ICLS2009 Seoul Korea Table of contents

ICLS2009 Seoul Korea
Table of contents
June 3(Wed), 2009
[ Keynote Speech 1 ]
K01 "Emerging Trends of Supply Chain Management in the United States"
Prof. Hokey Min(Bowling Green State University, USA)
[Opening Ceremony]
Opening Address
Welcome Address
Congratulatory address
[ Keynote Speech 2 ]
K02 "A World Class Supply Chain Management Strategy from the Experience in Singapore"
Prof. Mark Goh(National University of Singapore)
[Supply Chain Strategy 1]
A01( icls011)
A Study of Strategic optimization for Logistics & SCM in Automotive industry
Nyeonsik Choi, Gyunghyun Choi(Hanyang University, Korea)
A02(icls029)
Success Factor Analysis of Supplier Development : A Case study of Hard Disk Drive manufacturer in
Thailand
Arunee Eiampreecha, Montalee Sasananan(Thammasat University, Thailand)
A03( icls038)
Roles of Senior Executives for Global Supply Chain Management: A Brief Review and Illustrations
Paul Hong(University of Toledo, USA), He-Boong Kwon Concord University, USA), Youngwon Park Waseda
University, Japan), Jihye Lee SK Research Institute, Korea)
A04( icls046)
Supplier Selection for Strategic Purchasing and the case study of a South Korean Elevator
Manufacturer
Dong Myung Lee(Konkuk University, Korea), Paul R. Drake(University of Liverpool Management School,
UK)

A05(icls049)
Smart Label-supported Autonomous Supply Chain Control in the Apparel Industry
Scholz-Reiter, B.; Teucke, M.; Özsahin, M.-E.; Sowade(University of Bremen, Germany)
[Supply Chain Performance 1]
B01(icls014)
Measuring the bullwhip effect in a supply chain with stochastic lead time and seasonal ARMA
demand processes
Dong Won Cho, Young Hae Lee(Hanyang University, Korea)
B02(icls017)
A Study on the Effectiveness of Vendor-managed Inventory in the Two-echelon Retail Supply Chains
Sung-Chul Hong( CJ GLS Inc., Korea),Yang-Byung Park( Kyung Hee University, Rep. of Korea)
B03(icls109)
Analyzing the Dysfunction of Shared Information
Yongwon Seo(Chung-Ang University, Korea)
B04(icls054)
A Study of Business Performance through Key Performance Indicators (KPIs) in Thai Garment
Industry
Suthathip Suanmali, Ekkprawatt Phong-arjarn, Chawalit Jeenanunta,Veeris Ammarapala(Thammasat
University, Thailand), Kornthip Watcharapanyawong(Kasetsart University,Thailand)
B05(icls070)
On Evaluation and Improvement of Supplier Performance: An Effort for Mapping the Management
Knowledge
Arief A.R. Setiawan, Hiroshi Katayama(Waseda University, Japan)
[Logistics Planning & Control 1]
C01(icls019)
Logistic of Household Hazardous Waste in Thailand,Case study: Nontaburi Province
Sirawan Ruangchuay Tuprakay(Suan Dusit Rajabhat University, Thailand),
Seree Tuprakay(Ramkhamhaeng University, Thailand)

C02( icls056)
Theory of Constraints for Recycling Automobile Tires in the Reverse Logistics System
Yuxiang Yang(Zhejiang University of Technology, P.R.China),
Hokey Min(Bowling Green, USA), Gengui Zhou((Zhejiang University of Technology, P.R.China)
C03( icls057)
A Study of Efficient Recovery Methods for Consumer Electronic End-of-Life products in Korea and
Japan
Hyunsoo Kim, Daehee Han (Kyonggi University, Korea),
Yuji Yano, Gyeonghwa Hong(Ryutsu Keizai University)
C04(icls062)
Logistics Competitiveness in Emerging Countries:the VISTA Case
Yen-Chun Jim Wu(National Sun Yat-Sen University, Taiwan) ,
Hsiao-Ting Huang(National Kaohsiung First University of Science & Technology, Taiwan)
C05(icls067)
Factor Analysis on Overseas Offshoring of Japanese Automotive Industry by Logit Model
Hisashi Isahaya(Waseda University, Japan), Shunpei Osawa, Norio Ishiwatari, Mitsunobu Yoshino,Osamu
Kurashige(Sankyu Inc., Japan)Hiroshi Katayama( Waseda University, Japan)
[Emerging Technologies for Airport Operation]
D01(icls127)
Efficient ETV Systems For Air Cargo Terminals
Sanghyeon Kim, Junjae Chae, Yoonseok Chang(Korea Aerospace University, Korea)
D02(icls128)
Airside Movable Objects Pattern Analysis based on Multi-dimensional Clustering
Sang-Hyun OH(Java Information Technology Co., Korea),
Jae-Hyun HAN(The Korea Transport Institute, Korea )
D03(icls129)
Time Delay Reduction System for GSE vehicles in Airside
Jae-Hyun HAN , Yo-Sik KIM(The Korea Transport Institute, Korea)
D04(icls130)
RFID application environment for air cargo operation
Yoon Seok Chang, Min Gyu Son(Korea Aerospace Univ., Korea), Doo Won Lee(KPC, Korea)

