Determining the optimal location of control cabinet components requires the exploration of a large configuration space. For real-world control cabinets it is impractical to evaluate all possible cabinet configurations. Therefore, we need to apply methods for intelligent exploration of cabinet configuration space that enable to find a near-optimal configuration without evaluation of all possible configurations. In this paper, we describe an approach for multi-objective optimization of control cabinet layout that is based on Pareto Simulated Annealing. Optimization aims at minimizing the total wire length used for interconnection of components and the heat convection within the cabinet. We simulate heat convection to study the warm air flow within the control cabinet and determine the optimal position of components that generate heat during the operation. We evaluate and demonstrate the effectiveness of our approach empirically for various control cabinet sizes and usage scenarios.
Drexl and Nikulin @cite_17 use Pareto Simulated Annealing for solving the gate assignment problem in the context of an airport. Various aspects of airport operation are considered, such as, the total passenger walking distance, open flights, connection time, and gate assignment preferences.
@cite_17
2106120336
This paper addresses an airport gate assignment problem with multiple objectives. The objectives are to minimize the number of ungated flights and the total passenger walking distances or connection times as well as to maximize the total gate assignment preferences. The problem examined is an integer program with multiple objectives (one of them being quadratic) and quadratic constraints. Of course, such a problem is inherently difficult to solve. We tackle the problem by Pareto simulated annealing in order to get a representative approximation for the Pareto front. Results of computational experiments are presented. To the best of our knowledge, this is the first attempt to consider the airport gate assignment problem with multiple objectives. An anonymous Bluetooth tracking system was deployed at the new Indianapolis International Airport in Indiana from May 8 to June 2, 2009, to measure the time for passengers (a) to move from the nonsterile side of the airport (presecurity), (b) to clear the security screening checkpoint, and (c) to enter the walkway to Concourse B on the sterile side. The maximum passenger transit time between these checkpoints was observed on Monday mornings at approximately 0600, when it could take passengers up to 20 min to transit the security queue and screening and to walk to Concourse B. Depending on the day of the week, this approach was demonstrated to sample between 5 and 6.8 of passengers. This modest sample size provides a more robust measurement of screening times than the current system of manually distributing time-stamped cards as passengers enter the queue and collecting them where passengers pass through the magnetometer. Furthermore, because the final passenger reference point used in this study is on the sterile concourse, it captures the time associated with passengers repackaging their belongings and redonning their shoes. The data from this pilot study suggest the feasibility of using an automated system to provide quantitative information to managers for more effective allocation of scarce resources, as well as providing the traveling public with necessary information about the amount of time they should allocate for transiting the security screening process. The paper concludes by suggesting that additional pilot studies should be performed at several airports with alternative checkpoint configurations to develop a consensus on best practices for locating sensors to measure passage times at airport security screening. We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in large and busy airports. One particular aim is to bring travellers of connecting flights conveniently and efficiently from their arrival gate to the passport control. The uniqueness of the project stems from the strong demand of service robots for this application with a large potential impact for the aviation industry on one side, and on the other side from the scientific advancements in social robotics, brought forward and achieved in SPENCER. The main contributions of SPENCER are novel methods to perceive, learn, and model human social behavior and to use this knowledge to plan appropriate actions in real-time for mobile platforms. In this paper, we describe how the project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors. Emergency evacuation from airports is an important consideration given the continuing occurrence of both natural and human caused disasters. Unfortunately, the traditional evacuation-drill approach to prepare for emergency situations presents several practical challenges at the scale and magnitude required for airports. In this paper, we present an agent-based model (ABM) called exitus which is capable of determining the extent to which collective behavior and overall evacuation time of passenger groups is affected by changes in the built environment for large, complex structures. The model is unique because it explicitly considers the physical and psychological characteristics of individuals with disabilities. In our first experiment, several bomb simulation scenarios were conducted at an international airport using exitus. Several important findings were revealed including: 1) the importance of stairway and exit configurations; 2) the inherent weaknesses of the pier airport design in affecting timely evacuations; 3) who the most vulnerable groups of people