Network Design Approach Research Articles

A Lagrangian relaxation approach for algae-based biofuel supply chain network design under uncertainty and pricing issue

A Lagrangian relaxation approach for algae-based biofuel supply chain network design under uncertainty and pricing issue

An Integrated Approach for Ammunition Depot Location Selection and Ammunition Distribution Network Design Based on P-Median and Vehicle Routing Problems

During the war, the safe provision of the required ammunition at the desired place and time can affect the fate of the war and even the country. Determining the number and locations of ammunition depots needed in the war is as important as planning their distribution. Ammunition supply has always been a force multiplier for military units. Although the timely delivery of ammunition to the unit is not sufficient for success on its own, it is an important factor. These two issues are directly related to each other. This study presents a two-stage methodology to solve the problems in question. In the first stage, it was decided which of the 3 different types of depots, whose establishment locations were known with the p-median model, would be opened, and the unit-depot assignments were determined. In the second stage, the distribution network design for the distribution of ammunition from the ammunition depot to the deployed artillery units was realized with the Capacity-Constrained Vehicle Routing Problem (CCVRP) model. This methodology was tested for a fictitious situation where the geographical location and numerical information of the depots and artillery batteries were generated generically. By solving the mathematical model in the General Algebraic Modeling System (GAMS) program, it was determined which depot should distribute how many vehicles to which artillery batteries, and the results were presented visually.

Conjunctive optimal design of water and power networks

Water distribution systems (WDS) and power grids (PG) are critical infrastructure systems that are vital to all human activity. As such, their quality of service is of great importance for economic, environmental, and human welfare reasons. Although traditionally being analyzed separately, the two systems are interconnected and can mutually affect one another. In order to utilize the potential benefits that the two systems can produce for each other, their design and operation should be analyzed conjunctively. In this paper, a conjunctive optimal design approach for water and power networks is presented, with the objective of finding the dimensions of the systems’ facilities that will result in minimal overall costs, for both design and operation. The model is formulated and implemented on two example applications using an off-the-shelf nonlinear solver by MATLAB and compared to the optimal design of the independent WDS. A sensitivity analysis is performed to provide validity to the obtained results. The conjunctive design is compared to the design of an independent WDS to emphasize the effect of including the PG in the optimization problem. Results show a clear link between the availability of renewable energy and sizing of WDS components. The design of the independent WDS leads to the violation of PG constraints, which are satisfied when including both systems under a single optimization model, demonstrating the importance of a holistic design approach.

Designing a centralized storage hydrogen supply chain network with multi-period and bi-objective optimization

This study introduces a multi-period centralized storage optimization model aimed at designing an efficient hydrogen supply chain system, considering cost and emissions as dual objectives. It integrates multiple energy sources, production and storage methods, transport combinations, demand scenarios, and carbon capture systems, offering a comprehensive decision-making approach for hydrogen network design. Employing the mixed-integer linear programming methodology, the proposed model resolves these complexities. The research applies this model to a case study in France, generating six unique scenarios for 10 and 15 cities, and compares them against two distinct decentralized models. The findings consistently highlight the centralized storage model’s cost benefits across various demand scenarios, including cases of unrestricted emissions as well as cases with limited emission targets. The cost-effectiveness of this proposed model enhances its feasibility within the current context of decarbonization.

Multitier scalable clustering wireless network design approach using honey bee ratel optimization

Wireless sensor networks (WSNs) have emerged as a one of the most fascinating areas of lookup in the past few years principally due to its massive quantity of workable applications. However limited energy, node deployment strategy, routing techniques has considerable impact on the overall performance of network. This paper proposes sustainable honey bee ratel based hybrid wireless sensor network (SHHWSN) layering framework for improving load balancing and scalability issues in network. In SHHWSN tessellated hexagonal shape clusters are formed to cover entire geographical region without any gaps. Hybrid routing schema constrained with redundant data identification policy used in this, seeks to optimize energy utilization. The proposed SHHWSN was evaluated and validated by simulation, and the results were compared with earlier schematics where mostly one sided stochastic node deployments were followed. The experimental results show that SHHWSN has superior performance in terms of network alive nodes, delay, energy and throughput. The proposed SHHWSN obtained values of 50 nodes is 3, 0.061, 0.14, 0.89, 100 nodes is 3,0.037, 0.19, 0.551, 150 nodes is 13, 0.081, 0.23, 0.415, and 200 nodes is 23, 1.01, 0.25, 0.356, which is significantly more effective than current techniques.

