A system approach to information system: Design for a city planning agency

Information system design forms the most vital element in designing an organization. An information system for a city planning agency is a combination of people, computer hardware and software, a dynamic data base and a set of generalized interactive algorithms.City planning agencies require complete and current information in order to provide input into municipal government decision process. This paper presents a model for an information system based on the operational functions of various city departments. The emphasis of this paper is on system engineering considerations and there is no attempt to provide a detailed programming solution. Basic considerations include: operational requirements, planning requirements, political environment and level of computer expertise. The basic components of the model are an on-line interactive system with a data-base shared by the participating departments.     

A neural network approach to complete coverage path planning.

Complete coverage path planning requires the robot path to cover every part of the workspace, which is an essential issue in cleaning robots and many other robotic applications such as vacuum robots, painter robots, land mine detectors, lawn mowers, automated harvesters, and window cleaners. In this paper, a novel neural network approach is proposed for complete coverage path planning with obstacle avoidance of cleaning robots in nonstationary environments. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally simple. Simulation results show that the proposed model is capable of planning collision-free complete coverage robot paths.     

Modeling cooperative and competitive behaviors in emergency evacuation: A game-theoretical approach

A game-theoretical model to study evacuees’ cooperative and competitive behaviors during an emergency evacuation is proposed. The model integrated with evacuation dynamics model determines the density of cooperative and competitive evacuees and their related evacuation times. Computer simulation results show that (1) as urgency of evacuation increases, cooperation among evacuees’ decreases; (2) in an emergency situation, individual hyper-rationality among evacuees diminishes evacuation efficiency; (3) the imitation effect enhances cooperation among evacuees, yet reduces evacuation efficiency. This study provides a methodological pattern to research crowd behaviors in emergency evacuation.     

A minimum-cost network flow approach to preemptive parallel-machine scheduling

We model and solve the problems of preemptive scheduling of n independent jobs with release dates on m parallel machines with machine availability and eligibility constraints to minimize the makespan and maximum lateness as the minimum-cost network flow problem. We show that the approach can be extended to solve the corresponding scheduling problems on two uniform parallel machines.     

A resource enhanced HTN planning approach for emergency decision-making

Hierarchical resource reasoning is one of the key issues to successfully apply Hierarchy Task Network (HTN) planning into emergency decision-making. This paper proposes a Resource Enhanced HTN (REHTN) planning approach for emergency decision-making with the objective to enhance the expressive power and improve the processing speed of hierarchical resource reasoning. In the approach, resource timelines are defined to describe various resource variables and constraints. Top-down resource reasoning is used for decomposing the resource constraints of upper-level tasks into those of lower-level tasks. Meanwhile, resource and temporal constraints of tasks in different branches are processed by causal links. After the tasks are decomposed into primitive tasks, resource profiles of consumable resources and reusable resources are checked by separate resource allocation processes. Furthermore, a constraint propagation accelerator is designed to speed up hierarchal resource reasoning. The effectiveness and practicability of REHTN are confirmed with some experiments from emergency logistics distribution problems.     

Modeling human behavior during emergency evacuation using intelligent agents: A multi-agent simulation approach

It is costly and takes a lot of time for disaster employees to execute several evacuation drills for a building. One cannot glean information to advance the plan and blueprint of forthcoming buildings without executing many drills. We have developed a multi-agent system simulation application to aid in running several evacuation drills and theoretical situations. This paper combines the genetic algorithm (GA) with neural networks (NNs) and fuzzy logic (FL) to explore how intelligent agents can learn and adapt their behavior during an evacuation. The adaptive behavior focuses on the specific agents changing their behavior in the environment. The shared behavior of the agent places an emphasis on the crowd-modeling and emergency behavior in the multi-agent system. This paper provides a fuzzy individual model being developed for realistic modeling of human emotional behavior under normal and emergency conditions. It explores the impact of perception and emotions on the human behavior. We have established a novel intelligent agent with characteristics such as independence, collective ability, cooperativeness, and learning, which describes its final behavior. The contributions of this paper lie in our approach of utilizing a GA, NNs, and FL to model learning and adaptive behavior of agents in a multi-agent system. The planned application will help in executing numerous evacuation drills for what-if scenarios for social and cultural issues such as evacuation by integrating agent characteristics. This paper also compares our proposed multi-agent system with existing commercial evacuation tools as well as real-time evacuation drills for accuracy, building traffic characteristics, and the cumulative number of people exiting during evacuation. Our results show that the inclusion of GA, NNs, and fuzzy attributes made the evacuation time of the agents closer to the real-time evacuation drills.     

