| 000 | 03654nas|a2200445 a 4500 | ||
|---|---|---|---|
| 005 | 20260818115027.0 | ||
| 008 | 130513s2011 th uu|m rtt 0| a1eng d | ||
| 035 | _a.b1211781x | ||
| 099 | 9 | _aAIT Thesis no.TE-10-03 | |
| 100 | 0 | _aManasanan Titapunyapat | |
| 245 | 1 | 2 | _aA genetic algorithm for the heterogeneous fleet vehicle routing problem with delivery system |
| 260 |
_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2011 |
||
| 300 |
_a180 p. : _bill. |
||
| 490 | 1 |
_aThesis ; _vno. TE-10-03 |
|
| 500 | _aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Transportation Engineering, School of Engineering and Technology | ||
| 502 | _aThesis (M. Eng.) - Asian Institute of Technology, 2010 | ||
| 520 | _aA routing solution for delivery systems with a heterogeneous vehicle fleet was developed to address constraints and improve efficiency. The solution was designed as a prototype to assist in strategic and operational decisions in organizations. In the model, different types of vehicles were located at a depot. A set of geographically spread customers were served by a heterogeneous fleet of vehicles. The demand of the customers was known from orders. The objective was to determine the shortest travel distance for each route to reduce total cost while still satisfying customers' requirements. Each customer was visited by only one vehicle. A heuristic algorithm based on genetic algorithms was chosen to address the calculation time limit which in reality cannot be met by traditional optimization methods. The algorithms were designed and tested for several parameters. A genetic algorithm involves three main procedures. Initially, permutation encoding is used as format to construct a set of proto-chromosomes (parent). Second, a crossover procedure was applied to select two proto-chromosomes as parent strings for crossover and to define crossover positions. The final procedure was a mutation operation based on mutation probability. The results showed an optimized route algorithm that produced the best quality within a limited time compared to a cluster based heuristics method and the real route table from the company the data were derived from. The genetic algorithm's performance was evaluated and appropriate values of parameters were defined. | ||
| 650 | 0 |
_aTransportation _xCost of operation |
|
| 650 | 0 | _aGenetic algorithms | |
| 650 | 0 | _aDelivery of goods | |
| 700 | 0 |
_aKunnawee Kanitpong, _eChairperson |
|
| 700 | 1 |
_aSano, Kazushi, _eCo-chairperson |
|
| 700 | 0 |
_aThirayoot Limanond, _eExamination committee |
|
| 710 | 2 |
_aThailand (HM King), _eScholarship donor |
|
| 810 | 2 |
_aAsian Institute of Technology. _tThesis ; _vno. TE-10-03 |
|
| 856 |
_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B11370 |
||
| 907 |
_a.b1211781x _bmnait _cz |
||
| 902 | _a251123 | ||
| 998 |
_b0 _c130513 _dm _ea _fz _g2 |
||
| 962 |
_a000:001:PDF:b1211781:000956:0:0:0:0:0:0 _tAbstract _vn |
||
| 945 | _lmnait | ||
| 945 | _lmnait | ||
| 945 | _lmnarc | ||
| 942 | _c20 | ||
| 942 | _c40 | ||
| 909 |
_aBarcode : 30050120704571 _bCREATED : 2013-05-13 _cRECORD # : i12728433 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0 |
||
| 909 |
_aBarcode : 30050120704548 _bCREATED : 2013-05-13 _cRECORD # : i12728445 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 2 _iTOT CHKOUT : 0 _jTOT RENEW : 0 |
||
| 909 |
_aBarcode : 30050160041579 _bCREATED : 2016-03-24 _cRECORD # : i12908113 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0 |
||
| 999 |
_c49431 _d49431 |
||