► We review various types of Urban Transportation Network Design Problem (UTNDP). ► We focus on the topological design of urban transportation networks. ► We review studies on road network, transit ...network, and multi-modal network design. ► We propose possible future research directions for each type of UTNDP.
This paper presents a comprehensive review of the definitions, classifications, objectives, constraints, network topology decision variables, and solution methods of the Urban Transportation Network Design Problem (UTNDP), which includes both the Road Network Design Problem (RNDP) and the Public Transit Network Design Problem (PTNDP). The current trends and gaps in each class of the problem are discussed and future directions in terms of both modeling and solution approaches are given. This review intends to provide a bigger picture of transportation network design problems, allow comparisons of formulation approaches and solution methods of different problems in various classes of UTNDP, and encourage cross-fertilization between the RNDP and PTNDP research.
► Mixed network design problem (MNDP) can be formulated as a mixed-integer linear programming problem (MILP) with the link-based formulation which helps avoiding the path enumeration. ► The global ...optimality of the approximated MNDP can then be guaranteed following the property of the MILP. ► The proposed algorithm performed well with the tests with small and medium sized networks compared to other existing solution algorithms in the literature.
This paper proposes a global optimization algorithm for solving a mixed (continuous/discrete) transportation network design problem (MNDP), which is generally expressed as a mathematical programming with equilibrium constraint (MPEC). The upper level of the MNDP aims to optimize the network performance via both expansion of existing links and addition of new candidate links, whereas the lower level is a traditional Wardrop user equilibrium (UE) problem. In this paper, we first formulate the UE condition as a variational inequality (VI) problem, which is defined from a finite number of extreme points of a link-flow feasible region. The MNDP is approximated as a piecewise-linear programming (P-LP) problem, which is then transformed into a mixed-integer linear programming (MILP) problem. A global optimization algorithm based on a cutting constraint method is developed for solving the MILP problem. Numerical examples are given to demonstrate the efficiency of the proposed method and to compare the results with alternative algorithms reported in the literature.
The Relief Network Design Problem (RNDP) is particularly important in emergency management. Any uncertain factors caused by natural disasters, the equity measurement in network design, and the ...adequate analysis of relief behavior will affect the efficiency of the relief network. This paper provides a comprehensive basis to support this view. The scope of the review allowed for exploring all existing literature on RNDP, where screening for titles, abstracts, keywords, and main contents, a total of 629 relevant articles are preserved. To construct the review work, existing research perspectives on the Relief Logistics Network Design Problem (RLNDP) as well as the Relief Transport Network Design Problem (RTNDP) are addressed, and their research focus and main research approaches are discussed. The existing studies on RNDP seem to be reached a bottleneck on how to design a humanitarian relief network. Hence, this paper contributes to the existing body of knowledge by summarizing the literature in the field, identifying gaps, analyzing future challenges, and providing solutions for future research. Specifically, this review reveals that while a large number of studies have considered uncertainty in the network design, they have not considered it at both the management level and the residents' level. In addition, equity is often mentioned, but the definition of humanitarian equity is unclear, as most studies consider equity at the management level. In real disaster relief scenarios, people do not only wait for relief, but self-evacuation is also a main behavior in the relief process, yet there are few studies that consider it in the network design. This review also emphasizes the relief network design structure problem, and the interdependence and coupling of the relief infrastructure transport or logistics facility network with other networks, such as the electric network, energy network, etc., deserves to be focused. In summary, five valuable research highlights are proposed based on a review of the existing literature: (1) Explore uncertainties from both the government management and disaster victim perspectives and integrate them into network design approaches. (2) Define and consider relief equity from both the government management and disaster victim perspectives. (3) Analyze self-evacuation behavior in the emergency relief phase and explore how it affects RNDP. (4) Optimize the transfer point location and relief routing from the perspective of management and humanitarian equity. (5) Strengthen the resilience of disaster relief interdependent network.
•The research on RNDP is reviewed and a classification of RNDP is proposed and discussions are given for each classification.•Research gaps are identified in terms of uncertainty, equity, relief behavior, and disaster time phases, respectively.•Research challenges, research opportunities are proposed to lay the foundation for future research.
