Tower-Based Greenhouse Gas Measurement Network Design——The National Institute of Standards and Technology North East Corridor Testbed

The North-East Corridor （NEC） Testbed project is the 3rd of three NIST （National Institute of Standards and Technology） greenhouse gas emissions testbeds designed to advance greenhouse gas measurements capabilities. A design approach for a dense observing network combined with atmospheric inversion methodologies is described. The Advanced Research Weather Research and Forecasting Model with the Stochastic Time-Inverted Lagrangian Transport model were used to derive the sensitivity of hypothetical observations to surface greenhouse gas emissions （footprints）. Unlike other network design algorithms, an iterative selection algorithm, based on a k-means clustering method, was applied to minimize the similarities between the temporal response of each site and maximize sensitivity to the urban emissions contribution. Once a network was selected, a synthetic inversion Bayesian Kalman filter was used to evaluate observing system performance. We present the performances of various measurement network configurations consisting of differing numbers of towers and tower locations. Results show that an overly spatially compact network has decreased spatial coverage, as the spatial information added per site is then suboptimal as to cover the largest possible area, whilst networks dispersed too broadly lose capabilities of constraining flux uncertainties. In addition, we explore the possibility of using a very high density network of lower cost and performance sensors characterized by larger uncertainties and temporal drift. Analysis convergence is faster with a large number of observing locations, reducing the response time of the filter. Larger uncertainties in the observations implies lower values of uncertainty reduction. On the other hand, the drift is a bias in nature, which is added to the observations and, therefore, biasing the retrieved fluxes.

Preliminary Hydrogeologic Modeling and Optimal Monitoring Network Design for a Contaminated Abandoned Mine Site Area: Application of Developed Monitoring Network Design Software

In abandoned mine sites, i.e., mine sites where mining operations have ended, wide spread contaminations are often evident, but the potential sources and pathways of contamination especially through the subsurface, are difficult to identify due to inadequate and sparse geochemical measurements available. Therefore, it is essential to design and implement a planned monitoring net-work to obtain essential information required for establishing the potential contamination source locations, i.e., waste dumps, tailing dams, pits and possible pathways through the subsurface, and to design a remediation strategy for rehabilitation. This study presents an illustrative application of modeling the flow and transport processes and monitoring network design in a study area hydrogeologically resembling an abandoned mine site in Queensland, Australia. In this preliminary study, the contaminant transport process modeled does not incorporate the reactive geochemistry of the contaminants. The transport process is modeled considering a generic conservative contaminant for the illustrative purpose of showing the potential application of an optimal monitoring design methodology. This study aims to design optimal monitoring network to: 1) minimize the contaminant solute mass estimation error;2) locate the plume boundary;3) select the monitoring locations with (potentially) high concentrations. A linked simulation optimization based methodology is utilized for optimal monitoring network design. The methodology is applied utilizing a recently developed software package CARE-GWMND, developed at James Cook University for optimal monitoring network design. Given the complexity of the groundwater systems and the sparsity of pollutant concentration observation data from the field, this software is capable of simulating the groundwater flow and solute transport with spatial interpolation of data from a sparse set of available data, and it utilizes the optimization algorithm to determine optimum locations for implementing monitoring wells.

Intermodal Freight Network Design for Transport of Perishable Products

The amount of perishable products transported via the existing intermodal freight networks has significantly increased over the last years. Perishable products tend to decay due to a wide range of external factors. Supply chain operations mismanagement causes waste of substantial volumes of perishable products every year. The heretofore proposed mathematical models optimize certain supply chain processes and reduce decay of perishable products, but primarily deal with local production, inventory, distribution, and retailing of perishable products. However, significant quantities of perishable products are delivered from different continents, which shall increase the total transportation time and decay potential of perishable products as compared to local deliveries. This paper proposes a novel optimization model to design the intermodal freight network for both local and long-haul deliveries of perishable products. The objective of the model aims to minimize the total cost associated with transportation and decay of perishable products. A set of piecewise approximations are applied to linearize the non-linear decay function for each perishable product type. CPLEX is used to solve the problem. Comprehensive numerical experiments are conducted using the intermodal freight network for import of the seafood perishable products to the United States to draw important managerial insights. Results demonstrate that increasing product decay cost may significantly change the design of intermodal freight network for transport of perishable products, cause modal shifts and affect the total transportation time and associated costs.