[Supply Chain Strategy 2]
A06( icls050)
Critical factors and barriers in the implementation of customer relationship management
Chien-Ta Bruce Ho(National Chung Hsing University, Taiwan),
Kevin Cheng, Bose Sanjoy (New York Institute of Technology, United Arab Emirates)
A07( icls058)
Valuing Supply Contract under Real Options Approaches
Phounsakda Phimphavong, Matsumaru Masanobu(Tokai University, Japan)
A08(icls065)
Relevance of Lean Management for Global Operations -Issues Learnt from Cambridge Lean
Management Workshop-
Hiroshi Katayama (Waseda University, Japan)
A09(icls085)
Effect of Initial Conditions of Beer Game in a Supply Chain
Kyoung Jong Park(Gwangju University, Korea)
A10(icls090)
Elucidating the effect of outsourcing on the supplier relationship
Kevin Shihping Huang (National Sun Yat-sen University, Taiwan),
Yu-Tang Lo ( National Sun Yat-sen University, Taiwan),
Lopin Kuo (Tamkang University, Taiwan)
A11( icls091)
SCM – Quick Implementation to Compete in the Crisis Time. Case Study in Telecom Company
Adelaide dos Santos Figueiredo, José Eduardo Fernandes(Universidade Católica de Brasília, Brazil)
[Supply Chain Performance 2]
B06(icls071)
Improvement of Supply Performance between Parts Warehouse and Assembly Plant through
Simulation :Lean Initiatives in Design and Operation of Automotive Parts Logistics
Chong Wei Chua( Waseda University, Japan), Yousuke Takahashi (Honda Motor Co., Ltd), Hiroshi
Katayama( Waseda University, Japan)
B07(icls105)

Supply Chain Dynamic Network Equilibrium Model with Electronic Commerce
Li Chunfa, Xu Shiqin, Li Hongwei(Tianjin University of Technology, P.R.China)
B08( icls118)
Congestion Analysis through Empirical Survey of Container Terminal Gate
Hyung Rim Choi, Jae Joong Kim, Jung Rock Shon, Sung Pill Choi, Chae Soo Kim, Joong Jo Shin(Dong-A
University, Korea)
B09(icls093)
Analyzing IOS Impact from SME Supplier Perspective
Choi Young-Jin ( Eulji University, Korea) Hyun-Soo Han(Hanyang University, Korea)
B10( icls098)
An Empirical Study on Trust, Relationship Behavior and Firm Performance in Global Supply Chain of
Korean IT Exporting Firms
Hee Cheol Moon(Chungnam National University, Korea),
Beom-Soo Park (Electronics and Technology Research Institute, Korea),
Jing Xing(Chungnam National University, Korea)
B11(icls013)
Multilevel Postponement: Collaborating For Extreme Product Flexibility with High Inventory Turns
William T. Walker(StarTrak Systems, LLC, Morris Plains, New Jersey, USA)
[Logistics planning & Control 2]
C06(icls073)
Reverse Logistics for End-of-Life Consumer Electronic Appliances in Korea
Tai Woo Chang, Tae Young Hur, Hyun Soo Kim(Kyonggi University, Korea)
C07( icls084)
Real-time logistic process invocation based on RFID events using CEP engine
Shuzhu Zhang , Hyerim Bae(Pusan National University, Korea)
C08( icls080)
Service Planning of an Air Cargo Distribution Center with Discrete Event Simulation
Henry Y. K. Lau, Bill K. P. Chan(The University of Hong Kong),
Steve K. K. Chan(Hong Kong Air Cargo Terminals Limited, Hong Kong)

C09(icls095)
Lean Logistics Service Providers: Option or Utopia? Experiences from the Netherlands
Job de Haan, Mark Overboom ,Fons Naus(Tilburg University, Netherlands)
C10( icls119)
Exploring Reserve logistics in the Computer Industry – Australian Case Studies
Shams Rahman, William Ong(RMIT University, Australia)
C11( icls115)
Research and Application on Visual Simulation of Production Logistics System of Coal Mine
Feng Xiwen(Shandong University of Science and Technology, China)
Zhao Zhongling(Shandong University of Science and Technology, China)
Qu Mianyou(Shandong University of Science and Technology, China)
[Modeling & Optimization]
D05(icls018)
A Subproblem Based Compromised Large-Scale Neighborhood Heuristic for Capacitated Air-Cargo
Loading Planning with Fixed Commissions
Yanzhi Li(City University of Hong Kong), Yi Tao Fan Wang(Sun Yat-Sen University, China)
D06(icls020)
An Integer Programming Formulation of a University Timetabling Problem
Khaled El-Sahli, Fatma Kablan( Garyounis University, Libya)
D07(icls022)
Economic Production Lots for Deteriorating Items with Investing on Production Processes
Ping-Hui Hsu(De Lin Institute of Technology, Taiwan),
Hui-Ming Teng(Chihlee Institute of Technology, Chung Yuan Christian University, Taiwan),
Hui Ming Wee(Chung Yuan Christian University, Taiwan)
D08(icls030)
Planning of Loading-Unloading Spaces Based on Agent-Based Simulation
Yuichi Oyama(Kanagawa Institute of Technology, Japan),
Shingo Wakamatsu(CATS CO.,LTD. Japan),
Nobunori Aiura(Kanagawa Institute of Technology, Japan),
Yutaka Karasawa(Kanagawa University, Japan), Shigeyuki Yamabe(Tokyo University, Japan)

