Network Design Framework Research Articles

A two-level decision-support framework for reverse logistics network design considering technology transformation in Industry 4.0: a case study in Norway

Reverse logistics network design is a complex decision-making problem that involves the reuse, repair, remanufacturing, and recycling of end-of-life (EOL) under the tradeoff among conflicting objectives. The cutting-edge technologies in Industry 4.0 are now leading to an unprecedented and dynamic transformation of reverse logistics systems, which, however, further complicates the initial network design. In this paper, a two-level decision-support framework combined with both optimization and dynamic simulation is proposed to balance the cost, environmental impact, and service level in smart and sustainable reverse logistics network design under a dynamically evolving and stochastic environment. The results of a real-world case study in Norway show that the method can better support robust strategic decisions, eliminate dominated/near-dominated solutions, and yield holistic performance analyses considering smart reverse logistics transformation. The proposed two-level decision-support framework can better analyze the impact of the technology transformation of Industry 4.0 on reverse logistics systems, while it also provides a fundamental structure for digital reverse logistics twin.

A novel groundwater monitoring network design framework for long-term and economical data monitoring

The groundwater monitoring network (GMN) design balancing prediction accuracy with cost minimization has paramount importance in depth to groundwater (DTW) monitoring, modeling, and management. This study demonstrates a robust and transferable GMN design framework for the underexplored statistically homogenous DTW to facilitate appropriate, long-term, cost-effective, and regional-scale groundwater monitoring. The inherent homogeneity in regional scale DTW data was investigated to cluster the observation wells into 18 strongly structured clusters/strata using optimal cluster number (k = 18), corresponding to the highest mean silhouette score of 0.72 and the K-means clustering algorithm. The clustered observation wells were used to evaluate the cluster random sampling (CRS) and stratified random sampling (SRS) for GMN design. The sampling techniques were evaluated to capture the spatial variability of DTW over the whole study region through spatial modeling at an adequate accuracy by the inverse distance weighting interpolation tools. The performance metrics for SRS recommended it as a more robust and appropriate technique for regional-scale GMN design than CRS. Then, the GMN was designed with 540 wells corresponding to 70% sampling under SRS for accurate, cost-effective, and long-term groundwater monitoring. By reducing 30% of observation wells, the cost-saving can contribute to cutting-edge monitoring infrastructure development. Additionally, the need-based balancing of the accuracy in spatial modeling and monitoring cost may further moderate the number of observation wells using the performance metrics of SRS corresponding to sampling percentages. Conclusively, this novel and transferable framework can be adopted globally with internally homogenous DTW data for effective GMN design.

Whole-ocean network design and implementation pathway for Arctic marine conservation

Forestalling the decline of global biodiversity requires urgent and transformative action at all levels of government and society, particularly in the Arctic Ocean and adjacent seas where rapid changes are already underway. Amid growing scientific support and mounting pressure, the majority of nations have committed to the most ambitious conservation targets yet. However, without an approach that inclusively and equitably reconciles conservation and sustainable ocean use, these targets will likely go unmet. Here, we present ArcNet: a network design framework to help achieve ocean-scale, area-based marine conservation in the Arctic. The framework is centred around a suite of web-based tools and a ~ 5.9 million km2 network of 83 priority areas for conservation designed through expert-driven systematic conservation planning using conservation targets for over 800 features representing Arctic biodiversity. The ArcNet framework is intended to help adapt to new and emerging information, foster collaboration, and identify tailored conservation measures within a global context at different levels of planning and implementation.

Building a Network Design and Implementation Framework of a Campus Area Network for the Mariano Marcos State University

A framework for network design and implementation is imperative to ensure value in the delivery of network services. Frameworks serve as guides in identifying value propositions to address the changing demands of the organization’s stakeholders. Frameworks prescribe efficient methods and appropriate tools that can be used to ensure that the implementation of a technological innovation addresses the demands, regardless of the changing IT landscape. The demand to deliver an efficient network service requires that the network have optimized levels of scalability, availability, performance, security, and other desirable network goals. These goals have to be prioritized depending on the goals of the organization. A framework for identifying priority service delivery components must be in place in order for the governing body and IT managers of the organization to objectively prescribe appropriate technological solutions to employ. The development of the proposed framework is patterned on Plan Design Implement Operate Optimize network life cycle, anchored on the principles of the service value chain of ITIL and the principles of IT governance using the goals cascade of COBIT. A review of existing studies was carried out to determine the prevailing technical goals and technology enablers that are peculiar to IT service management in a campus environment, specifically the delivery of computer network service. A framework was drawn to serve as a guide to achieving network design goals that are aligned with business goals, compliant with standards, and provide value to customers of state universities and colleges like Mariano Marcos State University.

