Metaheuristics for Resource Deployment under Uncertainty in Complex Systems

Shuxin Ding, Chen Chen, Qi Zhang, Bin Xin, Panos Pardalos

• Editorial: CRC Press
• Año: 2021
• ISBN:
  9781000432916
• Edición: 1
• Categoría: Tecnología e ingeniería
• Categoría: Automatización en ingeniería

Metaheuristics for Resource Deployment under Uncertainty in Complex Systems analyzes how to set locations for the deployment of resources to incur the best performance at the lowest cost. Resources can be static nodes and moving nodes while services for a specific area or for customers can be provided. Theories of modeling and solution techniques are used with uncertainty taken into account and real-world applications used.

The authors present modeling and metaheuristics for solving resource deployment problems under uncertainty while the models deployed are related to stochastic programming, robust optimization, fuzzy programming, risk management, and single/multi-objective optimization. The resources are heterogeneous and can be sensors and actuators providing different tasks. Both separate and cooperative coverage of the resources are analyzed. Previous research has generally dealt with one type of resource and considers static and deterministic problems, so the book breaks new ground in its analysis of cooperative coverage with heterogeneous resources and the uncertain and dynamic properties of these resources using metaheuristics.

This book will help researchers, professionals, academics, and graduate students in related areas to better understand the theory and application of resource deployment problems and theories of uncertainty, including problem formulations, assumptions, and solution methods.

CHAPTER 1 Introduction

DOI: 10.1201/9781003202653-1

Resource deployment problem analyzes how to set the locations for deploying resources with the best performance and lowest cost. The resources can be static nodes and moving nodes. These resources can provide services for a specific area or some customers. This chapter introduces the resource deployment problem. It provides some real-world applications and fundamental issues of node deployment problem, and research progress of node deployment modeling and methods. This chapter provides a basic foundation for the whole work and gives the main issue and challenges that lead to the following chapters.

1.1APPLICATIONS OF NODE DEPLOYMENT PROBLEM

In this section, we outline a number of different applications related to the node deployment problem. They can be complex systems, e.g., unmanned systems, sensor networks, etc. Besides, there are many applications in facility location problems, e.g., healthcare, public sectors, railway network design, etc. There also exists resource deployment problems in distributed simulation systems.

1.1.1Unmanned Systems

Unmanned systems are man-made and can be operated or managed through advanced technologies. They are complex systems created by the fusion of various technologies related to mechanics, control, computer, communication, and materials [1]. They can be controlled by humans or perform tasks autonomously. Various types of unmanned systems are emerging include unmanned aerial (UAV), ground (UGV), and underwater (UUV) vehicles, etc. They may be applied in applications from the civil domain to the military domain, such as logistics, surveillance, building and environment monitoring, search and rescue, intruder detection and attacking, etc. [2]. Thus, one objective might be to minimize the number of vehicles for completing the tasks, and the other objective might be to maximize the payoff of the tasks [3]. Figure 1.1 shows the UAVs, UGVs, and UUVs in an unmanned system. A brief introduction and example applications of unmanned systems are presented as follows.

Figure 1.1 UAVs, UGVs, and UUVs in an unmanned system.

• Video surveillance. Camera-mounted UAVs can provide coverage of multiple-oriented targets. The positions of these UAVs are decided by the ground control station with a master camera. Except for surveillance [4] and crowd monitoring [5], they can be used for infrastructure inspections [6], cinematography [7], etc.
• Networks. The UAVs can help formulate coverage when there are disturbances and disruptions in the cellular networks caused by concerts, natural disasters, etc. [8]. Problems such as minimizing the number of UAVs required for continuous coverage, maximizing the area coverage, and preserving network connectivity require an optimized deployment strategy [2].

