scholarly journals Fuzzy Simheuristics for Optimizing Transportation Systems: Dealing with Stochastic and Fuzzy Uncertainty

2021 ◽  
Vol 11 (17) ◽  
pp. 7950
Author(s):  
Rafael D. Tordecilla ◽  
Leandro do C. Martins ◽  
Javier Panadero ◽  
Pedro J. Copado ◽  
Elena Perez-Bernabeu ◽  
...  
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Smart Cities ◽  
Hybrid Approach ◽  
Transportation Systems ◽  
Telecommunication Networks ◽  
Routing Problem ◽  
Fuzzy Uncertainty ◽  
Hard Problems ◽  
Team Orienteering

In the context of logistics and transportation, this paper discusses how simheuristics can be extended by adding a fuzzy layer that allows us to deal with complex optimization problems with both stochastic and fuzzy uncertainty. This hybrid approach combines simulation, metaheuristics, and fuzzy logic to generate near-optimal solutions to large scale NP-hard problems that typically arise in many transportation activities, including the vehicle routing problem, the arc routing problem, or the team orienteering problem. The methodology allows us to model different components–such as travel times, service times, or customers’ demands–as deterministic, stochastic, or fuzzy. A series of computational experiments contribute to validate our hybrid approach, which can also be extended to other optimization problems in areas such as manufacturing and production, smart cities, telecommunication networks, etc.

scholarly journals Nanolaser-based emulators of spin Hamiltonians

Nanophotonics ◽  
2020 ◽  
Vol 9 (13) ◽  
pp. 4193-4198 ◽  
Author(s):  
Midya Parto ◽  
William E. Hayenga ◽  
Alireza Marandi ◽  
Demetrios N. Christodoulides ◽  
Mercedeh Khajavikhan
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Exchange Interactions ◽  
Np Hard ◽  
Spin Models ◽  
Geometric Frustration ◽  
Spin Hamiltonians ◽  
Hard Problems ◽  
On Chip ◽  
Classical Spin Models

AbstractFinding the solution to a large category of optimization problems, known as the NP-hard class, requires an exponentially increasing solution time using conventional computers. Lately, there has been intense efforts to develop alternative computational methods capable of addressing such tasks. In this regard, spin Hamiltonians, which originally arose in describing exchange interactions in magnetic materials, have recently been pursued as a powerful computational tool. Along these lines, it has been shown that solving NP-hard problems can be effectively mapped into finding the ground state of certain types of classical spin models. Here, we show that arrays of metallic nanolasers provide an ultra-compact, on-chip platform capable of implementing spin models, including the classical Ising and XY Hamiltonians. Various regimes of behavior including ferromagnetic, antiferromagnetic, as well as geometric frustration are observed in these structures. Our work paves the way towards nanoscale spin-emulators that enable efficient modeling of large-scale complex networks.

A Hybrid Approach of Heuristic and Neural Network for Packing Problems

1994 ◽  
Author(s):  
Zuo Dai ◽  
Jianzhong Cha
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Hybrid Approach ◽  
Two Dimensions ◽  
Packing Problems ◽  
Combinatorial Optimization Problems ◽  
Complete Problems ◽  
Np Complete

Abstract Artificial Neural Networks, particularly the Hopfield-Tank network, have been effectively applied to the solution of a variety of tasks formulated as large scale combinatorial optimization problems, such as Travelling Salesman Problem and N Queens Problem [1]. The problem of optimally packing a set of geometries into a space with finite dimensions arises frequently in many applications and is far difficult than general NP-complete problems listed in [2]. Until now within accepted time limit, it can only be solved with heuristic methods for very simple cases (e.g. 2D layout). In this paper we propose a heuristic-based Hopfield neural network designed to solve the rectangular packing problems in two dimensions, which is still NP-complete [3]. By comparing the adequacy and efficiency of the results with that obtained by several other exact and heuristic approaches, it has been concluded that the proposed method has great potential in solving 2D packing problems.

Context-Aware Pervasive Services for Smart Cities

2011 ◽  
pp. 1-16 ◽  
Author(s):  
René Meier ◽  
Deirdre Lee
Keyword(s):  
Intelligent Transportation Systems ◽  
Data Model ◽  
Programming Model ◽  
Smart Cities ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Transportation Systems ◽  
Context Aware ◽  
Transportation Services ◽  
Context Modelling

Smart environments support the activities of individuals by enabling context-aware access to pervasive information and services. This article presents the iTransIT framework for building such context-aware pervasive services in Smart Cities. The iTransIT framework provides an architecture for conceptually integrating the independent systems underlying Smart Cities and a data model for capturing the contextual information generated by these systems. The data model is based on a hybrid approach to context-modelling that incorporates the management and communication benefits of traditional object-based context modelling with the semantic and inference advantages of ontology-based context modelling. The iTransIT framework furthermore supports a programming model designed to provide a standardised way to access and correlate contextual information from systems and ultimately, to build context-aware pervasive services for Smart Cities. The framework has been assessed based on a prototypical realisation of an architecture for integrating diverse intelligent transportation systems in Dublin and by building context-aware pervasive transportation services for urban journey planning and for visualising traffic congestion.