Evaluation of Efficient CCUS System Design from Chemical Industry Emission in Indonesia

Carbon dioxide (CO2) emissions from industry significantly contribute to increasing CO2 in the atmosphere as the main cause of the Green House Gas (GHG) effect and climate change. CO2 emissions cause the need for evaluation in finding emission reduction systems. The CCUS (Carbon Capture Utilization and Storage) system is one of the most studied emission reduction systems. This study aims to obtain an intuitive and quantitative CCUS network design framework using GAMS (General Algebraic Modeling System) software with a mathematical approach. Several sources of CO2 emissions and potential absorbers are scattered in several regions in Indonesia. A mathematical approach was developed to optimize the amount of CO2 stored and utilized by varying the minimum time difference (dt min) between source and sink from 0, 3, 5, 8, to 10 years. The economic potential of the source-sink pair decreases with the change in dt with an average of 6.50 x 106 USD. Based on the potential economic value, the CCUS system with industrial CO2 emission sources has a positive value that can be applied in Indonesia.

A network design approach for citizen science-satellite monitoring of surface water volume changes in Bangladesh

This study introduces a reproducible and integrated framework for gauge network design for tracking surface water availability in a citizen science framework. For identifying the optimal number and location of gauges, the framework uses Monte Carlo simulations. With randomized selection of gauges from an existing high-density network, the framework does not impose a single optimal solution but rather provides a range of optimal solutions, allowing water managers the freedom to select the number of gauges and locations in accordance with their specific needs. Using the methodology, the study revealed how a strategic placement of gauges can potentially improve the estimation of surface water storage variations. The study findings can be used to enhance the assessment of lake products derived from missions such as the Surface Water and Ocean Topography (SWOT).

An integrated decision support framework for resilient vaccine supply chain network design

Designing resilient supply chain networks for vaccine development and distribution requires reliable and robust infrastructure. This stud develops a novel two-stage decision support framework for configuring multi-echelon Supply Chain Networks (SCNs), resilient supplier selection, and order allocation under uncertainty. Resilient supplier selection is done using a hybrid Multi-Criteria Decision-Making (MCDM) approach based on Best-Worst Method (BWM), Weighted Aggregated Sum Product Assessment (WASPAS), and Type-2 Neutrosophic Fuzzy Numbers (T2NN). A robust multi-objective optimization model is then built for order allocation considering resiliency scores, reliability of facilities, and uncertain supply and demand. The objectives are to minimize the total cost of SCN design, maximize the resiliency score, and maximize the reliability of SC, respectively. A Non-dominated Sorting Genetic Algorithm II (NSGA-II) is developed to tackle the problem on large scales, tuned by the Taguchi design technique. The NSGA-II solution is compared to the ε-constraint and Multi-objective Particle Swarm Optimization (MOPSO) solutions using test problems. We demonstrate the superiority of the suggested NSGA-II method over the two competing methods according to five performance metrics. A case study is then investigated to illustrate the applicability and effectiveness of the offered methodology for COVID-19 vaccine distribution in a developing country. It is revealed that the models and algorithms can treat the problem optimally, such that Germany is the main source (approximately 25.61%) while India does not contribute to the supply of vaccines.

