An agent-based model of elephant crop consumption walks using combinatorial optimization

The article presents the developed calculation methodology for environmental regulation of water consumption of agricultural land and crops, taking into account the spatiotemporal variability of climatic, hydrological, soil and biological indicators of the natural system. It is noted that the human pressure in the form of a deficit in water consumption of agricultural land and crops should be based on the principles of energy balance of heat, moisture and nutrients, taking into account natural conditions that ensure targeted regulation and management of soil-forming processes in hydro-agrolandscapes. The developed model and algorithm for assessing water consumption in agriculture are built using two models for rationing water consumption - agricultural land and crops, which makes it possible to determine the most important criteria for environmental management in irrigated agriculture and is of great importance in ensuring food security of the population.

This study used cross-sectional data extracted from the 2011 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation to analyze individual trip-related expenditures associated with saltwater recreational fishing in the United States, based on the utility maximization framework in the double hurdle model. Empirical results (income, age, gender, ethnicity, urban setting, fishing license, fishing on a boat, fish types) showed significant effects on U.S. saltwater recreational fishing trip-related expenditures. The results of this study can provide insight into the determinants of U.S. saltwater recreational fishing trip-related expenditures, which can be used for saltwater recreational fisheries management and policy.

The problem of optimal scheduling of multicast traffic in time-varying wireless networks is studied in the framework of utility maximization. Since the wireless channel cannot be known exactly, only scheduling policies that take decisions based on a possibly inaccurate estimate of the wireless channel state are considered. A stationary, on-line, gradient-based scheduling and rate control policy is introduced which identifies at every decision instant the sources that should access the wireless medium along with their respective transmission rates. Furthermore, in the case that more than one optimal rate allocation is possible, the one that requires the minimum sum-power expenditure is selected by the policy. The optimality of the proposed policy among all policies that have access to the same estimate of the current wireless channel state is established through stochastic approximation arguments.

As a well‐structured and costly activity that pervades industries in both the public and private sector, vehicle fleet management would appear to be a splendid candidate for model‐based planning and optimization. And yet, until recently the combinatorial intricacies of vehicle routing and of vehicle scheduling have precluded the widespread use of optimization (exact) methods for this problem class. Our discussion in this paper identifies the extent and nature of these problem complexities and draws contrasts with other applications of combinatorial optimization. It also summarizes a number of successful uses of optimization for vehicle fleet planning and highlights potentially fruitful avenues for algorithmic development. In particular, we describe several alternative models and novel algorithms for the vehicle routing problem, show how various modeling approaches for this problem are intimately related, and illustrate the interplay between model formulations and the algorithms that they suggest. This discussion shows that prospects for applying exact methods, possibly in conjunction with heuristics, are far from fully realized and points to vehicle fleet planning as a tempting target of opportunity for further investigation.

Market equilibrium is characterized by a state wherein aggregate demand equals aggregate supply for all assets, a condition arising from consumers maximizing utility within budget constraints and producers maximizing profits. This paper investigates a financial market populated by non-homogeneous investors who may employ heterogeneous deviation measures to formulate their risk functions. By integrating the minimax risk diversification principle with the framework of individual utility maximization, we analytically derive the master fund for each investor. Furthermore, we establish the necessary and sufficient conditions for the existence of a unique non-negative equilibrium price system for risky assets and provide its explicit formula. A key finding is that the market portfolio is a convex combination of all investors’ master funds.

In this letter we address the problem of users' desired trade-off between optimal throughput and delay in wireless multihop networks under the framework of Network Utility Maximization (NUM). Via dynamic utility design and proper adaptation of the constraint set of the NUM problem, the impact of users' delay sensitivity on the resulting optimal throughput is considered. A cross-layer policy is proposed-stemming from Lyapunov drift techniques-that performs routing, scheduling and congestion control optimizing the above trade-off. Via modeling, analysis and simulations the performance of the proposed problem formulation and policy is evaluated.

Meaningfulness of conclusions from combinatorial optimization

Agent-based models have been developed and widely employed to assess the impact of disturbances or conservation management on animal habitat use, population development, and viability. However, the direct impacts of canopy disturbance on the arboreal movement of individual primates have been less studied. Such impacts could shed light on the cascading effects of disturbances on animal health and fitness. Orangutans are an arboreal primate that commonly encounters habitat quality deterioration due to land-use changes and related disturbances such as forest fires. Forest disturbance may, therefore, create a complex stress scenario threatening orangutan populations. Due to forest disturbances, orangutans may adapt to employ more terrestrial, as opposed to arboreal, movements potentially prolonging the search for fruiting and nesting trees. In turn, this may lead to changes in daily activity patterns (i.e., time spent traveling, feeding, and resting) and available energy budget, potentially decreasing the orangutan's fitness. We developed the agent-based simulation model BORNEO (arBOReal aNimal movEment mOdel), which explicitly describes both orangutans' arboreal and terrestrial movement in a forest habitat, depending on distances between trees and canopy structures. Orangutans in the model perform activities with a motivation to balance energy intake and expenditure through locomotion. We tested the model using forest inventory data obtained in Sebangau National Park, Central Kalimantan, Indonesia. This allowed us to construct virtual forests with real characteristics including tree connectivity, thus creating the potential to expand the environmental settings for simulation experiments. In order to parameterize the energy related processes of the orangutans described in the model, we applied a computationally intensive evolutionary algorithm and evaluated the simulation results against observed behavioral patterns of orangutans. Both the simulated variability and proportion of activity budgets including feeding, resting, and traveling time for female and male orangutans confirmed the suitability of the model for its purpose. We used the calibrated model to compare the activity patterns and energy budgets of orangutans in both natural and disturbed forests . The results confirm field observations that orangutans in the disturbed forest are more likely to experience deficit energy balance due to traveling to the detriment of feeding time. Such imbalance is more pronounced in males than in females. The finding of a threshold of forest disturbances that affects a significant change in activity and energy budgets suggests potential threats to the orangutan population. Our study introduces the first agent-based model describing the arboreal movement of primates that can serve as a tool to investigate the direct impact of forest changes and disturbances on the behavior of species such as orangutans. Moreover, it demonstrates the suitability of high-performance computing to optimize the calibration of complex agent-based models describing animal behavior at a fine spatio-temporal scale (1-m and 1-s granularity).

