Luận văn thạc sĩ Quản lý xây dựng: Phát triển thuật toán chó rừng vằn hông để tối ưu hóa quá trình vận chuyển và giảm ô nhiễm khí thải CO2 trong công nghiệp và dân dụng

43 Hình 7.1: BiӇXÿӗ Pareto thӇ hiӋn giá trӏ WuPÿѭӧc bҵng thuұt toán SSJ .... 50 Hình 7.4: BiӇXÿӗ Pareto thӇ hiӋn giá trӏ WuPÿѭӧc bҵng thuұt toán SSJ ..... FiFÿ{WKӏYjVӵSKiWWULӇQFӫDQJjQK[k

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5 TS NguyӉn Thanh Phong - Ӫy viên

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II NHIӊM VӨ VÀ NӜI DUNG:

ƒ Tìm hiӇu thuұt toán tӕLѭXEҫ\ÿjQ

ƒ Phát triӇn thuұt toán tӕi ѭXEҫ\ÿjQPӟi

ƒ Giҧi bài toán vұn chuyӇn bê tông bҵng thuұt toán mӟi

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Nowadays, the need for transportation of goods is increasing day by day to adapt

to social needs Therefore, exhaust fumes emitted by transportation vehicles also

rises and affect negatively to the living condition and health of the population

Besides, transportation consumes a large portion of fossil fuel, which drain and

exhaust energy and resources

The coordination for goods supply process effectively and sufficiently is a

hard-to-tackle issue in supply chain management It is the constant change in transporting

process and the high requirements of exhaust gas emitted that challenges the

distributors Our researchers have found a new algorithm, inspired by the hunting

instinct of side-striped jackals that can search more effectively than previous

algorithms Discrete Event Simulation (DES) is used to produce hypothesized

problems in order to optimize the various goals, so that the total of waiting time and

total length travel is minimized The results are then compared to the previous,

showing that the newly-developed algorithm is superior economically and

environmentally by reducing CO2 emission The research also solved the problem

of changing route when considering actual conditions Therefore, the new algorithm

makes managing goods transportation more active and reduces accrued cost when

unwanted condition happens

Previous researches mainly focused on distribution coordination while

delivering is still happening Researches considered incidents as a random factor in

any solution, therefore comparison among solutions could not be precise When

incidents occur, distributors do not have support tools to change the schedule

accordingly This research, on the other hand, approaches the issues more simply in

application, focuses on coordination whilst delivering and adjusting routes and

schedules for vehicles when there are any changes in the plan, which helps reduce

time and fuel, therefore reduce CO2 emission We developed a new algorithm,

called Side-Striped Jackal (SSJ) Optimization Algorithm, developed from the

tactics they use for hunting This algorithm is meant to help find the most optimized

transportation solutions, ensuring minimized time and distance traveled

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MӨC LӨC

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1.1 Lý do chӑQÿӅ tài 1

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1.3 3KѭѫQJSKiSQJKLrQFӭu 3

1.4 ĈyQJJySFӫa luұQYăQ 4

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ĈyQJJySY͉ m̿t th͹c ti͍n: 5

1.4.3 ĈyQJJySY͉ m̿t b̫o v͏ P{LWU˱ͥQJK˱ͣQJÿ͇n mͭc tiêu làm gi̫PO˱ͫng khí th̫i CO2: 5

1.5 6ѫÿӗ luұQYăQ 6

&KѭѫQJ7ӘNG QUAN Vӄ NGHIÊN CӬU 7

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4.1.2 Mô phӓng sӵ kiӋn rӡi rҥc 16

4.2 TӕLѭX 17

4.2.1 TӕLѭXÿѫQPөc tiêu 18

4.2.2 TӕLѭXÿDPөc tiêu 19

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5.2.1 HӋ thӕng phân cҩp xã hӝi 29

5.2.2 Bao vây con mӗi 29

6ăQEҳn 30

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5.2.6 Tҩn công con mӗi (khai thác) 33

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7.1.2 KӃt quҧ 49

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7.2.2 KӃt quҧ 53

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7.3.1 Khӣi tҥo thông tin chi tiӃt 55