A non-linear optimal control approach to central sewer network flow control

The problem of optimal water distribution to several retention reservoirs in an urban sewer network during rainfall is considered. The goal of the control actions is the minimization of overflows and eventually the reduction of their polluting impact on receiving waters. To this end, a non-linear optimal control approach is used and the numerical solution of the control problem is effectuated by use of a feasible direction algorithm. A detailed study of the central control problem for a particular large sewer network using this method is presented. Results demonstrate the efficiency and the real-time feasibility of the developed methodology.     

Resource planning in the emergency departments: A simulation-based metamodeling approach

Patient’s congestion and their long waiting times in Emergency Departments (EDs) are the most common problems in hospitals. This paper extends application domain of metamodels into decision-making in the EDs by developing a discrete event simulation (DES) model combined with suitable metamodels. This is used as a novel decision support system to improve the patients flow and relieve congestion by changing the number of ED resources (i.e., the number of receptionists, nurses, residents, and beds). This new tool could be used for decision-making in operational, tactical, and strategic levels. In the first step, we develop a simulation routine of the ED in order to evaluate the system performance measure (total average waiting times of patients) for each configuration of resources. In the next step, we use different metamodel techniques and choose one with the maximum efficiency through a cross validation technique to replace the computationally expensive DES model with an accurate and efficient metamodel. Then the proposed model is used to minimize the total average waiting times of patients subject to both budget and capacity constraints. We implement our proposed model in an emergency department in Iran. Experimental results with the current ED budget verify that after using the resource allocation obtained from the proposed model, the total waiting time of patients is reduced by about 48%. Furthermore, to evaluate the efficiency of the selected metamodel, we compare the respective results with those obtained through OptQuest in terms of both the accuracy and the time needed to perform the optimization process.     

A new deductive approach to planning

We introduce a new deductive approach to planning which is based on Horn clauses. Plans as well as situations are represented as terms and, thus, are first-class objects. We do neither need frame axioms nor state-literals. The only rule of inference is the SLDE-resolution rule, i.e. SLD-resolution, where the traditional unification algorithm has been replaced by anE-unification procedure. We exemplify the properties of our method such as forward and backward reasoning, plan checking, and the integration of general theories. Finally, we present the calculus and show that it is sound and complete. An earlier version of this paper was presented at the German Workshop on Artificial Intelligence, 1989.     

A case-based approach to heuristic planning

Most of the great success of heuristic search as an approach to AI Planning is due to the right design of domain-independent heuristics. Although many heuristic planners perform reasonably well, the computational cost of computing the heuristic function in every search node is very high, causing the planner to scale poorly when increasing the size of the planning tasks. For tackling this problem, planners can incorporate additional domain-dependent heuristics in order to improve their performance. Learning-based planners try to automatically acquire these domain-dependent heuristics using previous solved problems. In this work, we present a case-based reasoning approach that learns abstracted state transitions that serve as domain control knowledge for improving the planning process. The recommendations from the retrieved cases are used as guidance for pruning or ordering nodes in different heuristic search algorithms applied to planning tasks. We show that the CBR guidance is appropriate for a considerable number of planning benchmarks.     

A network flow approach to reconstruction of the left ventricle from two projections

A new method for binary reconstruction of the left ventricle from two orthogonal projections is presented. A priori knowledge has to be incorporated to reduce the ambiguity of the problem. A minimum cost capacitated network flow algorithm is discussed, which yields the optimal solution with respect to the priori information. It is shown that this method can also be used in the presence of observation noise. The method is demonstrated by reconstructing several cross sections of a dog's left ventricle.     

Two-phase evacuation route planning approach using combined path networks for buildings and roads

This paper addresses the problem of modeling evacuation routes from a building and out of an affected area. The evacuation route involves pathways such as corridors, and stairs in buildings and road networks and sidewalks outside the building. To illustrate such an approach, we consider the problem of finding evacuation paths from an urban building and out of a predetermined neighborhood of the building on foot. A case study for a college campus building and small set of road around it is provided. There are a pre-defined set of exit points out of the target building and out of the road network serving the building. A two-step approach with an uncapacitated network model for route finding and a capacitated scheduling algorithm for evacuation time computation is proposed. A recent efficient heuristic algorithm is selected as a reference for comparative analysis. The process of creating a combined building and road path network data is discussed. The key results are the competitive evacuation time provided by the proposed uncapacitated route planning model, simple pedestrian flow capacity formulas for corridors and roads from readily available geometric data, and the illustration of the creation and use of combined building and road path network.     