are; 4) the particular risk engendered from crowded or complex building interiors for individuals with disabilities; and 5) the potential problems caused by locating explosive detection system machines near passenger processing areas. The results of a second exploratory experiment also revealed the importance of realistically modeling psychological attributes of individuals with disabilities and their potential impact on collective evacuation performance. Overall the findings demonstrated the model’s ability to generate a common operational picture capable of guiding preparedness efforts for both public and private organizations encompassing a wide variety of professional endeavor. This study aimed to examine the effects of aspects of airline service quality, such as airline tangibles, terminal tangibles, and empathy on levels of customer satisfaction. The relationship between these levels of satisfaction and the general perceptions about service quality were also investigated. An airline passenger survey was conducted among the population of the Federal Territory of Labuan, Malaysia. A total of 300 respondents who had regularly patronized either Malaysia Airlines or AirAsia over the last six months were selected via convenience sampling method. Empirical results via structural equation modeling (SEM) approach revealed that the relationship between customer satisfaction with airline service quality and ‘word-of-mouth’ recommendations is a consistent one. Furthermore, customer satisfaction is widely influenced by empathy, which is why flight punctuality and good transportation links between city venues and airports are prioritized by providers. Direction for future research is presented. As smart surveillance technology becomes a critical component in security infrastructures, the system architecture assumes a critical importance. This paper considers the example of smart surveillance in an airport environment. We start with a threat model for airports and use this to derive the security requirements. These requirements are used to motivate an open-standards based architecture for surveillance. We discuss the critical aspects of this architecture and its implementation in the IBM S3 smart surveillance system. Demo results from a pilot deployment in Hawthorne, NY are presented. This paper develops a multi-objective optimization model for the passenger train-scheduling problem on a railroad network which includes single and multiple tracks, as well as multiple platforms with different train capacities. In this study, lowering the fuel consumption cost is the measure of satisfaction of the railway company and shortening the total passenger-time is being regarded as the passenger satisfaction criterion. The solution of the problem consists of two steps. First the Pareto frontier is determined using the [var epsilon]-constraint method, and second, based on the obtained Pareto frontier detailed multi-objective optimization is performed using the distance-based method with three types of distances. Numerical examples are given to illustrate the model and solution methodology. Airports are a prime target for terrorist organizations, drug traffickers, smugglers, and other nefarious groups. Traditional forms of security assessment are not real-time and often do not exist for each airport and port of entry. Thus, homeland security professionals must rely on measures of attractiveness of an airport as a target for attacks. We present an open source indicators approach, using news and social media, to conduct relative threat assessment, i.e., estimating if one airport is under greater threat than another. The three ingredients of our approach are a dynamic query expansion algorithm for tracking emerging threat-related chatter, news-Twitter reciprocity modeling for capturing interactions between social and traditional media, and a ranking scheme to provide an ordered assessment of airport threats. Case studies based on actual aviation incidents are presented. We consider the joint decision of placing public bads in each of two neighboring countries, modeled by two adjacent line segments. Residents of the two countries have single-dipped preferences, determined by the distance of their dips to the nearer public bad (myopic preferences) or, lexicographically, by the distance to the nearer and the other public bad (lexmin preferences). A (social choice) rule takes a profile of reported preferences as input and assigns the location of the public bad in each country. For the case of myopic preferences, all rules satisfying strategy-proofness, country-wise Pareto optimality, non-corruptibility, and the far away condition are characterized. These rules pick only border locations. The same holds for lexmin preferences under strategy-proofness and country-wise Pareto optimality alone. In many critical applications such as airport operations (for capacity planning), military simulations (for tactical training and planning), and medical simulations (for the planning of medical treatment and surgical operations), it is very useful to conduct simulations within physically accurate and visually realistic settings that are represented by real video imaging sequences. Furthermore, it is important that the simulated entities conduct autonomous actions which are realistic and which follow plans of action or intelligent behavior in reaction to current situations. We describe the research we have conducted to incorporate synthetic objects in a visually realistic manner in video sequences representing a real scene. We also discuss how the synthetic objects can be designed to conduct intelligent behavior within an