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Dynamic wireless charging technologies enable power transfer to electric vehicles while in motion. For transit applications, such technologies can reduce electric buses' idle time required for charging. In this study, we propose and analyze a self-charging transit network design problem. A self-charging transit network consists of routes such that the electric buses operating on these routes are charged solely while in motion. First, we introduce the self-charging route and transit network concepts. After that, the optimization model for establishing a self-charging transit network in a cost-minimum way is formulated. In this model, multiple dynamic wireless charging technologies can be used on the network to make the routes self-charging. The resulting model is NP-hard; and therefore, we develop a multi-start neighbor search heuristic as an alternative solution method. A set of numerical studies are conducted to demonstrate the self-charging transit network problem with an example, evaluate the efficiency of the proposed heuristic method, and present several sensitivity analyses. [ABSTRACT FROM AUTHOR]

With increased globalisation supply chain (SC) disruption significantly affects people, organisations and society. Supply chain network design (SCND) reduces the effects of disruption, employing mitigation strategies such as extra capacity and flexibility to make SCs resilient. Currently, no systematic literature review classifies mitigation strategies for SCND. This paper systematically reviews the literature on SCND, analysing proposed mitigation strategies and the methods used for their integration into quantitative models. First to understand the key failure drivers SCND literature is categorised using geography, with local, regional or global disruptions linked to vulnerable sections of a SC. Second, the strategies used in mathematical models to increase SC resilience are categorized as proactive, reactive, or SC design quality capabilities. Third, the relative performance of mitigation strategies is analysed to provide a comparison, identifying the most effective strategies in given contexts. Forth, mathematical modelling techniques used in resilient SCND are reviewed, identifying how strategies are integrated into quantitative models. Finally, gaps in knowledge, key research questions and future directions for researchers are described. [ABSTRACT FROM AUTHOR]

To provide better care for the elderly, health trustees establish home health care centers in cities. These centers offer services at homes, and a logistics network is essential for providing these services. An integrated framework is developed to design such a network by which various logistical decisions are made under inherent epistemic uncertainty of the travel and service times. A new mixed integer linear programming model is developed, and its equivalent deterministic formulation is obtained using a well-applied possibilistic programming approach. An improved genetic algorithm is also devised to solve large-sized instances and various sensitivity analyses are performed on key factors. Finally, a case study is presented to validate the developed model and its solution method. The numerical results demonstrate 20% reduction in the total time of employing care givers at almost 3.5% cost savings. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

For industries like the cement industry, switching to a carbon-neutral production process is impossible. They must rely on carbon capture, utilization and storage (CCUS) technologies to reduce their production processes' inevitable carbon dioxide ( CO 2 ) emissions. For transporting continuously large amounts of CO 2 , utilizing a pipeline network is the most effective solution; however, building such a network is expensive. Therefore minimizing the cost of the pipelines to be built is extremely important to make the operation financially feasible. In this context, we investigate the problem of finding optimal pipeline diameters from a discrete set of diameters for a tree-shaped network transporting captured CO 2 from multiple sources to a single sink. The general problem of optimizing arc capacities in potential-based fluid networks is already a challenging mixed-integer nonlinear optimization problem. The problem becomes even more complex when adding the highly sensitive nonlinear behavior of CO 2 regarding temperature and pressure changes. We propose an iterative algorithm splitting the problem into two parts: (a) the pipe-sizing problem under a fixed supply scenario and temperature distribution and (b) the thermophysical modeling, including mixing effects, the Joule–Thomson effect, and heat exchange with the surrounding environment. We demonstrate the effectiveness of our approach by applying our algorithm to a real-world network planning problem for a CO 2 network in Western Germany. Further, we show the robustness of the algorithm by solving a large artificially created set of network instances. [ABSTRACT FROM AUTHOR]

In this paper, we propose an augmented Lagrangian method with Backtracking Line Search for solving nonconvex composite optimization problems including both nonlinear equality and inequality constraints. In case the variable spaces are homogeneous, our setting yields a generic nonlinear mathematical programming model. When some variables belong to the real Hilbert space and others to the integer space, one obtains a nonconvex mixed-integer/-binary nonlinear programming model for which the nonconvexity is not limited to the integrality constraints. Together with the formal proof of its iteration complexity, the proposed algorithm is then numerically evaluated to solve a multi-constrained network design problem. Extensive numerical executions on a set of instances extracted from the SNDlib repository are then performed to study its behavior and performance as well as identify potential improvement of this method. Finally, analysis of the results and their comparison against those obtained when solving its convex relaxation using mixed-integer programming solvers are reported. [ABSTRACT FROM AUTHOR]

Gas networks are used to transport natural gas, which is an important resource for both residential and industrial customers throughout the world. The gas network design problem is generally modelled as a nonconvex mixed-integer nonlinear integer programming problem (MINLP). The challenges of solving the resulting MINLP arise due to the nonlinearity and nonconvexity. In this paper, we propose a framework to study the "design variant" of the problem in which the variables are the diameter choices of the pipes, the flows, the potentials, and the states of various network components. We utilize a nested loop that includes a two-stage procedure that involves a convex reformulation of the original problem in the inner loop and an efficient enumeration scheme in the outer loop. We conduct experiments on benchmark networks to validate and analyze the performance of our framework. [ABSTRACT FROM AUTHOR]

We address long-standing open questions raised by Williamson, Goemans, Vazirani and Mihail pertaining to the design of approximation algorithms for problems in network design via the primal-dual method (Williamson et al. in Combinatorica 15(3):435–454, 1995. https://doi.org/10.1007/BF01299747). Williamson et al. prove an approximation ratio of two for connectivity augmentation problems where the connectivity requirements can be specified by uncrossable functions. They state: "Extending our algorithm to handle non-uncrossable functions remains a challenging open problem. The key feature of uncrossable functions is that there exists an optimal dual solution which is laminar... A larger open issue is to explore further the power of the primal-dual approach for obtaining approximation algorithms for other combinatorial optimization problems." Our main result proves that the primal-dual algorithm of Williamson et al. achieves an approximation ratio of 16 for a class of functions that generalizes the notion of an uncrossable function. There exist instances that can be handled by our methods where none of the optimal dual solutions has a laminar support. We present three applications of our main result to problems in the area of network design. (1) A 16 -approximation algorithm for augmenting a family of small cuts of a graph G. The previous best approximation ratio was O (log | V (G) |) . (2) A 16 · ⌈ k / u min ⌉ -approximation algorithm for the Cap-k-ECSS problem which is as follows: Given an undirected graph G = (V , E) with edge costs c ∈ Q ≥ 0 E and edge capacities u ∈ Z ≥ 0 E , find a minimum-cost subset of the edges F ⊆ E such that the capacity of any cut in (V, F) is at least k; u min (respectively, u max ) denotes the minimum (respectively, maximum) capacity of an edge in E, and w.l.o.g. u max ≤ k . The previous best approximation ratio was min (O (log | V |) , k , 2 u max) . (3) A 20 -approximation algorithm for the model of (p, 2)-Flexible Graph Connectivity. The previous best approximation ratio was O (log | V (G) |) , where G denotes the input graph. [ABSTRACT FROM AUTHOR]

