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By incorporating the redefined error monitor function into network design, an error redefinition neural network (ERNN) is proposed to control mobile redundant manipulators to execute the tracking task in this paper. The global asymptotic stability and the strong anti-disturbance capability of the ERNN are proved theoretically. Furthermore, the ERNN can overcome the overshoot and constant disturbance. Meanwhile, the ERNN is input-to-state stable while the bounded time-varying disturbance is considered as the control input.

We study an integrated airline schedule design and fleet assignment model for constructing schedules by simultaneously selecting from a pool of optional flights and assigning fleet types to these scheduled flights. This is a crucial tactical decision which greatly influences airline profit. As passenger demand is often substitutable among available fare products between the same origin-destination pair, we study an optimization model that integrates within it a passenger choice model for fare product selections. To tackle the formidable computational challenge of solving this large-scale network design problem, we propose a decomposition approach based on partitioning the flight network into smaller subnetworks by exploiting weak dependency in the network structure. The decomposition relies on a series of approximation analyses and a novel fare split problem to reliably measure the approximation quality introduced by the partition by optimally allocating fares of products shared by flights in different subnetworks. We present several reformulations by representing fleet assignment and schedule decisions using composite variables and formally characterize the relationship of their strengths. This gives rise to a new reformulation that is able to flexibly trade off strength and size. We conduct detailed computational experiments using two realistically sized airline instances to demonstrate the effectiveness of our approach. Under a simulated passenger booking environment with both perfect and imperfect forecasts, we show that the fleeting and scheduling decisions informed by our approach deliver significant and robust profit improvement over benchmark implementations and previous models in the literature.

The increasing importance of integrated fulfillment concepts revitalizes bricks-and-mortar stores and puts them at the center of retail operations. So-called omnichannel (OC) concepts leverage stores to offer seamless and enhanced operations for offline and online shoppers. Stores are used to fulfill online orders, offer shorter lead times to customers, and extend the assortment across channels. The role of the store and the underlying store operations are thus impacted by profound changes. This transformation has not yet been assessed comprehensively from a practical or an Operations Research (OR) lens. This paper identifies cross-cutting store-related planning issues and develops a planning framework for OC operations. We apply industry interviews and a systematic literature analysis to derive five planning issues. Research gaps are revealed by matching the pertinent OR literature with managerial needs. The planning issues network design of fulfillment locations, assignment of customer orders, and assortment and inventory planning have been discussed in several store-related OC publications. Demand forecasting and inventory replenishment have received less coverage, and offer significant research opportunities.

In liner shipping, containers are generally transshipped in major hub ports in each region, between larger inter-region vessels and smaller feeder vessels. In this paper, we study the problem of designing the feeder vessel network, transporting containers between a regional hub and the surrounding feeder ports. The problem, as modelled, has many similarities with the split delivery vehicle routing problem, but with additional characteristics such as simultaneous pickups and deliveries and weekly departures. The problem also includes fleet sizing with a heterogeneous fleet and allows demand rejection at a penalty cost. We present a branch-and-price framework to solve the problem, where the subproblem is solved by enumerating vessel routes and subsequently assigning commodities by solving a min-cost flow problem for each commodity. The algorithm is tested on instances with up to 12 ports, which are all solved to optimality. Since the problem is similar to the liner shipping network design problem but without transshipments, we further study selected instances from the LINER-LIB instance suite. The Baltic instance is solved to proven optimality and we find the best known solution to the West Africa instance, significantly improving on what can be found in the literature.

We present a spectral approach to design approximation algorithms for network design problems. We observe that the underlying mathematical questions are the spectral rounding problems, which were studied in spectral sparsification and in discrepancy theory. We extend these results to incorporate additional non-negative linear constraints, and show that they can be used to significantly extend the scope of network design problems that can be solved. Our algorithm for spectral rounding is an iterative randomized rounding algorithm based on the regret minimization framework. In some settings, this provides an alternative spectral algorithm to achieve constant factor approximation for the classical survivable network design problem, and partially answers a question of Bansal about survivable network design with concentration property. We also show many other applications of the spectral rounding results, including weighted experimental design and additive spectral sparsification., Comment: Improved bound on one-sided spectral rounding by a randomized swapping algorithm. Added the proof of the deterministic algorithm for additive sparsifers

We explore a novel optimization model to support the planning and operation of an air service network at a package express carrier in China. The model simultaneously considers ground and air transportation, company-owned aircraft capacity and belly capacity purchased on commercial aircraft, multiple service classes for packages, and packages becoming available throughout the day. A column generation based algorithm is devised to solve real-life problems with more than 650,000 timed origin-destination demands (packages), a service network with 150 hubs at airports, and an air cargo fleet of 60 aircraft of varying capacities. An extensive computational study shows the efficacy of the model and algorithm, and provides managerial insight. In particular, we see that considering ground transportation when designing an air service network is critical to improving operational efficiency and company profits as well as to extending service coverage. Furthermore, we find that in an environment with ample and reasonably-priced capacity on commercial aircraft, an air service network design based on single-flight shipments can be more effective than the commonly used star network design with multi-flight shipments.

