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Operational Transportation Planning of Modern Freight Forwarding Companies : Vehicle Routing under Consideration of Subcontracting and Request Exchange.

By: Wang, Xin.
Material type: TextTextSeries: eBooks on Demand.Produktion und Logistik: Publisher: Wiesbaden : Gabler, 2014Copyright date: ©2015Edition: 1st ed.Description: 1 online resource (172 pages).Content type: text Media type: computer Carrier type: online resourceISBN: 9783658068691.Subject(s): Freight and freightage -- Planning | Transportation -- Planning | Vehicle routing problemGenre/Form: Electronic books.Additional physical formats: Print version:: Operational Transportation Planning of Modern Freight Forwarding Companies : Vehicle Routing under Consideration of Subcontracting and Request ExchangeDDC classification: 388.044 Online resources: Click here to view this ebook.
Contents:
Foreword -- Preface -- Contents -- List of Figures -- List of Tables -- Abbreviations -- 1 Introduction -- 1.1 Request fulfillment of freight forwarding companies -- 1.2 Objectives and Structure of the Thesis -- 2 Vehicle Routing -- 2.1 Introduction -- 2.2 Mathematical model for the PDPTW -- 2.3 Solution approaches for the PDPTW -- 2.3.1 Exact algorithms -- 2.3.2 Heuristic algorithms -- 2.4 The ALNS heuristic of Ropke and Pisinger (2006) -- 2.4.1 General ideas -- 2.4.2 Removal operators -- 2.4.3 Insertion operators -- 2.4.4 Adaptive operator choice -- 2.4.5 Further settings -- 3 Freight Consolidation -- 3.1 Introduction -- 3.2 Mathematical formulation -- 3.3 Solution methodology -- 3.3.1 A local search heuristic -- 3.3.2 A simulated annealing heuristic -- 3.4 Computational experiments -- 3.4.1 Cost function for the freight charge calculation -- 3.4.2 Test instances -- 3.4.3 Computational results -- 3.5 Conclusions -- 4 From Cherry-Picking to Integrated Operational Transportation Planning -- 4.1 External transportation resources in subcontracting -- 4.2 Cherry-Picking -- 4.3 Integrated operational transportation planning -- 4.3.1 Literature review -- 4.3.2 Mathematical model -- 4.4 Reducing long-term costs through integrated planning - A computational example based on the VRP -- 5 Solution approaches for the integrated operational transportation planning problem -- 5.1 Adaptive large neighborhood search -- 5.2 Heuristic II: An iterative approach -- 5.2.1 A set partitioning model of the IOTPP -- 5.2.2 Construction of candidate routes -- 5.2.3 Obtaining integer solutions -- 5.2.4 Overview of the heuristic -- 5.3 Computational experiments -- 5.3.1 Instance generation -- 5.3.2 Computational results -- 5.4 Conclusions -- 6 Collaborative transportation planning -- 6.1 Introduction -- 6.2 Design of request exchange mechanisms.
6.3 Mathematical formulation -- 6.4 Request exchange mechanisms in literature -- 7 A route-based request exchange mechanism for the collaborative transportation planning -- 7.1 The route-based request exchange mechanism -- 7.1.1 Preprocessing -- 7.1.2 Initial route generation -- 7.1.3 Temporary winner determination -- 7.1.4 Iterative route generation -- 7.1.5 Final winner determination -- 7.2 Computational experiments -- 7.2.1 Test instance generation -- 7.2.2 Route generator -- 7.2.3 Computational results -- 7.2.4 Discussion of results -- 7.3 Conclusions -- 8 Collaborative integrated operational transportation planning -- 8.1 Problem definition -- 8.2 Solution approach for collaborative planning -- 8.2.1 Preprocessing -- 8.2.2 Initial route generation -- 8.2.3 Temporary winner determination -- 8.2.4 Iterative route generation -- 8.2.5 Final winner determination and flow of payments -- 8.3 Computational Experiments -- 8.3.1 Instance generation -- 8.3.2 Isolated and centralized planning -- 8.3.3 Collaborative planning -- 8.4 Conclusions -- 9 Dynamic collaborative transportation planning -- 9.1 Literature review -- 9.2 Problem definition -- 9.3 Solution approaches -- 9.3.1 Rolling horizon planning with fixed interval -- 9.3.2 Request triggered rolling horizon planning -- 9.3.3 Determination of due requests -- 9.3.4 Planning strategies using advanced request information -- 9.3.5 Identification of requests for exchange -- 9.3.6 Extended route-based request exchange mechanism -- 9.4 Conclusions -- 10 Computational study on the dynamic collaborative transportation planning -- 10.1 Test 1: Dynamism of instances -- 10.1.1 Measuring dynamism of instances -- 10.1.2 Instance generation -- 10.1.3 Simulation results -- 10.2 Test 2: Value of advanced request information -- 10.2.1 Instance generation -- 10.2.2 Simulation settings -- 10.2.3 Results and discussion.
10.3 Test 3: Length of the planning period in RHP-INT -- 10.3.1 Simulation settings -- 10.3.2 Results and discussion -- 10.4 Test 4: Length of the planning horizon in RHP-RT -- 10.4.1 Simulation settings -- 10.4.2 Results and discussion -- 10.5 Test 5: Planning with high subcontracting costs -- 10.5.1 Instance adjustment and simulation settings -- 10.5.2 Results and discussion -- 10.6 Conclusions -- 11 Conclusions and future research -- 11.1 Summary and conclusions -- 11.2 Outlook of future research -- Bibliography.
Summary: Modern freight forwarding companies attempt to cooperate with each other to increase their efficiency. Xin Wang studies the new operational transportation planning problems that arise in the context of cooperation in both static and dynamic scenarios. To achieve the cost-savings embedded in cooperation, novel planning approaches that can help forwarders obtaining high-quality solutions are proposed. Computational studies show considerable benefits that can be achieved through cooperation.
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Item type Current location Call number URL Status Date due Barcode
Electronic Book UT Tyler Online
Online
HF4999.2-6182 (Browse shelf) https://ebookcentral.proquest.com/lib/uttyler/detail.action?docID=1802766 Available EBC1802766