Although numerous studies have investigated the validity of satellite-derived precipitation datasets, there has been a lack of emphasis on their practical applications. This study aims to explore the implications of such datasets in designing rain gauge networks, which are essential for acquiring reliable precipitation data. Initially, four satellite-derived precipitation datasets (PERSIANN, PERSIANN-CDR, PERSIANN-CCS, and TRMM 3B43 V.7) were statistically compared to ground-based observations from 23 synoptic stations within the Fars province in southwestern Iran, the designated study area, to assess their validity. Furthermore, to provide a technical comparison, the degree of spatial independence (variogram) derived from these datasets was compared to that obtained from ground-based observations. To meet the study's objectives, a detrending process was implemented to render the datasets isotropic and bounded. Among the aforementioned satellite-derived datasets, PERSIANN-CCS and TRMM 3B43 V.7 demonstrated promise for enhancement to be utilized in rain gauge network design through a hybrid method combining multivariate analysis incorporating factor analysis and a geostatistical approach incorporating ordinary (point and block) kriging. Based on the PERSIANN-CCS and TRMM 3B43 V.7 satellite-derived datasets, rain gauge grids containing 70 and 56 rain gauges were initially proposed using a scree diagram. However, after considering a predetermined level of accuracy (block variance of residuals set to 10 mm 2 ), the numbers were subsequently reduced to 56 and 28 rain gauges, respectively. Consequently, this research sheds light on the practical utility of satellite-based precipitation datasets in the development of rain gauge networks in regions with insufficient data coverage or for evaluating existing networks. [ABSTRACT FROM AUTHOR]

We present approximation algorithms for several network design problems in the model of flexible graph connectivity (Adjiashvili et al., in: IPCO, pp 13–26, 2020, Math Program 1–33, 2021). Let k ≥ 1 , p ≥ 1 and q ≥ 0 be integers. In an instance of the (p, q)-Flexible Graph Connectivity problem, denoted (p , q) -FGC , we have an undirected connected graph G = (V , E) , a partition of E into a set of safe edges S and a set of unsafe edges U , and nonnegative costs c : E → R ≥ 0 on the edges. A subset F ⊆ E of edges is feasible for the (p , q) -FGC problem if for any set F ′ ⊆ U with | F ′ | ≤ q , the subgraph (V , F \ F ′) is p-edge connected. The algorithmic goal is to find a feasible solution F that minimizes c (F) = ∑ e ∈ F c e . We present a simple 2-approximation algorithm for the (1 , 1) -FGC problem via a reduction to the minimum-cost rooted 2-arborescence problem. This improves on the 2.527-approximation algorithm of Adjiashvili et al. Our 2-approximation algorithm for the (1 , 1) -FGC problem extends to a (k + 1) -approximation algorithm for the (1 , k) -FGC problem. We present a 4-approximation algorithm for the (k , 1) -FGC problem, and an O (q log | V |) -approximation algorithm for the (p , q) -FGC problem. Finally, we improve on the result of Adjiashvili et al. for the unweighted (1 , 1) -FGC problem by presenting a 16/11-approximation algorithm. The (p , q) -FGC problem is related to the well-known Capacitatedk-Connected Subgraph problem (denoted Cap- k -ECSS ) that arises in the area of Capacitated Network Design. We give a min (k , 2 u max) -approximation algorithm for the Cap- k -ECSS problem, where u max denotes the maximum capacity of an edge. [ABSTRACT FROM AUTHOR]

In healthcare supply chain management particularly in emergency facilities, organ transplantation network plays a significant task. Donor hospital location and organ allocation can be essential aspects of the transplant network. In the allocation decisions area, weather conditions, quality of organs, and types of vehicles lead to life-challenging issues for the organ recipient. Due to the lack of studies covering location-allocation and organ transplant network design problems concerning uncertain conditions with climate change, quality of organs, and different types of vehicles, this paper proposes a new multi-objective mixed-integer non-linear programming (MINLP) model. The organ transplant network model aims to minimize the network cost and time and to maximize the quality of transmitted organs regarding economic and social dimensions simultaneously. Also, a new hybrid solution approach is presented based on robust possibilistic and ABS-compromise programming methods to handle uncertainties. The developed MINLP model is proper for adapting the design in a multi-organ transplant supply chain that employs multiple donations to deal with fluctuations of organs demands, climate change and organ quality in the supply. The hybrid solution procedure is investigated in an application example from the literature, referring to the organ transplant supply chain, which is exclusively vulnerable to such risks. The outcomes show that by employing the proposed approach, the objective functions increase 35.5%, 74.1%, and 19%, respectively. Also, the results indicate that utilizing the risk of climate change raises the total cost by 1.3% compared to operating without this condition. Furthermore, the time increases 2.3% by considering the risk of climate change in this network. The sensitivity and comparative analyses determine that the quality of the organ enhances 17.8% by increasing the range of the ischemia time from 5 to 55 h. [ABSTRACT FROM AUTHOR]

This research analyses the influence of data analytics in enhancing the supply chain management process. From the global perspective, companies are focusing to remain competitive and foster growth by controlling the cost. In a typical supply chain management (SCM), the factors like capacity management, demand and expenses are regarded as recognized constraints. However, in the reality, there are uncertainties revolving around the overall consumer demand, risk involved in transportation, lead time differences and other aspects. The demand uncertainties tend to impact the SC performance in a wider span; hence companies tend to apply data analytics as a unique tool to forecast the demand, analyse the risk aspects and frame strategies to reduce the lead time. Hence, this study will enable in analysing the nature of impact which data analytics influences in supporting the SC process in the organisation. Major theme of the paper is intended to apprehend the critical influence of the big data analytics towards the supply chain management in selected companies in Europe, the researchers intends to measure the critical drivers of BDA in enhancing the SCM process and thereby support in realising the goals of the organisation. The researchers has collated data from 135 managers from the supply chain process in 15 different companies from Europe, the study tries to apply Multiple regression analysis through SPSS and Structural equation modelling through partial least squares modelling was used to test the hypothesis. The final results obtained states that the data analytics tend to possess positive influence on the supply chain management process, supports the management in reducing the enhancing supplier relationship and enable in creating better supplier network design. This paper intends to provide clear and concise aspect on the current overview of literature related to data analytics and its effect on supply chain management process. It also reveals the theoretical aspects of the research and provides outlines on future research directions. The study will be unique in stating the role of data analytics on SCM process by integrating the procedural and management perspectives. [ABSTRACT FROM AUTHOR]

