Network Cost Research Articles

Designing a sustainable plastic bottle reverse logistics network: A data-driven optimization approach

Management of recovery options for plastic beverage containers involves some challenges. Most materials of used containers are recycled and are used to produce new products. The locations of collection facilities are the strategic decisions, affecting the amount collected and the total cost of a Reverse Logistics Network (RLN). In this study, a multi-objective (MO) optimization model is introduced to configure a plastic beverage container RLN, considering economic, environmental, and social objectives. This study also implements a scenario-based possibilistic approach to handle the uncertainty of the parameters. Furthermore, a data-driven fuzzy optimization framework is developed to consider the overlapping and multi-clustered characteristics of historical data samples. The application of the proposed method is demonstrated by considering a network in Vancouver, Canada. The numerical results reveal that the optimal configuration of the RLN resulting from the proposed MO model exhibits significant sensitivity to fluctuations in costs, demands, and the prioritization of the objective functions. Additionally, the proposed data-driven framework can incorporate decision makers' preferences when tuning the conservatism degree of uncertain parameters and the preference level of different objectives of the MO model. Moreover, the developed data-driven algorithm can reduce over-conservatism by 14% and guarantee the feasibility of optimal solutions compared to other data-driven strategies.

Soft independence guided filter pruning

Filter pruning (FP) is an effective method for reducing the computational costs of convolutional neural networks, and herein, the most critical task involves evaluating the significance of each convolutional filter and eliminating the less important ones while minimizing performance degradation. Most existing FP methods consider only local information, which may prevent them from accurately recognizing the most important filters. To address this limitation, we propose the soft filter independence (SFI) method, which leverages global information to identify the most important filters using their magnitude and correlation information in different functional layers. The SFI criterion measures the replaceability of filters from a global perspective in a network. Filters with low independence can be represented effectively by others, so their information can be accurately conveyed by other filters. In addition, we introduce a novel SFI-based asymptotic pruning ratio, which improves training and pruning stability. Compared to the most advanced FP methods, our method enables CNNs to achieve higher pruning rates and better classification performance.

Research on station-network planning of electricity-thermal -cooling regional integrated energy system considering multiple-load clusters and network costs

A reasonable regional integrated energy system (RIES) structure is the key to achieving its economic and efficient operation. However, in actual engineering construction, only the maximum load is considered as a prerequisite for energy design, which leads to waste of energy allocation. In view of this, this article proposes an electricity-thermal-cooling RIES energy stations and networks planning model that coordinates the operation of multiple-user loads, energy networks, and energy stations based on the idea of load clustering. Firstly, an average peak valley difference rate covering electricity, thermal, and cooling loads was proposed to evaluate the load characteristics of the RIES. Secondly, based on the load cluster characteristics and network costs of RIES, a RIES planning optimization framework is proposed, which includes load cluster division, RIES site selection and network planning, and RIES capacity planning. Thirdly, based on the P-median model and load cluster characteristics, establish an RIES location and network layout model. Fourthly, establish an RIES capacity planning model with the goal of annual total cost and carbon emissions. Finally, Dijkstra, improved particle swarm optimization, and VIKOR were used to solve the model. The results showed that the proposed model reduced the annual total cost by 7.13 % and the annual carbon emissions by 3.72 %.

A Liquidity Analysis System for Large-scale Video Streams in the Oilfield

This article introduces LinkStream, a liquidity analysis system based on multiple video streams designed and implemented for oilfield. LinkStream combines a variety of technologies to solve several problems in computing power and network latency. First, the system adopts an edge-central architecture and tailoring based on spatio-temporal correlation, which greatly reduces computing power requirements and network costs, and enables real-time analysis of large-scale video stream on limited edge devices. Second, it designed a set of liquidity information to describe the liquidity status in the oilfield. Finally, it uses object tracking technology to design a counting algorithm for the unique tubing object in the oilfield. We have deployed LinkStream in an oilfield in Iraq. LinkStream can perform real-time inference on over 200 video streams with acceptable resource overhead.