7.3.2 KӃt quҧ 57

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8.1 KӃt luұn 61

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Tài LiӋu Tham Khҧo 63

PHҪN LÝ LӎCH TRÍCH NGANG 66

QUÁ TRÌN+Ĉ¬27ҤO 66

QUÁ TRÌNH CÔNG TÁC 66

DANH MӨC BIӆU BҦNG

Bҧng 7.1: Chi tiӃWÿѫQKjQJWҥi thӡLÿLӇm xem xét 47

Bҧng 7.2: Vӏ trí xe tҥi thӡLÿLӇPÿDQJ[pW 47

Bҧng 7.3: Bҧng ma trұn chiӅXGjLTXmQJÿѭӡng 48

Bҧng 7.4: Bҧng ma trұn vұn tӕc xe di chuyӇn 48

BҧQJ3KѭѫQJiQSKkQSKӕi có STG nhӓ nhҩt 50

BҧQJ3KѭѫQJiQSKkQSKӕLFy64ĈQKӓ nhҩt 51

Bҧng 7.7: Chi tiӃWÿѫQKjQJWҥi thӡLÿLӇm xem xét 51

Bҧng 7.8: Vӏ trí xe tҥi thӡLÿLӇPÿDQJ[pW 52

Bҧng 7.9: Bҧng ma trұn chiӅXGjLTXmQJÿѭӡng 52

Bҧng 7.10: Bҧng ma trұn vұn tӕc xe di chuyӇn 53

BҧQJ3KѭѫQJiQSKkQSKӕi có STG nhӓ nhҩt 54

BҧQJ3KѭѫQJiQSKkQSKӕLFy64ĈQKӓ nhҩt 55

Bҧng 7.13: Chi tiӃWÿѫQKjQJWҥi thӡLÿLӇm xem xét 55

Bҧng 7.14: Vӏ trí xe tҥi thӡLÿLӇPÿDQJ[pW 56

Bҧng 7.15: Bҧng ma trұn chiӅXGjLTXmQJÿѭӡng 56

Bҧng 7.16: Bҧng ma trұn vұn tӕc xe di chuyӇn 57

BҧQJ3KѭѫQJiQSKkQSKӕi có STG nhӓ nhҩt 58

BҧQJ3KѭѫQJiQSKkQSKӕLFy64ĈQKӓ nhҩt 59

DANH MӨC HÌNH

Hình 4.1: Mô tҧ quá trình mô phӓng sӵ kiӋn liên tөc 16

Hình 4.2: Mô tҧ quá trình mô phӓng sӵ kiӋn rӡi rҥc 17

Hình 4.3: Mô tҧ TXiWUuQK[iFÿӏnh giá trӏ tӕLѭXFӫa bӝ Pareto 21

Hình 4.4: Mһt phҷng tӕLѭX3DUHWR 21

Hình 5.1 Chó rӯng vҵn hông 27

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+uQKĈjQFKyEҳt chim 28

Hình 5.6: Mô hình tә chӭc bao vây 30

Hình 5.7: Mô hình tә chӭFÿLVăQ 31

Hình 5.8: Lӵa chӑn tҩn công hoһc bӓ qua con mӗi 33

+uQK6ѫÿӗ thuұt toán 36

Hình 6.1: Ví dө ma trұn chiӅu dài tuyӃQÿѭӡQJÿѫQYӏ Km) 38

Hình 6.2: Ví dө ma trұn vұn tӕc trên các tuyӃQÿѭӡQJÿѫQYӏ Km/h) 39

Hình 6.3: Ví dө vӅ SKѭѫQJiQGLFKX\Ӈn cӫa các xe 40

Hình 6.4: Mô phӓng quá trình giao nhұQErW{QJWKѭѫQJSKҭm thông qua mô phӓng sӵ kiӋn rӡi rҥc 42

Hình 6.5: Mô phӓng quá trình phân phӕi bê tông mӝt trҥm trӝn 43

Hình 7.1: BiӇXÿӗ Pareto thӇ hiӋn giá trӏ WuPÿѭӧc bҵng thuұt toán SSJ 49

Hình 7.26ѫÿӗ di chuyӇQWKHRSKѭѫQJiQFy67*QKӓ nhҩt 50

Hình 7.36ѫÿӗ di chuyӇQWKHRSKѭѫQJiQFy64ĈQKӓ nhҩt 50

Hình 7.4: BiӇXÿӗ Pareto thӇ hiӋn giá trӏ WuPÿѭӧc bҵng thuұt toán SSJ 54

Hình 7.56ѫÿӗ di chuyӇQWKHRSKѭѫQJiQFy67*QKӓ nhҩt 54 Hình 7.66ѫÿӗ di chuyӇQWKHRSKѭѫQJiQFy64ĈQKӓ nhҩt 55 Hình 7.7: BiӇXÿӗ Pareto thӇ hiӋn giá trӏ WuPÿѭӧc bҵng thuұt toán SSJ và MOABC 58 Hình 7.86ѫÿӗ di chuyӇQWKHRSKѭѫQJiQFy67*QKӓ nhҩt 58 Hình 7.96ѫÿӗ di chuyӇQWKHRSKѭѫQJiQFy64ĈQKӓ nhҩt 59

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