Tracking and fleet optimization of Reusable Transport Items in the shipping industry

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 77-78).This thesis explores the strategies, methodologies and tools for an optimal management of Reusable Transport Items, such as containers or chassis, in an extensive multi-depots network. We use an ocean shipping company operating globally to propose a broad, comprehensive and integrated system for an optimal management of the fleet, embracing technology, processes and monitoring system. The ability to track these assets is the first step to visibility and fleet optimization and we will question the opportunity for a company to invest in a real time tracking technology. In highly complex logistic networks, the challenge is to get the right equipment at the right place at the right time, in a cost efficient manner and with a fleet size as small as possible. Beyond increased visibility through tracking capabilities, we show that choosing an appropriate utilization metrics helps identify and quantify other areas of improvement. Using actual data, we evaluate to what extent the fleet size can be reduced by improving asset utilization and how leasing also impacts operating costs. We also show how the structural imbalance of trade (some regions being net exporters while others are net importers) impacts both global repositioning policy and local inventory policy, with depots of different profiles requiring different policies. Understanding this systematic and systemic approach of fleet management, we assess the contribution of tracking technology capabilities to these potential improvements.by Jean-Marie Lefebvre and Dameng Yue.M.Eng.in Logistic

A simulation framework for the design of a station-based bike sharing system

Many cities and towns offer nowadays to citizens a bike sharing system (BSS). When a company starts the service, several decisions have to be taken on the location and size of the rental stations, and the number of vehicles to use to re-balance the bikes in the stations, in addition to the cost and policies for the payment of the service. Also, when the service is in place, it is often necessary to modify it, in many cases to expand it. In this paper, starting from the experience gained in a real-case application, we present a simulation framework to support the tactical decisions in the design or revision of a BSS. We will also present the application of the framework to the case of Bicimia in Brescia, Italy

Maritime Empty Container Repositioning with Inventory-based Control

Ph.DDOCTOR OF PHILOSOPH

A Literature Review, Container Shipping Supply Chain: Planning Problems and Research Opportunities

This paper provides an overview of the container shipping supply chain (CSSC) by taking a logistics perspective, covering all major value-adding segments in CSSC including freight logistics, container logistics, vessel logistics, port/terminal logistics, and inland transport logistics. The main planning problems and research opportunities in each logistics segment are reviewed and discussed to promote further research. Moreover, the two most important challenges in CSSC, digitalization and decarbonization, are explained and discussed in detail. We raise awareness of the extreme fragmentation of CSSC that causes inefficient operations. A pathway to digitalize container shipping is proposed that requires the applications of digital technologies in various business processes across five logistics segments, and change in behaviors and relationships of stakeholders in the supply chain. We recognize that shipping decarbonization is likely to take diverse pathways with different fuel/energy systems for ships and ports. This gives rise to more research and application opportunities in the highly uncertain and complex CSSC environment.</jats:p

Assessing the eco-efficiency benefits of empty container repositioning strategies via dry ports

Trade imbalances and global disturbances generate mismatches in the supply and demand of empty containers (ECs) that elevate the need for empty container repositioning (ECR). This research investigated dry ports as a potential means to minimize EC movements, and thus reduce costs and emissions. We assessed the environmental and economic effects of two ECR strategies via dry ports—street turns and extended free temporary storage—considering different scenarios of collaboration between shipping lines with different levels of container substitution. A multiparadigm simulation combined agent-based and discrete-event modelling to represent flows and estimate kilometers travelled, CO2 emissions, and costs resulting from combinations of ECR strategies and scenarios. Full ownership container substitution combined with extended free temporary storage at the dry port (FTDP) most improved ECR metrics, despite implementation challenges. Our results may be instrumental in increasing shipping lines’ collaboration while reducing environmental impacts in up to 32 % of the inland ECR emissions

Optimization of empty container movements using street-turn: Application to Valencia hinterland