Foreword -- Preface -- Contents -- List of Figures -- List of Tables -- Abbreviations -- 1 Introduction -- 1.1 Request fulfillment of freight forwarding companies -- 1.2 Objectives and Structure of the Thesis -- 2 Vehicle Routing -- 2.1 Introduction -- 2.2 Mathematical model for the PDPTW -- 2.3 Solution approaches for the PDPTW -- 2.3.1 Exact algorithms -- 2.3.2 Heuristic algorithms -- 2.4 The ALNS heuristic of Ropke and Pisinger (2006) -- 2.4.1 General ideas -- 2.4.2 Removal operators -- 2.4.3 Insertion operators -- 2.4.4 Adaptive operator choice -- 2.4.5 Further settings -- 3 Freight Consolidation -- 3.1 Introduction -- 3.2 Mathematical formulation -- 3.3 Solution methodology -- 3.3.1 A local search heuristic -- 3.3.2 A simulated annealing heuristic -- 3.4 Computational experiments -- 3.4.1 Cost function for the freight charge calculation -- 3.4.2 Test instances -- 3.4.3 Computational results -- 3.5 Conclusions -- 4 From Cherry-Picking to Integrated Operational Transportation Planning -- 4.1 External transportation resources in subcontracting -- 4.2 Cherry-Picking -- 4.3 Integrated operational transportation planning -- 4.3.1 Literature review -- 4.3.2 Mathematical model -- 4.4 Reducing long-term costs through integrated planning - A computational example based on the VRP -- 5 Solution approaches for the integrated operational transportation planning problem -- 5.1 Adaptive large neighborhood search -- 5.2 Heuristic II: An iterative approach -- 5.2.1 A set partitioning model of the IOTPP -- 5.2.2 Construction of candidate routes -- 5.2.3 Obtaining integer solutions -- 5.2.4 Overview of the heuristic -- 5.3 Computational experiments -- 5.3.1 Instance generation -- 5.3.2 Computational results -- 5.4 Conclusions -- 6 Collaborative transportation planning -- 6.1 Introduction -- 6.2 Design of request exchange mechanisms.

6.3 Mathematical formulation -- 6.4 Request exchange mechanisms in literature -- 7 A route-based request exchange mechanism for the collaborative transportation planning -- 7.1 The route-based request exchange mechanism -- 7.1.1 Preprocessing -- 7.1.2 Initial route generation -- 7.1.3 Temporary winner determination -- 7.1.4 Iterative route generation -- 7.1.5 Final winner determination -- 7.2 Computational experiments -- 7.2.1 Test instance generation -- 7.2.2 Route generator -- 7.2.3 Computational results -- 7.2.4 Discussion of results -- 7.3 Conclusions -- 8 Collaborative integrated operational transportation planning -- 8.1 Problem definition -- 8.2 Solution approach for collaborative planning -- 8.2.1 Preprocessing -- 8.2.2 Initial route generation -- 8.2.3 Temporary winner determination -- 8.2.4 Iterative route generation -- 8.2.5 Final winner determination and flow of payments -- 8.3 Computational Experiments -- 8.3.1 Instance generation -- 8.3.2 Isolated and centralized planning -- 8.3.3 Collaborative planning -- 8.4 Conclusions -- 9 Dynamic collaborative transportation planning -- 9.1 Literature review -- 9.2 Problem definition -- 9.3 Solution approaches -- 9.3.1 Rolling horizon planning with fixed interval -- 9.3.2 Request triggered rolling horizon planning -- 9.3.3 Determination of due requests -- 9.3.4 Planning strategies using advanced request information -- 9.3.5 Identification of requests for exchange -- 9.3.6 Extended route-based request exchange mechanism -- 9.4 Conclusions -- 10 Computational study on the dynamic collaborative transportation planning -- 10.1 Test 1: Dynamism of instances -- 10.1.1 Measuring dynamism of instances -- 10.1.2 Instance generation -- 10.1.3 Simulation results -- 10.2 Test 2: Value of advanced request information -- 10.2.1 Instance generation -- 10.2.2 Simulation settings -- 10.2.3 Results and discussion.

10.3 Test 3: Length of the planning period in RHP-INT -- 10.3.1 Simulation settings -- 10.3.2 Results and discussion -- 10.4 Test 4: Length of the planning horizon in RHP-RT -- 10.4.1 Simulation settings -- 10.4.2 Results and discussion -- 10.5 Test 5: Planning with high subcontracting costs -- 10.5.1 Instance adjustment and simulation settings -- 10.5.2 Results and discussion -- 10.6 Conclusions -- 11 Conclusions and future research -- 11.1 Summary and conclusions -- 11.2 Outlook of future research -- Bibliography.

Modern freight forwarding companies attempt to cooperate with each other to increase their efficiency. Xin Wang studies the new operational transportation planning problems that arise in the context of cooperation in both static and dynamic scenarios. To achieve the cost-savings embedded in cooperation, novel planning approaches that can help forwarders obtaining high-quality solutions are proposed. Computational studies show considerable benefits that can be achieved through cooperation.

Description based on publisher supplied metadata and other sources.

Author notes provided by Syndetics

Dr. Xin Wang wrote his dissertation under the supervision of Prof. Dr.-Ing. Herbert Kopfer at the Chair of Logistics at the University of Bremen.

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