The growing popularity for video based applications has put an enormous strain on the network and causes the network to be more prone to congestion. The study of congestion control and bandwidth allocation problems is often formulated into Network Utility Maximization (NUM) framework, and the existing solutions for NUM generally focus on single-layered applications. However, today's quality of video is divided into several layers, where each layer provides different level of enhancement of quality. In this paper, we study how multi-layered video based applications impact network performance and pricing through NUM formulation, in particularly traffic from video streaming. In our investigation, we design and implement a new multi-layered user utility model that leverages on studies of human visual perception. Then, using this new utility model to examine network activities, we demonstrate that solving NUM with multi-layered utility is intractable, and that rate allocation and network pricing may oscillate due to user behavior specific to multi-layered applications. To address this, we propose a new approach for admission control to ensure quality of service and experience.

Since the initial publication of Kelly et al.'s seminal paper on resource allocation in wired networks, a large number of studies based on the network utility maximization (NUM) framework have been conducted, for example, on resource allocations in wired networks as well as wireless networks. Congestion control, used to adapt the traffic rates injected into a network, can be controlled separately at the transport layer or mutually with other layers, using a cross-layer design. The focus of this paper is a review of the research following the NUM framework and addressing the problem of congestion control from several dimensions, specifically, elastic traffic or inelastic traffic, same-timescale or multi-timescale, and single-path or multipath transmissions. First, cross-layer designs for wireless networks, basics of optimization theory, and an example of cross-layer congestion control are summarized. Then, studies on NUM-based congestion control are sequentially presented from several points of view. In addition, we survey the recent literature on cross-layer resource allocation in next generation networks, software-defined networking, and cloud radio access networks, which can enhance the performances and address issues found in current networks. Finally, we outline what we consider to be the current challenges and open issues in the area of NUM-based congestion control for wireless networks.

This SpringerBrief explains how to leverage mobile users' social relationships to improve the interactions of mobile devices in mobile networks. It develops a social group utility maximization (SGUM) framework that captures diverse social ties of mobile users and diverse physical coupling of mobile devices. Key topics include random access control, power control, spectrum access, and location privacy. This brief also investigates SGUM-based power control game and random access control game, for which it establishes the socially-aware Nash equilibrium (SNE). It then examines the critical SGUM-based spectrum access game, and pseudonym change game for personalized location privacy. The authors propose future work on extending the SGUM framework to negative social ties, to demonstrate relevance to security applications and span the continuum between zero-sum game (ZSG) and non-cooperative game (NCG). Social Group Utility Maximization targets researchers and professionals working on mobile networks and social networks. Advanced-level students in electrical engineering and computer science will also find this material useful for their related courses.

We consider a group of mobile users, within proximity of each other, who are interested in watching the same online video. The common practice today is that each user downloads the video independently on her mobile device using her own cellular connection, which wastes access bandwidth and may also lead to poor video quality. We propose a novel cooperative system where each mobile device uses simultaneously two network interfaces: (i) cellular to connect to the video server and download parts of the video and (ii) WiFi to connect locally to all other devices in the group to exchange those parts. Devices cooperate to efficiently utilize all network resources and to adapt to varying wireless network conditions. In the local WiFi network, we exploit overhearing, which we further combine with network coding. The end result is savings in cellular bandwidth and improved user experience. We follow a complete approach, from theory to practice. First, we formulate the problem using a network utility maximization (NUM) framework, decompose the problem, and provide a distributed solution. Then, based on the structure of the NUM solution, we design a system called MicroCast, and we implement a prototype as an Android application. We provide both simulation results of the NUM solution and experimental evaluation. We demonstrate that the proposed approach brings significant performance benefits (namely, faster download on the order of the group size) without battery penalty.

In this paper, we consider lossy mobile ad hoc networks where the data rate of a given flow becomes lower and lower along its routing path. One of the main challenges in lossy mobile ad hoc networks is how to achieve the conflicting goal of increased network utility and reduced power consumption, while without following the instantaneous state of a fading channel. To address this problem, we propose a cross-layer rate-effective network utility maximization (RENUM) framework by taking into account the lossy nature of wireless links and the constraints of rate outage probability and average delay. In the proposed framework, the utility is associated with the effective rate received at the destination node of each flow instead of the injection rate at the source of the flow. We then present a distributed joint transmission rate, link power and average delay control algorithm, in which explicit broadcast message passing is required for power allocation algorithm. Motivated by the desire of power control devoid of message passing, we give a near-optimal power-allocation scheme that makes use of autonomous SINR measurements at each link and enjoys a fast convergence rate. The proposed algorithm is shown through numerical simulations to outperform other network utility maximization algorithms without rate outage probability/average delay constraints, leading to a higher effective rate, lower power consumption and delay. Furthermore, we conduct extensive network-wide simulations in NS-2 simulator to evaluate the performance of the algorithm in terms of throughput, delay, packet delivery ratio and fairness.

Models of Count with Endogenous Choices

Models of count with endogenous choices