Enhancing reclaimed water distribution network resilience with cost-effective meshing

Water Distribution Networks (WDNs) are critical infrastructures that ensure a continuous supply of safe water to homes. In the face of challenges, like water scarcity, establishing resilient networks is imperative, especially in regions vulnerable to water crises. This study evaluates the resilience of network designs through graph theory, including its hydraulic feasibility using EPANET software, an aspect often overlooked. Novel mathematical algorithms, including Resilience by Design (RbD) and Resilience-strengthening (RS) algorithms, provide cost-effective and resilient network designs, even with budget constraints. A novel metric, Water Availability (WA), is introduced to offer a comprehensive measure of network resilience, thereby addressing ongoing discrepancies in resilience evaluation methods. Practical benefits are illustrated through a case study in which a resilient-by-design reclaimed water network is created, and an existing equivalent non-resilient network is improved. The resilient-by-design network demonstrates remarkably better results compared to the equivalent non-resilient design, including up to a 36 % reduction in the probability of service disruptions and a nearly 65 % decrease in the annual average unserved water due to service disruptions. These findings underscore the enormous advantages of a resilience-focused network design approach. When compared to the equivalent non-resilient design, the resilient-by-design network generated effectively safeguards up to a significant 91,700m3 of water from the impacts of water disruption events over a 50-year operational period. In addition, the resilient-by-design WDN solution incurs a subtle decrease in overall costs compared to consuming tap water from the drinking WDN baseline over a 50-year operational period. These findings highlight the cost-effectiveness of the approach, even offering financial benefits. This paper builds on our previous research by expanding its scope to include resilience considerations, providing algorithms that can be easily adapted from reclaimed to drinking WDNs. Ultimately, we contribute to the enhancement of water resource management and infrastructure planning in ever-evolving urban environments.

Optimizing large-scale hydrogen storage: A novel hybrid genetic algorithm approach for efficient pipeline network design

As hydrogen production technologies continue to advance, efficient and high-capacity hydrogen storage solutions become increasingly crucial. To address the complex optimization challenges in the design of large-scale hydrogen storage pipeline networks, the study introduces a mixed integer nonlinear optimization model inspired by hydrogen pipeline design optimization theory. The model not only focuses on optimizing network design parameters but also aims to minimize investment costs. To achieve this, the study proposes an innovative hybrid genetic algorithm that combines the Modified Feasible Directions Method (MFDM) and Genetic Algorithm Theory (GAT), specifically targeting the optimization of pipeline diameter's discrete distribution challenges. Comparative analyses with SUMT and GA algorithms demonstrate the superiority of the approach. In continuous space, the algorithm achieves a lower convergence cost of 232.4437 × 104 Yuan, while in discrete space, the cost further decreases to 212.1636 × 104 Yuan. Additionally, the study delves into the sensitivity of the HGA-MFDM algorithm to ambient temperature and wellhead temperature in hydrogen storage facilities. The analysis reveals that wellhead temperature has a more significant impact on pipeline network investment, whereas ambient temperature exhibits a more pronounced effect on iteration curves. The findings provide valuable insights for the precise adjustment and optimization of hydrogen storage pipeline networks in practical applications.