D09(icls031)
Development of Sales Forecasting Model for Farmers’ Markets
Ma Xin,Uetake Toshifumi, Horikawa Mitsuyoshi, Takeno Takeo, Sugawara Mitsumasa (Iwate Prefectural
University, Japan)
D10(icls034)
Optimal Ordering Decisions with Returns and Shortages Backordering
Hui-Ming Teng(Chihlee Institute of Technology, Chung Yuan Christian University, Taiwan),
Ping-Hui Hsu ( De Lin Institute of Technology, Taiwan)
Yu-Fang Chiu,Hui Ming Wee(Chung Yuan Christian University, Taiwan)
June 4(Thu), 2009
[ Keynote Speech 3 ]
K03 "Meeting Supply Chain Challenges in Global Economic Stress"
Prof. Ik-Whan G. Kwon(Saint Louis University)
[ Keynote Speech 4 ]
K04 "Sustainable Supply Chain: Concept and Perspective"
Prof. Bongju Jeong(Yonsei University)
[ Keynote Speech 5 ]
K05 " Green SCM in turbulent economy”
Mr. Won Joon Hyoung(CEO & President, SAP Korea)
[Vehicle Routing and Transportation 1]
A12(icls074)
An Adaptive Memetic Algorithm for Dynamic Vehicle Routing Problems
Hongfeng Wang(Northeastern University, P. R. China), Il-Kyeong Moon(Pusan National University, Korea),
Dingwei Wang(Northeastern University, P. R. China)
A13(icls075)
A Study on Dock Door Assignment for Cross-docking Terminal with Multiple estinations in a Single
Dock
Ferdinand Friska Natalia, Geun Hwa Song, Hae Kyeong Lee, Chang Seong Ko (Kyungsung University,
Korea)

A14(icls092)
Genetic Algorithm for Workload Capacity Planning with Stochastic Service Time
Chawalit Manisri(Sripatum University, Thailand)
A15( icls076)
Heuristic Procedure for Vehicle Routing Problem with Manual Materials Handling (VRPMMH)
Prachaya Boonprasurt, Suebsak Nanthavanij(Thammasat University, Thailand)
A16(icls068)
Solving a Vehicle Routing Problem with Uncertain Number of Vehicles and Time Windows by Multiobjective
Genetic Algorithm
Shunsuke Suganuma, ReaKook Hwang(Waseda University, Japan),
Shunpei Osawa, Norio Ishiwatari, Mitsunobu Yoshino,
Osamu Kurashige(Sankyu Inc., Japan), Hiroshi Katayama(Waseda University, Japan)
[Logistics Network]
B12( icls023)
A Boltzmann random key-based Genetic Algorithm for Flexible Logistics Network Model with
Inventory
Shinichiro Ataka, Byungki Kim, Mitsuo Gen(Waseda University, Japan)
B13(icls024)
Designing Multi-product and Multi-time Period Logistics Network with Inventory by Hybrid Genetic
Algorithm
Mitsuo Gen, Su-jin Jin, Jeong-eun Lee(Waseda University, Japan)
B14(icls026)
Genetic Algorithm for Reverse Logistics Networks Problem in Product Reuse System: A Case Study
Jeong-Eun Lee, Mitsuo Gen(Waseda University, Japan ),
Kyong-Gu Rhee(Dongeui University, Korea)
B15(icls035)
Advantage Analysis of the Global Supply Network Configuration using Air-Cargo Logistics Center in
the Free Trade Zone
Jun-Der Leu, Yu-Tsung Huang(National Central University, Taiwan)
B16( icls116)
Simulation Analysis on the Air Cargo Network
Dongjin Ko, Kwanryul Lee, Chulung Lee(Korea University, Korea)

[Planning & Scheduling 1]
C12(icls088)
Spatial Characteristics in Aggregate and Dump Truck Industry
Seung-Bum Ahn, Won-Dong Lee, S. Y. Lim(Univ. of Incheon, Korea)
C13( icls027)
Quay Crane Scheduling considering Yard Cranes Workload by Multiobjective Genetic Algorithm
Yang Yang(Waseda University, Japan), Kap Hwan Kim(Pusan National University),
Mitsuo Gen(Waseda University, Japan)
C14(icls048)
Optimization Model of Hydro and Thermal Electricity Generation System for Daily Load Dispatch
Scheduling
Anwida Prompijit, Chawalit Jeenanunta, Aussadavut Dumrongsiri, Somrote Komolavanij,
Pisal Yenradee(Thammasat University, Thailand)
C15(icls053)
Improve: A New Approach for Solving the Machine Part Cell Formation Problem
Houda Elmogassabi(Garyounis University, Libya)
C16(icls066)
A Procedure for Kaizen Case Development with Related Technology Assets: A Case of Visual
Management
Koichi Murata(Waseda University, Japan), Takashi Imamura(Yamatake Corporation, Japan),
Hiroshi Katayama(Waseda University, Japan)
[Vehicle Routing and Transportation 2]
D11(icls096)
Effect of Getting Backhaul Loads in Short-to-Medium Range Transportation
Keizo Wakabayashi, Akihiro Watanabe(Nihon University, Japan),
Yu Fujita(SANNO University, Japan), Yutaka Karasawa(Kanagawa University, Japan),
Susumu Ishii(Nihon University, Japan)

