Dynamic Rerouting In Multimodal Transportation Networks

Author : Yibo Dang
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (136 download)

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Download or read book Large-scale Transportation Routing and Mode Optimization written by Yibo Dang and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the logistics industry, many resources are spent on handling the challenges in complex transportation planning problems. The growth of e-commerce demand scalable and hybrid delivery solutions that are in-time, cost-efficient, and clean. Increased workloads demand sophisticated scheduling and routing timelines. Therefore, governments and major logistics companies are launching different programs to promote sustainable distribution processes that leverage multiple transportation modes. In this way, the transportation network costs will be decreased and the carbon efficiency will be improved. Such increasing need for detailed and multimodal plans to larger transportation problems makes the current decision support tools indispensable. The low-quality solutions and the lack of big-data capability have concerned the practitioners for a long time. The problem of routing and scheduling with different types of carriers and modes have drawn limited attentions in previous research. There is no comprehensive research that discusses implementable models and algorithms with most real-world constraints on such transportation mode decision problem. The problem implies the design of long-haul transport, efficient last-mile deliveries to fulfill the demands within in strict time windows. Because it may take several days to cover a long-haul route, it is mandatory to consider breaks or layovers during the trip. Due to the capacity limit, some non-profitable deliveries are subcontracted to third-party carriers. This strategy allows the company to fill their vehicle fleets to the maximum. To further optimize the cost and carbon efficiencies of the vehicle fleets, different types of vehicles are to be evaluated. While the traditional diesel trailers can efficiently handle large amount of cargo, the travel costs and the greenhouse gas emissions cannot be negligible. While the technologies of electric vehicles are becoming mature, the driving-range issue and the high purchase cost still worry the industry. These challenges inhibit the success of a multimodal transportation network. In this dissertation, we investigate the three related optimization problems via meta-heuristics and mathematical programming – 1) a vehicle routing problem with common carriers and time regulation (VRPCCTR), in which the working hour limit is specified and third-party common carriers’ services are considered for unprofitable deliveries; 2) an extended version of the first problem where heterogeneous-sized vehicle fleets are involved (called HVRPCCTR); 3) the electric vehicles are introduced to the problem so that the mode decision becomes determining optimal routes between mixed vehicle types and sizes (called MVRPC) observing the time windows, recharging needs, service of hours regulations, and other constraints. In short, we study a new variant of the vehicle routing problem with three shipping modes – heterogeneous internal combustion vehicles, electric vehicles, and common carriers. Our main contributions are three-fold: We propose a novel meta-heuristic algorithm called Red-Black Ant Colony System for the problems, where the black ants are searching for regular routes using different sizes of vehicles whereas the red ants re-optimize within the common carrier deliveries to build efficient dedicated fleet routes. A developed tool can solve the mode decision problem for 150,000 deliveries. And its implementation has conducted over $5 million of dollars of savings for DHL and its customers annually. For each of the three problems, we give mixed integer linear programming (MILP) models and introduce strong valid inequalities. We prove the strengths of each set of valid inequalities by analyzing the linear relaxation bounds of the models. The endeavors of reducing the symmetry in the MILP models can be seen as the complexities grow from VRPCCTR through HVRPCCTR to MVRPC. We extend our work to incorporate green transportation plans. The recharges for electric vehicles and layover needs are efficiently modeled as renewable resources. This dissertation presents the first exact method based on a branch-and-cut- and-pricing algorithm for variants of MVRPC. The algorithm is tested on real data set from small sizes to medium sizes. Its performance and sensitivity are analyzed. The results show that the our algorithm can solve the problems up to 120 customers to exact optimality. Our results provide analytical insights and implementable large-scale algorithms for multimodal transportation networks of the next-generation logistic operations. The algorithms can also be utilized to study other types of scaling networks such as social networks, electrical grid, 3D printing models, etc. Other industries, such as manufacturing, digital communication, shared economy, etc., can also benefit from our results.