Electric vehicle charging network design with capacity and service considerations

Electric Vehicles (EVs) have a critical role in the future of sustainable transportation systems. Recently, improving the infrastructure for EVs has been a major focus of organizations around the world, such as local and federal governments, energy companies, and automotive original equipment manufacturers. In this article, we present a service network design framework to improve the infrastructure for EVs with quality of service constraints taken into consideration. We present a novel charging station location model with capacity allocation. The model maximizes the EV traffic flow captured and minimizes the average service time for drivers. The model formulation allows for assessing capacity and quality of service trade-offs, and operational insights are presented to reflect drivers’ expectations. We demonstrate an application for the solution approach on a real road network and evaluate the performance based on both computational efficiency and solution quality. Our results highlight how charging times at stations affect the quality of service in the whole service system by quantifying the improvements in flow coverage while reducing waiting times. The value of faster chargers in increasing coverage and reducing the average waiting times is also highlighted in relation to a service provider’s budget constraints.

Distributionally robust optimization for collaborative emergency response network design

The post-disaster emergency response capacity of a single country is limited, and a collaborative approach that pools emergency resources can improve disaster resilience. In this paper, we study a multi-country collaborative emergency response network design problem with uncertain demand and transportation time. Considering the benefits of inter-regional cooperation in sharing emergency facilities and resources, we construct a collaborative emergency response network (CERN) design framework. The cost of CERN is allocated among the partner countries according to their expect standalone response cost and the level of economic development. In practice, the distribution information of random parameters is not perfectly known, so we propose a distributionally robust optimization (DRO) model to design the CERN. A scenario-wise ambiguity set is constructed to characterize the uncertain parameters based on disaster-level-related events. To solve the proposed DRO model, we propose a decomposition-based algorithm with a valid inequality. In the numerical study, we first verify the advantage of the CERN design approach. The proposed scenario-wise DRO method is subsequently compared with alternative modeling approaches to assess its out-of-sample performance. The findings confirm the efficacy of the constructed ambiguity set in capturing the uncertainty stemming from varying magnitudes of catastrophic events. The computational study demonstrates that the proposed algorithm exhibits superior computational efficiency compared to the commercial solver CPLEX for large-scale problems. Additionally, we conduct sensitivity analyses on various parameter configurations and provide managerial insights for the CERN design problem.

A scenario-based metaheuristic and optimization framework for cost-effective machine-trail network design in forestry

Designing an optimal machine trail network is a complex locational problem that requires an understanding of different machines’ operations and terrain features as well as the trade-offs between various objectives. With the overall goal to minimize the operational costs of the logging operation, this paper proposes a mathematical optimization model for the trail network design problem and a greedy heuristic method based on different randomized search scenarios aiming to find the optimal location of machine trails —with potential to reduce negative environmental impact. The network is designed so that all trees can be reached and adapted to how the machines can maneuver while considering the terrain elevation’s influence. To examine the effectiveness and practical performance of the heuristic and the optimization model, it was applied in a case study on four harvest units with different topologies and shapes. The computational experiments show that the heuristic can generate solutions that outperform the solutions corresponding to conventional, manual designs within practical time limits for operational planning. Moreover, to highlight certain features of the heuristic and the parameter settings’ effect on its performance, we present an extensive computational sensitivity analysis.

Optimization of maritime support network with relays under uncertainty: A novel matheuristics method

Construction and optimization of maritime support networks with relays have received extensive attention due to their implications for maritime economy and national interests. To improve total revenues under uncertain risk scenarios, we propose a general two-stage stochastic optimization framework for maritime support networks with relays. A novel matheuristics method (i.e., the interoperation of metaheuristics and mathematical programming techniques) is proposed as a general algorithm framework for reconfiguration under the serialized network disruption. Small, medium, and large benchmark groups are generated to verify the effectiveness of the proposed general network design framework, and show strong robustness and high efficiency of the proposed novel matheuristics method, compared with an exact algorithm and three types of representative heuristic algorithms.

A mathematical model for vaccine cold chain network design considering social sustainability

Breakages that may occur in the cold chain cause serious economic, environmental, and social costs, as well as a substantial risk for human and public health. Therefore, it is necessary to design an effective, robust, and strong vaccine cold chain network. Sustainable Development Goal 3 titled “Good Health and Well Being” emphasizes children's health and specifies reducing the mortality rate for under five ages. In this study, we consider the Expanded Programme on Immunization (EPI) vaccine cold chain in Türkiye and develop a linear programming model for a multi-product, multi-period, multi-stage vaccine cold chain network in light of Sustainable Development Goal 3. The study aims to maximize fully immunized children for up to five years and propose a framework for a vaccine cold chain network design. The proposed model is applied to a real case. Finally, various scenario analyzes are applied to show the results of the model under different conditions.