On Network Designs with Coding Error Detection and Correction Application

The detection of error and its correction is an important area of mathematics that is vastly constructed in all communication systems.Furthermore,combinatorial design theory has several applications like detecting or correcting errors in communication systems.Network(graph)designs(GDs)are introduced as a generalization of the symmetric balanced incomplete block designs(BIBDs)that are utilized directly in the above mentioned application.The networks(graphs)have been represented by vectors whose entries are the labels of the vertices related to the lengths of edges linked to it.Here,a general method is proposed and applied to construct new networks designs.This method of networks representation has simplified the method of constructing the network designs.In this paper,a novel representation of networks is introduced and used as a technique of constructing the group generated network designs of the complete bipartite networks and certain circulants.A technique of constructing the group generated network designs of the circulants is given with group generated graph designs(GDs)of certain circulants.In addition,the GDs are transformed into an incidence matrices,the rows and the columns of these matrices can be both viewed as a binary nonlinear code.A novel coding error detection and correction application is proposed and examined.

GUI-Based DL-Network Designer for KISTI’s Supercomputer Users

With the increase in research on AI(Artificial Intelligence),the importance of DL(Deep Learning)in various fields,such as materials,biotechnology,genomes,and new drugs,is increasing significantly,thereby increasing the number of deep-learning framework users.However,to design a deep neural network,a considerable understanding of the framework is required.To solve this problem,a GUI(Graphical User Interface)-based DNN(Deep Neural Network)design tool is being actively researched and developed.The GUI-based DNN design tool can design DNNs quickly and easily.However,the existing GUI-based DNN design tool has certain limitations such as poor usability,framework dependency,and difficulty encountered in changing GUI components.In this study,a deep learning algorithm that solves the problem of poor usability was developed using a template to increase the accessibility for users.Moreover,the proposed tool was developed to save and share only the necessary parts for quick operation.To solve the framework dependency,we applied ONNX(Open Neural Network Exchange),which is an exchange standard for neural networks,and configured it such that DNNs designed with the existing deep-learning framework can be imported.Finally,to address the difficulty encountered in changing GUI components,we defined and developed the JSON format to quickly respond to version updates.The developed DL neural network designer was validated by running it with KISTI’s supercomputer-based AI Studio.

Analysis of Transit Network Design Using GIS and Honeybee Algorithm in the City of Sanandaj, Iran

Public transit planning is a user-oriented problem, respectful of financial issues and involves different stakeholders such as the general public, the transportation provider and the local government. One of the main components of public transit planning is the transit network design (TND) problem. This research is an attempt to perform transit network design and analysis in the city of Sanandaj, Iran using the capabilities of GIS and Honeybee algorithm. Objectives of this study are formulating a multi-objective model of the TND problem, developing a GIS-based procedure for solving the TND problem and examination of the solutions using artificial metaheuristic methods such as honeybee algorithm. The transit network design approach in this research, aims to reduce the walking distance, the total travel distance and the total number of stops needed for a suitable transit service in Sanandaj, Iran. One of the contributions of this research is developing a transit network design with utilizing a spectrum of GIS software modelling functionalities and using the abilities of the artificial intelligence in modelling and assessment of the transit network.