Building upon earlier research, we revisit a bilevel formulation of service design and pricing for freight networks, with the aim of investigating its algorithmic aspects. The model adds substantial computational challenges to the existing literature, as it deals with general integer network design variables. An iterative heuristic algorithm is introduced, based on the concepts of inverse optimization and neighbourhood search. The procedure alternates between two versions of restricted formulations of the model while inducing promising changes into the service assignments. The approach has proven a high performance for all of the considered real-world instances. Its efficiency rests on its ability to deliver results within a close proximity to those obtained by the exact solver in terms of quality, yet within a significantly smaller amount of time, and to land feasible solutions for the large-sized instances that could not be previously solved. In line with the sustainable transport goals, a deeper observation of the transport management side highlights the strategy of the algorithm favouring freight consolidation and achieving high load factors.

International audience; The objective of the paper is to study the integration of revenue management considerations into service network design models targeting the tactical planning of intermodal consolidation-based freight transportation carriers. Revenue management strategies and mechanisms are broadly used within passenger transportation. Although identified as a desirable feature for freight transportation, interest growing within the industry, few contributions have addressed the topic. Moreover, almost none of those target the challenging issue of the interactions between the planning of the carrier’s services and operations, on the one hand, and the revenue-management strategy it could implement, on the other hand. We propose a new scheduled service network design model with resource and revenue management model, which selects the services and schedule to be repeatedly operated over the next season, allocates and routes the main resources supporting the selected services, and routes the demand flows between their respective origins and destinations. The objective of the model is the maximization of the expected net revenue of the carrier when several customer categories, with specific service requirements, as well as several tariff and operation classes are considered. Our interest goes beyond the modeling challenges raised by the problem setting, to exploring the impacts of this new approach on the decision types and on the structure of the service network solutions obtained. The results of extensive experiments, in terms of demand distribution, network topology, fare class and quality-of-service, provide a proof of concept of the proposed modeling framework and its capability for insightful analyses. Experimentation was conducted using an off-the-shelf software to solve the corresponding mixed-integer linear programming formulation for realistically dimensioned barge intermodal transportation instances.

Exclusive bus lanes (XBLs) have been widely discussed and implemented worldwide in recent years. An improved and more flexible transit lane management strategy—intermittent bus lanes (IBLs)—prove to be potentially more efficient and car-friendly than XBLs. The common benefit of XBLs and IBLs arises from the fact that they separate the bus and car traffic and hence eliminate the impacts of slowly moving buses on the car traffic. Compared to XBLs, the application of IBLs has not been broadly adopted in the real world so far except very few cases, because managing an urban IBL segment or area typically involves extra traffic signals and message signs and the switch between its regular and protected statuses could potentially cause unexpected traffic chaos and safety issues. The recent emergence of connected and automated vehicle (CAV) technologies, however, favors relieving these concerns and bring the dawn to a future application of this theoretically sound traffic control strategy. This paper is dedicated to evaluating the network performance of XBLs and IBLs and optimizing the networkwide configurations of these two strategies for future urban traffic networks where CAVs prevail. For this purpose, we formulate a system-optimal dynamic traffic assignment (SO-DTA) problem with car-exclusive lane segments, on the basis of a cell transmission model for separate car and bus traffic (CTM-SCB). By limiting its applicable context to morning commute networks and emergency evacuation networks, this model is set with only one real or virtual destination, which greatly eases its modeling and solution complexity. On the basis of the single-destination SO-DTA model, an XBL-based network design problem and an IBL-based network design problem are then both formulated into mixed integer programming models and solved by a discrete optimization solver, where the former problem statically configures bus lanes while the later one allows a dynamic allocation of bus lanes. Insightful findings obtained by applying the models and solver to a synthetic corridor network and a real commute network illustrate the great promise of these two lane-based strategies in mixed car and bus traffic management.