Development of optimum waste water using network

The present study deals with three case studies are discussed in detail. All cases had been considered as retrofitting problems and solved very precisely considering existing fixed network costs, present cost of power consumption and recent market price of different sizes of pipes. The first case study involves four processes and single contaminant problem and three approaches of water minimization i.e. reuse, recycle reuse, regeneration reuse are applied. The GAMS software using CONOPT was used to mathematical programming. The second case study is identified as a multi contaminant, reuse problem. The results obtained from the present analysis is compared well and validated with the ASPEN WATER and WATER PINCH software's which uses mathematical programming and pinch technology. The third case study is also multiple contaminant problem which involves eight operations and only water reuse approach is applied. The results are also verified with both software's solutions. The initial fresh water consumption is 185 tph and after modification using mathematical programming it reduces to 90 tph (reuse), 90 tph (recycle reuse) and 46 tph (regeneration reuse).

Low-Cost Approach to an Instream Water Depth Sensor Construction Using Differential Pressure Sensors and Arduino Microcontrollers.

Accurate hydrological data with high spatial resolution is important for flood risk and water resource management, particularly under the context of climate change. The cost of monitoring networks, as well as the characteristics of the hydrological environment itself, can be a barrier to meeting these data requirements, however. This study covers the design and testing of a low-cost, "build-it-yourself", instream water depth sensor providing an assessment of its potential in future hydrological monitoring projects. The low-cost sensor was built using an Arduino microcontroller, a differential pressure sensor and a thermistor, a real-time clock, and an SD card module. The low-cost logger was deployed in tandem with a factory-calibrated Solinst®LevelLogger® 5 Junior for 6 months in the River Wissey, UK. We found the mean absolute error of the Arduino-based logger relative to the commercial setup to be ±0.69 cm for water depth and ±0.415 °C for water temperature. Economically, the Arduino-based logger offers an advantage, costing a total of £133.35 (USD 168.26 at time of publication) comparative to the industrial comparison's cost of £408 (USD 514.83 at time of publication). This study concludes that the low cost of the Arduino-based logger gives a strong advantage to its incorporation in hydrological data collection, if the trade-offs (i.e., time investment and accuracy) are considered acceptable and appropriate for a project.

Multi-period fourth-party logistics network design from the viability perspective: a collaborative hyper-heuristic embedded with double-layer Q-learning algorithm

In the ‘new normal’ setting of a mega-crisis, the viability becomes the driving force for the fourth party logistics (4PL) network design. In this paper, the viability is characterised in terms of agility, resilience and survival sustainability as the response to changes in demand, disruption and survivability. The fortification and recovery strategies are considered in possible disruptions at transfer centres and third-party logistics providers. A novel mixed integer non-linear programming model is proposed to obtain the multi-period 4PL network solution with minimum total cost under viability constraints. Considering the NP-hard characteristic of problem and the non-convex of proposed model, the hyper-heuristic algorithm is designed. To take advantage of both global optimality seeking and local search ability, a collaborative hyper-heuristic embedded with double-layer Q-learning (CHHDLQL) algorithm is proposed. The effectiveness and efficiency of the proposed algorithm is demonstrated by the promising numerical results. By stress-testing the existing network, appropriate adjustments to fortification and recovery strategies can effectively cope with changes in demand and disruption. Furthermore, the impact of 4PL strategy, fortification and recovery strategies, and viability constraints are investigated. The demand satisfaction, network resilience and capacity can be improved by adjusting agility, resilience and survival sustainability to influence different component network costs. Highlights A novel multi-period 4PL network model under viability constraints is presented. The CHHDLQL algorithm for construction and perturbation coevolution is proposed. The stress tests apply to the 4PL network, including demand, disruption and 3PL. 4PL improves demand satisfaction, resilience and capacity by adjusting viability.