We address the Multicommodity Capacitated Fixed-charge Network Design problem with uncertain demands, which we formulate as a two-stage stochastic program. We rely on the progressive hedging (PH) algorithm of Rockafellar and Wets where the subproblems are defined using scenario groups. To address the problem, we propose an efficient matheuristic approach which we refer to as the Integrated Learning and Progressive Hedging. The proposed method takes advantage of a specialized learning-based matheuristic that is able to quickly produce high-quality solutions to multi-scenario subproblems. Furthermore, we propose a novel reference point definition, at each aggregation step of the PH algorithm, which leverages subproblem information regarding promising design variables. Extensive computational experiments illustrate that the proposed approach should be the method of choice when high-quality solutions to large instances of stochastic network problems need to be found quickly. [ABSTRACT FROM AUTHOR]

Precipitation is a key constituent of the water cycle and its accurate measurement is essential for a wide range of hydroclimatic studies. Although advances in technology have provided new sources of precipitation data (e.g., satellite data), gauge measurements are still considered as the most reliable source of information. Accuracy of ground measurements highly depends on the network density and positions of the rain gauges and thus, many studies have attempted to re-design the rain gauge networks around the globe. This is while, a limited number of researches have investigated the potential of utilizing global gridded precipitation products in network optimization context. In this study, a two-step framework was proposed to remove redundant rain gauge stations of the Central Plateau watershed of Iran. In the first step, number of excess rain gauges was detected based on transinformation–distance (T–D) analysis. In the second step, exact location of the extra rain gauges was determined through high degree of freedom and low degree of freedom (LDF) scenarios by a combination of harmony search optimization algorithm and a geostatistical model. According to the results, 58 out of 580 rain gauges were detected to be removed. Results of the proposed scenarios differed in detecting 13 out of 58 gauges. While LDF scenario required less computational time, network re-designed by this scenario was about 32% less accurate than the other scenario. Performance of the both optimized networks was equally similar to the initial network in terms of developing precipitation rasters. [ABSTRACT FROM AUTHOR]

Studies conducted at sites across ecological research networks usually strive to scale their results to larger areas, trying to reach conclusions that are valid throughout larger enclosing regions. Network representativeness and constituency can show how well conditions at sampling locations represent conditions also found elsewhere and can be used to help scale-up results over larger regions. Multivariate statistical methods have been used to design networks and select sites that optimize regional representation, thereby maximizing the value of datasets and research. However, in networks created from already established sites, an immediate challenge is to understand how well existing sites represent the range of environments in the whole area of interest. We performed an analysis to show how well sites in the USDA Long-Term Agroecosystem Research (LTAR) Network represent all agricultural working lands within the conterminous United States (CONUS). Our analysis of 18 LTAR sites, based on 15 climatic and edaphic characteristics, produced maps of representativeness and constituency. Representativeness of the LTAR sites was quantified through an exhaustive pairwise Euclidean distance calculation in multivariate space, between the locations of experiments within each LTAR site and every 1 km cell across the CONUS. Network representativeness is from the perspective of all CONUS locations, but we also considered the perspective from each LTAR site. For every LTAR site, we identified the region that is best represented by that particular site—its constituency—as the set of 1 km grid locations best represented by the environmental drivers at that particular LTAR site. Representativeness shows how well the combination of characteristics at each CONUS location was represented by the LTAR sites' environments, while constituency shows which LTAR site was the closest match for each location. LTAR representativeness was good across most of the CONUS. Representativeness for croplands was higher than for grazinglands, probably because croplands have more specific environmental criteria. Constituencies resemble ecoregions but have their environmental conditions "centered" on those at particular existing LTAR sites. Constituency of LTAR sites can be used to prioritize the locations of experimental research at or even within particular sites, or to identify the extents that can likely be included when generalizing knowledge across larger regions of the CONUS. Sites with a large constituency have generalist environments, while those with smaller constituency areas have more specialized environmental combinations. These "specialist" sites are the best representatives for smaller, more unusual areas. The potential of sharing complementary sites from the Long-Term Ecological Research (LTER) Network and the National Ecological Observatory Network (NEON) to boost representativeness was also explored. LTAR network representativeness would benefit from borrowing several NEON sites and the Sevilleta LTER site. Later network additions must include such specialist sites that are targeted to represent unique missing environments. While this analysis exhaustively considered principal environmental characteristics related to production on working lands, we did not consider the focal agronomic systems under study, or their socio-economic context. [ABSTRACT FROM AUTHOR]

Nowadays, convolutional neural networks (CNNs) have led the developments of machine learning. However, most CNN architectures are obtained by manual design, which is empirical, time-consuming, and non-transparent. In this paper, we aim at offering better insight into CNN models from the perspective of optimization theory. We propose a unified framework for understanding and designing CNN architectures with the family of Newton’s methods, which is referred to as Newton design. Specifically, we observe that the standard feedforward CNN model (PlainNet) solves an optimization problem via a kind of quasi-Newton method. Interestingly, residual network (ResNet) can also be derived if we use a more general quasi-Newton method to solve this problem. Based on the above observations, we solve this problem via a better method, the Newton-conjugate-gradient (Newton-CG) method, which inspires Newton-CGNet. In the network design, we translate binary-value terms in the optimization schemes to dropout layers, so dropout modules naturally appear in the derived CNN structures with specific locations, rather than being an empirical training strategy. Extensive experiments on image classification and text categorization tasks verify that Newton-CGNets perform very competitively. Particularly, Newton-CGNets surpass their counterparts ResNets by over 4% on CIFAR-10 and over 10% on CIFAR-100, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to solve the problem of bus network design and frequency setting (BNDFS). Transit network design must satisfy the needs of both service users and transit operators. Numerous optimisation techniques have been proposed for BNDFS in the literature. Previous approaches tend to adopt a sequential optimisation strategy that conducts network routing and service frequency setting in two separate steps. To address the limitation of sequential optimisation, our new algorithm uses Reinforcement Learning for a simultaneous optimisation of three key components of BNDFS: the number of bus routes, the route design and service frequencies. The algorithm can design the best set of bus routes without defining the total number of bus routes in advance, which can reduce the overall computational time. The proposed algorithm was tested on the benchmark Mandl Swiss network. The algorithm is further extended to the routing of express services. The validation includes additional test scenarios which modify the transit demand level on the Mandl network. The new algorithm can be useful to assist transit agencies and planners in improving existing routing and service frequency to cope with changing demand conditions. [ABSTRACT FROM AUTHOR]