D12(icls097)
Effect Analysis of Abandonment pallet Causing at Public Truck Terminal by VSP Method
Akihiro Watanabe, Keizo Wakabayashi(Nihon University, Japan),
Yu Fujita(SANNO University, Japan), Yutaka Karasawa(Kanagawa University, Japan),
Susumu Ishii(Nihon University, Japan)
D13(icls059)
An Analysis on the Preference for Mutual Development of Busan North Port and New Port
Gyeong-Gu Lee(Korea Maritime University, Korea), Ju Tae Kan(Busan Port Authority, Korea)
Ki-Chan Nam, Kyu-Seok Kwak(Korea Maritime University, Korea)
D14(icls060)
Performance analysis of quay crane systems in seaport container terminals
Pyung Hoi Koo(Pukyong National University, Korea)
D15(icls079)
A Hybrid Evolutionary Algorithm for Optimal Container Repositioning
Henry Y. K. Lau(The University of Hong Kong, Hong Kong)
Eugene Y. C. Wong(Orient Overseas Container Line Limited, Hong Kong)
[Plant & Warehouse Management]
A17(icls125)
Warehouse Optimization with Visibility : Heesuk Kang(emFrontier)
A18( icls041)
Towards a Sustainable RFID Value in the Supply Chain: RFID based performance management
Yoon Min Hwang, Jae Jeung Rho(Korea Advanced Institute of Science and Technology, Korea)
A19(icls131)
Pantos change logistics: K.C.Park (Pantos Logistics Co.,Ltd., Korea)
A20(icls078)
Determining the Number of Logistics Resources in Warehouse System Using Simulation: A case
study
Dong Sik Kim , Young Hae Lee, Dong Won Cho( Hanyang University, Korea)

A21(icls083)
A Basic Research for an Opportunity Loss of the Automated Warehouse Caused by Distribution
Y. Nakama(Sumitomo Heavy Industry, Japan), Y. Karasawa(Kanagawa University, Japan)
K. Wakabayashi(Nippon University, Japan),
N. Aiura(Kanagawa Institute of Technology, Japan)
A22(icls101)
Performance Comparison of Different Warehouse Layouts under a Class-based Storage Policy
Natanaree Sooksaksun, Voratas Kachitvichyanukul(Asian Institute of Technology, Thailand), Dah-Chuan
Gong(Chung Yuan Christian University, Taiwan)
A23(icls077)
Use of RFID and Activity-Based Costing in Direct Labor Budgeting
Lawrence Yang(Youric Consultant, Inc., Taiwan ),
Yen-Chun Jim Wu(National Sun Yat-Sen University, Taiwan)
[Supply Chain Strategy & Risk Management]
B17( icls040)
An Entropy Approach to Assessment of Small-Businesses’ Credit Based On Daily Transaction Data
DONG Yanwen, HAO Xiying(Fukushima University, Japan)
B18(icls124)
SCM Success Factors and Performance regarding Middle and Down Streams of Textiles and Fashion
Business
Sangmoo Shin(Soongsil University, Korea), Jin-Hyeok Choe(Soongsil University, Graduate school, Korea)
B19(icls104)
Materials management for remanufacturing purpose in closed loop supply chain
Paulina Golinska(Poznan University of Technology, Poland)
B20(icls121)
A Review of the Roles of Suppliers in Large Scale System Integration: A Proposal for New
Paradigm for Supply Chain Management
Seong-Jong Joo(Colorado State University-Pueblo,USA), Ik-Whan G. Kwon(Saint Louis University, USA),
Seock-Jin Hong(Bordeaux Management School, France)
B21(cls015)
Risk Management for Logistics Outsourcing under Public Emergency
FENG Shaojuan, HU Zhenbang(Wuhan Economics Institute, China)

B22(icls037)
Effective Governance of Global Supply Chain Risks: A Research Framework and Lessons from Case
Studies
Paul Hong(University of Toledo, USA), Youngwon Park, Hideaki Miyajima(Waseda University, Japan), Sachin
Modi(University of Toledo, USA),Takeshi Abe(Parametric Technology Co., Japan)
[Planning & Scheduling 2]
C17(icls069)
A Performance Evaluation of Job Sequencing for Mixed-model Assembly Line: A Comparison with
Target Chasing Method
Rea Kook Hwang , Hiroshi Katayama(Waseda University, Japan)
C18(icls081)
Multi-Chip Package (MCP) Scheduling Problem in Semiconductor Manufacturing
Yong-Hee Han ( Samsung Electronics, Korea), Jin Young Choi(Ajou University, Korea)
C19(icls082)
SCM Approach to the Mixed-model Sequencing Problem
Shusaku Hiraki(Hiroshima Shudo University, Japan)
C20(icls086)
Batch family scheduling model under setup and maintenance constraints in the Semiconductor
manufacturing
Jun-Ho Lee, Sun Hoon Kim, Young Hoon Lee(Yonsei University, Korea)
C21(icls126)
Design and Implementation of Workflow Based Yard Management System
Dong Won Cho, Young Hae Lee, Hyun Jin Hwang(Hanyang University, Korea)
C22(icls120)
A comparative study on the perception of logistics service quality by shippers, logistics service
providers and service type
Pal-Seon Jang,Oh Kyoung Kwon(Inha University, Korea)