A multi-objective optimization approach for green supply chain network design for the sea cucumber (Apostichopus japonicus) industry

In China, aquatic supply chain network design does not include the green concept or the coordination of environmental and economic performance. Sea cucumber (Apostichopus japonicus) is an aquatic product of high economic value; however, studies on sea cucumber supply chain network optimization are lacking. This study is the first to design the sea cucumber supply chain and construct an optimization model. Considering the characteristics of the sea cucumber industry, LCA for Experts software and the CML-IA-Aug. 2016-world method were used to assess each aquaculture model's global warming potential (GWP), as the environmental performance indicator. In addition, multi-objective genetic algorithm (MOGA) coupled with Modified Technique for Order of Preference by Similarity to Ideal Solution (M-TOPSIS) integrates yield production, economic benefits, and environmental performance. The results demonstrated that cage seed rearing (CSR) combined bottom sowing aquaculture (BSA) represents the best production strategy upstream of the sea cucumber supply chain. In the downstream, the best proportion of sales channels in supermarkets, boutique stores and online shops accounted for 14.79 %, 58.02 % and 27.19 % of the production, respectively. The proposed optimization scenario 4 (S4) can increase product profit by 27.88 % and reduce GWP by 56.89 %. The following improvement measures are proposed: using sea cucumber aquaculture industry standards (cleaner production and green supplier selection) to regulate the behavior of enterprises, adopting an ecological and green production strategy, eliminating high-energy consumption and high emission production practices, and promoting widespread adoption of green consumption concepts. Finally, these measures may improve the sea cucumber supply chain, achieve coordinated environmental and economic performance development in the sea cucumber industry, and provide guidance for green optimization of other aquatic product supply chains in China.

A Distributionally Robust Fuzzy Optimization Approach for Resilient Manufacturing Supply Chain Network Design: An RCEP Perspective

A Distributionally Robust Fuzzy Optimization Approach for Resilient Manufacturing Supply Chain Network Design: An RCEP Perspective

Smart loading zones. A data analytics approach for loading zones network design

Urban public space is often provided for freight delivery operations in the form of on-street (un)loading zones (LZ). Since public space is scarce and demanded by several users, city authorities have the challenge of managing LZ by gaining knowledge about freight curbside needs and utilization. Although technological solutions and enforcement practices have become popular among policymakers to capture curbside dynamics, there is still an open and promising research field for designing analytical frameworks that shape LZ decision-making processes. This fact has motivated the authors to define the concept of Smart Loading Zones (SLZ) as the involvement of technology and data analytics in the planning and management of LZ in a responsive and user-oriented way. Besides proposing a conceptual approach for the study of SLZ, this paper implements data analytics tools for enhancing decisions on LZ network design, using the City of Vic (Spain) as a case study. The machine learning techniques k-means++, DBSCAN, and integer linear programming prescribed the LZ number, location and service assignment based on establishments' coordinates, walking distances and freight demand. Results from the case study showed how an optimized number, location, and size of LZ improved occupation levels, i.e., from 18 % to 80 %, while freeing up curbside space for other users. Service coverage was also improved by allocating LZ to establishments within walking distances no greater than 75 m. Further development of methods and tools for SLZ at tactical and operational decisions are recommended for future studies.

A stochastic programming approach for EOL electric vehicle batteries recovery network design under uncertain conditions

With the development of the electric vehicle industry, the number of power batteries has increased dramatically. Establishing a recycling EOL (end-of-life) battery network for secondary use is an effective way to solve resource shortage and environmental pollution. However, existing networks are challenging due to the high uncertainty of EOL batteries, e.g., quantity and quality, resulting in a low recycling rate of the recovery network. To fill this gap, this paper proposes a stochastic programming approach for recovery network design under uncertain conditions of EOL batteries. Firstly, a multi-objective model for battery recovery network is established, considering carbon emissions and economic benefits. Secondly, a stochastic programming approach is proposed to clarify the model. Subsequently, the genetic algorithm is employed to solve the proposed model. Finally, a recovery network case of Region T is given to verify the credibility and superiority of the proposed method. The results demonstrate that the proposed model reduces carbon emissions by 20 metric tons and increases overall economic benefits by 10 million yuan in Region T compared to the deterministic model. Furthermore, the two portions affecting the optimization results are also discussed to provide a reference for reducing carbon emissions and improving economic efficiency in recycling networks.