Hematopoietic stem cell transplantation is a usual treatment used against cancers and blood diseases. To diminish the incidence and severity of graft-versus-host disease, donors and recipients should be sufficiently matched in terms of human leukocyte antigen. Iranians' stem cell bank was established to help patients search for their compatible donors. However, in many cases, matched donors are not found in the domestic bank. The low efficiency of the national adult stem cell bank and the expensive cost of adding donors are the main challenges of this study, driving us to design the stem cell donor network for the first time. Initially, using the common weights data envelopment analysis approach, we identified the most efficient donation zones. Afterward, we proposed a mixed-integer programming model in order to design the donor network. Finally, to show the applicability of the model, we provided a real case study of Iran. The results of this study shed light on three important questions: Where should the stem cell donors be attracted from? How many donors does the stem cell bank need to increase the odds of finding a matched donor within the country? And what would be the optimal location and allocation of the donor network facilities? [ABSTRACT FROM AUTHOR]

Many network design problems deal with the design of low-cost networks that are resilient to the failure of their elements (such as nodes or links). One such problem is Connectivity Augmentation, with the goal of cheaply increasing the (edge- or node-)connectivity of a given network from a value k to k + 1 . The problem is NP-hard for k ≥ 1 , and the most studied setting focuses on the case of edge-connectivity with k = 1 . In this work, we give a 1.892-approximation algorithm for the NP-hard problem of augmenting the node-connectivity of any given graph from 1 to 2, which improves upon the state-of-the-art approximation previously developed in the literature. The starting point of our work is a known reduction from Connectivity Augmentation to some specific instances of the Node-Steiner Tree problem, and our result is obtained by developing a new and simple analysis of the iterative randomized rounding technique when applied to such Steiner Tree instances. Our results also imply a 1.892-approximation algorithm for the problem of augmenting the edge-connectivity of a given graph from any value k to k + 1 . While this does not beat the best approximation factor known for this problem, a key point of our work is that the analysis of our approximation factor is less involved when compared to previous results in the literature. In addition, our work gives new insights on the iterative randomized rounding method, that might be of independent interest. [ABSTRACT FROM AUTHOR]

This paper proposes an enhanced version of Moth Flame Optimization (MFO) algorithm, called Enhanced Chaotic Lévy Opposition-based MFO (ECLO-MFO) for solving the mesh router nodes placement problem in wireless mesh network (WMN-MRNP). The proposed ECLO-MFO incorporates three strategies including the chaotic map concept, the Lévy flight strategy, and the Opposition-Based Learning (OBL) technique to enhance the optimization performance of MFO. Firstly, chaotic maps are used to increase the chaotic stochastic behavior of the MFO algorithm. Lévy flight distribution is adopted to increase the population diversity of MFO. Finally, OBL is introduced to improve the convergence speed of MFO and to explore the search space effectively. The effectiveness of the proposed ECLO-MFO is tested based on various scenarios under different settings, considering network connectivity and client coverage metrics. The results of simulation obtained using MATLAB 2020a demonstrate the accuracy and superiority of ECLO-MFO in determining the optimal positions of mesh routers when compared with the original MFO and ten other optimization algorithms such as Genetic Algorithm (GA), Simulated Annealing (SA), Harmony Search (HS), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Cuckoo Search Algorithm (CS), Bat Algorithm (BA), Firefly optimization (FA), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). [ABSTRACT FROM AUTHOR]

Radionuclide monitoring for nuclear explosions includes measuring radioactive aerosol and noble gas concentrations in the atmosphere. The International Monitoring System (IMS) of the Comprehensive Nuclear Test-Ban Treaty has made such measurements for decades, revealing much about how atmospheric radioactivity impacts the sensitivity of the network. For example, civilian emissions of radioiodine make a substantial regional impact, but a minor global impact, while civilian radioxenon emissions create major regional and complex global impacts. The impacts are strongly influenced by the minimum releases anticipated to be interesting. The original design of the IMS anticipated relatively large releases, and the current IMS network substantially meets or exceeds the sensitivity needed to detect those levels. Much lower signal levels can be motivated from historical tests. Using a release that corresponds roughly to a one-ton equivalent of fission in the atmosphere rather than the design level of one-kiloton equivalent, the network detection probabilities for 140Ba and 131I are quite good (~ 75%) and for 133Xe is still considerable (~ 45%). Using measured and simulated background concentrations, various possible desired signal levels, and an innovative anomaly threshold, maps of sensitivity and a station ranking are developed for IMS radionuclide stations. These provide a strong motivation for additional experimentation to learn about sources and the potential plusses of new technology. [ABSTRACT FROM AUTHOR]

Network realization problems require, given a specification π for some network parameter (such as degrees, distances or connectivity), to construct a network G conforming to π , or to determine that no such network exists. In this paper we study composed profile realization, where the given instance consists of two or more profile specifications that need to be realized simultaneously. To gain some understanding of the problem, we focus on two classical profile types, namely, degrees and distances, which were (separately) studied extensively in the past. We investigate a wide spectrum of variants of the composed distance and degree realization problem. For each variant we either give a polynomial-time realization algorithm or establish NP hardness. In particular: We consider both precise specifications and range specifications, which specify a range of permissible values for each entry of the profile. We consider realizations by both weighted and unweighted graphs. We also study settings where the realizing graph is restricted to specific graph classes, including trees and bipartite graphs. [ABSTRACT FROM AUTHOR]

Brain tumor segmentation is one of the most challenging problems in medical image analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain tumor regions. In recent years, deep learning methods have shown promising performance in solving various computer vision problems, such as image classification, object detection and semantic segmentation. A number of deep learning based methods have been applied to brain tumor segmentation and achieved promising results. Considering the remarkable breakthroughs made by state-of-the-art technologies, we provide this survey with a comprehensive study of recently developed deep learning based brain tumor segmentation techniques. More than 150 scientific papers are selected and discussed in this survey, extensively covering technical aspects such as network architecture design, segmentation under imbalanced conditions, and multi-modality processes. We also provide insightful discussions for future development directions. [ABSTRACT FROM AUTHOR]