A collaborative approach to assembly sequence planning

Distributed product development requires collaborative work among team members. For the sake of supporting assembly planning activities involving geographically dispersed designers, this paper presents an approach of collaborative assembly sequence planning to validate the assemblability of parts and subassemblies rapidly. In order to increase the planning efficiency and support the collaborative planning, role-based model is exploited to compress or simplify the product. In role-based model, the B-rep models are simplified according to the permissions associated with the role, so the surfaces invisible from outside of the model are removed. In collaborative planning, the planning tasks are assigned to different designers that carry out the collaborative planning, respectively. In this paper, a knowledge-based approach is proposed to the assembly sequence planning problem. This research shows that the typical or standard CSBAT (Connection Semantics Based Assembly Tree) can be applied to a given assembly problem. This paper presents the structure of the Co-ASP (Collaborative Assembly Sequence Planning System) and provides an example to illustrate the collaborative planning approach.     

A knowledge-based approach to maintenance project planning

Maintenance issues comprise an important, though frequently overlooked, area of manufacturing operations. In smaller companies in particular, expertise for effective maintenance project planning may be lacking. In this paper we discuss the development of an object-oriented expert system to support such project planning. The system was developed to meet the needs of an automotive components manufacturer, and focuses solely on its material-handling-oriented projects. The microcomputer-based expert system considers issues of schedule, inventory availability and cost.     

A network flow based approach for network selection in dynamic spectrum access networks

In this paper, we present a network flow based approach for dynamic network and channel selection for secondary users in dynamic spectrum access networks. Most approaches in the current literature on dynamic spectrum access networks do not consider dynamic network and channel selection. We present a network flow framework for network selection. We show that our approach can enable re-assignment of networks to secondary users and also re-assignment of channels to secondary users within the same network. The assignments and re-assignments take into account, the interference caused to primary users, the price each secondary user is willing to pay and the quality of service (QoS) obtained by each secondary user. We obtain a bound for the maximum number of re-assignments.     

A neural network approach to a Bayesian statistical decisionproblem

Generalized mean-squared error (GMSE) objective functions are proposed that can be used in neural networks to yield a Bayes optimal solution to a statistical decision problem characterized by a generic loss function.     

基于动态DCVS网络模型的流量分析及规划方法

针对目前机场远端行李运输目的地编码车系统未对流量进行规划而造成的资源浪费现状,提出了一种基于动态DCVS网络模型的流量分析及规划方法。该方法首先基于图论建立了DCVS静态网络模型,接着根据系统在特定时间段的流量需求,采用最大流最小时间动态流量分析和规划算法对DCVS资源进行动态调度。实验结果显示,该方法可以在很短的时间内完成对DCVS流量的整体分析和规划,实现远端行李运输任务的可行性预测,从而为DCVS资源的控制提供有效的参考依据。     

A network flow based heuristic approach for optimising AGV movements

Automated Guided Vehicles (AGVs) are driverless carriers that automatically navigate along planned paths by means of several guidance and control methods. This paper proposes an approach for solving the dispatching problem in an AGV system. The problem is modelled through a network by relying on the formulation of a Minimum Cost Flow Problem. In the defined graph, the nodes represent transportation tasks and AGVs while the arcs consider, through the associated weights, several system’s aspects such as pick, drop, and travel times, battery recharging, capacity constraints, congestion and error issues. Two objectives can be achieved: (i) minimisation of the average time for carrying out transportation tasks or (ii) maximisation of the utilisation degree of AGVs. The modelling and solution approach adopted has provided a novel Vehicle–Initiated dispatching rule and parameters settings for the dynamic assignments of transportation missions to AGVs. The decision making process concurrently and dynamically considers several factors. The results show a relevant reduction in the average time for transportation order fulfilment and a decrease in its variability. The proposed approach has been exploited for optimising the AGVs performance in a pharmaceutical production system.