[Vehicle Routing and Transportation 3]
D16(icls112)
Korea’s Port Development Strategy to be a World Top-class Hub Port in Global SCM
Gim Jin-Goo(LSE INSTITUTE, Korea)
D17(icls039)
Algorithm for the Multi-Objective Vehicle Routing Problem with Time Windows
Tharinee Manisri(Sripatum University, Thailand),
Anan Mungwattana(Kasetsart University, Thailand), Gerrit K. Janssens(Hasselt University, Belgium)
D18(icls102)
Key Elements of Cross-border Transportation Infrastructure: A Comprehensive Approach of Logistics
Infrastructure Development
Pichet Sooksaksun, Wutthipong Moungnoi, Santi Charoenpornpattana(King Mongkut’s University of
Technology Thonburi, Thailand)
D19(icls108)
Container Terminal Location Model Using Set-covering Method
Suk Tae Bae, Ki Wook Lee, Chang Sung Ha,Heung Suk Hwang(TongMyong Univ., Korea)
D20(icls055)
Innovative load carrier management solution for seaport terminals
-based on positioning, identification and communication technologies-
Bernd Scholz-Reiter, Marc-André Isenberg, Anne Virnich, Mehmet-Emin Özsahin(University of Bremen,
Germany)
D21(icls113)
A Study of e-RTGC Introduction Effects in the Container Terminal: Based on D Container Terminal
Case Study
Hyung Rim Choi, Yong Sung Park, Moo Hong Kang, Seung Hong Lee, Hee Yoon Kim,
Ki Nam Choi(Dong-A University, Korea)

The 5th International Congress on Logistics and SCM Systems(ICLS2009)
Algorithm for the Multi-Objective Vehicle Routing
Problem with Time Windows
Tharinee Manisri *A , Anan Mungwattana *B , and Gerrit K. Janssens *C
*A Sripatum University, Thailand, e-mail:tharinee_i@hotmail.com
*B Kasetsart University, Thailand, e-mail:fenganm@ku.ac.th
*C Hasselt University, Belgium, e-mail: gerrit.janssens@uhasselt.be
Abstract
This paper focuses on an algorithm for the
vehicle routing problem with time windows
(VRPTW). It involves servicing a set of customers,
with earliest and latest time deadlines, a constant
service time including when the vehicle arrives to the
customers. The demands are served by capacitated
vehicles with limited travel times to return to the
depot. The purpose of this research is to develop a
hybrid algorithm that includes a heuristic, a local
search and a meta-heuristic algorithm to solve
optimization problems with multiple objectives. The
first priority aims to find the minimum number of
vehicles required and the second priority aims to
search for the solution that minimizes the total travel
time. The algorithm performances are measured with
two criteria: quality of solution and running time.
A set of well-known benchmark data and the
genetic algorithm are used to compare the quality of
solution and running time of the algorithm,
respectively. The algorithm is applied to solve the
Solomon’s 56 VRPTW benchmarking problems
which have 100-customer instances. The results show
that 22 solutions are better than or competitive as
compared to the best solutions of the Solomon
benchmark problem instances. The running time
results display that the hybrid algorithm has higher
performance than the genetic algorithm when the
number of customers less than 25 nodes.
Keywords: Vehicle routing problem with time
windows, Heuristic, Local search, Meta-heuristic
1. Introduction
The vehicle routing problem (VRP) is an
operational decision problem for the delivery of
goods from a depot to customers by a fleet of
vehicles. The vehicle routing problem with time
windows (VRPTW) is an extension of the VRP with
earliest, latest, service times for customers and travel
times.
The objective is to minimize the number of
vehicles and the total travel time to service the
customers by using an evolutionary hybrid algorithm.
This paper proposes a multi-objective algorithm that
incorporates a heuristic, local search and a
meta-heuristic for solving the multi-objective
optimization in VRPTW. The algorithm is designed
by the modified push-forward insertion heuristic
(MPFIH), a λ-interchange local search descent
method (λ-LSD) and tabu search (TS). The route is
constructed based on the MPFIH as initial solution
which is improved by using the λ-LSD and TS. The
constraints of the problem are to service all the
customers within the earliest and latest service time
of the customer without exceeding the route time of
the vehicle and overloading the vehicle. The route
time of the vehicle is defined as the sum of the
waiting times, the service times and the travel times.
A vehicle that reaches a customer before the earliest
time, after the latest time and after the maximum
route time incurs waiting time, tardiness time and
overtime, respectively. The total of the customer
demands in each route can not exceed the total
capacity of the vehicle.
The rest of this paper is organized as follows.
Section 2 reviews relevant VRPTW and algorithms.
Section 3 presents tools and the methods to solve this
problem. Section 4 presents the results and
discussion. Finally, conclusions and future work are
formulated in section 5.
2. Literature Review
The VRPTW arises in retail distribution, school
bus routing, mail and newspaper delivery, airline and
railway fleet routing and scheduling. It is well-known
and complex combinatorial problem with
considerable economic significance [1]. Savelsbergh
[2] has shown that finding a feasible solution to the
traveling salesman problem with time windows
(TSPTW) is a NP-complete problem. Therefore the
VRPTW is more complex as it involves servicing
customers with time windows using multiple vehicles
that vary with respect to the problem. By this case,
almost researchers tend to heuristic and metaheuristic
methods which often produce optimal or
near optimal solutions in a reasonable amount of
computer time. Thus, there is still a considerable
interest in the design of new heuristics for solving
large-sized practical VRPTW.
Evaluation of any heuristic and meta-heuristic
method is subject to the comparison of a number of