The vehicles may also perform different functional roles. Four main functional roles are defined as sensors, actuators, decision makers (DMs), and auxiliary facilities [9, 10]. For example, in UAV-UGV coordination systems, the characteristics of UAVs and UGVs are strongly complementary. The combination of different functional roles makes cooperative UAV-UGV coordination systems promising. UGVs can act as actuators limited by their speed and environmental occlusion, while UAVs can act as sensors quickly deployed for finding targets. UAVs can also help in formulating communication links for UGVs, which may be blocked by obstacles. Besides, small-scale UAVs are restricted by their short voyage due to the energy limitation, while UGVs can act as carriers providing auxiliary facilities. This also suggests that we would like to make better use of the systems by optimizing the deployment of the vehicles.

1.1.2Wireless Sensor Networks

Wireless sensor networks (WSNs) are formed by small, inexpensive, low-powered sensors. WSNs have recently become a popular research area for many applications in military, environmental, industrial, home, medical, etc. [11]. The objective is to monitor the environment and communicate information with each sensor. Figure 1.2 shows a target detection scenario in the three-dimensional (3D) space of WSNs. Decision makers need to decide the numbers and locations for the WSNs. Some performance metrics to be optimized in WSNs are introduced in Section 1.3.1. A brief introduction and example applications of wireless sensor networks are presented as follows.

Figure 1.2 Wireless sensor networks in 3D space for target detection.

• Military applications. In command, control, communications, computers, intelligence, surveillance, and reconnaissance (C4ISR) systems, WSNs can be rapidly scattered in critical terrains, routes to provide battlefield intelligence. They can be used to detect and track enemy targets.
• Environmental applications. Environmental problem is a critical issue for human on the earth. WSNs can be used for wildlife monitoring, fire detection, measuring ${\text{CO}}_{\text{2}}$ level, flood detection, air pollution detection, etc. [12].
• Industrial applications. WSNs can be used in manufacturing process management, monitoring the gas, water, and electric, lighting control, etc. They can also be used for monitoring the structural health of buildings, bridges, roads, physical condition of water and gas pipes, smart railway stations, etc.
• Home applications. WSNs in home environments connect everyday objects and devices at home through networks and create an Internet of Things (IoT)-based smart environment [13]. It is an environment that learns from our daily activity. People can conduct remote control of the home devices through IoT-based smart home systems [14].
• Medical applications. The Body Area Sensor Network (BASN) consists of multiple interconnected nodes for sensing, data processing, and wireless communication. These sensor nodes are placed on, near, or within the human body. The BASN sensor nodes constantly monitor and analyze different physiological signals, e.g., the electrical activities of the heart, muscles, and the brain; body temperature, blood glucose, blood pressure, blood oxygen saturation, etc. [15].

1.1.3Healthcare

Healthcare is defined as the prevention and treatment for illness or injury through professional medical services. The facility location problems related to healthcare covers from locating healthcare facilities to layout problems in hospitals [16]. Figure 1.3 shows the locations of historical cardiac arrests and candidate sites for AED deployment. A brief introduction and example applications of healthcare are presented as follows.

Figure 1.3 Locations of historical cardiac arrests and candidate sites for AED deployment.

• Healthcare facility location. The facility location problems for healthcare are mainly related to healthcare facilities, e.g., community health clinics, public and private hospitals, etc. The optimization criteria for healthcare facilities are minimizing access cost for healthcare consumers, maximizing population with access, etc.
• AED location. Optimizing the deployment of public automated external defibrillators (AEDs) can help to increase the probability of survival when sudden cardiac arrest occurs [17, 18].
• Ambulance location. Ambulance location belongs to the emergency vehicles sitting problems. The goal is to find the locations for the ambulances (or ambulance bases) with a minimal number and provide a certain level of service. Meanwhile, relocation decisions for ambulances should be periodically made to avoid areas unprotected [19].
• Hospital layout planning. The layout planning problems for hospitals aim at minimizing in-house travel distances or costs inside the building. It is classified as a resource capacity planning problem, which directly influences the quality and efficiency of healthcare, and patient satisfaction.

1.1.4Public Sectors

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