Space-time clustering-based method to optimize shareability in real-time ride-sharing

Real-time ride-sharing has become popular in recent years. However, the underlying optimization problem for this service is highly complex. One of the most critical challenges when solving the problem is solution quality and computation time, especially in large-scale problems where the number of received requests is huge. In this paper, we rely on an exact solving method to ensure the quality of the solution, while using AI-based techniques to limit the number of requests that we feed to the solver. More precisely, we propose a clustering method based on a new shareability function to put the most shareable trips inside separate clusters. Previous studies only consider Spatio-temporal dependencies to do clustering on the mobility service requests, which is not efficient in finding the shareable trips. Here, we define the shareability function to consider all the different sharing states for each pair of trips. Each cluster is then managed with a proposed heuristic framework in order to solve the matching problem inside each cluster. As the method favors sharing, we present the number of sharing constraints to allow the service to choose the number of shared trips. To validate our proposal, we employ the proposed method on the network of Lyon city in France, with half-million requests in the morning peak from 6 to 10 AM. The results demonstrate that the algorithm can provide high-quality solutions in a short time for large-scale problems. The proposed clustering method can also be used for different mobility service problems such as car-sharing, bike-sharing, etc.

Dynamic Two-sided Matching Group Decision-making Model With Multi-form Preference Information for Configuring Tasks and Resources in Cloud Manufacturing

Enterprises have been faced with the problem of how to optimize resource allocation in an uncertain environment by the expanding of manufacturing informatization. In the process of cloud manufacturing matching, group decision making organizations may provide uncertain preference information. However, preference information at various points have led to differing impacts of the final matching decision. it is necessary to study the dynamic two-sided matching. In this paper, the dynamic two-sided matching problem under the multi-form preference information was studied. Primarily, the problem of two-sided matching is described, then through group decision-making and uncertain preference information, an ordinal vector matrix is constructed. Afterwards, the comprehensive satisfaction matrix is calculated by using dynamic time-series weight and matching competition degree. Further, by introducing stable matching constraints, a multi-objective optimization model considering the satisfaction, fairness and stability of matching is constructed. Then the optimal matching result is obtained by solving the model. In addition, the presented method was verified through a case of cloud manufacturing. At the end, advantages of the presented model were demonstrated by comparison. Research results of this paper enrich the theoretical research of two-sided matching and provide an effective solution for cloud manufacturing matching in uncertain environments.

Modeling HMM Map Matching Using Multi-label Classification

Map matching deals with matching GPS coordinates to corresponding points or segments on a road network map. The work has various applications in both vehicle navigating and tracking domains. Traditional rule-based approach for solving the Map matching problem yielded great matching results. However, its performance depends on the underlying algorithm and Mathematical/Statistical models employed in the approach. For example, HMM Map Matching yielded O(N2) time complexity, where N is the number of states in the underlying Hidden Markov Model. Map matching techniques with large order of time complexity are impractical for providing services, especially within time-sensitive applications. This is due to their slow responsiveness and the critical amount of computing power required to obtain the results. This paper proposed a novel data-driven approach for projecting GPS trajectory onto a road network. We constructed a supervised-learning classifier using the Multi-Label Classification (MLC) technique and HMM Map Matching results. Analytically, our approach yields O(N) time complexity, suggesting that the approach has a better running performance when applied to the Map matching-based applications in which the response time is the major concern. In addition, our experimental results indicated that we could achieve Jaccard Similarity index of 0.30 and Overlap Coefficient of 0.70.

A novel grasshopper optimization algorithm based on swarm state difference and its application

The grasshopper optimization algorithm (GOA) has received extensive attention from scholars in various real applications in recent years because it has a high local optima avoidance mechanism compared to other meta-heuristic algorithms. However, the small step moves of grasshopper lead to slow convergence. When solving larger-scale optimization problems, this shortcoming needs to be solved. In this paper, an enhanced grasshopper optimization algorithm based on solitarious and gregarious states difference is proposed. The algorithm consists of three stages: the first stage simulates the behavior of solitarious population learning from gregarious population; the second stage merges the learned population into the gregarious population and updates each grasshopper; and the third stage introduces a local operator to the best position of the current generation. Experiments on the benchmark function show that the proposed algorithm is better than the four representative GOAs and other metaheuristic algorithms in more cases. Experiments on the ontology matching problem show that the proposed algorithm outperforms all metaheuristic-based method and beats more the state-of-the-art systems.

Hierarchical Approach to Identifying Fluid Flow Models in a Heterogeneous Porous Medium

A three-dimensional numerical hydrodynamic model fairly accurately describes the processes of developing oil and gas fields, and has good predictive properties only if there are high-quality input data and comprehensive information about the reservoir. However, under conditions of high uncertainty of the input data, measurement errors, significant time and resource costs for processing and analyzing large amounts of data, the use of such models may be unreasonable and can lead to ill-posed problems: either the uniqueness of the solution or its stability is violated. A well-known method for dealing with these problems is regularization or the method of adding some additional a priori information. In contrast to full-scale modeling, currently there is active development of reduced-physics models, which are used, first of all, in conditions when it is required to make an operational decision, and computational resources are limited. One of the most popular simplified models is the material balance model, which makes it possible to directly capture the relationship between reservoir pressure, flow rates and the integral reservoir characteristics. In this paper, it is proposed to consider a hierarchical approach when solving the problem of oil field waterflooding control using material balance models in successive approximations: first for the field as a whole, then for hydrodynamically connected blocks of the field, then for wells. When moving from one level of model detailing to the next, the modeling results from the previous levels of the hierarchy are used in the form of additional regularizing information, which ultimately makes it possible to correctly solve the history matching problem (identification of the filtration model) in conditions of incomplete input information.

Subspace Detours Meet Gromov–Wasserstein

In the context of optimal transport (OT) methods, the subspace detour approach was recently proposed by Muzellec and Cuturi. It consists of first finding an optimal plan between the measures projected on a wisely chosen subspace and then completing it in a nearly optimal transport plan on the whole space. The contribution of this paper is to extend this category of methods to the Gromov–Wasserstein problem, which is a particular type of OT distance involving the specific geometry of each distribution. After deriving the associated formalism and properties, we give an experimental illustration on a shape matching problem. We also discuss a specific cost for which we can show connections with the Knothe–Rosenblatt rearrangement.

Dynamic Two-sided Matching Group Decision-making Model with Multi-form Preference Information for Configuring Tasks and Resources in Cloud Manufacturing

Enterprises have been faced with the problem of how to optimize resource allocation in an uncertain environment by the expanding of manufacturing informatization. In the process of cloud manufacturing matching, group decision making organizations may provide uncertain preference information. However, preference information at various points have led to differing impacts of the final matching decision. it is necessary to study the dynamic two-sided matching. In this paper, the dynamic two-sided matching problem under the multi-form preference information was studied. Primarily, the problem of two-sided matching is described, then through group decision-making and uncertain preference information, an ordinal vector matrix is constructed. Afterwards, the comprehensive satisfaction matrix is calculated by using dynamic time-series weight and matching competition degree. Further, by introducing stable matching constraints, a multi-objective optimization model considering the satisfaction, fairness and stability of matching is constructed. Then the optimal matching result is obtained by solving the model. In addition, the presented method was verified through a case of cloud manufacturing. At the end, advantages of the presented model were demonstrated by comparison. Research results of this paper enrich the theoretical research of two-sided matching and provide an effective solution for cloud manufacturing matching in uncertain environments.