The supply chain is a dynamic and uncertain system consisting of material, information, and fund flows between different organizations, from the acquisition of the raw materials to the delivery of the finished products to the end customers. Closed-loop supply chains do not end with the delivery of the finished products to the end customers, the process continues until economic value is obtained from the returned products or they are disposed properly in landfills. Incorporating reverse flows in supply chains increases the uncertainty and complexity, as well as complicating the management of supply chains that are already composed of different actors and have a dynamic structure. Since agent-based modeling and simulation is a more efficient method of handling the dynamic and complex nature of supply chains than the traditional analytical methods, in this study agent-based modeling methodology has been used to model a generic closed-loop supply chain network design problem with the aims of integrating customer behavior into the network, coping with the dynamism, and obtaining a more realistic structure by eliminating the required assumptions for solving the model with analytical methods. The actors in the CLSC network have been defined as agents with goals, properties and behaviors. In the proposed model dynamic customer arrivals, the changing aspects of customers' purchasing preferences for new and refurbished products and the time, quantity and quality uncertainties of returns have been handled via the proposed agent-based architecture. To observe the behavior of the supply chain in several conditions various scenarios have been developed according to different parameter settings for the supplier capacities, the rate of customers being affected by advertising, the market incentive threshold values, and the environmental awareness of customers. From the scenarios, it has been concluded that the system should be fed in the right amounts for the new and refurbished products to increase the effectiveness of factors such as advertising, incentives, and environmental awareness for achieving the desired sales amounts and cost targets. [ABSTRACT FROM AUTHOR]

The hub covering problem can be viewed as a combination of the set covering and the hub location problem. In a traditional hub covering problem, all parameters are assumed to be deterministic. However, neglecting uncertainties associated with demand and transportation costs may lead to inferior solutions. Thus, we introduce three novel stochastic optimization models to formulate a multiple allocation hub covering problem in which deterministic transportation cost and demand assumptions are relaxed. Further, we also reflect scale economy to the models via inter-hub transportation cost discount factor. The L-Shaped Algorithm is applied to solve the sculpted problems. To test the efficiency of the proposed models, several test instances are generated and solved based on simulated data. The effects of the change of coverage radius, inter-hub discount factor, and hub opening cost are also examined. Results of this study validate that modeling the hub covering problem as a stochastic optimization model yields up 13.05 % efficiency when compared to the expectation of the expected value method. [ABSTRACT FROM AUTHOR]

In view of the perishable nature and complex storage requirements of certain blood products, the delivery of blood groups from blood banks to hospitals is a key aspect of the healthcare system. The centralization of blood supply facilities for economic reasons and an increase in traffic volume have led to significant delays in the use of traditional emergency vehicles. The aim of the proposed mathematical model is to minimize logistics costs by strategically positioning launch and refueling stations and assigning requests to these stations. The proposed approach employs integer binary linear programming to offer four possible scenarios that consider the flight range and supply node capacity of the drone. The study conducted a scenario-based analysis to examine the primary decision-making process for transporting blood groups and identified the optimal configuration for launching and refueling stations based on 50 group requests. The study uncovered two essential factors, NL and NR, which signify the ideal number of launching stations and the number of refueling stations situated away from the optimal launching sites. The findings offer decision-makers the precise number of stations necessary for an ideal outcome, whereas information on refueling station locations assists in resource distribution planning. Introducing refueling stations for blood supply can extend the mission range and improve coverage in nonemergency situations. Gradual implementation can prevent operational disruptions, such as station closures. This approach can also reduce delivery times and minimize delays, potentially saving lives, as refueling stations have a significant impact on the management of blood supply and logistics. [ABSTRACT FROM AUTHOR]

Medical waste management (MWM) is an important and necessary problem in the COVID-19 situation for treatment staff. When the number of infectious patients grows up, the amount of MWMs increases day by day. We present medical waste chain network design (MWCND) that contains health center (HC), waste segregation (WS), waste purchase contractor (WPC), and landfill. We propose to locate WS to decrease waste and recover them and send them to the WPC. Recovering medical waste like metal and plastic can help the environment and return to the production cycle. Therefore, we proposed a novel viable MWCND by a novel two-stage robust stochastic programming that considers resiliency (flexibility and network complexity) and sustainable (energy and environment) requirements. Therefore, we try to consider risks by conditional value at risk (CVaR) and improve robustness and agility to demand fluctuation and network. We utilize and solve it by GAMS CPLEX solver. The results show that by increasing the conservative coefficient, the confidence level of CVaR and waste recovery coefficient increases cost function and population risk. Moreover, increasing demand and scale of the problem makes to increase the cost function. [ABSTRACT FROM AUTHOR]

In this paper, we bound the integrality gap and the approximation ratio for maximum plane multiflow problems and deduce bounds on the flow-multicut-gap. We consider instances where the union of the supply and demand graphs is planar and prove that there exists a multiflow of value at least half the capacity of a minimum multicut. We then show how to convert any multiflow into a half-integer flow of value at least half the original multiflow. Finally, we round any half-integer multiflow into an integer multiflow, losing at most half the value thus providing a 1/4-approximation algorithm and integrality gap for maximum integer multiflows in the plane. [ABSTRACT FROM AUTHOR]

Understanding flow traffic patterns in networks, such as the Internet or service provider networks, is crucial to improving their design and building them robustly. However, as networks grow and become more complex, it is increasingly cumbersome and challenging to study how the many flow patterns, sizes and the continually changing source-destination pairs in the network evolve with time. We present Netostat, a visualization-based network analysis tool that uses visual representation and a mathematics framework to study and capture flow patterns, using graph theoretical methods such as clustering, similarity and difference measures. Netostat generates an interactive graph of all traffic patterns in the network, to isolate key elements that can provide insights for traffic engineering. We present results for U.S. and European research networks, ESnet and GEANT, demonstrating network state changes, to identify major flow trends, potential points of failure, and bottlenecks. [ABSTRACT FROM AUTHOR]