In typical operations research courses, optimization problems, such as transportation and assignment problems, are frequently discussed and taught as stand-alone problems. An integrated approach may prove to be necessary in order to enable students to have a holistic understanding of a complex problem (e.g., a project). In this paper, a global supply network design problem is presented where the case company can source from multiple “suppliers” using multiple modes of transport (including the use of containers with different capacities), allowing lateral supply between warehouses, etc. As more factors are considered, the problem becomes much more complex than any isolated problem in a typical course. The case was tested in an undergraduate course in Australia, and students found this case challenging but at the same time rewarding once solved.

In the periods following sales on online shopping carnivals, demand for express transportation is expected to precipitously surge within a short time, potentially causing express system overload and delivery delays. In this study, we address a service network design problem of a hub-and-spoke structured express transportation network during periods of surging demands. We consider a new strategy of upgrading existing spokes into temporary hubs to expand the network capacity with low costs. We propose a two-stage bi-objective robust model based on a time-space network to integrate the spoke-upgrading selection, service-scheduling, and vehicle-routing for designing an express service network that is both efficient and economic. To solve the model, we develop an enhanced column-and-constraint generation algorithm. Finally, a case study of a real express logistics company in China is demonstrated to validate the effectiveness of the proposed strategy and to offer decision support in real problems.

We consider the following general network design problem. The input is an asymmetric metric (V, c), root [Formula: see text], monotone submodular function [Formula: see text], and budget B. The goal is to find an r-rooted arborescence T of cost at most B that maximizes f(T). Our main result is a simple quasi-polynomial time [Formula: see text]-approximation algorithm for this problem, in which [Formula: see text] is the number of vertices in an optimal solution. As a consequence, we obtain an [Formula: see text]-approximation algorithm for directed (polymatroid) Steiner tree in quasi-polynomial time. We also extend our main result to a setting with additional length bounds at vertices, which leads to improved [Formula: see text]-approximation algorithms for the single-source buy-at-bulk and priority Steiner tree problems. For the usual directed Steiner tree problem, our result matches the best previous approximation ratio but improves significantly on the running time. For polymatroid Steiner tree and single-source buy-at-bulk, our result improves prior approximation ratios by a logarithmic factor. For directed priority Steiner tree, our result seems to be the first nontrivial approximation ratio. Under certain complexity assumptions, our approximation ratios are the best possible (up to constant factors).

Previous video salient object detection (VSOD) approaches have mainly focused on the perspective of network design for achieving performance improvements. However, with the recent slowdown in the development of deep learning techniques, it might become increasingly difficult to anticipate another breakthrough solely via complex networks. Therefore, this paper proposes a universal learning scheme to obtain a further 3% performance improvement for all state-of-the-art (SOTA) VSOD models. The major highlight of our method is that we propose the ‘motion quality’, a new concept for mining video frames from the ‘buffered’ testing video stream for constructing a fine-tuning set. By using our approach, all frames in this set can all well-detect their salient object by the ‘target SOTA model’ — the one we want to improve. Thus, the VSOD results of the mined set, which were previously derived by the target SOTA model, can be directly applied as pseudolearning objectives to fine-tune a completely new spatial model that has been pretrained on the widely used DAVIS-TR set. Since some spatial scenes in the buffered testing video stream are shown, the fine-tuned spatial model can perform very well for the remaining unseen testing frames, outperforming the target SOTA model significantly. Although offline model fine tuning requires additional time costs, the performance gain can still benefit scenarios without speed requirements. Moreover, its semisupervised methodology might have considerable potential to inspire the VSOD community in the future.

This study proposes a unified optimization framework for strategic planning of shared autonomous vehicle (SAV) systems that explicitly and endogenously considers their operational aspects based on macroscopic dynamic traffic assignment. Specifically, the proposed model optimizes fleet size, road network design, and parking space allocation of an SAV system with optimized SAVs' dynamic routing with passenger pickup/delivery and ridesharing. It is formulated as a multi-objective optimization problem that simultaneously minimizes total travel time of travelers, total distance traveled by SAVs, total number of SAVs, and infrastructure construction cost; thus, both the user-side cost and the system-side cost are taken into account, and their trade-off relations can be explicitly investigated. Furthermore, the problem is formulated as a linear programming problem, making it easy to solve. By leveraging the linearity, we mathematically derive a useful property of the problem: introduction of ridesharing can weakly monotonically and simultaneously decrease the user-side cost and system-side cost. The proposed model is evaluated by applying it to actual travel records obtained from New York City taxi data.

