Marxan with Zones: Software for optimal conservation based land- and sea-use zoning

Marxan is the most widely used conservation planning software in the world and is designed for solving complex conservation planning problems in landscapes and seascapes. In this paper we describe a substantial extension of Marxan called Marxan with Zones, a decision support tool that provides land-use zoning options in geographical regions for biodiversity conservation. We describe new functions designed to enhance the original Marxan software and expand on its utility as a decision support tool. The major new element in the decision problem is allowing any parcel of land or sea to be allocated to a specific zone, not just reserved or unreserved. Each zone then has the option of its own actions, objectives and constraints, with the flexibility to define the contribution of each zone to achieve targets for pre-specified features (e.g. species or habitats). The objective is to minimize the total cost of implementing the zoning plan while ensuring a variety of conservation and land-use objectives are achieved. We outline the capabilities, limitations and additional data requirements of this new software and perform a comparison with the original version of Marxan. We feature a number of case studies to demonstrate the functionality of the software and highlight its flexibility to address a range of complex spatial planning problems. These studies demonstrate the design of multiple-use marine parks in both Western Australia and California, and the zoning of forest use in East Kalimantan.     

Achieving full connectivity of sites in the multiperiod reserve network design problem

The conservation reserve design problem is a challenge to solve because of the spatial and temporal nature of the problem, uncertainties in the decision process, and the possibility of alternative conservation actions for any given land parcel. Conservation agencies tasked with reserve design may benefit from a dynamic decision system that provides tactical guidance for short-term decision opportunities while maintaining focus on a long-term objective of assembling the best set of protected areas possible. To plan cost-effective conservation over time under time-varying action costs and budget, we propose a multi-period mixed integer programming model for the budget-constrained selection of fully connected sites. The objective is to maximize a summed conservation value over all network parcels at the end of the planning horizon. The originality of this work is in achieving full spatial connectivity of the selected sites during the schedule of conservation actions.     

Transit network design by Bee Colony Optimization

The transit network design problem is one of the most significant problems faced by transit operators and city authorities in the world. This transportation planning problem belongs to the class of difficult combinatorial optimization problem, whose optimal solution is difficult to discover. The paper develops a Swarm Intelligence (SI) based model for the transit network design problem. When designing the transit network, we try to maximize the number of satisfied passengers, to minimize the total number of transfers, and to minimize the total travel time of all served passengers. Our approach to the transit network design problem is based on the Bee Colony Optimization (BCO) metaheuristics. The BCO algorithm is a stochastic, random-search technique that belongs to the class of population-based algorithms. This technique uses a similarity among the way in which bees in nature look for food, and the way in which optimization algorithms search for an optimum of a combinatorial optimization problem. The numerical experiments are performed on known benchmark problems. We clearly show that our approach, based on the BCO algorithm, is competitive with other approaches in the literature, and it can generate high-quality solutions.     

Metro Transit‐Centric Visualization for City Tour Planning

In general, city trip planning consists of two main steps: knowing Points‐Of‐Interest (POIs), and then planning a tour route from the current point to next preferred POIs. We mainly consider the metro for traveling around touristic cities as the main means of transportation. In this context, existing tools lack a capability to effectively visualize POIs on the metro map for trip planning. To bridge this gap, we propose an interactive framework that holistically combines presentations of POIs and a metro network. Our idea is to identify popular POIs based on visual worth computation, and to introduce POI discovery for effectively identifying POIs within reach of a metro network for users. We use octilinear layouts to highlight the metro network, and show representative POI images in the layout space visualized within a user‐specified viewing window. We have implemented our working prototype showing touristic cities with a metro network. We have factored out various design guidelines that are basis for designing our method, and validated our approach with a user study surveying 70 individuals.     

A Model-Based Performance Management Tool for ATM and Frame Relay Networks

In this paper we describe a network modelingapproach intended to assist in the performancemanagement, design, and optimization of broadbandtraffic networks. Switch and source models, as well asrouting optimization and decision support algorithmshave been integrated in a prototype software tool,called DATANMOT (Data Network Modeling and OptimizationTool). The switch models developed are based on standard Frame Relay and ATM switch implementations.Specifically, an analytical model of the Fujitsu FETEX150 ATM switch is described here in detail. Fluid-flowapproximation methods were used for performance evaluation, with computational complexity lowenough for near-real time applications. As a result,given the network configuration and input traffic, anevaluation of the quality of service can be derived and used in optimal routing, admission controland network planning. These techniques have beenincorporated in our modeling tool to demonstrate themodel-based approach to network management. In addition, all configuration, modeling, and managementfunctions of the software tool are supported by agraphical user interface, and a databasesystem.     