Multivariate low-carbon scheduling of distribution network based on improved dynamic carbon emission factor

The “load-following” characteristic of the power system makes the electricity consumption behavior on the load side crucial for the low-carbon operation of the distribution network. To address this, this paper proposes an improved dynamic carbon emission factor for the distribution network, taking into account the spatiotemporal characteristics of carbon emission intensity and the integration capacity of photovoltaics (PV). Based on this, a calculation method for the carbon emissions of the distribution network load is provided. Subsequently, for commercial and industrial user scenarios, demand response models are separately constructed for commercial and industrial loads based on different driving mechanisms. Using time-of-use electricity prices as decision variables, optimization scheduling of the distribution network is carried out with the objectives of minimizing scheduling costs and carbon emissions. At the same time, a case study is conducted in an improved IEEE-33 node distribution network. The results indicate that, under the guidance of the improved dynamic carbon emission factor, load transfer can be achieved through fluctuating electricity prices, effectively reducing the scheduling costs of the distribution network, decreasing carbon emissions, and enhancing the PV integration capacity of the distribution network in different user scenarios. Finally, it is hoped that in the future, this optimization method can be widely applied, and further research can explore coordinated strategies among generation, network, load, and storage to advance the development of the power industry.

Regulating Roof-Top Solar Generation: Defending Sustainable Projects from Utility Proposals

Residential owned solar panels mounted on roof-tops generated 1.4% of the U.S. electricity supply in 2022. Utilities compensate residential customers using a net metering system by which each kilowatt-hour generated by the customer reduces the customer’s bill by the amount the utility charges for one kilowatt. Despite the small impact of roof-top solar on the total market, in 2022, thirty-two states conducted ninety-four proceedings investigating whether to change the net metering system. In these proceedings, utilities advocated changing the rates to discourage residential investment in solar panels. Utilities argue that net metering provides a subsidy to solar customers because the rate the utility pays is greater than the avoided cost of energy. That excess payment allows solar customers to avoid paying for the distribution network. The cost of the distribution network is shifted to non-solar customers. This article will evaluate these claims. It will show that if the social cost of carbon were in the calculation, it would generate a Kapp cost shift from solar customers to non-solar customers. The size of the Kapp cost shift is greater than the cost of distribution networks, reversing any private cost shift that utilities have estimated.

Model analysis on the economic impact of paid peering: Implications of the Netflix vs. SK broadband dispute

In April 2020, Netflix, Inc. and its Korean subsidiary Netflix Services Korea Ltd. filed a lawsuit against SK Broadband, Inc., seeking confirmation that there were no obligations to bear network costs. On June 25, 2021, the Seoul Central District Court rejected Netflix’s argument and acknowledged the existence of an obligation to negotiate fees. Netflix subsequently appealed the decision on November 5, 2021. However, on September 18, 2023, SK Broadband and parent SK Telecom suddenly announced in a joint statement with Netflix that they had agreed to establish a strategic partnership to end all disputes. This study provides an overview of the case, discusses the related concepts, and analyzes the meaning of the case outcome in terms of efficient resource allocation within the broadband ecosystem. A model analysis revealed that, with the freedom of price setting, the effect of introducing paid peering is neutral with respect to resource allocation; however, when that freedom is impaired, for example by price regulations or net neutrality obligations, the introduction of paid peering not only generates benefits for the introducing players and their customers but it also results in damage to overall social welfare. In addition, this study shows that paid peering can be used as a means of internalizing externalities when the external economies of network/content investment are considered. These arguments are expected to make a theoretical contribution to the debate on shifting the economic burden of network infrastructure to over-the-top players, and help the telecom regulator to better design universal broadband scheme.

Optimizing satellite and core networks for a global quantum network

Quantum key distribution (QKD) promises information theoretic security. However, the exponential decay of the secure key in optical fibers leads to limitations in long distance QKD distribution across fibers, which is necessary for global quantum networks (QNs). Satellite QKD can be used to generate keys over long distances bypassing fiber limitations and is thus a promising approach for global QNs. In this paper, we construct mixed integer linear program (MILP) models to investigate how to best connect the core fiber network to ground stations to minimize the overall network cost. We design one MILP that can provide a quantitative value for the number of satellites needed for a given configuration and another one to optimize the allocation of the core network nodes to ground stations to minimize the overall network cost. We use these models to investigate different strategies to allocate satellites to ground stations during a satellite overpass, showing that allocating satellites based on the expected transmission requirements can reduce the number of satellites needed in a network by up to 40% compared to randomly allocating the satellites to ground stations. Furthermore, we use these models to investigate securing the data center traffic in two networks, one local European network and one global network, and show that costs in the optimal configuration can be up to 40% cheaper than simply connecting core network sites to their geographically closest ground station.

