000			09024nam a22002657a 4500
008			201210b2023 a|||f bm|| 00| 0 eng d
024	7		_a0009-0008-3325-1216 _2ORCID
040			_aEG-CaNU _cEG-CaNU
041	0		_aeng _beng
082			_a658.4
100	0		_aNour Sameh Mahmoud Abdulatif _93234
245	1		_aManagerial considerations for Fuzzy EVRP _c/Nour Sameh Mahmoud Abdulatif
260			_c2023
300			_a 97p. _bill. _c21 cm.
500			_3Supervisor: Tarek Khalil
502			_aThesis (MS.c)—Nile University, Egypt, 2023 .
504			_a"Includes bibliographical references"
505	0		_aContents: ACKNOWLDGEMENT ....................................................................................................................................V ABSTRACT ......................................................................................................................................................VI LIST OF Abbrevations....................................................................................................................................... X LIST OF TABLES ............................................................................................................................................ XI LIST OF FIGURES.......................................................................................................................................... XII 1 INTRODUCTION................................................................................................................................... 13 2 DISCUSSION OF THE FUZZY ELECTRIC VEHICLE PROBLEM................................................... 15 2.1 ROAD FREIGHT TRANSPORTATION.......................................................................................................... 15 2.1.1 Road Freight Transportation: Global outlook ..................................................................................... 15 2.1.2 Road Freight Transportation : Egypt’s Outlook.................................................................................. 16 2.1.3 Road Freight Transportation : Repercussions..................................................................................... 16 2.2 ELECTRIC Road Friegt Transportation...................................................................................................... 17 2.2.1 Electric Vehicles in Transportation: Global Outlook.......................................................................... 17 2.2.2 Electric Vehicles in Transportation: Egypt’s outlook ......................................................................... 19 2.2.3 ELECTRIC VEHICLES CHALLENGES.......................................................................................... 20 2.3 Routing Problems .................................................................................................................................... 21 2.3.1 Vehicle Routing Problem (VRP) ........................................................................................................ 21 2.3.2 Electric Vehicle routing Problem (EVRP).......................................................................................... 21 2.4 FUZZY Optimization ................................................................................................................................. 22 2.4.1 Fuzzy logic & logistical optimzation.................................................................................................. 22 2.4.2 Fuzzy Routing Problems & Variation................................................................................................. 23 2.4.3 Lexicographic Methods for Fuzzy Optimization ................................................................................ 24 2.5 LITERATURE GAP ANALYSIS ..................................................................................................................... 25 3 RESEARCH METHODOLOGY ............................................................................................................ 26 3.1 Research objectives ................................................................................................................................. 26 IX 3.2 Research method..................................................................................................................................... 26 3.2.1 Methodology....................................................................................................................................... 26 3.2.2 Addressing Fuzzy Demands ............................................................................................................... 29 3.2.3 Proposed model definition. ................................................................................................................. 31 3.2.4 Proposed Model.................................................................................................................................. 34 4 RESULTS and discussion....................................................................................................................... 38 4.1 Simulation Experiment and Result Analysis: Solomon Dataset ............................................................... 38 4.1.1 Description of Instances and Experimental Environment................................................................... 38 4.1.2 Experimental Results.......................................................................................................................... 39 4.2 Simulation Experiment and Result Analysis: Case Study.......................................................................... 68 4.2.1 Description of Instance and Experimental Environment .................................................................... 68 4.2.2 Experimental Results.......................................................................................................................... 69 5 CONCLUSION AND RECOMMENDATIONS ..................................................................................... 84 5.1 Managerial Implications.......................................................................................................................... 84 5.2 Recommendations................................................................................................................................... 88 6 Conclusion............................................................................................................................................... 89 7 References ............................................................................................................................................... 91
520	3		_aAbstract: This study presents a significant contribution to the Electric Vehicle Routing Problem (EVRP) domain by addressing the challenges posed by fuzzy demands, soft time windows, and the need for recharging at demand points. The research aims to provide valuable insights into electric vehicle routing optimization, specifically focusing on the dynamic nature of fuzzy demand and the incorporation of soft time windows, while simultaneously assessing the economic and environmental implications. The problem is formulated as a mixed-integer linear programming model, accommodating uncertainties in demand levels and allowing for flexibility with penalties for time window violations. Utilizing LINGO V19 software, the model is solved using both Solomon Datasets and a case study dataset, yielding exact solutions. To gauge the impact of fuzzy demand, a Depth-First Lexicographic Parametric Analysis is conducted by varying the fuzzy demand parameters. The model's validity and effectiveness are rigorously examined using Solomon's benchmark dataset and subsequently applied to a case study derived from the Egyptian local market. The solutions obtained are evaluated based on various metrics, including total incurred costs, penalty costs, total distance traveled, and total CO2 emissions. The analysis of these solutions provides managerial implications, establishing a decision-making framework for electric vehicle fleet management. Recommendations are proposed for decision-makers encompassing fuzzy demand modeling techniques, charging infrastructure considerations, pricing and incentive strategies, and effective management systems. This study significantly advances EVRP research, offering practical solutions for real-world transportation planning and logistics management. The integration of fuzzy demand dynamics and soft time windows into the optimization model enhances its applicability to complex and uncertain real-world scenarios, contributing to the sustainable development of electric vehicle routing practices
546			_aText in English, abstracts in English
650		4	_aMOT _9309
655		7	_2NULIB _aDissertation, Academic _9187
690			_aMOT _9309
942			_2ddc _cTH
999			_c10297 _d10297