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 Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1928 ◽  
Author(s):  
Alfonso González-Briones ◽  
Fernando De La Prieta ◽  
Mohd Mohamad ◽  
Sigeru Omatu ◽  
Juan Corchado
Keyword(s):  
Optimization Problems ◽  
State Of The Art ◽  
Smart Cities ◽  
Energy Optimization ◽  
Sensor Data ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Research Perspectives ◽  
Wide Range ◽  
Multi Agent

This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent-based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore, we can argue that MAS is a widespread approach in the field of energy optimization and that it is commonly used due to its capacity for the communication, coordination, cooperation of agents and the robustness that this methodology gives in assigning different tasks to agents. Finally, this article considers how MASs can be used for various purposes, from capturing sensor data to decision-making. We propose some research perspectives on the development of electrical optimization solutions through their development using MASs. In conclusion, we argue that researchers in the field of energy optimization should use multi-agent systems at those junctures where it is necessary to model energy efficiency solutions that involve a wide range of factors, as well as context independence that they can achieve through the addition of new agents or agent organizations, enabling the development of energy-efficient solutions for smart cities and intelligent buildings.

Logistics for the Garbage Collection through the use of Ant Colony Algorithms

2012 ◽  
pp. 33-51
Author(s):  
Julio Cesar Ponce Gallegos ◽  
Fatima Sayuri Quezada Aguilera ◽  
José Alberto Hernandez Aguilar ◽  
Christian José Correa Villalón
Keyword(s):  
Waste Management ◽  
Garbage Collection ◽  
Optimization Problems ◽  
Decision Makers ◽  
Ant Colony ◽  
Ant Colony System ◽  
Waste Collection ◽  
Combinatorial Optimization Problems ◽  
Ant Colony Algorithms ◽  
Acs Algorithm

The contribution of this chapter is to present an approach to explain the Ant Colony System applied on the Waste Collection Problem, because waste management is moving up to the concern over health and environmental impacts. These algorithms are a framework for decision makers in order to analyze and simulate various spatial waste management problems. In the last decade, metaheuristics have become increasingly popular for effectively confronting difficult combinatorial optimization problems. In the present work, an individual metaheuristic Ant Colony System (ACS) algorithm is introduced, implemented and discussed for the identification of optimal routes in the case Solid Waste collection. This algorithm is applied to a waste collection and transport system, obtaining recollection routes with the less total distance with respect to the actual route utilized and to the solution obtained by a previously developed approach.

scholarly journals Abstract Cores in Implicit Hitting Set MaxSat Solving (Extended Abstract)

2021 ◽  
Author(s):  
Jeremias Berg ◽  
Fahiem Bacchus ◽  
Alex Poole
Keyword(s):  
Linear Programming ◽  
Optimization Problems ◽  
State Of The Art ◽  
Empirical Evaluation ◽  
Boolean Satisfiability ◽  
Compact Representation ◽  
Maximum Satisfiability ◽  
Hitting Set ◽  
Combinatorial Optimization Problems ◽  
Core Extraction

Maximum satisfiability (MaxSat) solving is an active area of research motivated by numerous successful applications to solving NP-hard combinatorial optimization problems. One of the most successful approaches for solving MaxSat instances from real world domains are the so called implicit hitting set (IHS) solvers. IHS solvers decouple MaxSat solving into separate core-extraction (i.e. reasoning) and optimization steps which are tackled by a Boolean satisfiability (SAT) and an integer linear programming (IP) solver, respectively. While the approach shows state-of-the-art performance on many industrial instances, it is known that there exists instances on which IHS solvers need to extract an exponential number of cores before terminating. Motivated by the simplest of these problematic instances, we propose abstract cores, a compact representation for a potentially exponential number of regular cores. We demonstrate how to incorporate abstract core reasoning into the IHS algorithm and report on an empirical evaluation demonstrating, that including abstract cores into a state-of-the-art IHS solver improves its performance enough to surpass the best performing solvers of the 2019 MaxSat Evaluation.

scholarly journals Bin Completion Algorithms for Multicontainer Packing, Knapsack, and Covering Problems

2007 ◽  
Vol 28 ◽  
pp. 393-429 ◽  
Author(s):  
A. S. Fukunaga ◽  
R. E. Korf
Keyword(s):  
Bin Packing ◽  
Optimization Problems ◽  
State Of The Art ◽  
Cutting Stock ◽  
Multi Agent Systems ◽  
Covering Problems ◽  
Combinatorial Optimization Problems ◽  
Bin Covering ◽  
Multiple Knapsack ◽  
Dominance Criterion

Many combinatorial optimization problems such as the bin packing and multiple knapsack problems involve assigning a set of discrete objects to multiple containers. These problems can be used to model task and resource allocation problems in multi-agent systems and distributed systms, and can also be found as subproblems of scheduling problems. We propose bin completion, a branch-and-bound strategy for one-dimensional, multicontainer packing problems. Bin completion combines a bin-oriented search space with a powerful dominance criterion that enables us to prune much of the space. The performance of the basic bin completion framework can be enhanced by using a number of extensions, including nogood-based pruning techniques that allow further exploitation of the dominance criterion. Bin completion is applied to four problems: multiple knapsack, bin covering, min-cost covering, and bin packing. We show that our bin completion algorithms yield new, state-of-the-art results for the multiple knapsack, bin covering, and min-cost covering problems, outperforming previous algorithms by several orders of magnitude with respect to runtime on some classes of hard, random problem instances. For the bin packing problem, we demonstrate significant improvements compared to most previous results, but show that bin completion is not competitive with current state-of-the-art cutting-stock based approaches.

