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Dynamic Fleet Management Concepts, Systems, Algorithms& Case Studies

:	Vasileios Zeimpekis, Christos D. Tarantilis, George M. Giaglis, Ioannis Minis
:	Vasileios S. Zeimpekis, Christos D. Tarantilis, George M. Giaglis, Ioannis E. Minis
:	Dynamic Fleet Management Concepts, Systems, Algorithms& Case Studies
:	Springer-Verlag
:	9780387717227
:	1
:	CHF 85.90
:

:	Management
:	English

:	242
:	Wasserzeichen
:	PC/MAC/eReader/Tablet
:	PDF

This book focuses on real time management of distribution systems, integrating the latest results in system design, algorithm development and system implementation to capture the state-of-the art research and application trends. The book important topics such as goods dispatching, couriers, rescue and repair services, taxi cab services, and more. The book includes real-life case studies that describe the solution to actual distribution problems by combining systemic and algorithmic approaches.

	TABLE OF CONTENTS	7
	PREFACE	9
	ACKNOWLEDGMENTS	14
	Chapter 1 PLANNED ROUTE OPTIMIZATION FOR REAL- TIME VEHICLE ROUTING	15
	1.1 INTRODUCTION	15
	1.2 ADAPTATION OF STATIC ALGORITHMS	18
	1.2.1 Many-to-one (One-to-many) Problems	19
	1.2.1.1 Local update procedures	19
	1.2.1.2 Reoptimization procedures	20
	1.2.2 Many-to-many Problems	22
	1.2.2.1 Local update procedures	22
	1.2.2.2 Reoptimization procedures	22
	1.2.3 Multiple Plan Approach (MPA)	24
	1.3 DIVERSION	24
	1.4 ANTICIPATION OF FUTURE REQUESTS	26
	1.4.1 Double Horizon	26
	1.4.2 Waiting Strategies	26
	1.4.3 Fruitful Regions	28
	1.4.4 Multiple Scenario Approach (MSA)	28
	1.5 CONCLUSION	28
	ACKNOWLEDGEMENTS	30
	REFERENCES	30
	Chapter 2 CLASSIFICATION OF DYNAMIC VEHICLE ROUTING SYSTEMS	33
	2.1 INTRODUCTION	33
	2.2 THE DYNAMIC VEHICLE ROUTING PROBLEM	35
	2.3 STATIC VERSUS DYNAMIC VEHICLE ROUTING	37
	2.4 THE DEGREE OF DYNAMISM	40
	2.4.1 Dynamism Without Time Windows	41
	2.4.1.1 The degree of dynamism	41
	2.4.1.2 Effective Degree of Dynamism - EDOD	43
	2.4.2 Dynamism and Time Windows	43
	2.4.3 Effective Degree of Dynamism EDOD-TW	44
	2.5 MEASURING THE PERFORMANCE OF DVRP S	45
	2.5.1 Competitive Analysis	46
	2.5.2 Determining the Objectives	47
	2.6 THREE-ECHELON FRAMEWORK FOR DVRP s	48
	2.6.1 Echelon I Weakly Dynamic Systems	48
	2.6.2 Echelon II Moderately Dynamic Systems	49
	2.6.3 Echelon III Strongly Dynamic Systems	50
	2.6.4 System Classification	51
	2.7 CONCLUDING REMARKS	53
	REFERENCES	53
	Chapter 3 DYNAMIC AND STOCHASTIC VEHICLE ROUTING IN PRACTICE	55
	3.1 INTRODUCTION	55
	3.2 THE DYNAMIC AND STOCHASTIC VEHICLE ROUTING PROBLEM	56
	3.3 APPLICATION EXAMPLES	59
	3.4 A FORMAL DESCRIPTION OF DYNAMIC AND STOCHASTIC VRPS	61
	3.5 A DYNAMIC AND STOCHASTIC VRP SOLVER	63
	3.5.1 Overall Architecture	63
	3.5.2 Requirements	65
	3.5.3 The SPIDER DSVRP Solver	67
	3.6 A ROBUST APPROACH TO DYNAMIC AND STOCHASTIC VRPS	68