Computationally Efficient Hybrid Method for Transmission Network Expansion Planning

2017 ◽  
Vol 18 (2) ◽  
Author(s):  
Ashu Verma ◽  
Soumya Das ◽  
P. R. Bijwe
Keyword(s):  
Power System ◽  
Large Scale ◽  
Optimization Problems ◽  
Hybrid Approach ◽  
Solution Process ◽  
System Optimization ◽  
Mixed Integer ◽  
Transmission Network ◽  
Network Expansion ◽  
Expansion Planning

Abstract Transmission network expansion planning (TNEP) is an important and computationally very demanding problem in power system. Many computational approaches have been proposed to handle TNEP in the past. The problem is mixed integer, large scale and its complexity increases exponentially with the size of the system. Metaheuristic techniques have gained a lot of importance in last few years to solve the power system optimization problems, due to their ability to handle complex optimization functions and constraints. Many of them have been successfully applied for TNEP. The biggest challenge in these techniques is the requirement of large computational efforts. This paper uses a two-stage solution process to solve the TNEP problems. The first stage uses compensation based method to generate a quick, suboptimal solution. The valuable information contained in this solution is used to generate a set of heuristics aimed at drastically reducing the number of population for fitness evaluations required in the 2nd stage with application of metaheuristic method. The resulting hybrid approach produces very good quality solutions very efficiently. Results for 24 bus and 93 bus test systems have been obtained with the proposed method to ascertain the potential of the method in comparison to earlier approaches.

scholarly journals Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

2020 ◽  
Vol 12 (2) ◽  
pp. 22 ◽  
Author(s):  
Thays A. Oliveira ◽  
Yuri B. Gabrich ◽  
Helena Ramalhinho ◽  
Miquel Oliver ◽  
Miri W. Cohen ◽  
...  
Keyword(s):  
Operations Research ◽  
Optimization Problems ◽  
State Of The Art ◽  
Smart Cities ◽  
Transportation Systems ◽  
Decision Makers ◽  
Current Generation ◽  
Combinatorial Optimization Problems ◽  
Innovation And Technology ◽  
Transportation Technologies

Cities are constantly transforming and, consequently, attracting efforts from researchers and opportunities to the industry. New transportation systems are being built in order to meet sustainability and efficiency criteria, as well as being adapted to the current possibilities. Moreover, citizens are becoming aware about the power and possibilities provided by the current generation of autonomous devices. In this sense, this paper presents and discusses state-of-the-art transportation technologies and systems, highlighting the advances that the concepts of Internet of Things and Value are providing. Decentralized technologies, such as blockchain, are been extensively investigated by the industry, however, its widespread adoption in cities is still desirable. Aligned with operations research opportunities, this paper identifies different points in which cities’ services could move to. This also study comments about different combinatorial optimization problems that might be useful and important for an efficient evolution of our cities. By considering different perspectives, didactic examples are presented with a main focus on motivating decision makers to balance citizens, investors and industry goals and wishes.

scholarly journals Parallel ACO with a Ring Neighborhood for Dynamic TSP

2012 ◽  
Vol 5 (4) ◽  
pp. 1-13 ◽  
Author(s):  
Camelia M. Pintea ◽  
Gloria Cerasela Crisan ◽  
Mihai Manea
Keyword(s):  
Ant Colony Optimization ◽  
Large Scale ◽  
Optimization Problems ◽  
Large Data ◽  
Ant Colony ◽  
Data Sets ◽  
Routing Problem ◽  
Computing Technique ◽  
Slight Movement ◽  
Scale Optimization

The current paper introduces a new parallel computing technique based on ant colony optimization for a dynamic routing problem. Ant Colony Optimization is a metaheurisitc that is able to solve large scale optimization problems. In the dynamic traveling salesman problem, the distances between cities as travel times are no longer fixed. The new technique uses a parallel model for a problem variant that allows a slight movement of nodes within their neighborhoods. The algorithm is tested with success on several large data sets. The paper concludes with a discussion of the results provided by both the sequential and parallel approaches and calls for further research on the subject.

scholarly journals Alpha-T: Learning to Traverse over Graphs with An AlphaZero-inspired Self-Play Framework

2021 ◽  
Author(s):  
Qi Wang
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Optimal Path ◽  
Solution Space ◽  
Approximate Solutions ◽  
Small Scale ◽  
Traditional Methods ◽  
Routing Problem ◽  
Monte Carlo Tree Search ◽  
Combinatorial Optimization Problems

