraj Gajpal, Systems Engineering Department, King Fahd<br />
University of Petroleum and Minerals, KFUPM Box 634, 31261,<br />
Dhahran, Saudi Arabia, gajpal@kfupm.edu.sa, Prakash Abad<br />
The paper presents saving based algorithm for the multi-depot version of VRP-<br />
SPD. We developed four saving based algorithms for the problem. These algorithms<br />
are 1) Partition based algorithm, 2) Nearest depot algorithm, 3) Saving<br />
algorithm and 4) Tillman’s saving algorithm. We also use cumulative-net pick<br />
approach for checking the feasibility when two existing routes are merged. The<br />
numerical results performed on benchmark problem instances show that the<br />
performance of the proposed heuristics is qualitatively better than the existing<br />
insertion based heuristics.<br />
3 - Heuristic Methods for Formation of Compact Pickup<br />
and Delivery Districts<br />
Ronald Askin, Industrial Engineering, Arizona State University,<br />
Computing, Informatics and Dec. Systems Engineering, PO Box<br />
8809, 85287-8809, Tempe, AZ, United States,<br />
ron.askin@asu.edu, Rosa Gonzalez-Ramirez, Neale Smith, Jose<br />
Luis Gonzalez-Velarde<br />
We consider partitioning a geographical region into smaller districts to balance<br />
expected workloads and district size while meeting demand and other<br />
constraints. Demand varies daily. Each district is assigned a single vehicle that<br />
departs daily from the central depot and must satisfy all service requests in that<br />
district with high probability. The problem is motivated by a parcel company<br />
that picks up and delivers packages daily. A solution approach based on a hybrid<br />
algorithm that combines GRASP and Tabu Search is compared to CPLEX<br />
under a variety of geographical and demand conditions.<br />
24<br />
� MC-10<br />
<strong>Monday</strong>, 16:00-17:30<br />
Meeting Room <strong>11</strong>1<br />
Metaheuristics for Time-Definite Logistics<br />
Stream: Time-Definite Logistics<br />
Invited session<br />
Chair: Christine Vanovermeire, University of Antwerp, 2000,<br />
Antwerp, Belgium, christine.vanovermeire@ua.ac.be<br />
1 - Sustainable Supply Chains through Flexible Horizontal<br />
Collaboration<br />
Christine Vanovermeire, University of Antwerp, 2000, Antwerp,<br />
Belgium, christine.vanovermeire@ua.ac.be, Kenneth Sörensen<br />
Horizontal collaboration among distributors is one of the most promising approaches<br />
to decrease supply chain costs and increase supply chain sustainability.<br />
In order to exploit the possibilities of such alliances to the fullest, this<br />
research investigates how and to which extent orchestrating the supply chain<br />
—i.e. synchronizing orders in time to optimize truck loads- can positively influence<br />
such alliances. Additionally, a profit allocation method based on concepts<br />
from game theory is proposed as an incentive for partners to allow flexibility in<br />
their due dates.<br />
2 - Deconstructing Record-To-Record Travel for the Vehicle<br />
Routing Problem with Time Windows<br />
Kenneth Sörensen, Faculteit Toegepaste Economische<br />
Wetenschappen, Universiteit Antwerpen, Prinsstraat 13, 2000,<br />
Antwerpen, Belgium, kenneth.sorensen@ua.ac.be, Patrick<br />
Schittekat<br />
In this paper, a simple and well-performing metaheuristic, Record-To-Record<br />
Travel, is deconstructed for the Vehicle Routing Problem with Time Windows<br />
in order to gain insight in the inner workings of each of its components. A<br />
detailed statistical experiment is set up to determine how each of its components<br />
contribute to the effectiveness of the metaheuristic, and what the optimal<br />
parameter levels are.<br />
3 - Exact Solutions to the Pickup and Delivery Problem<br />
with Time Windows using Search Algorithms<br />
Richard Kelly, Information Technology, Monash University,<br />
6/200 Glen Eira Road, 3185, Elsternwick, VIC, Australia,<br />
richard.adam.kelly@monash.edu.au<br />
We compare the performance of four search algorithms for exact solving of<br />
the pickup and delivery problem with time windows (PDPTW). The four algorithms<br />
used are A*, DFS*, IDA* and depth-first branch and bound. The<br />
performance impact of using different methods for calculating lower bounds<br />
and the techniques for determining query order is also investigated. We present<br />
a new method for calculating the lower bound using a minimum spanning tree<br />
with time window constraints.<br />
� MC-<strong>11</strong><br />
<strong>Monday</strong>, 16:00-17:30<br />
Meeting Room <strong>11</strong>2<br />
Queueing Models and Analyses II<br />
Stream: Submodular Structures and Optimization<br />
Invited session<br />
Chair: Yutaka Takahashi, Graduate School of Informatics, Kyoto<br />
University, Sakyo-ku, 606-8501, Kyoto, Japan,<br />
takahashi@i.kyoto-u.ac.jp<br />
1 - Analysis of Thinning Input Queue<br />
Takaichi Fujiwara, Information Sciences Dept., Kanagawa<br />
University, 2946, Tsuchiya, Hiratsuka-shi, 259-1293,<br />
Kanagawa-ken, Japan, r200970213oj@kanagawa-u.ac.jp<br />
Closed-form solution of T(m,n)-thinning input queue with an exponential single<br />
server is given in this paper. The T(m,n)-thinning input process is constructed<br />
by thinning customers from a Poisson arrival process repeating the<br />
thinning procedure such that m-consecutive arrivals are picked up and next<br />
n-consecutive arrivals are discarded. Interarrival time sequence of the T(m,n)thinning<br />
input process is not a sequence of i.i.d.r.v.’s. To analyse the T(m,n)thinning<br />
input queue, generating function, phase-method and Rouche’s theorem<br />
are used.
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