This paper proposes a method which generates a set of weights to synthesize sum or difference pattern with precisely controlled sidelobe level (SLL), null, and dynamic range ratio (DRR) for arbitrary arrays. Our pattern synthesis approach reduces mutual coupling between the neighboring elements and complexity of the feeding network design. However, the formulated optimization problem is nonconvex due to the nonconvex objective function and fractional DRR constraint. To tackle it, we firstly introduce two sets of auxiliary variables: one for DRR constraint and the other for sidelobe and null constraints. By doing so, we then decompose the original optimization problem into three sets of subproblems characterized by the auxiliary variables and weight variables. To facilitate the subproblem with weight variables reaching its optimum values, we derive an appropriate range of step size. Finally, we iteratively solve these subproblems to obtain the solution to the original problem. Extensive experiments employing non-equispaced linear and rectangular arrays, concentric ring array, and cylinder array, are implemented to demonstrate that the developed approach can accurately control SLLs, null and DRR for arbitrary arrays.

The pricing and reverse logistics network design problem in remanufacturing has attracted considerable attention in recent years due to increasingly serious environmental problems. In this study, we consider a pricing and reverse logistics network design problem with price-dependent return quality uncertainty. To handle the high uncertainty in return quality, we propose a distributionally robust risk-averse model to safeguard the profits of investors in extreme situations. We propose a Benders decomposition approach to solve the proposed model. It is enhanced through valid inequalities, local branching, in-out variant methods and scenario-based aggregated cuts. Computational experiments demonstrate that the distributionally robust model can effectively hedge against high uncertainty and that the enhanced Benders decomposition methods significantly outperform their classical counterparts and the off-the-shelf solver Gurobi. Lastly, managerial insights are explored, and future research directions are outlined.

Blockchain based cryptocurrencies have received great attention but long confirmation times of transactions hinder its adoption for micro-payments. Payment network is one of the solutions proposed to address the scalability issue. It transforms the problem into a routing problem by leveraging Hash Time Locked Contracts (HTLC). Payment networks possess unique characteristics to be considered when routing protocol is designed. Channels must stay balanced to have a sustainable network. In this paper, we present a payment network design that aims to keep the channels balanced by using a common weight policy across the network. We additionally utilize multi-point connection for unbalanced payment scenarios. The results show that we can keep the channels in the network more equally balanced compared to minimal fee approach and multiple connections from customers to stores increases the success ratio.

In telecommunications, network service accessibility as a requirement is closely related to equitably serving the population residing at locations that can most appropriately be described as remote. Remote connectivity, however, would have benefited from a more inclusive consideration in the existing generations of mobile communications. To remedy this, sustainability and its social impact are being positioned as key drivers of sixth generation's (6G) research and standardization activities. In particular, there has been a conscious attempt to understand the demands of remote wireless connectivity, which has led to a better understanding of the challenges that lie ahead. In this perspective, this article overviews the key challenges associated with constraints on network design and deployment to be addressed for providing broadband connectivity to rural areas, and proposes novel approaches and solutions for bridging the digital divide in those regions., Comment: This paper has been accepted for publication in IEEE Wireless Communications. 9 pages, 5 figures, 1 table

This study addresses a healthcare facility location/network design problem considering equity and accessibility. Locating the healthcare facilities, planning the capacity of healthcare facilities, and designing the transfer network are the primary goals of the given problem. The presented model aims to minimize system costs, maximize accessibility, and minimize inequality among all demand nodes. A real-world case study is presented to elucidate the performance and applicability of the proposed model. Moreover, a fix-and-optimize (FO) approach is proposed for tackling the problem on a large scale. The obtained results from experiments on several test problems indicate the efficiency of the developed FO.

In many cases, some programs often have some milestone activities that decision makers need to pay attention to. In this case, managers must efficiently describe these different decision-making spaces and use this as a basis to optimize possible solutions. Choosing a suitable decision-making space has always been a major factor in decision-making management, and it is also one of the key factors that managers must determine when making plans. Multi-objective optimization is to optimize multiple objectives at the same time. Multi-objective optimization algorithms have been widely used in many technology industries. This article researched from two parts: model display and optimization algorithm. The first is to deepen the basic characteristics of project planning and decision-making when there are more decision-making spaces. Based on the existing decision-making network planning methods, combined with the situation analysis method, a decision-making network planning model that can show more decision-making space is established. Secondly, based on the actual improvement requirements, the multi-objective optimization method in the multi-decision network design model and the simulation model of activity exercise data are established. The purpose is to create a relatively complete project planning decision model and optimization algorithm that can select multiple decision spaces at the same time through the above experimental research.