Integrated Sequential Groundwater Contaminant Source Characterization and Pareto-Optimal Monitoring Network Design Application for a Contaminated Aquifer Site

Accurate and reliable groundwater contaminant source characterization with limited contaminant concentration monitoring measurement data remains a challenging problem. This study presents an illustrative application of developed methodologies to a real-life contaminated aquifer. The source characterization and optimal monitoring network design methodologies are used sequentially for a contaminated aquifer site located in New South Wales, Australia. Performance of the integrated optimal source characterization methodology combining linked simulation-optimization, fractal singularity mapping technique (FSMT) and Pareto optimal solutions is evaluated. This study presents an integrated application of optimal source characterization with spatiotemporal concentration measurement data obtained from sequentially designed monitoring networks. The proposed sequential source characterization and monitoring network design methodology shows efficiency in identifying the unknown source characteristics. The designed monitoring network achieves comparable efficiency and accuracy utilizing much smaller number of monitoring locations as compared to a more ideal scenario where concentration measurements from a very large number of widespread monitoring wells are available. The proposed methodology is potentially useful for efficient characterization of unknown contaminant sources in a complex contaminated aquifer site, where very little initial concentration measurement data are available. The illustrative application of the methodology to a real-life contaminated aquifer site demonstrates the capability and efficiency of the proposed methodology.

Transit Network Design Using GIS and Ant Colony Optimization in Sanandaj

The public transit system in Sanandaj has been under review and modification for the last several years. The goal is to reduce the traffic congestion and the share of private car usage in the city and increase the very low share of the public transit. The bus routes in Sanandaj are not connected. There is no connected transit network with the ability to transfer between the routes in locations outside of the downtown terminal. The routes mostly connect the downtown core directly to the peripheries without providing travel options for passengers between peripheries. Although there has been some improvement in the transit system, lack of service in many populated districts of Sanandaj and town nearby makes the transit system unpopular and unreliable. This research is an attempt to provide solutions for the transit network design (TND) problem in Sanandaj using the capabilities of GIS and artificial intelligence methods. GIS offers several tools that enable the decision-makers to investigate the spatial correlations between different features. One of the contributions of this research is developing a transit network design with utilizing a spectrum of GIS software modeling functionalities. The visual ability of GIS is used to generate TNDs. Many studies focus on artificial intelligence as the main method to generate the TNDs, but the focus of this research is to combine GIS and artificial intelligence capabilities in order to generate a multi-objective GIS-based procedure to construct different bus network designs and explore and evaluate them to find the suitable transit network alternative.

Implementation of Hybrid Genetic Algorithm for CLSC Network Design Problem—A Case Study on Fashion Leather Goods Industry

The implementation of closed loop supply chain system is becoming essential for fashion leather products industry to ensure an economically sustainable business model and eco-friendly industrial practice as demanded by the environmental regulations, consumer awareness and the prevailing social consciousness. In this context, this research work addresses a closed loop supply chain network problem of fashion leather goods industry, with an objective of minimizing the total cost of the entire supply chain and also reducing the total waste from the end of life product returns. The research work commenced with a literature review on the reverse and closed loop supply chain network design problems of fashion and leather goods industry dealt in the past. Then, the identified CLSCND problem is solved using a mathematical model based on Mixed Integer Non-Linear Programme (MINLP) and then a suitable Hybrid Genetic Algorithm (HGA) developed for the CLSCND is implemented for obtaining optimum solution. Both the MINLP model and HGA are customized as per the CLSCND problem chosen and implemented for the industrial case of an Indian Fashion Leather Goods Industry. Finally, the solutions obtained for MINLP model in LINGO 15 and for HGA in VB.NET platform are compared and presented. The optimum solution obtained from the suitable HGA is illustrated as an optimum shipment pattern for the closed loop supply chain network design problem of the fashion leather goods industry case.

