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Integrating routing decisions in public transportation problems / [electronic resource] : Marie E. Schmidt.

Contemporary masterworks / editor Colin Naylor ; picture editor Leanda Shrimpton.

A dangerous arrangement / Lee Christine.

Paradise fever : growing up in the shadow of the New Age / Ptolemy Tompkins.

Macau, China : a political history of the Portuguese colony's transition to Chinese rule = [Ao-men Chung-kuo] / [Shipp].

Intelligent Techniques for Cyber-Physical Systems / edited by Mohammad Sajid [and four others].

Passing Clouds : a winemaker's journey / Graeme Leith.

Sustainability of the sugar and sugar-ethanol industries / Gillian Eggleston, editor ; sponsored by the ACS Division of Carbohydrate Chemistry ... [et al.].

Nujeen : one girl's incredible journey from war-torn Syria in a wheelchair / Nujeen Mustafa ; with Christina Lamb.

The roots of goodness and resistance to evil : inclusive caring, moral courage, altruism born of suffering, active bystandership, and heroism / Ervin Staub.

The art of resistance : my four years in the French underground : a memoir / Justus Rosenberg.

IEEE International Symposium on Electromagnetic Compatibility : [record].

Gestion du patrimoine d'infrastructure dans le secteur routier [electronic resource] / Organisation de coopération et de développement économiques

We consider a class of routing optimization problems under uncertainty in which all decisions are made before the uncertainty is realized. The objective is to obtain optimal routing solutions that would, as much as possible, adhere to a set of specified requirements after the uncertainty is realized. These problems include finding an optimal routing solution to meet the soft time window requirements at a subset of nodes when the travel time is uncertain, and sending multiple capacitated vehicles to different nodes to meet the customers’ uncertain demands. We introduce a precise mathematical framework for defining and solving such routing problems. In particular, we propose a new decision criterion, called the
Requirements Violation (RV) Index
, which quantifies the risk associated with the violation of requirements taking into account both the frequency of violations and their magnitudes whenever they occur. The criterion can handle instances when probability distributions are known, and ambiguity when distributions are partially characterized through descriptive statistics such as moments. We develop practically efficient algorithms involving Benders decomposition to find the exact optimal routing solution in which the RV Index criterion is minimized, and we give numerical results from several computational studies that show the attractive performance of the solutions.

Operations research, 2016-01, Vol.64 (1), p.186-200

Routing Optimization Under Uncertainty

In several transportation systems, vehicles can choose where to meet customers rather than stopping in fixed locations. This added flexibility, however, requires coordination between vehicles and customers that adds complexity to routing operations. This paper develops scalable algorithms to optimize these operations. First, we solve the one-stop subproblem in the
ℓ
1
space and the
ℓ
2
space by leveraging the geometric structure of operations. Second, to solve a multistop problem, we embed the single-stop optimization into a tailored coordinate descent scheme, which we prove converges to a global optimum. Third, we develop a new algorithm for dial-a-ride problems based on a subpath-based time–space network optimization combining set partitioning and time–space principles. Finally, we propose an online routing algorithm to support real-world ride-sharing operations with vehicle–customer coordination. Computational results show that our algorithm outperforms state-of-the-art benchmarks, yielding far superior solutions in shorter computational times and can support real-time operations in very large-scale systems. From a practical standpoint, most of the benefits of vehicle–customer coordination stem from comprehensively reoptimizing “upstream” operations as opposed to merely adjusting “downstream” stopping locations. Ultimately, vehicle–customer coordination provides win–win–win outcomes: higher profits, better customer service, and smaller environmental footprint.
This paper was accepted by Chung Piaw Teo, optimization.
Funding:
This research was supported by the National Natural Science Foundation of China [Grants 72288101, 52221005 and 52220105001].
Supplemental Material:
The e-companion and data are available at
https://doi.org/10.1287/mnsc.2023.4739
.

Management science, 2023-11, Vol.69 (11), p.6876-6897

Routing Optimization with Vehicle–Customer Coordination

Routing optimization is a relevant problem in many contexts. Solving directly this type of optimization problem is often computationally intractable. Recent studies suggest that one can instead turn this problem into one of solving a dynamical system of equations, which can instead be solved efficiently using numerical methods. This results in enabling the acquisition of optimal network topologies from a variety of routing problems. However, the actual extraction of the solution in terms of a final network topology relies on numerical details which can prevent an accurate investigation of their topological properties. In fact, in this context, theoretical results are fully accessible only to an expert audience and ready-to-use implementations for non-experts are rarely available or insufficiently documented. In particular, in this framework, final graph acquisition is a challenging problem in-and-of-itself. Here we introduce a method to extract network topologies from dynamical equations related to routing optimization under various parameters’ settings. Our method is made of three steps: first, it extracts an optimal trajectory by solving a dynamical system, then it pre-extracts a network, and finally, it filters out potential redundancies. Remarkably, we propose a principled model to address the filtering in the last step, and give a quantitative interpretation in terms of a transport-related cost function. This principled filtering can be applied to more general problems such as network extraction from images, thus going beyond the scenarios envisioned in the first step. Overall, this novel algorithm allows practitioners to easily extract optimal network topologies by combining basic tools from numerical methods, optimization and network theory. Thus, we provide an alternative to manual graph extraction which allows a grounded extraction from a large variety of optimal topologies. The analysis of these may open up the possibility to gain new insights into the structure and function of optimal networks. We provide an open source implementation of the code online.