Energy-efficient Data Aggregation in Low-power Wireless Networks with Sensors of Discrete Transmission Ranges: A Mathematical Framework for Network Design

The advancements in sensor technology and the evolution of new generation technologies such as the Internet of Things have led to extensive deployment of low-power wireless sensor networks for various surveillance and monitoring applications. The volume and velocity of the data generated by a large number of sensors and monitors used in such applications are huge. The harnessing of such big data is critical to the real-time control of underlying real-world processes. Future generation networks favor the deployment of sensors with different discrete transmission ranges. Such multiple transmission ranges introduce different connectivity constraints in the network. Advanced strategies are under investigation for designing energy-efficient data aggregation schemes in such connectivity-constrained networks. To aid such works, we introduce a mathematical framework that captures the salient features of low-power networks with sensors of multiple transmission ranges. The proposed framework can serve as a baseline platform to investigate different networking problems in such systems. Further, we consider a cluster-based multihop network with fixed intra-cluster and inter-cluster transmission ranges and address the problem of designing an optimal network configuration for minimizing the data transmission in the network. We model the problem as an integer linear program, which can be applied only to networks of small sizes because of the hardness of the problem. To address the problem in large-scale networks, we design a polynomial-time approximation method using the proposed mathematical framework. The performance of the proposed methods is evaluated under compression schemes based on compressive sensing.

A framework for human-computer interactive street network design based on a multi-stage deep learning approach

Limited attention has been given to human-computer interactions in the plan-making process to capitalize on the relative strengths of both. This paper proposes a methodological framework for an interactive street network design that complements user-driven (i.e., procedural-based tools) and example-driven (i.e., learning-based tools) approaches in urban planning and design. The proposed framework consists of three components: (1) a data preparation module to link open-source road networks with human-labeled planning guidance, (2) a multi-stage deep learning (MSDL) model to reinforce the user-defined guidance in the automatic generation of street networks, and (3) a human-computer interaction (HCI) interface to enable the progressive design process. The performance and the working mechanism of the proposed framework were examined through experiments in four European cities (i.e., Amsterdam, Barcelona, Berlin, and Prague). The experiments demonstrate that the proposed MSDL model can achieve a better predictive performance compared to benchmark models, particularly when limited planning guidance is given. These finding are revealed using either computer vision- or street network-related metrics. With less than 40% of the ground truth planning guidance used as an input, the MSDL model can perform as well as other models using 100% of the information. Furthermore, when embedded within an HCI system for user trials, the model can facilitate a human-computer collaborative design process. This advantage is derived from the model's ability to provide initial prototypes, timely responses to changed guidance, and quantifiable evaluations of the generated proposals. Suitable for professionals and laypersons, the proposed tool can inform plan-making and public engagement by offering realistic, enriched, and human-centered spatial proposal alternatives for comparison.

An optimal control framework for joint-channel parallel MRI reconstruction without coil sensitivities

GoalThis work aims at developing a novel calibration-free fast parallel MRI (pMRI) reconstruction method incorporate with discrete-time optimal control framework. The reconstruction model is designed to learn a regularization that combines channels and extracts features by leveraging the information sharing among channels of multi-coil images. We propose to recover both magnitude and phase information by taking advantage of structured convolutional networks in image and Fourier spaces. MethodsWe develop a novel variational model with a learnable objective function that integrates an adaptive multi-coil image combination operator and effective image regularization in the image and Fourier spaces. We cast the reconstruction network as a structured discrete-time optimal control system, resulting in an optimal control formulation of parameter training where the parameters of the objective function play the role of control variables. We demonstrate that the Lagrangian method for solving the control problem is equivalent to back-propagation, ensuring the local convergence of the training algorithm. ResultsWe conduct a large number of numerical experiments of the proposed method with comparisons to several state-of-the-art pMRI reconstruction networks on real pMRI datasets. The numerical results demonstrate the promising performance of the proposed method evidently. ConclusionThe proposed method provides a general deep network design and training framework for efficient joint-channel pMRI reconstruction. SignificanceBy learning multi-coil image combination operator and performing regularizations in both image domain and k-space domain, the proposed method achieves a highly efficient image reconstruction network for pMRI.