Fire and smoke detection using deep learning have recently proven to be a robust and efficient detection approach in contrast to traditional vision-based techniques. Efforts are made by researchers to leverage this promising direction but are always faced with a trade-off between performance accuracy and model size. To tackle this, we present Light-FireNet, an enhanced lightweight, fast, and cost-effective system based on a combination of lighter convolution mechanisms inspired by Hard Swish (H-Swish), and a novel architectural design built from scratch. Experimental results and performance analysis reveal that our proposed method has 32% fewer parameters than AlexNet, 3.03 MB lighter than MobileNetV2, and achieves a better detection accuracy of 97.83%, which is higher than most existing fire detection techniques in the literature. [ABSTRACT FROM AUTHOR]

Due to industrialization, copper demand has increased over the last decades. Recycling rate of copper is high and its scrap requires less energy than primary production, so sustainable closed-loop supply chain network design is considered a primary decision. Besides, the uneven distribution of copper has exaggerated the destructive effects of natural disasters such as earthquakes on mines. To the best of the authors' knowledge, there is no research about copper supply chain network design. In this paper, a copper network is designed and backup suppliers are used as a resilience strategy to reduce the effects of earthquakes on mining operations. Without backup model and with backup model are presented as multi-objective and are compared with each other. In each model, the economic objective is to maximize the supply chain profit; the environmental objective is to minimize water consumption and air pollutants; and the social objective is to maximize social desirability by considering security and unemployment rates. The models are formulated using mixed-integer linear programming and they are solved by ε -constraint and weighted sum methods. Results show that, with backup model increases the supply chain responsiveness. Also, the model is able to improve the economic and social performances of the supply chain. But in environmental aspect, it performs worse than without backup model. This is because the backup suppliers are added to the supply chain and their exploitation will create negative environmental effects. In addition, using copper scraps saves costs, energy and the consumption of this non-renewable metal. [ABSTRACT FROM AUTHOR]

We address the problem of constructing large circulant networks with given degree and diameter, and efficient routing schemes. First we discuss the theoretical upper bounds and their asymptotics. Then we apply concepts and tools from the change-making problem to efficient routing in circulant graphs. With these tools we investigate some of the families of circulant graphs that have been proposed in the literature, and we construct tables of large circulant graphs and digraphs with efficient routing properties. [ABSTRACT FROM AUTHOR]

A graph is said to be two connected if between every pair of nodes there are at least two node-disjoint paths. Given weights on the edges of the graph, the two connected subgraph problem is to find a two connected spanning subgraph of G whose weight is minimum. This problem has many applications in telecommunications. In this paper we consider a new variant of this problem with additional disjunctive constraints (called also conflict constraints) related to the survivability of telecommunication networks. This can be called the Disjunctive Two-Connected Subgraph Problem (DTCSP). First, we give an extended formulation for the problem whose variables are the cycles of the graph. Then, we use a column generation algorithm to solve its linear relaxation, and further show that the pricing reduces to finding a specific cycle in the graph which can be formulated as an integer programming problem. We also describe several valid inequalities for the polytope. Moreover, we study the related separation problems and devise separation routines for these inequalities. Using this, we devise a Branch-and-Cut-and-Price algorithm for the problem along with an extensive experimental study. [ABSTRACT FROM AUTHOR]

In a highly competitive environment, characterized by shorter product life cycles, mass customization and fluctuating markets, the supply chain network design is considered as one of the most important steps for reducing their costs, increasing their performance, competitiveness and profits. In addition, due to the growing social, governmental and industrial concerns about environmental protection and greenhouse gas reduction, policy makers are now forced to incorporate the environmental issue and sustainability constraints into their logistics network design process. The purpose of the present paper is to address the issue of sustainable supply-chain-network design by integrating simultaneously the strategic decisions related to the location of facilities with limited capacity and transport network design with capacity constraints on the links to be built, and constraints related to environmental protection. A multi-objective model is proposed to minimize the cost and environmental impact associated with transport activities by considering different environmental impacts of the links to be built. An adaptation of the Non-dominated Sorting Genetic Algorithm II, using mixed coding is developed to effectively solve the proposed model. In order to evaluate the performance of the proposed approach, a number of numerical experiments on instances of different sizes have been performed. Finally, a sensitivity analysis is presented to analyse the behaviour of the proposed model in response to changes in the key parameters of the problem. [ABSTRACT FROM AUTHOR]

Estimations of the amount of lithium-ion batteries reaching their end-of-life in 2025 and the amount being recycled indicates large deviations. To enable an efficient recycling process a well-defined and efficient supply chain network for the recovery of discarded lithium-ion batteries must be put in place. This includes analyzing the needs and restrictions of such a network. The aim of this paper is to provide decision support tools, to analyze input, and optimize a future supply chain for discarded lithium-ion batteries. A mixed integer programming model is developed and applied to the Swedish market. The findings show that several aspects will affect a reverse supply chain for discarded lithium-ion batteries, many of which are still uncertain and hard to predict. [ABSTRACT FROM AUTHOR]

The maturing of autonomous driving technology in recent years has led to several pilot projects and the initial integration of autonomous pods and buses into the public transport (PT) system. An emerging field of interest is the design of public transport networks operating autonomous buses and the potential to attract higher levels of travel demand. In this work a multi-objective optimization and multi-agent simulation framework is developed to study potential changes in the network design and frequency settings compared to conventional PT systems when autonomous vehicles (AV) systems are deployed on fixed-route networks. During the optimization process multiple deployment scenarios (network configurations and service frequency) are evaluated and optimized considering the operator cost, user cost and infrastructure preparation costs of the system. User-focused network design and operator-focused network design are studied for a real-world urban area in Sweden. The results provide insights into the network design and level of service implications brought about by the deployment of autonomous bus (AB) when those are integrated in route-based PT systems. We show that the deployment of autonomous buses result with a network design that increases service ridership. In the context of our case study this increase is likely to primarily substitute walking. [ABSTRACT FROM AUTHOR]

We introduce and study the problem Flexible Graph Connectivity, which in contrast to many classical connectivity problems features a non-uniform failure model. We distinguish between safe and unsafe resources and postulate that failures can only occur among the unsafe resources. Given an undirected edge-weighted graph and a set of unsafe edges, the task is to find a minimum-cost subgraph that remains connected after removing at most k unsafe edges. We give constant-factor approximation algorithms for this problem for k = 1 as well as for unit costs and k ≥ 1 . Our approximation guarantees are close to the known best bounds for special cases, such as the 2-edge-connected spanning subgraph problem and the tree augmentation problem. Our algorithm and analysis combine various techniques including a weight-scaling algorithm, a charging argument that uses a variant of exchange bijections between spanning trees and a factor revealing min–max–min optimization problem. [ABSTRACT FROM AUTHOR]