Empty maritime container logistics is one of the most relevant costs for shipping companies. In this paper two mathematical models (based on two different container movement patterns, i.e. with and without street-turns) were defined to optimize land empty container movements among shippers, consignees, terminals and depots, along with minimizing storage costs. One of the proposed optimization models was embedded in a simple Decision Support System (DSS) and then tested with real data, based on the operations in Valencia s (Spain) hinterland. The results obtained confirm the benefits of implementing these kinds of models for the company, and additional experiments assess and quantify the advantage of using the more complex approach that is able to implement street-turn patterns.This research has been funded by the Spanish Ministry of Science and Innovation through Grant DPI2010-16201 and FEDER.Furió, S.; Andrés Romano, C.; Adenso Díaz, B.; Lozano Segura, S. (2013). Optimization of empty container movements using street-turn: Application to Valencia hinterland. Computers and Industrial Engineering. 66(4):909-917. https://doi.org/10.1016/j.cie.2013.09.003S90991766

공유 자율주행 차량 서비스를 활용한 최적 교통 경로 문제

학위논문(석사)--서울대학교 대학원 :공과대학 산업공학과,2019. 8. Moon, Ilkyeong.This thesis describes a traffic-aware routing problem with shared autonomous vehicles by incorporating jams along traffic flow due to the large population of vehicles in the network. This anticipates that autonomous vehicles will replace privately owned vehicles in the future. To provide an efficient shared common service, the dial-a-ride problem is combined with the traffic flow model to satisfy demand (origin-destination pairs), producing a system-optimal traffic assignment problem solution. Macroscopic traffic flow is modelled via the two--regime transmission model (TTM), utilizing inflow and outflow for each link. The optimal solution demonstrates that an appropriate number of vehicles is utilized regardless of the demand or fleet size due to congestion limitations.본 연구는 네트워크 내 교통 흐름 혼잡을 고려하는 공유 자율주행 차량 경로문제(Shared Autonomous Vehicle Routing Problem)를 다루고 있다. 이 문제는 향후 자율주행차가 개인 소유의 차를 대체할 것이라는 관점에서 시작되었다. 효율적인 공유 서비스를 제공하기 위해, 기존의 다이얼 어 라이드(Dial-A-Ride) 문제에 출발지와 도착지 간의 수요를 만족하도록 하는 교통 흐름 모델을 결합해 최적의 교통 할당 문제를 제안한다. 거시적인 교통 흐름은 네트워크 각 링크에 유입 및 유출을 활용한 이중 체제 전송(Two Regime Transmission) 모델을 활용한다. 혼잡으로 인한 제약들로 인해 수요 및 차량 크기와 관계없이 최적의 해에서는 최대 차량 수가 활용되고 있음을 보여준다. 또한, 피크 교통 시간대에서는 수요에 따른 최적의 교통 할당과 차량 크기를 얻어 교통 혼잡에 활용할 수 있다.Chapter 1: Introduction 1 1.1. Background and Purpose 1 1.2. Literature Survey 3 1.2.1. Shared Autonomous Vehicle 3 1.2.2. VRP and DARP 5 1.2.3. Traffic-flow Model 9 Chapter 2: Mathematical Model 15 2.1. Model Development 16 2.2. Traffic Network 17 2.3. Explanations on Constraints 19 2.4. Objective Function 28 2.5. Mathematical Formulation 31 Chapter 3: Computational Experiments 35 3.1. Test Network 35 3.2. Comparison with Static Traffic Assignment Formulation 38 3.3. Experiments 39 3.3.1. Effects of Change in Demand on Utilization Rate 40 3.3.2. Effects of Change in Demand on VMT 41 3.3.3. Effects of Change in Demand on Total Travel Time 42 3.3.4. Effects of Change in Fleet Size on Total Travel Time 44 3.3.5. Effects of Change in Time Intervals on Computational Time and Complexity 45 Chapter 4: Conclusions 49 Acknowledgements 52 국문초록 59 Appendix 60 i) IBM CPLEX ILOG Linear Programming Code 60 ii) Two Regime Transmission Model Mathematical Proof 64Maste