Efforts to enhance greenhouse gas (GHG) emission reduction in East Asia play a pivotal role on both global and regional scales in advancing climate mitigation strategies. This study aimed to better ...constrain anthropogenic CO2 emission estimates by expanding the network of near-surface in-situ stations for CO2 observations across South Korea. To achieve an optimal CO2 network design, we conducted an Observing System Simulation Experiment (OSSE) coupled with the Stochastic Lagrangian Transport model (STILT), utilizing meteorological data from the Korean Integrated Model (KIM). Our inversion setup incorporated two CO2 emission datasets with a 0.1o resolution: EDGAR v6 for prior emissions and GRACED for truth emissions. A uniform model-mismatch error of 3 ppm was introduced across sites. The effectiveness of the existing five in-situ stations, termed the base network, in South Korea was evaluated to gauge their ability to constrain CO2 surface flux estimates. However, the findings revealed a reduction in flux uncertainty of only 29.2%, which fell short of the desired uncertainty reduction goal. In this base network, the Lotte World Tower (LWT: 37.5126°E, 127.1025°E) in Seoul and the Anmyeondo (AMY: 36.538576° N, 126.330071° E) site in Taean county stood as major contributors, with estimated reductions of 17.48% and 6.35%, respectively. Consequently, we proposed and developed an extended network, identifying seven candidate sites based on consideration of logistical factors, existing infrastructures, and proximity to the emission source regions. An incremental optimization scheme ranked their contributions, resulting in an additional 25% reduction, bringing the total to 54.13%. However, it is noteworthy that diminishing returns (ranging from 13% to less than 0.1%) were observed with an increase in station count mainly due to the possibility that adding a station earlier in the sequence might render subsequent stations redundant. Despite this, the proposed CO2 network successfully reduced uncertainty in emissions, narrowing the gap with the objectives of the Global Greenhouse Gas Watch (G3W).
•Designing CO2 near-surface observation network over South Korea.•New stations selected considering logistic factors, existing infrastructures, and proximity to emission sources.•The base network was able to reduce 29.18% of the uncertainty in CO2 emissions.•Extending network, by adding new seven candidate sites, led to a total uncertainty reduction of 54.13%.
We study a network design problem (NDP) where the planner aims at selecting the optimal single-link intervention on a transportation network to minimize the travel time under Wardrop equilibrium ...flows. Our first result is that, if the delay functions are affine and the support of the equilibrium is not modified with interventions, the NDP may be formulated in terms of electrical quantities computed on a related resistor network. In particular, we show that the travel time variation corresponding to an intervention on a given link depends on the effective resistance between the endpoints of the link. We suggest an approach to approximate such an effective resistance by performing only local computation, and exploit it to design an efficient algorithm to solve the NDP. We discuss the optimality of this procedure in the limit of infinitely large networks, and provide a sufficient condition for its optimality. We then provide numerical simulations, showing that our algorithm achieves good performance even if the equilibrium support varies and the delay functions are non-linear.
The recent research explosion around implicit neural representations, such as NeRF, shows that there is immense potential for implicitly storing high‐quality scene and lighting information in compact ...neural networks. However, one major limitation preventing the use of NeRF in real‐time rendering applications is the prohibitive computational cost of excessive network evaluations along each view ray, requiring dozens of petaFLOPS. In this work, we bring compact neural representations closer to practical rendering of synthetic content in real‐time applications, such as games and virtual reality. We show that the number of samples required for each view ray can be significantly reduced when samples are placed around surfaces in the scene without compromising image quality. To this end, we propose a depth oracle network that predicts ray sample locations for each view ray with a single network evaluation. We show that using a classification network around logarithmically discretized and spherically warped depth values is essential to encode surface locations rather than directly estimating depth. The combination of these techniques leads to DONeRF, our compact dual network design with a depth oracle network as its first step and a locally sampled shading network for ray accumulation. With DONeRF, we reduce the inference costs by up to 48× compared to NeRF when conditioning on available ground truth depth information. Compared to concurrent acceleration methods for raymarching‐based neural representations, DONeRF does not require additional memory for explicit caching or acceleration structures, and can render interactively (20 frames per second) on a single GPU.