The 5th International Congress on Logistics and SCM Systems(ICLS2009)
criteria that relate to various aspects of algorithm
performance [3]. Examples of such criteria are
running time, quality of solution, ease of
implementation, robustness and flexibility [4].
Almost all algorithms for the VRPTW use route
construction, route improvement or methods that
integrate both route construction and route
improvement. Solomon [5] designed and analyzed a
number of route construction heuristics, namely: the
savings, time-oriented nearest neighbor insertion and
a time oriented sweep heuristic for solving the
VRPTW. In his study, the time-oriented nearest
neighbor insertion heuristic has shown to be very
successful. Berger and Barkaoui [1] proposed a
parallel version of a new hybrid genetic algorithm for
the VRPTW. This approach is based upon the
simultaneous evolution of two populations of
solutions focusing on separate objectives subject to
temporal constraint relaxation. Bräysy and Gendreau
[3] presented a survey of the research on the VRPTW.
Both traditional heuristic route construction methods
and recent local search algorithm are examined in
Part I. Meta-heuristics are general solution
procedures that explore the solution space to identify
good solutions and often embed some of the standard
route construction and improvement heuristics [6].
Recently, several researches involve algorithms to
solve the multi-objective VRPTW. The primary
objective is defined as the minimization of the
number of routes or vehicles. Minimization of the
total travel cost is the secondary objective. Qi and
Sun [7] proposed an improved algorithm based on the
ant colony system (ACS), which hybridized with
randomized algorithm (RACS-VRPTW). For this
multi-objective problem, Ombuki et al.[8] presented a
genetic algorithm solution using the Pareto ranking
technique. An advantage of this approach is that it is
unnecessary to derive weights for a weighted sum
scoring formula. An evolutionary algorithm for the
VRPTW was developed by incorporating various
heuristics for local exploitation in the evolutionary
search and the concept of Pareto’s optimality [9].
All approaches in the literature are quite
effective, as they provide solutions competitive with
the well-known benchmark data, thus the benchmark
Solomon’s 56 VRPTW instances with 100 customers
[10].
3. Tools and Methods
3.1 Tools
The experiments for the research are run on
personal computer, Pentium 4 3.40 GHz. and using
MATLAB computing software.
3.2 Notation
K : total number of vehicles, k = 1,2,...
K
: lower bound of the number of vehicles,
K
LB
where K
N
∑
d
i=
LB
= 2
qk
i
N : total number of customers, including the depot
Ci
: customer i , where i = 2,3...,
N
C1
: depot
d
i
: demand of customer i
Dk
: total demand for the vehicle k
qk
: capacity of vehicle k
tij
: travel time between customer i to customer j
where i , j = 1,...,
N , i ≠ j and i , j = 1 is
depot
ei
: earliest arrival time at customer i
li
: latest arrival time at customer i
Ai
: arrival time to customer i
bi
: service time at customer i
w : waiting time between customer i and j
ij
where w
ij
= max[ e
j
− ( Ai
+ tij
),0]
,
i , j = 2,...,
N and i ≠ j
M
k
: maximum route time, where k = 1,2,...
K
Rk
: vehicle route k , where k = 1,2,...
K
Wk
: total waiting time for vehicle k ,
where k = 1,2,...
K
Bk
: total service time for vehicle k ,where
k = 1,2,...K
Ok
: total overtime for vehicle k ,where
k = 1,2,...K
Lk
: total tardiness for vehicle k ,where
k = 1,2,...K
Tk
: total travel times for vehicle k ,where
k = 1,2,...K
Tot
k
: total travel time for vehicle k ,or
Tot
k
= Tk
+ Wk
+ Bk
where k = 1,2,...
K
α : penalty weight factor for the waiting time
γ : penalty weight factor for the tardiness time
η : penalty weight factor for the overtime