Remanufacturing closed-loop supply chain network design based on genetic particle swarm optimization algorithm

As the huge computation and easily trapped local optimum in remanufacturing closed-loop supply chain network (RCSCN) design considered, a genetic particle swarm optimization algorithm was proposed. The total cost of closed-loop supply chain was selected as fitness function, and a unique and tidy coding mode was adopted in the proposed algorithm. Then, some mutation and crossover operators were introduced to achieve discrete optimization of RCSCN structure. The simulation results show that the proposed algorithm can gain global optimal solution with good convergent performance and rapidity. The computing speed is only 22.16 s, which is shorter than those of the other optimization algorithms.

Discrete logistics network design model under interval hierarchical OD demand based on interval genetic algorithm

Aimed at the uncertain characteristics of discrete logistics network design,an interval hierarchical triangular uncertain OD demand model based on interval demand and network flow is presented.Under consideration of the system profit,the uncertain demand of logistics network is measured by interval variables and interval parameters,and an interval planning model of discrete logistics network is established.The risk coefficient and maximum constrained deviation are defined to realize the certain transformation of the model.By integrating interval algorithm and genetic algorithm,an interval hierarchical optimal genetic algorithm is proposed to solve the model.It is shown by a tested example that in the same scenario condition an interval solution[3275.3,3 603.7]can be obtained by the model and algorithm which is obviously better than the single precise optimal solution by stochastic or fuzzy algorithm,so it can be reflected that the model and algorithm have more stronger operability and the solution result has superiority to scenario decision.

Synchronous hybrid transport network design

Delay,as an inevitable real-world phenomenon,is usually ignored in transport network design.A model of urban hybrid transport system with stochastic delay was created on the basis of the idealized public transport system design.After formulating the total trip time cost composed of accessing time in the sub-region of the city,waiting time at the public transport station,and in-vehicle time in the public transit network,the analytical properties of the total trip time cost function were investigated.The results show that in the urban hybrid transport network design,the total trip time cost reaches its approximate minimum in a δ-neighbourhood of buffer time of 1.5 min,and that through modelling optimal delay in hybrid transport system,the maximal synchronization can be achieved and operational efficiency and passenger satisfaction can be improved.The proposed modelling and analytical investigations are attempts to contribute to more realistic modelling of future idealized public transport system that involves more practical constraints.

Identification of Contaminant Source Characteristics and Monitoring Network Design in Groundwater Aquifers: An Overview

The groundwater system is often polluted by different sources of contamination where the sources are difficult to detect. The presence of contamination in groundwater poses significant challenges to its delineation and quantification. The remediation of a contaminated site requires an optimal decision making system to identify the pollutant source characteristics accurately and efficiently. The source characteristics are generally identified using contaminant concentration measurements from arbitrary or planned monitoring locations. To effectively characterize the sources of pollution, the monitoring locations should be selected appropriately. An efficient monitoring network will result in satisfactory characterization of contaminant sources. On the other hand, an appropriate design of monitoring network requires reliable source characteristics. A coupled iterative sequential source identification and dynamic monitoring network design, improves substantially the accuracy of source identification model. This paper reviews different source identification and monitoring network design methods in groundwater contaminant sites. Further, the models for sequential integration of these two models are presented. The effective integration of source identification and dedicated monitoring network design models, distributed sources, parameter uncertainty, and pollutant geo-chemistry are some of the issues which need to be addressed in efficient, accurate and widely applicable methodologies for identification of unknown pollutant sources in contaminated aquifers.

Literature Review on Network Design Problems in Closed Loop and Reverse Supply Chains

The productivity of an organization is very much affected by non-value adding activity like logistics, which moves the resources from suppliers to factory, raw materials/semi-finished items within the factory and finished goods from factory to customers via a designated distribution channel called as forward logistics. In some cases, parts of the products such as automobiles, computers, cameras, mobile phones, washing machines, refrigerators, garments, footwear and empty glass bottles of beverages, etc. will be brought back to the factories as a product recovery strategy through reverse logistics network which is integrated in a sustainable closed loop supply chain network. So, it is highly essential to optimize the movement of the items in the reverse logistics network. This paper gives a comprehensive review of literature of the design of networks for the reverse logistics as well as for the reverse logistics coupled with forward logistics. The contributions of the researchers are classified into nine categories based on the methods used to design the logistics network.

