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考慮燃油消耗與碳排成本下物流車輛配送路線之研究 = = Research...

徐婕瑀

考慮燃油消耗與碳排成本下物流車輛配送路線之研究 = = Research on Logistics Vehicle Distribution Routes Considering Fuel Consumption and Carbon Emission Costs /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	考慮燃油消耗與碳排成本下物流車輛配送路線之研究 =/ 徐婕瑀.
Reminder of title:	Research on Logistics Vehicle Distribution Routes Considering Fuel Consumption and Carbon Emission Costs /
remainder title:	Research on Logistics Vehicle Distribution Routes Considering Fuel Consumption and Carbon Emission Costs.
Author:	徐婕瑀
Published:	雲林縣 :國立虎尾科技大學 , : 民113.07.,
Description:	[8], 89面 :圖, 表 ; : 30公分.;
Notes:	指導教授: 陳盈彥.
Subject:	城市物流. -
Online resource:	電子資源

考慮燃油消耗與碳排成本下物流車輛配送路線之研究 = = Research on Logistics Vehicle Distribution Routes Considering Fuel Consumption and Carbon Emission Costs /
徐婕瑀

考慮燃油消耗與碳排成本下物流車輛配送路線之研究 =Research on Logistics Vehicle Distribution Routes Considering Fuel Consumption and Carbon Emission Costs /Research on Logistics Vehicle Distribution Routes Considering Fuel Consumption and Carbon Emission Costs.徐婕瑀. - 初版. - 雲林縣 :國立虎尾科技大學 ,民113.07. - [8], 89面 :圖, 表 ;30公分.

指導教授: 陳盈彥.

碩士論文--國立虎尾科技大學工業管理系工業工程與管理碩士班.

含參考書目.

城市物流隨著經濟快速發展而逐漸繁盛，又因新冠疫情的肆虐，改變顧客的傳統消費模式，進一步促進電子商務的崛起。然而，物流業的快速發展也帶來污染問題，因此減少運輸過程中的碳排放視為本研究探討之議題。本研究採用異質車輛，並以多艙種(Multi-Compartment)、多趟次(Multi-Trip)之條件進行配送，且具有軟時窗限制，即允許延遲送達，以應對實際運輸過程中的不確定性及變動；同時，考量燃油消耗成本。綜合上述問題特性，構建最佳化數學模型，透過作業研究最佳化軟體於小規模試驗範例驗證其正確性。本研究主要目標為最小化車輛固定成本，次要目標為減少因延遲送達而導致的懲罰成本及燃油消耗成本，從而設計最佳配送路徑。為解決大規模且複雜的車輛途程問題，開發適應性遺傳演算法(Adaptive genetic algorithm, AGA)於實際案例。最終，藉由敏感度分析評估不同參數對模型結果的影響，以及提供改善策略作為決策者的參考框架。.

(平裝)Subjects--Topical Terms:

1449083
城市物流.

考慮燃油消耗與碳排成本下物流車輛配送路線之研究 = = Research on Logistics Vehicle Distribution Routes Considering Fuel Consumption and Carbon Emission Costs /
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245 1 0 $a 考慮燃油消耗與碳排成本下物流車輛配送路線之研究 = $b Research on Logistics Vehicle Distribution Routes Considering Fuel Consumption and Carbon Emission Costs / $c 徐婕瑀.
246 1 1 $a Research on Logistics Vehicle Distribution Routes Considering Fuel Consumption and Carbon Emission Costs.
250 $a 初版.
260 # $a 雲林縣 : $b 國立虎尾科技大學 , $c 民113.07.
300 $a [8], 89面 : $b 圖, 表 ; $c 30公分.
500 $a 指導教授: 陳盈彥.
500 $a 學年度: 112.
502 $a 碩士論文--國立虎尾科技大學工業管理系工業工程與管理碩士班.
504 $a 含參考書目.
520 3 $a 城市物流隨著經濟快速發展而逐漸繁盛，又因新冠疫情的肆虐，改變顧客的傳統消費模式，進一步促進電子商務的崛起。然而，物流業的快速發展也帶來污染問題，因此減少運輸過程中的碳排放視為本研究探討之議題。本研究採用異質車輛，並以多艙種(Multi-Compartment)、多趟次(Multi-Trip)之條件進行配送，且具有軟時窗限制，即允許延遲送達，以應對實際運輸過程中的不確定性及變動；同時，考量燃油消耗成本。綜合上述問題特性，構建最佳化數學模型，透過作業研究最佳化軟體於小規模試驗範例驗證其正確性。本研究主要目標為最小化車輛固定成本，次要目標為減少因延遲送達而導致的懲罰成本及燃油消耗成本，從而設計最佳配送路徑。為解決大規模且複雜的車輛途程問題，開發適應性遺傳演算法(Adaptive genetic algorithm, AGA)於實際案例。最終，藉由敏感度分析評估不同參數對模型結果的影響，以及提供改善策略作為決策者的參考框架。.
520 3 $a Urban logistics have thrived amidst rapid economic growth, while concurrently being influenced by the COVID-19 pandemic, altering traditional consumer behaviors and boosting e-commerce. However, this rapid expansion in the logistics industry has also heightened environmental concerns, leading this study to focus on reducing carbon emissions during transportation. Using heterogeneous vehicles, the study investigates multi-compartment, multi-trip deliveries within soft time windows constraints, accommodating delays. Fuel consumption costs are considered. Constructing an optimization mathematical model and validating it with small-scale pilot studies using operational research software. The primary goal is to minimize vehicle fixed costs, with secondary aims of reducing penalties from delayed deliveries and optimizing fuel consumption to design efficient delivery routes. Addressing large-scale and complex vehicle routing challenges, an Adaptive Genetic Algorithm (AGA) is developed and validated in practical scenarios. Finally, sensitivity analysis assesses parameter impacts and proposes enhancement strategies for decision-makers..
563 $a (平裝)
650 # 4 $a 城市物流. $3 1449083
650 # 4 $a 軟時窗限制. $3 1422518
650 # 4 $a 多艙種. $3 1381245
650 # 4 $a 多趟次. $3 1381244
650 # 4 $a 燃油消耗. $3 1422519
650 # 4 $a 適應性遺傳演算法. $3 1381248
650 # 4 $a Urban Logistics. $3 1449097
650 # 4 $a Soft Time Window Constraints. $3 1449098
650 # 4 $a Multi-Compartment. $3 1381250
650 # 4 $a Multi-Trip. $3 1381249
650 # 4 $a Fuel Consumption. $3 1449099
650 # 4 $a Adaptive Genetic Algorithm. $3 1381253
856 7 # $u https://handle.ncl.edu.tw/11296/37pv28 $z 電子資源 $2 http