Abstract The combinatorial optimization problems on the graph are the core and classic problems in artificial intelligence and operations research. For example, the Vehicle Routing Problem (VRP) and Traveling Salesman Problem (TSP) are not only very interesting NP-hard problems but also have important significance for the actual transportation system. Traditional methods such as heuristics methods, precise algorithms, and solution solvers can already find approximate solutions on small-scale graphs. However, they are helpless for large-scale graphs and other problems with similar structures. Moreover, traditional methods often require artificially designed heuristic functions to assist decision-making. In recent years, more and more work has focused on the application of deep learning and reinforcement learning (RL) to learn heuristics, which allows us to learn the internal structure of the graph end-to-end and find the optimal path under the guidance of heuristic rules, but most of these still need manual assistance, and the RL method used has the problems of low sampling efficiency and small searchable space. In this paper, we propose a novel framework (called Alpha-T) based on AlphaZero, which does not require expert experience or label data but is trained through self-play. We divide the learning into two stages: in the first stage we employ graph attention network (GAT) and GRU to learn node representations and memory history trajectories, and in the second stage we employ Monte Carlo tree search (MCTS) and deep RL to search the solution space and train the model.

Context-Aware Services for Ambient Environments

2010 ◽  
pp. 488-501
Author(s):  
René Meier ◽  
Deirdre Lee
Keyword(s):  
Intelligent Transportation Systems ◽  
Data Model ◽  
Large Scale ◽  
Programming Model ◽  
Hybrid Approach ◽  
Transportation Systems ◽  
Context Aware ◽  
Context Modeling ◽  
Transportation Services ◽  
Ambient Services

This article presents the iTransIT framework for building context-aware pervasive services in large-scale ambient environments. The iTransIT framework provides an architecture for conceptually integrating the independent systems underlying an ambient environment and a data model for capturing the contextual information generated by these systems. The data model is based on a hybrid approach to context-modeling that incorporates the management and communication benefits of traditional object-based-context modeling with the semantic and inference advantages of ontology-based context modeling. The iTransIT framework furthermore supports a programming model designed to provide a standardized way to access and correlate information from systems and their devices based on context and ultimately, to build context-aware ambient services. The framework has been assessed based on a prototypical realization of an architecture for integrating diverse intelligent transportation systems in Dublin and by building context-aware ambient transportation services for urban journey planning and for visualizing traffic congestion.

Context-Aware Pervasive Services for Smart Cities

2012 ◽  
pp. 880-896
Author(s):  
René Meier ◽  
Deirdre Lee
Keyword(s):  
Intelligent Transportation Systems ◽  
Data Model ◽  
Programming Model ◽  
Smart Cities ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Transportation Systems ◽  
Context Aware ◽  
Transportation Services ◽  
Context Modelling

Smart environments support the activities of individuals by enabling context-aware access to pervasive information and services. This article presents the iTransIT framework for building such context-aware pervasive services in Smart Cities. The iTransIT framework provides an architecture for conceptually integrating the independent systems underlying Smart Cities and a data model for capturing the contextual information generated by these systems. The data model is based on a hybrid approach to context-modelling that incorporates the management and communication benefits of traditional object-based context modelling with the semantic and inference advantages of ontology-based context modelling. The iTransIT framework furthermore supports a programming model designed to provide a standardised way to access and correlate contextual information from systems and ultimately, to build context-aware pervasive services for Smart Cities. The framework has been assessed based on a prototypical realisation of an architecture for integrating diverse intelligent transportation systems in Dublin and by building context-aware pervasive transportation services for urban journey planning and for visualising traffic congestion.

An Improved Immune Clone Algorithm Logistics Delivery Strategy

2019 ◽  
Vol 23 (2) ◽  
pp. 309-312 ◽  
Author(s):  
Yan Song Tan ◽  
Keyword(s):  
High Frequency ◽  
Large Scale ◽  
Optimization Problems ◽  
Clonal Selection ◽  
Path Optimization ◽  
Routing Problem ◽  
Distribution Point ◽  
Np Hard Problem ◽  
Logistics Delivery ◽  
Frequency Variations

Logistics routing problem is a typical NP hard problem, which is very difficult to solve accurately. On the basis of establishing logistics path optimization model, an immune clone algorithm is proposed. To improve the accuracy of search algorithms, the clonal selection and high frequency variations in the immune algorithm method are introduced. Then the antibody encoding virtual distribution point algorithm is designed to improve search efficiency. The benchmark problem of logistics delivery path optimization is simulated and analyzed. Experimental results show that the proposed immune cloning algorithm expands the range of population search and it have obvious advantages in solving large-scale complex physical distribution optimization problems. Also, the proposed algorithm can solve the optimal distribution of logistics effectively.

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