scholarly journals Selected Soft Computing Algorithms for Job Shop Problem (JSP)

2021 ◽  
Vol 10 (9) ◽  
pp. 125-131
Author(s):  
Adedeji Oluyinka Titilayo ◽  
Alade Oluwaseun Modupe ◽  
Makinde Bukola Oyeladun ◽  
OYELEYE Taye E
Keyword(s):  
Combinatorial Optimization ◽  
Computer Science ◽  
Soft Computing ◽  
Operations Research ◽  
Optimization Problem ◽  
Job Shop ◽  
Optimization Problems ◽  
Combinatorial Optimization Problems ◽  
Flower Pollination

Job Shop Problem (JSP) is an optimization problem in computer science and operations research in which jobs are assigned to resources at particular times. Each operation has a specific machine that it needs to be processed on and only one operation in a job can be processed at a given time. This problem is one of the best known combinatorial optimization problems. The aim of this project is to adapt Bat, Bee, Firefly, and Flower pollination algorithms, implement and evaluate the developed algorithms for solving Job Shop Problem.

Logistics for the Garbage Collection through the use of Ant Colony Algorithms

2013 ◽  
pp. 1809-1827 ◽  
Author(s):  
Julio Cesar Ponce Gallegos ◽  
Fatima Sayuri Quezada Aguilera ◽  
José Alberto Hernandez Aguilar ◽  
Christian José Correa Villalón
Keyword(s):  
Waste Management ◽  
Garbage Collection ◽  
Optimization Problems ◽  
Decision Makers ◽  
Ant Colony ◽  
Ant Colony System ◽  
Waste Collection ◽  
Combinatorial Optimization Problems ◽  
Ant Colony Algorithms ◽  
Acs Algorithm

The contribution of this chapter is to present an approach to explain the Ant Colony System applied on the Waste Collection Problem, because waste management is moving up to the concern over health and environmental impacts. These algorithms are a framework for decision makers in order to analyze and simulate various spatial waste management problems. In the last decade, metaheuristics have become increasingly popular for effectively confronting difficult combinatorial optimization problems. In the present work, an individual metaheuristic Ant Colony System (ACS) algorithm is introduced, implemented and discussed for the identification of optimal routes in the case Solid Waste collection. This algorithm is applied to a waste collection and transport system, obtaining recollection routes with the less total distance with respect to the actual route utilized and to the solution obtained by a previously developed approach.

scholarly journals Dichotomous Binary Differential Evolution for Knapsack Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Hu Peng ◽  
Zhijian Wu ◽  
Peng Shao ◽  
Changshou Deng
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
State Of The Art ◽  
Experimental Studies ◽  
Continuous Optimization ◽  
Knapsack Problems ◽  
Binary Particle Swarm Optimization ◽  
Combinatorial Optimization Problems ◽  
Multidimensional Knapsack ◽  
Binary Differential Evolution

Differential evolution (DE) is one of the most popular and powerful evolutionary algorithms for the real-parameter global continuous optimization problems. However, how to adapt into combinatorial optimization problems without sacrificing the original evolution mechanism of DE is harder work to the researchers to design an efficient binary differential evolution (BDE). To tackle this problem, this paper presents a novel BDE based on dichotomous mechanism for knapsack problems, called DBDE, in which two new proposed methods (i.e., dichotomous mutation and dichotomous crossover) are employed. DBDE almost has any difference with original DE and no additional module or computation has been introduced. The experimental studies have been conducted on a suite of 0-1 knapsack problems and multidimensional knapsack problems. Experimental results have verified the quality and effectiveness of DBDE. Comparison with three state-of-the-art BDE variants and other two state-of-the-art binary particle swarm optimization (PSO) algorithms has proved that DBDE is a new competitive algorithm.

scholarly journals An Assessment Framework for the Transformation of Mobility 4.0 in Smart Cities

Systems ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 1
Author(s):  
Hakan Inac ◽  
Ercan Oztemel
Keyword(s):  
Mathematical Model ◽  
Reference Model ◽  
Smart Cities ◽  
Transportation Systems ◽  
Transformation Process ◽  
Digital Transformation ◽  
Decision Makers ◽  
Strategic Action ◽  
Related Services ◽  
Transport Services

Investing in digital transformation turns out to be a strategic action to tackle contemporary issues and to improve competitiveness for enterprises. The high variability of options in the digital transformation process enforces a higher complexity level in configuring and setting up objectives and goals based on cities’ needs; hence, a systematic approach is required to assist decision makers for better and sustainable transformation. A reference model is described in this paper to support decision makers with comprehensive assessment data for digital transformation cities transport. The proposed reference model assesses the cities based on digital transformation of transport services to assist policy makers for better decisions in transforming the Mobility 4.0. The proposed model in this study functions as a knowledge-based systematic framework for assessing the capabilities of the cities, diagnosing their needs under given circumstances and identifying the best fitting workflow for digital transformation of urban transportation systems and related services. The reference model takes on board a group of smart city indices with respective assessment criteria in determining a smartness level of transportation components. A conceptual 4-tier smartness scale has been proposed to establish a consistent assessment subject to cities circumstances in many respects. The reference model has been formalised into a mathematical model to characterise the assessments. The mathematical model encompasses strategic assessments by experts to identify priorities of investments in the digitalization process, which are aligned with strategic goals and policies of cities’ management.

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.

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