Designing a Reverse Logistics Network for End-of-Life Vehicles in an Uncertain Environment

The strategic development of reverse logistics networks is crucial for addressing the common challenge of low recovery rates for end-of-life vehicles (ELVs) in China. To minimize the total cost of the reverse logistics network for ELVs, this paper proposes a mixed-integer linear programming (MILP) model. The model considers the recycling volume of different vehicle types, facility processing capacity, and the proportions of parts and materials. Building on this foundation, a fuzzy mixed-integer nonlinear programming (FMINLP) model is developed to account for the inherent uncertainty associated with recycling volumes and facility processing capacities. The model was solved using Lingo, and its effectiveness was validated using Jiangsu Province of China as a case study, followed by a sensitivity analysis. The results indicate that dismantling and machining centers incur the highest processing costs. Variations in recycling volume and facility handling capacity significantly impact total costs and site selection, with the former having a more pronounced effect. Increasing facility processing capacity effectively increases the recovery rate. Moreover, a higher confidence level corresponds to higher total costs and a greater demand for facilities.

Downlink Learning-aided Precoding for Cell-Free networks Specially D esigned for beyond 5G Communication System

Objectives: With increase in number of access points in Cell-Free Massive Multiple Input Multiple Output (CFMM) system, spatial complexity of calculating precoding vector increases with traditional precoding schemes. So, to reduce computational complexity, network cost, and run time, a new deep learning -aided precoder is specially designed for CFMM system. The performance of downlink (DL) Cell-Free Massive Multiple Input Multiple Output (CFMM) system operating under Rayleigh fading channel model is analyze using new deep learning-based precoding scheme. Methods: We introduce new deep learning-aided precoder and an improved version of basic scalable pilot assignment algorithm which enhances system performance. We derive closed- form expression for average DL spectral efficiency (SE) for the proposed scheme, which is then compared with Minimum Mean Square Error Successive Interference Cancellation (MMSESIC), Regularized Zero Forcing (RZF), Least Square (LS) and Maximum Ratio (MR) combining techniques. We analyze the proposed scheme with perfect channel state information (CSI), instantaneous CSI, coherent transmission, non-coherent transmission, different pilot configuration. Findings: The spectral efficiency obtained with CFMM-MMSESIC and the proposed scheme is 3 bits/s/Hz, and 2.5bits/s/Hz respectively, which clearly indicates the difference of 20 % only. So, the proposed scheme gives near optimal results, and we can use simple linear combining techniques instead of complex non-linear combining techniques. Also, the proposed scheme with coherent data transmission gives performance curve, which is very close to MMSESIC scheme as desired. The performance of the proposed system is improved by reducing run time and computational complexity as compared to conventional linear precoding schemes. Novelty: The proposed precoder improves performance by reducing run time and computational complexity. Advanced pilot assignment algorithm enhances performance by reducing pilot contamination. Keywords: Cell-Free Massive Multiple Input Multiple Output, Pilot Contamination, Precoding, Minimum Mean Square Error Successive Interference Cancellation, Maximum Ratio, Regularized Zero Forcing (RZF), Least Square (LS)

A Multiobjective Model for a Multilevel Blood Supply Chain to Improve the Attractiveness of Blood Centers during the COVID-19 Pandemic