An Integrated Multi-Echelon Model for a Sustainable Closed Loop Supply Chain Network Design

The integration of entire supply and value chain into a closed loop network is gaining more importance in recent times in order to ensure a business to be economically and environmentally sustainable with the changing trends in business and social environments, growing environmental consciousness in the society and government legislations to protect the environment as well as the business. In this context, this paper considers a multi-echelon closed loop supply chain network design with forward and reverse logistics components. An attempt has been made to develop a mixed integer non-linear programming model for this problem with different costs so that the sum of the total cost is minimized subject to different constraints pertaining to capacities of the entities of the system, demands of first customers and second customers. A generalized model is presented and then its application is illustrated using an example problem by solving the model using LINGO14. This model forms as a tool to compare future meta-heuristics to check the closeness of their solutions with corresponding optimal solutions.

Development and Comparison of Hybrid Genetic Algorithms for Network Design Problem in Closed Loop Supply Chain

This paper presents four different hybrid genetic algorithms for network design problem in closed loop supply chain. They are compared using a complete factorial experiment with two factors, viz. problem size and algorithm. Based on the significance of the factor “algorithm”, the best algorithm is identified using Duncan’s multiple range test. Then it is compared with a mathematical model in terms of total cost. It is found that the best hybrid genetic algorithm identified gives results on par with the mathematical model in statistical terms. So, the best algorithm out of four algorithm proposed in this paper is proved to be superior to all other algorithms for all sizes of problems and its performance is equal to that of the mathematical model for small size and medium size problems.

A Structured Methodology for Local Network Design Engineering

This paper presents a structured methodology for local network design engineering (SMLNDE). A complex and fuzzy project for local network design can be decomposed into a set of simple and particular activities using the SMLNDE. The SMLNDE allows rigorous requirements definition and permits the exhaustive consideration of the large number of factors influencing local network design engineering. The complete and clear design documentations and an optimal design can also be provided by the methodology. The SMLNDE has been implemented using the structured analysis and design technique. The study shows that the SMLNDE is an effective design methodology for the large and complex local networks.

Reliable Network Design Problem under Node Failure with Benders Decomposition

The design of telecommunication network with capacity constraints of links, routers and ports of routers is considered in this paper. Specially, we limit each demand flow traversed through a pre-specified maximal number of links (called hops) under node failure scenarios in IP layer network. Such a design must be the most cost-effective and ensure that feasible flows continue to exist even when any relay node of the network fails. We propose a reliable mixed-integer programming (MIP) model with multi-scenario constraints to optimally design a minimum-cost survivable IP network that continues to support a good communication under any node failure scenario. Then we transform the MIP model into many single scenario models, that is, simplified MIPs, nonlinear programming (NLP) models and MIP models under Benders decomposition Then we transform the MIP model into many single scenario models, that is, simplified MIPs, nonlinear programming (NLP) models and MIP models under Benders decomposition. Three heuristic methods are proposed to solve these models including branch-and-bound algorithm, global algorithm for NLP, and heuristic algorithm based on benders decomposition. We mainly study the application of Benders decomposition method, where dual model and bounding procedures are given for each MIP model under Benders decomposition at each scenario. The results of our computational experiments validate the effectiveness of the proposed models and algorithms.

Computational Tools to Support Ethanol Pipeline Network Design Decisions

This paper considers the pipeline network design problem (PND) in ethanol transportation, with a view to providing robust and efficient computational tools to assist decision makers in evaluating the technical and economic feasibility of ethanol pipeline network designs. Such tools must be able to address major design decisions and technical characteristics, and estimate network construction and operation costs to any time horizon. The specific context in which the study was conducted was the ethanol industry in Sao Paulo. Five instances were constructed using pseudo-real data to test the methodologies developed.