augmented reality setting. The paper discusses both the computer vision aspects that we have addressed and solved, and the issues related to the insertion of intelligent autonomous objects within an augmented reality simulation. We consider the problem of assigning agents to slots on a line, where only one agent can be served at a slot and each agent prefers to be served as close as possible to his target. Our focus is on aggregate gap minimizing methods, i.e., those that minimize the total gap between targets and assigned slots. We first consider deterministic assignment of agents to slots, and provide a direct method for testing if a given deterministic assignment is aggregate gap minimizing. We then consider probabilistic assignment of agents to slots, and make use of the previous method to propose an aggregate gap minimizing modification of the classic random priority method to solve this class of problems. We also provide some logical relations in our setting among standard axioms in the literature on assignment problems, and explore the robustness of our results to several extensions of our setting. In order to be successful, guide robots in public space require socially-intelligent navigation behaviors. Evaluation of these behaviors can be done through lab studies, though these do not always capture the complexities of interactions in "the wild". In this extended abstract we present initial results of a field trial of a multi-year project in which we developed and deployed a robot which provided guiding services to real passengers at one of the top-20 busiest airports in the world. During this field trial 9 groups of passengers were guided by the robot. We will present initial results and implications for field studies. Most of existing works on complex network assumed that the nodes and edges were uncapacitated during the evolving process, and displayed “rich club” phenomenon. Here we will show that the “rich club” could be changed to “common rich” if we consider the node capacity. In this paper, we define the node and edge attractive index with node capacity, and propose a new evolving model on the base of BBV model, with evolving simulations of the networks. In the new model, an entering node is linked with an existing node according to the preferential attachment mechanism defined with the attractive index of the existing node. We give the theoretical approximation and simulation solutions. If node capacity is finite, the rich node may not be richer further when the node strength approaches or gets to the node capacity. This is confirmed by analyzing the passenger traffic and routes of Chinese main airports. Due to node strength being function of time t, we can use the theoretical approximation solution to forecast how node strength changes and the time when node strength reaches its maximum value. The problem of eliciting honest preferences from individuals who must be assigned to a set of positions is considered. Individuals know that they will be charged for the positions to which they are assigned. A set of prices that provide no incentive for the individual to misrepresent his preferences is suggested. It is shown that these prices constitute an element of the optimal solution to the dual of a linear programming assignment problem. Both the optimal allocation and the prices to be charged can be derived by solving two linear programming problems once preferences have been elicited. The procedure can usefully be viewed as a simulation of a competitive market under conditions where such a market cannot be expected to function well. It results in an efficient allocation where all resources are valued at their opportunity costs and "consumer surplus" is maximized; its outcome thus has the desirable properties of competitive market equilibria. An important aspect of the packaging of digital networks is the allocation of logic gates to modules such that a predetermined objective function is minimized. In order to develop techniques for this partitioning of a logic network we have considered the following problem: Given an acyclic combinational network composed of various primitive blocks such as NOR gates, assume that a maximum of M gates can be "clustered" together into larger modules, and that a maximum of P pins can be accommodated in each larger module. Assume also that in a network composed of such larger modules, no delay is encountered on the interconnections linking two gates internal to a module and a delay of one time unit is encountered on interconnections linking two gates in different modules . Find an easily applied algorithm that will result in a network such that the maximum delay through the network is minimized. This paper deals with fair assignment problems in decision contexts involving multiple agents. In such problems, each agent has its own evaluation of costs and we want to find a fair compromise solution between individual point of views. Lorenz dominance is a standard decision model used in Economics to refine Pareto dominance while favoring solutions that fairly share happiness among agents. In order to enhance the discrimination possibilities offered by Lorenz dominance, we introduce here a new model called infinite order Lorenz dominance. We establish a representation result for this model using an ordered weighted average with decreasing weights. Hence we exhibit some properties of infinite order Lorenz dominance that explain how fairness is achieved in the aggregation of individual preferences. Then we explain how to solve fair assignment problems of m items to n agents, using infinite order Lorenz dominance and other models used for measuring