Lack of rapidly available neurological expertise, especially in rural areas, is one of the key obstacles in stroke care. Stroke care networks attempt to address this challenge by connecting hospitals with specialized stroke centers, stroke units, and hospitals of lower levels of care. While the benefits of stroke care networks are well-documented, travel distances are likely to increase when patients are transferred almost exclusively between members of the same network. This is particularly important for patients who require mechanical thrombectomy, an increasingly employed treatment method that requires equipment and expertise available in specialized stroke centers. This study aims to analyze the performance of the current design of stroke care networks in Bavaria, Germany, and to evaluate the improvement potential when the networks are redesigned to minimize travel distances. To this end, we define three fundamental criteria for assessing network design performance: 1) average travel distances, 2) the populace in the catchment area relative to the number of stroke units, and 3) the ratio of stroke units to lower-care hospitals. We generate several alternative stroke network designs using an analytical approach based on mathematical programming and clustering. Finally, we evaluate the performance of the existing networks in Bavaria via simulation. The results show that the current network design could be significantly improved concerning the average travel distances. Moreover, the existing networks are unnecessarily imbalanced when it comes to their number of stroke units per capita and the ratio of stroke units to lower-care hospitals. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of clustering nodes in a wireless visible light communication (VLC) network while simultaneously forming a spanning tree backbone. More precisely, let G = (V , E) be a complete Euclidean input graph instance with a set of wireless node devices V and connection links E representing the VLC network. We consider the problem of partitioning k ≤ | V | nodes into disjoint cliques of the size of at most ⌊ k t ⌋ + 1 nodes where t ≤ k (k , t ∈ Z +) in such a way that a unique vertex of each clique is used to form a spanning tree backbone. The underlying idea is to form clusters of nodes while simultaneously providing connectivity between them. Thus, we maximize the total power received and total residual energy of the k chosen nodes of the network for such a structure. We also consider the simplified version of the problem in which no backbone structure is required. Recall that clustering the nodes of a wireless network allows handling efficiently problems related to scalability and routing. In order to achieve the grouping task optimally, we propose mixed-integer linear and quadratic programming models based on classical combinatorial optimization problems. In order to compare our proposed models, we assume that every node in the network can communicate through a direct-line-of-sight VLC channel. Our numerical results indicate that the linearized quadratic models are preferable as they allow solving to optimality most of the tested instances and in less computational effort. [ABSTRACT FROM AUTHOR]

In this study, we consider two classes of multicriteria two-stage stochastic programs in finite probability spaces with multivariate risk constraints. The first-stage problem features multivariate stochastic benchmarking constraints based on a vector-valued random variable representing multiple and possibly conflicting stochastic performance measures associated with the second-stage decisions. In particular, the aim is to ensure that the decision-based random outcome vector of interest is preferable to a specified benchmark with respect to the multivariate polyhedral conditional value-at-risk or a multivariate stochastic order relation. In this case, the classical decomposition methods cannot be used directly due to the complicating multivariate stochastic benchmarking constraints. We propose an exact unified decomposition framework for solving these two classes of optimization problems and show its finite convergence. We apply the proposed approach to a stochastic network design problem in the context of pre-disaster humanitarian logistics and conduct a computational study concerning the threat of hurricanes in the Southeastern part of the United States. The numerical results provide practical insights about our modeling approach and show that the proposed algorithm is computationally scalable. [ABSTRACT FROM AUTHOR]

Among the current trends in waste management and circular economy is the involvement of new fractions of waste for sorting and collection. One of them is fats and cooking oils, especially those coming from households. Now, the nascent fat waste recycling becomes promoted as regulations and waste recovery targets have been set in the European Union. The traditional manner of discarding household fat waste usually causes sewage problems. However, utilisation of this waste brings the potential for contributing to the energy supply and material recovery. This research presents a mathematical model for the optimal location of fat waste bins and containers in the given municipalities. The container network should comprise as few containers as possible, while the walking distance for the citizens towards the container is as short as possible. The objective of the proposed optimisation model is to minimise the total number of collection points (infrastructure cost). The collection points represent the citizens' addresses in a municipality. The average walking distance towards a container is a novel feature in the model, which is pertinent to waste fractions with low production per person. Cluster analysis describes the variability between municipalities, and further, it is possible to use regression analysis to model the number of containers for any municipality or region. The proposed general decision support tool estimates the total cost and number of bins needed for any region or a country. The region from the Czech Republic, which was used as a study area, revealed the requirement for 609 containers, with only EUR 30,000 of investment cost. There are around 950 inhabitants assigned to a single collection point on average. [ABSTRACT FROM AUTHOR]

Environmental and social awareness are the key elements of the sustainable tire industry. End-of-life tire (ELT) waste flow is an important environmental problem worldwide since it produces severe air, water, and soil pollution issues. Significant advancements have been made in ELT management in the last few years. As a result, ELTs should not only be regarded as waste but also as a source of environmentally friendly materials. Besides, sound ELT management has vital importance for circular economy and sustainable development. Over the last decade, ELT management has attracted many researchers and practitioners. Unfortunately, a comprehensive review of the ELT management area is still missing. This study presents the first critical review of the whole ELT management area. It aims to present an extensive content analysis overview of state-of-the-art research, provide its critical analysis, highlight major gaps, and propose the most significant research directions. A total of 151 peer-reviewed studies published in the journals between 2010–2020 are collected, analyzed, categorized, and critically reviewed. This review study redounds comprehensive insights, a valuable source of references, and major opportunities for researchers and practitioners interested in not only ELT material flow but also the whole waste management area. [ABSTRACT FROM AUTHOR]

Designing low-cost network layouts is an essential step in planning linked infrastructure. For the case of capacitated trees, such as oil or gas pipeline networks, the cost is usually a function of both pipeline diameter (i.e. ability to carry flow or transferred capacity) and pipeline length. Even for the case of incompressible, steady flow, minimizing cost becomes particularly difficult as network topology itself dictates local flow material balances, rendering the optimization space non-linear. The combinatorial nature of potential trees requires the use of graph optimization heuristics to achieve good solutions in reasonable time. In this work we perform a comparison of known literature network optimization heuristics and metaheuristics for finding minimum-cost capacitated trees without Steiner nodes, and propose novel algorithms, including a metaheuristic based on transferring edges of high valency nodes. Our metaheuristic achieves performance above similar algorithms studied, especially for larger graphs, usually producing a significantly higher proportion of optimal solutions, while remaining in line with time-complexity of algorithms found in the literature. Data points for graph node positions and capacities are first randomly generated, and secondly obtained from the German emissions trading CO2 source registry. As political will for applications and storage for hard-to-abate industry CO2 emissions is growing, efficient network design methods become relevant for new large-scale CO2 pipeline networks. [ABSTRACT FROM AUTHOR]