Optimal restoration of altered habitats

We describe a new conservation decision support tool: ‘Optimal Restoration of Altered Habitats’ (OPRAH). OPRAH combines species landscape configuration preferences and habitat quality information to optimally select habitat restoration priorities for single or multiple species. OPRAH uses a simulated annealing algorithm and information about extant native vegetation, land use and rehabilitation costs, to find solutions to non-linear landscape-scale problems. The landscape is divided into planning units, some of which represent native vegetation, while others can be restored for a given budget. A habitat restoration problem for declining woodland bird species is demonstrated as a case study.     

Assembly sequence planning based on structure cells in open design

Advances in computer network technologies have enabled firms to increasingly utilize external resources to remain competitive. Based on the function-behavior-structure cell (FBSC) modeling and computer network technologies, consumers with design knowledge and experience, called co-designers in this research, can involve in the process of open design (OD) to share their requirements, experiences and knowledge. The structure cells (SCs) provided by the co-designers in OD and the relationships among them are critical for generating the optimal design scheme and assembly sequence planning. However, the existing assembly sequence planning (ASP) approaches mainly focus on identification of the assembly plan based on precedence relations of operations from the predefined parts in the design scheme without considering the utilization of resources available in OD. In this study, a new approach for ASP based on SCs in OD is proposed to tackle this problem. First, the assembly features of the SCs and their matching rules are described in OD, and an approach for calculating the matching intensity between SCs is developed for identifying the assembly relationships between SCs. The design scheme is generated according to the SCs and their assembly relationships. Second, the base part of the design scheme is determined by its correlation degree with other parts. The feasible assembly sequences are derived by reversing the disassembly sequences. As the increase of the number of parts in design scheme will result in the combinatorial explosion of feasible assembly sequences, a particle swarm optimization algorithm is presented to achieve the optimal assembly sequence. A case study is provided to demonstrate the feasibility and effectiveness of the proposed approach.     

Land cover heterogeneity in Great Britain as identified in Land Cover Map 2000

Land cover is an important component of landscape character. The spatial configuration and heterogeneity of land cover can influence species distributions and patterns of biodiversity. Sustainable countryside planning and land management policy in Great Britain require the mapping of land cover heterogeneity at a national scale. Here, we map four measures of land cover heterogeneity across Great Britain using Land Cover Map 2000. We calculate land cover richness, diversity, evenness and similarity within and between 1 × 1 km grid cells of the British National Grid. From this we are able to identify assemblages of land cover types that are associated with high or low landscape heterogeneity, and where they occur geographically.     

城市景观生态网络连接的典型范式

城市景观生态网络连接研究是景观规划设计的重要研究领域,是理性规划的重要尝试,也是城市景观科学规划的重要发展趋势,本文在探讨城市景观生态系统特征、城市景观生态网络结构的基础上,初步分析了城市景观生态网络的典型图示,并进一步探讨了景观生态网络连接度理论评价城市景观生态系统并指导规划的发展趋势。     

Graph-based optimal reconfiguration planning for self-reconfigurable robots

The goal of optimal reconfiguration planning (ORP) is to find a shortest reconfiguration sequence to transform a modular and reconfigurable robot from an arbitrary configuration into another. This paper investigates this challenging problem for chain-type robots based on graph representations and presents a series of theoretical results: (1) a formal proof that this is an NP-complete problem, (2) a reconfiguration planning algorithm called MDCOP which generates the optimal graph-based reconfiguration plan, and (3) another algorithm called GreedyCM which can find a near-optimal solution in polynomial time. Experimental and statistical results demonstrate that the solutions found by GreedyCM are indeed near-optimal and the approach is computationally feasible for large-scale robots.     

Scaffolding student teachers' information‐seeking behaviours with a network‐based tutoring system

Student teachers' instructional planning requires them to regulate certain aspects of their own learning while designing lessons. The aim of this study is to support student teachers' self‐regulated learning through the convergence effect, where network‐based tutors are designed to optimize system recommendations of online resources based on information‐seeking behaviours. A total of 68 student teachers were randomly assigned to either a dynamic or static version of nBrowser, which converged a network or not towards an optimal configuration. The structural equation model suggests that student teachers spent less time during the learning session using the dynamic version of nBrowser. Although student teachers were found to be more efficient in seeking and acquiring information and reported knowledge gains, they failed to perform better than those assigned to the static condition on the lesson plan design task. We discuss the implications for the convergence effect in the context of network‐based tutors.     