Ecological Robustness-Oriented Grid Network Design for Resilience Against Multiple Hazards

Power systems are critical infrastructure for reliable and secure electric energy delivery. Incidents are increasing, as unexpected multiple hazards ranging from natural disasters to cyberattacks threaten the security and functionality of society. Inspired by resilient ecosystems, this paper presents a resilient network design approach with an ecological robustness (R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ECO</sub> )-oriented optimization to improve power systems' ability to maintain a secure operating state throughout unknown hazards. The approach uses R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ECO</sub> , a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">surprisal</i> -based metric that captures key features of an ecosystem's resilient structure, as an objective to strategically design the electrical network. The approach enables solvability and practicality by introducing a stochastic-based candidate branch creation algorithm and a Taylor series expansion for relaxation of the R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ECO</sub> formulation. Finally, studies are conducted on the R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ECO</sub> -oriented approach using the IEEE 24 Bus RTS and the ACTIVSg200 systems. Results demonstrate improvement of the system's reliability under multiple hazards, network properties of robust structure and equally distributed power flows, and survivability against cascading failures. From the analysis, we observe that a more redundant network structure with equally distributed power flows benefits its resilience.

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).

A multi‐emission‐driven efficient network design for green hub‐and‐spoke airline networks

AbstractThe green hub‐and‐spoke airline network (GHSAN) is emerging as a dominant feature due to its excellent economic and environmental‐friendly capabilities. However, environmental GHSAN designs still have some concerns, including single pollutant‐domain oversimplification and lack of comprehensive network‐level operation impacts. This paper proposes a multi‐emission‐driven efficient network design approach for GHSAN, utilizing a system, green, and user threefold optimization methodology. The approach includes a multi‐objective optimization model and a two‐layer solving method. The multi‐objective optimization aims at minimizing multiple emissions, including carbon dioxide, carbonic oxide hydrocarbon, and nitric oxide, while also considering transportation system costs and journey user costs. A two‐layer optimization algorithm is adopted to address different scales of optimization. Real‐world results demonstrate that the proposed method mitigates environmental impact and user costs and increases overall airline density in airline networks. The proposed method can have a 16.29% reduction in green‐fold (10 nodes) and a 12.06% decrease in user costs for the user‐fold (10 nodes). As the number of nodes (15, 25, 50 nodes) and hubs (3, 4, 5, 6, 7 hubs) increase, the genetic algorithm (GA) proves to be more efficient and suitable in large‐scale GHSAN. This work is further significant for the long‐term and sustainable development of the future air transport industry.

A fuzzy multiobjective linear programming approach for green multiechelon distribution network design

PurposeDistribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.Design/methodology/approachThe problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.FindingsFor the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.Originality/valueThe proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.

Regulating hazardous material transportation: a scenario-based network design approach with integrated risk-mitigation mechanisms

Integrating both proactive and reactive risk-mitigation mechanisms, this research constructs a bi-level network design problem for hazardous materials (hazmats) to regulate the carriers' use of roads, such that the environmental impact is minimized. In more detail, by embedding the emergency response time into the risk assessment, the locations of hazmat response teams are determined along with toll schemes, road closures, and new road constructions. The uncertainties of demand, including differences in the number of shipments, origin/destination changes, and amount variations, are also taken into account by applying a scenario-based approach. The proposed bi-level model is first solved optimally by a single-level reformulation, and then by a three-stage heuristic method for larger instances. Through numerical experiments on a real-world road network in Nanchang, a city in China, a set of management insights are derived to facilitate policy-making in regulating hazmat transportation.

A service network design for scheduled advanced air mobility using human-driven and autonomous air metro

Emerging modes of advanced air mobility are potential alternatives to current ground transport. This study proposes a service network design approach for the air metro, a pre-scheduled service with fixed routes that accommodate passengers for intra- or inter-city trips. The scenarios of human-driven and autonomous air metro are then compared, where the former has a labour cost for pilots and the latter has a higher capital costs such as vehicle and automation costs. Then, a rolling horizon optimisation approach is proposed, where the temporal length of a single rolling horizon is an early confirmation period plus a safety margin. The rolling horizon introduces decision and marginal arcs with different fleet, passenger, and pilot network capabilities. The optimised outputs on critical arcs are determined and fixed, while the marginal arcs can be continuously adjusted in the subsequent rolling horizons. Numerical studies are undertaken across all variables in the context of the Greater Metropolitan Area of Sydney, Australia. Results suggest that the human-driven air metro would be economically preferable until the utilisation cost of an autonomous aircraft can reduce by 60%. Furthermore, confirming the actual passenger demand at least 45 min in advance is recommended, and a single rolling horizon should be longer than 150 min.