inequalities. We show that this problem can be reformulated as a 0--1 non-linear optimization problems that can be solved, after a linearization step, by standard LP solvers. We provide numerical results showing the efficiency of the proposed approach on various instances of the paper assignment problem. In this paper we present a model based on the parallel computational tool of cellular automata CA capable of simulating the process of disembarking in a small airplane seat layout, corresponding to Airbus A320 Boeing 737 layout, in search of ways to make it faster and safer under normal evacuation conditions, as well as emergency scenarios. The proposed model is highly customizable, with the number of exits, the walking speed of passengers, depending on their sex, age and height, and the effects of retrieving and carrying luggage. Additionally, the presence of obstacles in the aisles as well as the emergence of panic being parameters whose values can be varied in order to enlighten the disembarking and emergency evacuation processes are considered in detail. The simulation results were compared to existing aircraft disembarking and evacuation times and indicate the efficacy of the proposed model in investigating and revealing passenger attributes during these processes in all the examined cases. Moreover, we parallelized our code in order to run on a graphics processing unit GPU using the CUDA programming language, speeding up the simulation process. Finally, in order to present a fully dynamical anticipative real-time system helpful for decision-making we implemented the proposed CA model in a field programmable gate array FPGA device, and recreated the results given by the software simulations in a fraction of the time. We then compared and exported the performance results among a sequential software implementation, the implementation running on a GPU, and a hardware implementation, proving the consequent acceleration that results from the parallel CA implementation in specific hardware. In the capacitated facility location problem with hard capacities, we are given a set of facilities, @math , and a set of clients @math in a common metric space. Each facility i has a facility opening cost f i and capacity u i that specifies the maximum number of clients that may be assigned to this facility. We want to open some facilities from the set @math and assign each client to an open facility so that at most u i clients are assigned to any open facility i. The cost of assigning client j to facility i is given by the distance c ij , and our goal is to minimize the sum of the facility opening costs and the client assignment costs. The only known approximation algorithms that deliver solutions within a constant factor of optimal for this NP-hard problem are based on local search techniques. It is an open problem to devise an approximation algorithm for this problem based on a linear programming lower bound (or indeed, to prove a constant integrality gap for any LP relaxation). We make progress on this question by giving a 5-approximation algorithm for the special case in which all of the facility costs are equal, by rounding the optimal solution to the standard LP relaxation. One notable aspect of our algorithm is that it relies on partitioning the input into a collection of single-demand capacitated facility location problems, approximately solving them, and then combining these solutions in a natural way. Recently, algorithms for computing game-theoretic solutions have been deployed in real-world security applications, such as the placement of checkpoints and canine units at Los Angeles International Airport. These algorithms assume that the defender (security personnel) can commit to a mixed strategy, a so-called Stackelberg model. As pointed out by Kiek- (2009), in these applications, generally, multiple resources need to be assigned to multiple targets, resulting in an exponential number of pure strategies for the defender. In this paper, we study how to compute optimal Stackelberg strategies in such games, showing that this can be done in polynomial time in some cases, and is NP-hard in others. Taxi services are a vital part of urban transportation, and a considerable contributor to traffic congestion and air pollution causing substantial adverse effects on human health. Sharing taxi trips is a possible way of reducing the negative impact of taxi services on cities, but this comes at the expense of passenger discomfort quantifiable in terms of a longer travel time. Due to computational challenges, taxi sharing has traditionally been approached on small scales, such as within airport perimeters, or with dynamical ad hoc heuristics. However, a mathematical framework for the systematic understanding of the tradeoff between collective benefits of sharing and individual passenger discomfort is lacking. Here we introduce the notion of shareability network, which allows us to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets. We apply this framework to a dataset of millions of taxi trips taken in New York City, showing that with increasing but still relatively low passenger discomfort, cumulative trip length can be cut by 40 or more. This benefit comes with reductions in service cost, emissions, and with split fares, hinting toward a wide passenger acceptance of such a shared service. Simulation of a realistic online system demonstrates the feasibility of a shareable taxi service in New York City. Shareability as a function of trip density saturates fast, suggesting effectiveness of the taxi sharing system also in cities with much sparser taxi fleets or when willingness to share is low.