Scientific reports, 2020-11, Vol.10 (1), p.20806-20806, Article 20806

Network extraction by routing optimization

Optimizing passengers routes is crucial to design efficient transportation networks. Recent results show that optimal transport provides an efficient alternative to standard optimization methods. However, it is not yet clear if this formalism has empirical validity on engineering networks. We address this issue by considering different response functions—quantities determining the interaction between passengers—in the dynamics implementing the optimal transport formulation. Particularly, we couple passengers’ fluxes by taking their sum or the sum of their squares. The first choice naturally reflects edges occupancy in transportation networks, however the second guarantees convergence to an optimal configuration of flows. Both modeling choices are applied to the Paris metro. We measure the extent of traffic bottlenecks and infrastructure resilience to node removal, showing that the two settings are equivalent in the congested transport regime, but different in the branched one. In the latter, the two formulations differ on how fluxes are distributed, with one function favoring routes consolidation, thus potentially being prone to generate traffic overload. Additionally, we compare our method to Dijkstra’s algorithm to show its capacity to efficiently recover shortest-path-like graphs. Finally, we observe that optimal transport networks lie in the Pareto front drawn by the energy dissipated by passengers, and the cost to build the infrastructure.

Scientific reports, 2022-05, Vol.12 (1), p.7474-11, Article 7474

Multicommodity routing optimization for engineering networks

This paper demonstrates a recapitulated historic evolution further to a future overview of all vehicular ad-hoc network (VANET) routing problems that concern either directly related routing tasks or targeting a set of diverse routing-related techniques with the aid of the bio-inspired approaches. In this lecture, we serialize, in a synchronous observation, the evolution and tendencies of the VANET routing problem’s solving simultaneously with the emergence of different classes of nature-based meta-heuristics, by bringing a proposed taxonomy of different major VANET routing problems seen their nature, studied range and metaheuristic types used for their optimization. Then, we follow with a visionary deduction of the other appearing routing issues of VANETs that can be approached or already began to be solved by nature-inspired optimization algorithms. Noting that each spread routing problem is illustrated with notable related works, describing initially realized conventional protocols to vulgarize different routing modules, then detailing bio-inspired protocols for VANET routing to explain the utility of nature-inspired optimization techniques. The motivation of this work came from the lack of a reference classifying the VANET-related routing problems within the notion of nature-inspired optimization. That’s further to giving and up-to-date literature on the context for opening out a visionary opinion on the tendencies of either emerging recent bio-inspired optimization approaches or the different metaheuristic-based combinations on specific VANET routing problems.

The Artificial intelligence review, 2021-02, Vol.54 (2), p.1005-1062

Bio-inspired VANET routing optimization: an overview: A taxonomy of notable VANET routing problems, overview, advancement state, and future perspective under the bio-inspired optimization approaches

We study an a priori Traveling Salesman Problem with Time Windows (
tsptw
) in which the travel times along the arcs are uncertain and the goal is to determine within a budget constraint, a route for the service vehicle in order to arrive at the customers’ locations within their stipulated time windows as well as possible. In particular, service at customer’s location cannot commence before the beginning of the time window and any arrival after the end of the time window is considered late and constitutes to poor customer service. In articulating the service level of the
tsptw
under uncertainty, we propose a new decision criterion, called the
essential riskiness index
, which has the computationally attractive feature of convexity that enables us to formulate and solve the problem more effectively. As a decision criterion for articulating service levels, it takes into account both the probability of lateness and its magnitude, and can be applied in contexts where either the distributional information of the uncertain travel times is fully or partially known. We propose a new formulation for the
tsptw
, where we explicitly express the service starting time at each customer’s location as a convex piecewise affine function of the travel times, which would enable us to obtain the tractable formulation of the corresponding distributionally robust problem. We also show how to optimize the essential riskiness index via Benders decomposition and present cases where we can obtain closed-form solutions to the subproblems. We also illustrate in our numerical studies that this approach scales well with the number of samples used for the sample average approximation. The approach can be extended to a more general setting including Vehicle Routing Problem with Time Windows with uncertain travel times and customers’ demands.

Mathematical programming, 2019-05, Vol.175 (1-2), p.263-305

Routing optimization with time windows under uncertainty

Unmanned aerial vehicles (UAVs) are widely used in Internet-of-Things (IoT) networks, especially in remote areas where communication infrastructure is unavailable, due to flexibility and low cost. However, the joint optimization of locations of UAVs and relay path selection can be very challenging, especially when the numbers of IoT devices and UAVs are very large. In this paper, we formulate the joint optimization of UAV locations and relay paths in UAV-relayed IoT networks as a graph problem, and propose a graph neural network (GNN)-based approach to solve it in an efficient and scalable way. In the training procedure, we design a reinforcement learning-based relay GNN (RGNN) to select the best relay path for each user. The theoretical analysis shows that the time complexity of RGNN is two orders lower than the conventional optimization method. Then, we jointly exploit location GNN (LGNN) and RGNN trained to optimize the locations of all UAVs. Both GNNs can be trained without relying on the training data, which is usually unavailable in the context of wireless networks. In inference procedure, LGNN is first used to optimize the location of UAVs, and then RGNN is used to select the best relay path based on the output of LGNN. Simulation results show that the proposed approach can achieve comparable performance to brute-force search with much lower time complexity when the network is relatively small. Remarkably, the proposed approach is highly scalable to large-scale networks and adaptable to dynamics in the environment, which can hardly be achieved using conventional methods.

Remote sensing (Basel, Switzerland), 2022-09, Vol.14 (17), p.4377

Joint Flying Relay Location and Routing Optimization for 6G UAV–IoT Networks: A Graph Neural Network-Based Approach