Comparative analysis of machine learning algorithms on prediction of the sound absorption coefficient for reconfigurable acoustic meta-absorbers

Exploring the potential of sound absorption is foremost for improving room acoustics and unleashing the power of noise remediation. Acoustic metamaterials are considered promising candidates for tunable broadband sound absorption in the low-to-high frequency range. In this work, an acoustic meta-absorber with multi-order Helmholtz resonator cavities is proposed. The meta-absorber unit cell comprises multiple disks, spacers, and a bottom cavity arranged in a series configuration. Therefore, the assembled acoustic meta-absorber is reconfigurable to have different absorption characteristics. Experimental results demonstrate that the proposed meta-absorber unit cell exhibits high sound absorption, with multiple absorption peaks observed within the 80-1600 Hz frequency range. Additionally, tunable broadband and quasi-perfect absorption (≥0.9) can be achieved at low frequencies (≤600 Hz) by adjusting the positions of individual disks and other structural parameters. Furthermore, a feedforward network-design approach based on machine learning models is proposed and experimentally demonstrated for the meta-absorber. Two types of meta-absorber features and different machine learning algorithms, including artificial neural network (ANN), k-Nearest Neighbor (kNN), and radial basis function neural network (RBFN), are utilized to compare their predictive abilities. The implementation of machine learning models allows for efficient design optimization of the meta-absorber's geometrical parameters to achieve specific functionalities without relying on physical models. The geometrical parameters are adaptively tuned during the design process to enhance sound absorption performance, particularly at low frequencies. The predicted sound absorption values are compared with the experimental results, and among the machine learning algorithms employed, ANN demonstrates the best performance, followed by the kNN method.

A link criticality approach for pedestrian network design to promote walking

Measures of walkability generally do not provide a detailed quantitative assessment of pedestrian infrastructure development prioritization. In this study, a link-based composite measure of walkability and walking is introduced to overcome this limitation. This measure, called ‘pednet score’, is based on a weighted pedestrian network (‘pednet’) made of sidewalks and crosswalks whose edge weights are descriptive of their popularity. Edge popularity is derived from home-based walk trip assignments derived from simulated pedestrian demand. Properties of the pednet score are studied using three hypothetical variants of the pednet in three North American cities, each involving the addition of candidate sidewalk and/or crosswalk segments. It is shown that a strategic selection of these segments based on pednet score can substantially increase walking trips, in some cases up to 236%, and reduce current mean pedestrian trip distances by up to 340 m. A mixed development approach involving both sidewalks and crosswalks also shows considerably higher improvement than those segments considered alone. Results from marginal benefit curves strongly indicate the utility of the pednet score as a measure of link criticality for segment prioritization in pedestrian network design.

Network design with consideration of hours‐of‐service regulation and drop‐and‐swap trailer operations

AbstractThis research, motivated by collaboration with a leading manufacturer and distributor of building products, introduces two distribution network design models considering federally mandated hours of service restrictions on truck drivers and drop‐and‐swap trailer operations. A key feature of the drop‐and‐swap transport mode is the decoupling of linehaul and last‐mile delivery operations, which enables geographic extension of last‐mile delivery routes while complying with hours of service driver regulations and providing next‐day delivery service. The models simultaneously determine the profit‐maximizing size, number, and location of order fulfillment facilities and satellite drop‐and‐swap terminals, and systemwide transportation flows. A case study of the firm's distribution network reveals that the new location models improve profitability by approximately 5% when compared with traditional network design approaches. An experimental study of five marketing and operational characteristics shows when the models are most advantageously applied. Overall, profit improvements range from 2.32% to 10.97% on a set of 288 test problems. The research provides new insights for integrated facility location and transportation strategic design. The potential application of the models in a variety of e‐commerce scenarios is promising.