Failure to control crisis conditions leads to irreparable damage to many supply chains around the world, including blood supply chains (BSCs) as critical networks in the health system. Consequently, it significantly reduces the supply of blood and its products, as vital materials, and exerts detrimental effects on the activities of blood organizations and facilities as well as the health of individuals in society. In the present study, the proposed model seeks to simultaneously minimize the operating costs and the shortage of blood products with the aim of improving the attractiveness of blood centers during the COVID-19 pandemic. Accordingly, by optimizing the overall cost and the attractiveness of blood donation centers, an attractive efficient environment is provided. It can help to remove barriers to blood donation and improve blood health. To this end, the model takes certain strategies into account for the proper establishment of new local blood collection centers (BCCs) and mobile BCCs. It also arranges suitable transportation vehicles for the efficient transfer of blood products to the provincial centers of the candidate country and sets various incentive policies for blood donation. In order to minimize the costs of the entire supply chain network and maximize the attractiveness of the BCCs, a two-objective mathematical model is developed. It produces Pareto solutions using the ε-constraint method. Finally, the efficiency of the proposed approach and the sensitivity of the corresponding parameters are analyzed through a practical case study. The obtained results represent that a growth in the attractiveness of blood centers induces a raise in the number of donors, and, consequently, the amount of the donated blood grows. This depends on more investment at all levels of the supply chain, including collection, production, storage, and transportation. Moreover, the performance and attractiveness of a BSC can be enhanced significantly if the number of collection centers and the amount of blood sent from the receiving centers to the demand nodes are increased.

Analysis of willingness to pay of farmers on irrigation networks maintenance fees in the Gonggang reservoir, Magetan

Irrigation network maintenance is very important to ensure the function of irrigation run well. Therefore, maintenance fee is necessary to maintain the irrigation network. The problem is the maintenance cost of irrigation network is expensive, the second highest cost after rehabilitation costs. Thus, participation of farmers in irrigation network maintenance is essential. One of participation by farmers is in the form of regular fees. This study aims to determine the number of fees farmers are willing to pay to maintain irrigation networks through the willingness-to-pay approach. This study was conducted in the irrigation area of the Gonggang reservoir in Magetan District. The subject is farmers who use irrigation – the data collection technique used by observation, interview, questionnaire, and documentation. The data analysis technique used is the contingent valuation method with steps (1) determining the hypothetical market, (2) obtaining bids, (3) estimating average willingness to pay, (4) estimating bid curves, and (5) aggregating data. The result shows that the amount of farmers’ willingness to pay the maintenance fees for irrigation networks at Gonggang reservoir irrigation area in Magetan District by using the contingent valuation method is IDR 90,621 (USD 5.45) for each hectare for each planting period with total willingness to pay in the population of IDR 116,188,708 (USD 7,951.59) for each planting period.

Distribution network tariff design: Facilitate flexible resource under uncertain future energy scenarios

The uncertainty embedded in low-carbon transition pathways and future energy scenarios will reshape the way of distribution network planning and operation. Flexible solutions provided by flexible resources could help distribution network operators (DNOs) to reduce network costs and risks under uncertainties. However, the current tariff paradigm neglects such contributions, which cannot generate sufficient incentives to facilitate flexible resource development. To tackle this issue, this paper first introduces an improved price control method that caps DNOs' revenue while encouraging DNOs' sustainable investment under uncertainties. A bi-level planning model is then proposed to determine the optimal investment plan and evaluate the risk reduction effect by scheduling flexible loads. Finally, a novel pricing method is proposed to derive network tariffs for existing and potential flexible loads based on their contributions to risk reduction and demonstrated in a test system. The result shows a 24% network cost reduction given by 6% DNO risk compensation rate, and 0%–43% and 1%–22% network tariff reductions for existing and future flexible demands, respectively. This evidence proves the proposed method can incentivize DNOs to well manage risks, meanwhile promote the connection and engagement of flexible loads. Regulators and DNOs are recommended to review the existing pricing framework, enhance risk management in price regulation, and explore suitable tariff designs or business models for flexible loads to cope with future uncertainties through flexibility.

A novel navigation and charging strategy for electric vehicles based on customer classification in power-traffic network

With electric vehicles getting increasingly popular, there has been a lot of interest in encouraging future electric vehicle development in terms of greater charging efficiency, greater benefits to the environment, and greater reliability. One of the most prominent research is on the scheduling of electric vehicle charging and navigation. High-quality navigation and charging scheduling strategy must depend on the state of the coupled network between the transportation and power networks, while the outcomes of scheduling can also have a significant impact on these networks. In this paper, we propose a novel dynamic navigation and charging strategy that fully considers the coupled traffic and power distribution network. Firstly, electric vehicles with similar travel and charging requirements are classified. Secondly, an elastic scheme including fixed route navigation and flexible charging scheduling at charging stations is provided. We also propose an information integration framework for the implementation of electric vehicle routing and charging scheduling, information interaction between sectors including the status of heterogeneous electric vehicles, day-ahead power scheduling, and base load demands. The novel strategy aims at optimal electric vehicle navigation and charging at the system level achieving a lower overall travel cost, charging cost, carbon emission, and load variance of the power distribution network. Classification algorithms, stochastic dynamic programming, and queuing theory are used for mathematical modeling. Numerical results demonstrate the effectiveness of the proposed novel strategy for the navigation and charging scheduling of electric vehicles.