	3.7 LEARNING EVENT MODELS	71
	3.7.1 Bayesian Networks	72
	3.7.2 Modeling of Stochastic VRP Events	73
	3.8 CONCLUSIONS AND FURTHER RESEARCH	75
	REFERENCES	75
	Chapter 4 A PARALLELIZABLE AND APPROXIMATE DYNAMIC PROGRAMMING- BASED DYNAMIC FLEET MANAGEMENT MODEL WITH RANDOM TRAVEL TIMES AND MULTIPLE VEHICLE TYPES	78
	4.1 INTRODUCTION AND RELEVANT LITERATURE	78
	4.2 PROBLEM DESCRIPTION DESCRIPTION DESCRIPTION DESCRIPTION DESCRIPTION	82
	4.3 MODEL FORMULATION	83
	4.3.1 Deterministic Travel Times Times Times Times Times Times Times	83
	4.3.2 Random Travel Times	85
	4.4 STRUCTURE OF THE APPROXIMATE SUBPROBLEMS AND PARALLELIZATION PARALLELIZATION PARALLELIZATION	88
	4.5 CHARACTERIZING THE ARRIVAL RANDOM VARIABLES VARIABLES VARIABLES VARIABLES VARIABLES VARIABLES VARIABLES VARIABLES VARIABLES VARIABLES VARIABLES VARIABLES VARIABLES VARIABLES VARIABLES	93
	4.6 UPDATING AND IMPROVING THE VALUE FUNCTION APPROXIMATIONS	94
	4.7 COMPUTATIONAL EXPERIMENTS	98
	4.7.1 Experimental Setup	98
	4.7.2 Computational Results Results	101
	4.8 CONCLUSIONS AND RESEARCH PROSPECTS	104
	REFERENCES	105
	Chapter 5 INTEGRATED MODEL FOR THE DYNAMIC ON- DEMAND AIR TRANSPORTATION OPERATIONS	107
	5.1 INTRODUCTION	107
	5.2 BACKGROUND AND LITERATURE SURVEY	108
	5.2.1 Background of the Problem	109
	5.2.2 Previous Work	110
	5.3 THE INTEGRATED MODEL	111
	5.3.1 Crew Network and Crew Reassignment	112
	5.3.2 The Fleet-station Time Line	114
	5.3.3 The Model Formulation	115
	5.3.4 Solution Algorithm	116
	5.3.5 Dynamic Plan Adjustment to Handle Uncertainty	118
	5.3.5.1 Demand uncertainty	119
	5.3.5.2 Uncertainty on aircraft availability	119
	5.4 COMPUTATIONAL EXPERIMENTS	120
	5.4.1 New Demand without Time Window	120
	5.4.2 New Demand with Time Window	122
	5.5 CONCLUSIONS	122
	REFERENCES	123
	Chapter 6 AN INTERMODAL TIME-DEPENDENT MINIMUM COST PATH ALGORITHM	124
	With an Application to Hazmat Routing	124
	6.1 INTRODUCTION	124
	6.2 BACKGROUND	126
	6.2.1 Shortest Path Algorithms	127
	6.2.2 Hazmat Transportation Problem	128
	6.3 PROBLEM FORMULATION FORMULATION	129
	6.4 ALGORITHM AND PROPERTIES PROPERTIES PROPERTIES PROPERTIES PROPERTIES PROPERTIES PROPERTIES PROPERTIES PROPERTIES PROPERTIES PROPERTIES PROPERTIES PROPERTIES PROPERTIES	132
	6.5 EXTENSION OF THE TDIMCP ALGORITHM TO MINIMUM RISK HAZMAT ROUTING ROUTING ROUTING ROUTING ROUTING ROUTING ROUTING	136
	6.6 NUMERICAL TESTS OF HAZMAT ROUTING PROBLEM	138
	6.7 CONCLUDING REMARKS	141
	REFERENCES	142
	Chapter 7 REAL- TIME EMERGENCY RESPONSE FLEET DEPLOYMENT: CONCEPTS, SYSTEMS, SIMULATION	142
	144	142
	7.1 INTRODUCTION	144
	7.2 AN INTEGRATED EMERGENCY VEHICLE FLEET MANAGEMENT SYSTEM	146
	7.3 PROBLEM STATEMENT	148
	7.4 LITERATURE REVIEW	151
	7.5 SIMULATION	154
	7.5.1 Emergency Module	155
	7.5.2 Vehicle Module	156
	7.5.3 Optimizer Module	156
	7.5.4 Calibration of Simulation Model	156
	7.5.5 Output Analysis	156
	7.6 MATHEMATICAL MODEL	157
	7.6.1 Notat