In order to improve the quality of Microgrid data, an automatic detection method of Microgrid bad data considering manifold ordering is proposed. The ELM network design and training model was ...constructed to extract the characteristic quantities of Microgrid data. Then, after analyzing the generation conditions of bad data, the quartile distance method is used to divide the identification interval of bad data, so as to improve the detection accuracy fundamentally. Then the Microgrid data are mapped to corresponding points in multi-dimensional vectors to form a weighted graph model. Considering the approximate manifold structure of data, a high-dimensional data automatic detection method based on manifold ordering is designed. The sorting scores of data nodes are calculated by confidence propagation, and the automatic detection results of bad data are obtained. Experimental results show that the root-mean-square error of monitoring results decreases obviously when the proposed method is used to detect bad data of Microgrid, indicating that the proposed method improves the accuracy of data detection.
There are two broad categories of risk, which influence the supply chain design and management. The first category is concerned with uncertainty embedded in the model parameters, which affects the ...problem of balancing supply and demand. The second category of risks may arise from natural disasters, strikes and economic disruptions, terroristic acts, and etc. Most of the existing studies surveyed these types of risk, separately. This paper proposes a robust and reliable model for an integrated forward–reverse logistics network design, which simultaneously takes uncertain parameters and facility disruptions into account. The proposed model is formulated based on a recent robust optimization approach to protect the network against uncertainty. Furthermore, a mixed integer linear programing model with augmented p-robust constraints is proposed to control the reliability of the network among disruption scenarios. The objective function of the proposed model is minimizing the nominal cost, while reducing disruption risk using the p-robustness criterion. To study the behavior of the robustness and reliability of the concerned network, several numerical examples are considered. Finally, a comparative analysis is carried out to study the performance of the augmented p-robust criterion and other conventional robust criteria.
A robust bi-level model of the single-product multi-period network design problem is proposed for a competitive green supply chain considering pricing and inventory decisions under uncertainty and ...disruption risks. The bi-level programming approach is used through this model to demonstrate the competition among two supply chains; the leader and the follower, respectively. After modelling the competition and applying pricing decisions by defining a price-dependent demand, disruption risks are analysed through the model. The proposed model simultaneously considers demand uncertainty and disruption risks and is capable of dealing with such uncertainties by implementing resilience strategies including, inventory decisions, and having a contract with reliable suppliers. Moreover, to consider the environmental issues, controlling CO2 emissions and managing the reverse flow were added to the model. Our approach to mitigate the problem uncertainties is to use the possibilistic programming method. The Karush-Kuhan-Tucker (K-K-T) optimality conditions are deployed to make a single-level equivalent form. Since the integrated model was bi-objective, the ϵ-constraint method is implemented to make a single objective integrated model. Finally, some managerial implications are discussed through an industrial case example.
•Robust fuzzy modeling can address the effect of uncertainty in parameters.•The priority-based solution encoding is useful in construction of meta-heuristics.•The proposed whale optimization ...algorithm provides fast high-quality solutions.•The solution quality is consistent without parameter-dependent behavior.
The closed-loop supply chain (CLSC) management as one of the most significant management issues has been increasingly spotlighted by the government, companies and customers, over the past years. The primary reasons for this growing attention mainly down to the governments-driven and environmental-related regulations which has caused the overall supply cost to reduce while enhancing the customer satisfaction. Thereby, in the present study, efforts have been made to propose a facility location/allocation model for a multi-echelon multi-product multi-period CLSC network under shortage, uncertainty, and discount on the purchase of raw materials. To design the network, a mixed-integer nonlinear programming (MINLP) model capable of reducing total costs of network is proposed. Moreover, the model is developed using a robust fuzzy programming (RFP) to investigate the effects of uncertainty parameters including customer demand, fraction of returned products, transportation costs, the price of raw materials, and shortage costs. As the developed model was NP-hard, a novel whale optimization algorithm (WOA) aimed at minimizing the network total costs with application of a modified priority-based encoding procedure is proposed. To validate the model and effectiveness of the proposed algorithm, some quantitative experiments were designed and solved by an optimization solver package and the proposed algorithm. Comparison of the outcomes provided by the proposed algorithm and exact solution is indicative of high quality performance of the applied algorithm to find a near-optimal solution within the reasonable computational time.
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