Two-stage robust planning method for distribution network energy storage based on load forecasting

A two-stage robust planning method for energy storage in distribution networks based on load prediction is proposed to address the uncertainty of active load in energy storage planning. First, considering the uncertainty of active load, a short-term load forecasting model combining the mutual information method and BiLSTM is established based on k-means++ clustering. Second, based on the results of load forecasting, a comprehensive norm-constrained uncertainty set is constructed, and a two-stage robust model for distribution network energy storage planning is established. The first stage aims to minimize the annual investment cost of the energy storage system, while the second stage aims to minimize the daily operating cost of the distribution network. At the same time, a second-order cone relaxation transformation model with non-convex constraints is introduced to ultimately achieve the optimal economy of the distribution network in energy storage planning. Finally, the effectiveness of the proposed method and model is validated on the IEEE 33-node distribution network model using the MATLAB platform.

Game of banks - biform game theoretical framework for ATM network cost sharing

Automated teller machines (ATM) play a major role in the world economy as they enable financial transactions and hence good exchanges and consumption. ATM transaction fees are incurred to cover the cost of running the network and these are often settled among the members including banks and cash machine operators. In this paper, we develop a novel biform game theoretic model for members to optimally invest in the ATM network and to share the cost. This biform game includes both a cooperative game theory mechanism for interchange fee sharing and a non-cooperative counterpart to model the fact that members also wish to maximize their utilities. While the proposed coopetition framework is applicable to general ATM networks, we focus the case study on the UK ATM network thanks to the accessibility of the data in addition to the notable stability issues that the network is currently experiencing as has been widely featured by the mainstream media. On the technical side, we prove the existence of a pure Nash equilibrium, which can be computed efficiently. We also show that, under some settings, the Shapley allocation belongs to the core and hence it is not only fair to all members but also leads to a stable ATM network. In addition, we show that the Shapley value allocation dominates the current mechanism in terms of social welfare. Finally, we provide numerical analysis and managerial insights using real data on the complete UK ATM network.

Coordinated Configuration of SOPs and DESSs in an Active Distribution Network Considering Social Welfare Maximization

In recent years, the penetration rate of intermittent distributed generation (DG) in active distribution networks has been steadily increasing, leading to prominent issues such as voltage violations and network congestion. Coordinated operation of soft open points (SOPs) and distributed energy storage systems (DESSs) enable flexible resource management in both spatial and temporal dimensions, allowing real-time voltage regulation and flow control. This enhances the controllability, sustainability, and economic efficiency of the distribution network, ultimately improving user satisfaction. The optimization of this coordinated configuration has become a central challenge in research. Taking the different characteristics of DESSs and SOPs into account, this paper proposes a coordinated configuration method by introducing local marginal price (LMP) under the configuration scheme. The framework is modeled as a three-level problem, including planning and operation levels. Initially, typical scenarios are generated to address the uncertainty of distributed generation operation. At the upper level, the DESS configuration is optimized to minimize annual operational costs, while at the middle level, SOPs are planned based on the annual comprehensive operational cost of the distribution network. At the lower level, the objective is set as social welfare maximization to reflect user satisfaction by incorporating LMP as a planning indicator. It is then transformed and solved as a mixed-integer second-order programming model with a hybrid optimization algorithm. The model is established with the modified IEEE 33-node distribution system as a case study to validate the feasibility of the proposed configuration method. The case study results demonstrate the effectiveness of the proposed approach in optimally configuring SOPs with DESSs to reduce overall annual operating costs and enhance the economic efficiency of the system.