A generic tool for optimising land-use patterns and landscape structures

We present a flexible and easy to use genetic algorithm-based library for optimising the spatial configurations of land-use. LUPOlib, the Land-Use Pattern Optimisation-library, can be applied to a variety of spatial planning problems to derive target-driven scenarios that identify trade-offs between conflicting objectives and solve optimum allocation problems (e.g. allocation of reserve sites or management actions). A major novelty is that spatial changes are performed according to a patch topology that allows to simultaneously integrate changes of different landscape elements (e.g. in agricultural fields and linear changes along corridors). The objective function evaluation is based on a grid representation of the landscape where neighbourhood dependencies like lateral flows or the landscape pattern can explicitly be considered. A parameter file allows the user to control the optimisation, the modelled land-use changes, objective weightings and constraints as well as input data. Only the case study-specific objective function needs to be specified in the source code. LUPOlib has been applied so far in two case studies to find optimum trade-offs between habitat requirements of three different bird species and to analyse cost-effectiveness of management actions for species conservation. The results suggest that LUPOlib can be a useful tool to support management decisions. It could be used as an extension to a GIS and for spatially explicit decision support tools.     

A heuristic search approach to planning with temporally extended preferences

Planning with preferences involves not only finding a plan that achieves the goal, it requires finding a preferred plan that achieves the goal, where preferences over plans are specified as part of the planner's input. In this paper we provide a technique for accomplishing this objective. Our technique can deal with a rich class of preferences, including so-called temporally extended preferences (TEPs). Unlike simple preferences which express desired properties of the final state achieved by a plan, TEPs can express desired properties of the entire sequence of states traversed by a plan, allowing the user to express a much richer set of preferences. Our technique involves converting a planning problem with TEPs into an equivalent planning problem containing only simple preferences. This conversion is accomplished by augmenting the inputed planning domain with a new set of predicates and actions for updating these predicates. We then provide a collection of new heuristics and a specialized search algorithm that can guide the planner towards preferred plans. Under some fairly general conditions our method is able to find a most preferred plan—i.e., an optimal plan. It can accomplish this without having to resort to admissible heuristics, which often perform poorly in practice. Nor does our technique require an assumption of restricted plan length or make-span. We have implemented our approach in the HPlan-P planning system and used it to compete in the 5th International Planning Competition, where it achieved distinguished performance in the Qualitative Preferences track.     

Hierarchical multi-robot navigation and formation in unknown environments via deep reinforcement learning and distributed optimization

Compared with a single robot, Multi-robot Systems (MRSs) can undertake more challenging tasks in complex scenarios benefiting from the increased transportation capacity and fault tolerance. This paper presents a hierarchical framework for multi-robot navigation and formation in unknown environments with static and dynamic obstacles, where the robots compute and maintain the optimized formation while making progress to the target together. In the proposed framework, each single robot is capable of navigating to the global target in unknown environments based on its local perception, and only limited communication among robots is required to obtain the optimal formation. Accordingly, three modules are included in this framework. Firstly, we design a learning network based on Deep Deterministic Policy Gradient (DDPG) to address the global navigation task for single robot, which derives end-to-end policies that map the robot’s local perception into its velocity commands. To handle complex obstacle distributions (e.g. narrow/zigzag passage and local minimum) and stabilize the training process, strategies of Curriculum Learning (CL) and Reward Shaping (RS) are combined. Secondly, for an expected formation, its real-time configuration is optimized by a distributed optimization. This configuration considers surrounding obstacles and current formation status, and provides each robot with its formation target. Finally, a velocity adjustment method considering the robot kinematics is designed which adjusts the navigation velocity of each robot according to its formation target, making all the robots navigate to their targets while maintaining the expected formation. This framework allows for formation online reconfiguration and is scalable with the number of robots. Extensive simulations and 3-D evaluations verify that our method can navigate the MRS in unknown environments while maintaining the optimal formation.     

多智能体专家型策略梯度的目标跟踪与清障

为适应复杂环境下目标跟踪机器人高效运动规划需求,本文提出一种基于多智能体强化学习的专家型策略梯度(ML-DDPG) 方法。为此首先构建了基于最小化任务单元的分布式多Actor-Critic网络架构;随后针对机器人主动障碍清除和目标跟踪任务建立了强化学习运动学模型和视觉样本预处理机制,由此提出一种专家型策略引导的最优目标价值估计方法;进一步通过并行化训练与集中式经验共享,提升了算法的训练效率;最后在不同任务环境下测试了ML-DDPG 算法的目标跟踪与清障性能表现,和其它算法对比验证了其在陌生环境中良好的迁移与泛化能力。     

Modeling broad-scale patterns of avian species richness across the Midwestern United States with measures of satellite image texture