We introduce the problem of finding a spanning tree along with a partition of the tree edges into the fewest number of feasible sets, where constraints on the edges define feasibility. The motivation comes from wireless networking, where we seek to model the irregularities seen in actual wireless environments. Not all node pairs may be able to communicate, even if geographically close—thus, the available pairs are specified with a link graph G = (V , E) . Also, signal attenuation need not follow a nice geometric formula—hence, interference is modeled by a conflict (hyper)graph C = (E , F) on the links. The objective is to maximize the efficiency of the communication, or equivalently, to minimize the length of a schedule of the tree edges in the form of a coloring. We find that in spite of all this generality, the problem can be approximated linearly in terms of a versatile parameter, the inductive independence of the conflict graph. Specifically, we give a simple algorithm that attains a O (ρ log n) -approximation, where n is the number of nodes and ρ is the inductive independence. For an extension to Steiner trees, modeling multicasting, we obtain a O (ρ log 2 n) -approximation. We also consider a natural geometric setting when only links longer than a threshold can be unavailable, and analyze the performance of a geometric minimum spanning tree. [ABSTRACT FROM AUTHOR]

Building robust logistics networks against facility disruptions has been gaining more attention from both researchers and practitioners. However, in pursuit of high built-in network robustness, one usually has to increase the network redundancy and complexity, which adds to the difficulty of management and induces additional operating costs. In this paper, we aim to balance the trade-off between robustness and complexity in the logistics network design problem and propose a model that explicitly considers the demand uncertainty. Due to the non-convexity of our model, we present a linear reformulation method that transforms the model into a MILP. From our numerical studies, in some cases, we can simultaneously enhance robustness and reduce complexity. When the demand uncertainty increases, the network will become less robust. It will require more backup links to guarantee a given robustness requirement. Consequently, the network can exhibit higher complexity and incur additional costs. We also observe it might be possible to dramatically reduce the complexity at a reasonable cost. One can achieve a relatively simple and robust network by only maintaining backup links for customer zones with large demand volumes. Finally, we provide discussions on managerial implications. [ABSTRACT FROM AUTHOR]

The problems of structural optimization of networks are considered in order to obtain the most reliable topologies under the conditions of random failure of their elements. As a reliability criterion, several indicators are given; nodes of the network are subject to failures. To accelerate the search for optimal solutions, we use the approach with cumulative refinement of the boundaries of network reliability which allows eliminating unreliable variants at earlier steps. It is proposed, at the intermediate stages of optimization within this approach, to provide the reliability estimate with the use of the trend for a priori reliable variants. The results of numerical experiments are given. [ABSTRACT FROM AUTHOR]

Today, growth in the population and the use of vehicles have led to a growth in the production of waste tires and subsequently the creation of environmental concerns. Thus, choosing common strategies (such as retreading, recycling, burning, and disposal) to deal with these wastes and improve environmental conditions has become one of the most significant concerns of today's industries. In this study, a mixed-integer linear programming model has been used to develop a stochastic closed-loop supply chain network design (SCND). The proposed formulation has two objectives: (i) minimizing Eco-indicator 99 and (ii) maximizing profit in a multi-product, multi-echelon, and multi-period problem for tires. It is implemented in a practical case study in tire production industry. Also, uncertain parameters such as the return rate of products, demand, and the percentage of tire material provided by external suppliers are considered as possible scenarios. The improved version of the augmented ɛ-constraint, named AUGMECON2, is applied to solve the proposed problem. Finally, comprehensive sensitivity analysis is carried out to measure the efficiency. Obtained results represent that concerning global factors, an optimal closed-loop SCND can be very different and the problem is sensitive to the customs duty rate and exchange rate parameters. Besides, without considering the limitations of supplying raw materials by external suppliers, profits can increase by about 12%. [ABSTRACT FROM AUTHOR]

The use of automated network planning systems is crucial for reducing the deployment cost and planning time of passive optical telecommunication networks. Mixed-integer linear programming (MILP) is well suited for the purpose of modeling passive optical networks; however, excessive computing times for solving large-scale problem instances render these approaches impractical. This research presented a formulation that was based on MILP. It is for the issue of optimum dimensions of fiber equipment of the FTTH optical-access network (FTTH-OANs). The key objective is to minimize the capital expenditure associated with the implementation of FTTH-OAN, i.e. the expense of passive and active tools (splices, fiber terminations, closures, cables, optical splitters, and OLT cards, etc.) site planning costs and the cost of labor needed. This formulation is installed and tested. The key results are derived from practical physical systems installed in homes. [ABSTRACT FROM AUTHOR]

In recent years, due to governmental legislation, environmental groups' pressures, customer green expectation, etc., closed-loop green supply chains have gained paramount consideration. Accordingly, this study develops a novel multi-objective mixed integer nonlinear programming model for a closed-loop green supply chain network design problem. The proposed model aims to minimize the total costs, total CO2 emissions, and robustness costs in both forward and reverse directions, simultaneously. To cope with flexible constraints and epistemic uncertainty in the model's parameters, a robust flexible-possibilistic programming approach is tailored. The model is solved using an efficient interactive solution approach, in which, the presented model is analyzed under various carbon emission mechanisms to assess the influence of these mechanisms on the achieved solution. An illustrative example in the copier industry is also provided to validate the applicability of the presented optimization model. Numerical results indicate the superiority of the carbon cap-and-trade policy in most of the cases. [ABSTRACT FROM AUTHOR]

The majority of academic studies on the optimisation of public transport routes consider passenger trips to be fixed between pairs of stop points. This can lead to barriers in the use of the developed algorithms in real-world planning processes, as these usually utilise a zone-based trip representation. This study demonstrates the adaptation of a node-based optimisation procedure to work with zone-to-zone trips. A core element of this process is a hybrid approach to calculate zone-to-zone journey times through the use of node-based concepts. The resulting algorithm is applied to an input dataset generated from real-world data, with results showing significant improvements over the existing route network. The dataset is made publicly available to serve as a potential benchmark dataset for future research. [ABSTRACT FROM AUTHOR]