The 5th International Congress on Logistics and SCM Systems(ICLS2009)
We consider a set of vehicles, K and a set of
customer nodes, C
i
. We identify C
1
as the depot
node and C = Ci ∪ C1
represent the set of all nodes.
Let x be the set of the decision variables, they are
evaluated using the function F(x)
as equation (1):
F ( x)
= Tk
+ ( α × Wk
) + ( γ × Lk
) + ( η × Ok
) (1)
3.3 Methods
In this paper develops the hybrid algorithm.
There are two phases of this algorithm. The first
phase is route construction heuristic, namely, the
modified push-forward insertion heuristic (MPFIH).
The MPFIH is a heuristic method for inserting a
customer into a route based on push-forward insertion
method of Solomon [5] and Thangiah [11][15]. It is
an efficient method for computing the insertion of a
new customer into the route. Let us assume a route
R
k
= { Cik
,..., Cmk
} where Cik
is the first set of
customer and C
mk
is the last set of customer in each
route k . The earliest arrival and latest arrival time
are defined as e
ik
, lik
and e
mk
, lmk
respectively.
The number of routes k in this method is defined as
the minimum of number of vehicles that satisfies the
total customer demand. The feasibility of inserting a
set of customers into route Rk
is checked by inserting
the customer between all the edges in the current
route and selecting the edge that satisfies the vehicle
capacity. The MPFIH algorithm is shown below.
Step1: Sort the customer nodes which have ei
and l
i
by ascending and descending method,
respectively
Step2: Construct the initial matrix, R
k
, where
k = K LB
Step3: Construct the set of Clk
and Cmk
which the
first k minimum, ei
and the first k maximum,
l
i
, respectively
Step4: Remove the customer nodes that have been
selected to matrix, R
k
Step5: Select the set of Cik
which the next k
minimum, e
i
Step6: Check the feasible route, each row of matrix,
R that satisfy the constraints,
D
k
k
m
= ∑ d
i=
l
i
≤ q
Tot ≤ M and L = 0
k
,
k k
If all rows satisfy the constraints go to step7, else
go to step9
Step7: Insert the set of Cik
between set of C
lk
and
C then repeat step4 to step6
mk
k
Step8: If all of set Cik
has been inserted to routes or
matrix, R then the algorithm terminate, else go
to step5
Step9: Select the remainder,
minimum, e
i
k
C
i
which the next
Step10: Check the feasible route, each the remainder
row of matrix, R that satisfy the constraints,
D
k
m
= ∑ d
i=
l
i
≤ q
k
Tot ≤ M and L = 0
k
,
k k
If the remainder rows satisfy the constraints go to
step11, else go to step14
Step11: Insert C in the remainder routes or rows of
R
k
i
matrix,
Step12: Remove the customer nodes that have been
selected and then repeat step9 to step12
Step13: If all of C
i
has been inserted to routes or
matrix, Rk
then the algorithm terminates, else go
to step14
Step14: Construct a new route or row of matrix,
R +
, where i = 1,2,...,
n and then repeat step9
k
i
to step13
The second phase is the route improvement
method. This algorithm applies local search and a
meta-heuristic based on the concept of iteratively
improving the solution to a problem by exploring
neighboring ones. To design a λ-interchange local
search descent method (λ-LSD), one typically needs
to specify the following choices: how an initial
feasible solution is generated, what move-generation
mechanism to use, the acceptance criterion and the
stopping test [3]. The λ-LSD is a type of
neighborhood search that the set of all neighbors
generated by the LSD for a given integer λ equal to 1
and 2. The move generation mechanism creates the
neighboring solutions by the move operators (0, 1),
(1, 0), (1, 1), (0, 2), (2, 0), (1, 2), (2, 1) and (2, 2).
Here attribute could refer, for example, The operator
(0, 1) on routes ( R
p
, Rq
) indicates a shift of one
customer from route q to route p . The operator (0,
1), (1, 0), (2, 0) and (0, 2) indicates a shift of one or
two customers between two routes. The operator (1,
1), (1, 2), (2, 1) and (2, 2) indicate an exchange of a
customer between two routes.
It is a sequential search which selects all possible
combinations of different pair of routes. The first
generation mechanism was introduced by Osman and
Christofides [12]. If the neighboring solution is better,
it replaces the current solution and the search
continues. The acceptance strategy, the first best (FB)
is used to selects the first neighbor that satisfies the
pre-defined acceptance criterion.
k

The 5th International Congress on Logistics and SCM Systems(ICLS2009)
Fig. 1 The move operator (0, 1)
Fig. 2 The move operator (1, 2)
Then the TS is used as a diversification method
to prevent that the algorithm falls into a local
optimum. The TS is used to swap node or re-arranges
a sequence of customers for each route. It is a
memory-based search strategy which guides the local
search descent method (LSD) to continue its search
beyond local optimum [13][14]. When a local
optimum is encountered, a move to the best neighbor
is made to explore the solution space, even if it may
cause of deterioration in the objective function value
in equation (1). The TS seeks the best available move
that can be determined in a reasonable amount of
time. If the neighborhood is large or its elements are
expensive to evaluate, candidate list strategies are
used to help restrict the number of solutions
examined on a given iteration. This hybrid algorithm
for the VRPTW can be summarized as follows:
Step1: Construct the travel times matrix, where using
Euclidean distances
Step2: Set the penalty weight factor parameters:
α = 0.01, γ = 0.1 and η = 0.05
Step3: Set the parameters for λ -LSD and TS, the
number of iterations = 100 and the length of the
tabu list =5
Step4: Obtain an initial MPFIH solution, x
0
Step5: Improve x
0
using the λ -LSD with the
first-best selection strategy and prevent local
optima by using TS
Step6: Evaluate the fitness function
Δ f = F( x′
) − F(
x0)
, when x′ is a possible
solution that satisfies the constraints.
If Δf > 0 then x = x′
else x = x0
Step7: If the stopping criterion is found then
terminate the algorithm else go to step6.
The algorithms’ performance is measured by two
indicators. The first one refers to the quality of
solution and the second one refers to the computer
run time. The quality of the solution is compared with
the best solution published in literature. The computer
run time is hard to compare because there are many
constraints must to considering. According to the type
of computer, the type of computing software and the
environments between runs are used. We select the
best known algorithm, GA for benchmark test
computer run time. GA is an efficient meta-heuristic
method for a range of general applications. We design
a GA, using MATLAB computing software and the
same type of personal computer. We construct a
simple GA involves three types of operators, thus,
selection, crossover and mutation in order to solve
VRPTW problems. The comparison shows CPU(s) by
using the Solomon’s 56 VRPTW benchmark
instances with 100 customers.
4. Results and Discussion
To implement the algorithm, we created a source
code using MATLAB computing software. We tested
the algorithm on 6 types of Solomon’s VRPTW
benchmarking problems including R1, R2, C1, C2,
RC1 and RC2. The experimental runs on 56 VRPTW
instances. All instances have 25, 50 or 100 customer
nodes and a single depot node. First, the quality of the
solution is shown in Tables 1-3. The comparison
results are separated to two objective functions, the
minimum number of vehicles and the minimum total
travel times as follows.
Table 1 The hybrid algorithm
Problems
Number of customers
25 50 100 All
R1 4.83 8.33 14.58 9.25
482.13 840.82 1391.43 904.79
R2 2.44 4.33 6.82 4.69
487.19 848.61 1321.58 915.85
C1 3.33 5.78 12.78 7.30
289.42 637.04 1755.68 894.05
C2 2.00 3.13 6.88 4.09
279.29 595.30 1332.43 755.51
RC1 3.75 8.25 14.75 8.92
394.56 864.74 1584.88 948.06
RC2 2.50 5.29 7.63 5.13
449.14 972.84 1555.16 993.23