Exact reliability optimization for series‐parallel graphs using convex envelopes

Given its wide spectrum of applications, the classical problem of all-terminal network reliability evaluation remains a highly relevant problem in network design. The associated optimization problem -- to find a network with the best possible reliability under multiple constraints -- presents an even more complex challenge, which has been addressed in the scientific literature but usually under strong assumptions over failures probabilities and/or the network topology. In this work, we propose a novel reliability optimization framework for network design with failures probabilities that are independent but not necessarily identical. We leverage the linear-time evaluation procedure for network reliability in the series-parallel graphs of Satyanarayana and Wood(1985) to formulate the reliability optimization problem as a mixed-integer nonlinear optimization problem. To solve this nonconvex problem, we use classical convex envelopes of bilinear functions, introduce custom cutting planes, and propose a new family of convex envelopes for expressions that appear in the evaluation of network reliability. Furthermore, we exploit the refinements produced by spatial branch-and-bound to locally strengthen our convex relaxations. Our experiments show that, using our framework, one can efficiently obtain optimal solutions in challenging instances of this problem.

Learning‐based robust control methodologies under information constraints

Learning‐based robust control methodologies under information constraints

A methodological framework for efficient and resilient supply network design

The growing competition and the destabilising effects of climate, disease and other external perils bring important challenges to companies worldwide. Supply chain (SC) networks become more and more complex with a growing exposure to a broad range of uncertainties, some of which may cause network disruptions. Neglecting these kinds of risks may lead to adverse consequences, such as negative financial effects, higher transportation costs, order delays, inventory shortages, and loss of market shares. Frequent disruption events that have been continuously increasing over recent years have clearly shown the key role of supply chain resilience. To hedge against SC disruptions, a well-designed and reliable supply network that performs efficiently in normal situations and resiliently during unstable conditions is a top priority. In this paper, starting from the discussion of the main design drivers for a resilient SC, a methodological framework is proposed to support both efficient and resilient supply network design. The procedure gets foundation from the recent literature, and it is directly derived from its application in a numerical example and an industrial case.

A comprehensive framework for sustainable closed-loop supply chain network design

Many companies face challenges in reducing their supply chain costs while increasing sustainability and customer service levels. A comprehensive framework for a sustainable closed-loop supply chain (CLSC) network is a practical solution to these challenges. Hence, for the first time, this study considers an integrated multi-objective mixed-integer linear programming (MOMILP) model to design sustainable CLSC networks with cross-docking, location-inventory-routing, time window, supplier selection, order allocation, transportation modes with simultaneous pickup, and delivery under uncertainty. An intelligent simulation algorithm is proposed to produce CLSC network data with probabilistic distribution functions and feasible solution space. In addition, a fuzzy goal programming approach is proposed to solve the MOMILP model under uncertainty. Eight small and medium-size test problems are used to evaluate the performance of the proposed model with the simulated data in GAMS software. The results obtained from test problems and sensitivity analysis show the efficacy of the proposed model.

A performance evaluation framework of public health distribution network for essential medicines: lessons from the select Indian states

AbstractThe present work has been carried out to develop a performance evaluation framework of distribution network design in public health with specific reference to essential medicines based on six different types of public health distribution networks (PHDNs) prevalent across six Indian states. We evaluated the performance of PHDNs on the criteria of cost and service and also on the sub‐criteria of cost and service by following the analytic hierarchy process. We utilized the pairwise comparison method in determining the relative importance of criteria and sub‐criteria by seeking responses from several experts and subsequently evaluated the PHDNs on all sub‐criteria in a 7‐point Likert scale by seeking views from the same experts. Finally, we developed the “cost–service matrix,” in which cost and service have been depicted in the horizontal and vertical axes, respectively. We demonstrated the performance of six PHDNs in this matrix in terms of cost and service by plotting scores obtained by them on cost and service, respectively. The findings of the study provide rich insights to the policy planners of public health.