Avian biodiversity is threatened, and in order to prioritize limited conservation resources and conduct effective conservation planning a better understanding of avian species richness patterns is needed. The use of image texture measures, as a proxy for the spatial structure of land cover and vegetation, has proven useful in explaining patterns of avian abundance and species richness. However, prior studies that modeled habitat with texture measures were conducted over small geographical extents and typically focused on a single habitat type. Our goal was to evaluate the performance of texture measures over broad spatial extents and across multiple habitat types with varying levels of vertical habitat structure. We calculated a suite of texture measures from 114 Landsat images over a study area of 1,498,000 km² in the Midwestern United States, which included habitats ranging from grassland to forest. Avian species richness was modeled for several functional guilds as a function of image texture. We subsequently compared the explanatory power of texture-only models with models fitted using landscape composition metrics derived from the National Land Cover Dataset, as well as models fitted using both texture and composition metrics. Measures of image texture were effective in modeling spatial patterns of avian species richness in multiple habitat types, explaining up to 51% of the variability in species richness of permanent resident birds. In comparison, landscape composition metrics explained up to 56% of the variability in permanent resident species richness. In the most heavily forested ecoregion, texture-measures outperformed landscape metrics, and the two types of measurements were complementary in multivariate models. However, in two out of three ecoregions examined, landscape composition metrics consistently performed slightly better than texture measures, and the variance explained by the two types of measures overlapped considerably. These results show that image texture measures derived from satellite imagery can be an important tool for modeling patterns of avian species richness at broad spatial extents, and thus assist in conservation planning. However, texture measures were slightly inferior to landscape composition metrics in about three-fourths of our models. Therefore texture measures are best considered in conjunction with landscape metrics (if available) and are best used when they show explanatory ability that is complementarity to landscape metrics.     

A decomposition approach for commodity pickup and delivery with time-windows under uncertainty

We consider a special class of large-scale, network-based, resource allocation problems under uncertainty, namely that of multi-commodity flows with time-windows under uncertainty. In this class, we focus on problems involving commodity pickup and delivery with time-windows. Our work examines methods of proactive planning, that is, robust plan generation to protect against future uncertainty. By a priori modeling uncertainties in data corresponding to service times, resource availability, supplies and demands, we generate solutions that are more robust operationally, that is, more likely to be executed or easier to repair when disrupted. We propose a novel modeling and solution framework involving a decomposition scheme that separates problems into a routing master problem and Scheduling Sub-Problems; and iterates to find the optimal solution. Uncertainty is captured in part by the master problem and in part by the Scheduling Sub-Problem. We present proof-of-concept for our approach using real data involving routing and scheduling for a large shipment carrier’s ground network, and demonstrate the improved robustness of solutions from our approach.     

动态灾害环境下多对多物资配送路径规划方法

动态灾害环境下多对多物资配送路径规划问题具有重大的现实意义,它需要在路径规划的同时应对路网环境随时间的变化,并找到不同应急物资储备点、配送点之间的最佳对应关系,同时保证求解的时效性和成功率.目前的静态预案规划方法(SPO)和动态路径规划方法(DPO)难以确保动态灾害环境下求解效果的理论最优性,甚至可能导致部分配送点不能...     

Path planners based on the wave expansion neural network

In the potential field approach to path planning, the development of the artificial potential field (APF) is a computationally intensive operation. The realization that the APFs can be developed by parallel distributed techniques has prompted interest in using neural networks for developing APFs. This paper describes a neural network called the wave expansion neural network (WENN) and shows that it is capable of developing a variety of APFs that are useful for path planning. The discretized environment including information about the target configuration (position and orientations) and the obstacles are applied to the WENN as input. Activity is then propagated in the form of waves throughout the WENN neural field and at equilibrium, the resulting neural activity distribution forms the desired APF. We analyze the computational complexity of the WENN based APF generation and compare it with conventional development of APFs. We also describe different path planners which use these APFs to plan paths for moving objects with two and three degrees of freedom.     

Ant Colony Search Algorithm for Optimal Strategical Planning of Electrical Distribution Systems Expansion

Strategical planning is one of many research fields in the design of electrical distribution systems. The problem of strategical planning is a multiobjective combinatorial problem and the search space may often be quite large concerning to the options. The aim is to identify a strategy of expansion of a given distribution system in a given timeframe. For this problem, the search space is created beforehand by running a multiobjective optimisation algorithm for the optimal design of distribution networks for different load levels related to different years. The sets of Pareto-optimal solutions obtained for each load level at each year are equivalent in terms of the considered objectives, these being minimum losses, installation costs, and minimum unavailability. The problem of the identification of the optimal expansion strategy through these chronologically intermediate solutions leading to the final target configuration at the last year has been solved herein using an ACS (Ant Colony Search) algorithm. In order to verify the efficiency of the ACS algorithm, a small size application has been carried out and results have been compared to those obtained with enumeration. Then, a Simulated Annealing (SA) approach was used for a larger size test problem and results were compared to those obtained using the ACS. For this problem, the ACS demonstrated to be more robust than SA with higher quality results.