The 5th International Congress on Logistics and SCM Systems(ICLS2009)
Note. For each column two average results for
Solomon’s benchmarks are presented. First row in
each problem is the average number of vehicles and
second row is the average total travel times. Column
“All” is the average results for all instances.
Table 2 The best solutions
Problems
Number of customers
25 50 100 All
R1 4.92 7.75 13.08 8.58
463.37 766.13 1178.80 802.77
R2 2.89 4.11 3.09 3.34
381.93 634.03 941.98 672.60
C1 3.00 5.00 10.00 6.00
190.59 361.69 826.70 459.66
C2 2.00 2.75 3.00 2.61
214.44 357.50 587.38 393.92
RC1 3.25 6.50 12.38 7.38
350.24 730.31 1341.39 807.31
RC2 2.88 4.43 4.88 4.04
325.53 585.24 1048.97 656.20
From Table 1 and Table 2 illustrate the result of
the hybrid algorithm is effective, as it provides
solutions competitive with best solutions, as well as
new solutions that are not biased toward the number
of vehicles. There are some new solutions that better
than Solomon problem instances. They are shown in
Table 3.
Table 3 New best-computed solutions for some
Solomon benchmark problem instances
Best solutions New best solutions
Problems
Travel
Travel
Vehicles
Vehicles
Times
Times
R101.25 8 617.1 7* 613.2*
R102.25 7 547.1 5* 494.7*
R110.25 4 444.1 4 433.5*
R111.25 5 428.8 4* 471.3
R102.50 11 909 9* 932.9
R103.50 9 772.9 8* 823.3
R101.100 20 1637.7 17* 1915.5
R102.100 18 1466.6 17* 1694.3
R201.25 4 463.3 3* 577.1
R203.25 3 391.4 2* 468.3
R207.25 3 316.6 2* 457
R210.25 3 404.6 2* 513.1
R203.50 5 605.3 4* 822.2
R210.50 4 645.6 3* 767.7
C205.50 3 359.8 2* 493.8
C206.50 3 359.8 2* 574.4
RC101.25 4 461.1 4 439.4*
RC203.25 3 326.9 2* 462.2
RC204.25 3 299.7 2* 406.5
RC206.25 3 324 2* 488.8
RC207.25 3 298.3 2* 403.2
RC203.50 4 555.3 3* 780.3
Note. * is the new best objective
The results from Table 3 show 22 new best
solutions. There are 20 solutions in the first objective
(minimum number of vehicles) and 4 solutions in the
second objective better than or competitive as
compared to the best solutions in Solomon’s
benchmark problem instances.
The computer run time comparison between the
hybrid algorithm and GA is shown in Fig. 3.
Avg.CPU(s)
35000
30000
25000
20000
15000
10000
5000
0
Computer Run Time Comparison
R1_25
R2_25
C1_25
C2_25
RC1_25
RC2_25
R1_50
R2_50
C1_50
C2_50
RC1_50
RC2_50
R1_100
R2_100
C1_100
C2_100
RC1_100
RC2_100
Problems
Fig. 3 Computer run time comparison
Hybrid
GA
The results show a trend. The hybrid algorithm
shows higher performance than the GA when the
number of customers is lower than 25 nodes. The
performance of the algorithm is lower than the GA
when the number of customers increases over 50
nodes. The number of customers is an important
factor in the performance of the hybrid algorithm but
it has little effect in the GA. It is reasonable cause
because of the main structure of the hybrid algorithm
is local search algorithm, otherwise, GA is random
search. This result demonstrates the effectiveness of
the hybrid algorithm in the quality of solution more
than running time. However, if the problem has the
numbers of customers not exceed 25 nodes. The
algorithm might be hold in this case and more
effectiveness than GA.
In addition to the results, the types of problem
which have a significant effect to computer run time
of the algorithm, are of Type1: R1, C1 and RC1 (short
scheduling horizon) and of type2: R2, C2 and RC2
(long scheduling horizon). The algorithm consumes
more computer run time for Type1 than of Type2.
5. Conclusions and Future work
The modeling of VRPTW aims to optimize a
multi-objective problem by using the hybrid
algorithm. The results are compared according to two
criteria, the quality of solution and computer run
time. The quality of solution of the algorithm is
effective, as it provides solutions competitive with the
best solutions in the Solomon benchmark problem
instances. In addition it provides the 20 new best
solutions in the first priority objective that is
proposed by this research.
The running time criterion, the experiments show
clearly that the algorithm is higher performance than

The 5th International Congress on Logistics and SCM Systems(ICLS2009)
GA when the number of customers is lower than 25
nodes. The performance of the algorithm decreases
rapidly when the number of customers is over than 50
nodes. In addition to the types of benchmarking
problems, there is significant effect to the computer
run time.
For future work, we will improve this hybrid
algorithm by using the meta-heuristic techniques,
thus, simulated annealing algorithm, ant colony
algorithm or GA to solve larger scale VRPTW
problems, i.e. n = 200 to 1000 to illustrate its
performance when the number of customers
increases.
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