Online and omnichannel retailers are proposing increasingly tight delivery deadlines, moving toward instant on-demand delivery. To operate last-mile distribution systems with such tight delivery deadlines efficiently, defining the right strategic distribution network design is of paramount importance. However, this problem exceeds the complexity of the strategic design of traditional last-mile distribution networks for two main reasons: (1) the reduced time available for order handling and delivery and (2) the absence of a delivery cut-off time that clearly separates order collection and delivery periods. This renders state-of-the-art last-mile distribution network design models inappropriate, as they assume periodic order fulfillment based on a delivery cutoff. In this study, we propose a metamodel simulation-based optimization (SO) approach to strategically design last-mile distribution networks with tight delivery deadlines. Our methodology integrates an in-depth simulator with traditional optimization techniques by extending a traditional black-box SO algorithm with an analytical model that captures the underlying structure of the decision problem. Based on a numerical study inspired by the efforts of a global fashion company to introduce on-demand distribution with tight delivery deadlines in Manhattan, we show that our approach outperforms contemporary SO approaches as well as deterministic and stochastic programming methods. In particular, our method systematically yields network designs with superior expected cost performance. Furthermore, it converges to good solutions with a lower computational budget and is more consistent in finding high-quality solutions. We show how congestion effects in the processing of orders at facilities negatively impact the network performance through late delivery of orders and reduced potential for consolidation. In addition, we show that the sensitivity of the optimal network design to congestion effects in order processing at the facilities increases as delivery deadlines become increasingly tight.

Robust channel models for indoor areas are a crucial part of network planning and are immensely valuable for the small cell and indoor 5G network evolution. As the main input for many resource allocation and network planning problems, the accuracy of the path loss model can improve the overall accuracy of these techniques. Previous measurement campaigns exist for outdoor areas and higher frequencies, however extensive indoor measurements at these frequencies is missing from the literature. Both WLAN and LTE networks use 2.4 GHz and 5 GHz bands. For this work, indoor measurements were carried out in two distinct indoor environments, at two frequencies, and various models were compared. The measurements were made at the Deutsches Museum Bonn and the ICT cubes, an office space at RWTH Aachen University. Both empirical and deterministic models are tested on the data, the free space path loss model, the single and dual slope models with line-of-sight and nonline-of-sight, ray tracing models, and artificial neural network models were all tested and evaluated. Overall, the artificial neural network combined with the free space path loss model proved to be the most robust model which accurately predicted the propagation in the indoor environments, at both frequencies.

Optical Wireless Communication (OWC) is being explored for application in the next-generation broadcasting networks, where possessing accurately determined user locations becomes increasingly important. Received signal strength (RSS) Visible Light Positioning (VLP)-based localisation systems aim to deliver these centimetre-level location data at a low cost by featuring but a single photodiode (PD). Maximising the VLP accuracy requires optimising the LED transmitter locations, which is missing currently. An evolutionary optimisation algorithm is proposed to determine the optimal LED locations and the associated positioning error values for various configurations. The sensitivity of the planning on the number of VLP-enabled LEDs, the LEDs' characteristics, the room dimensions and the positioning parameters is investigated. Experimental data, i.e., two datasets with 157(2) measurement points each, serve to validate the simulations.

Objectives. The quality of a radio receiver preselector largely determines its main characteristics, including sensitivity. A preselector usually consists of linear elements: inductors, capacitors, low noise amplifiers, and switches. At high frequencies, the components cannot be considered as ideal ones, since active and reactive parasitic parameters significantly affect the frequency response of the components and, as a consequence, the network. Therefore, simulation of the networks requires more sophisticated component models, which take into account parasitic parameters. However, if refined components models are applied, it is still possible to obtain unsatisfactory results, since interconnections and footprints pads also affect the frequency response. This is true even if short lines with a length of about 5 mm are used at frequencies of about 100 MHz. These features must be taken into account for RF network design. The purpose of the work is to ensure the required characteristics of the preselector in the design process based on computer simulation.Methods. Egor Gurov’s methodology for analog VHF LC-filters was applied to radio receiver preselector design. The methodology contains the methods of discrete optimization, Monte-Carlo method, momentum analysis with Green’s functions. Experimental results were obtained by prototype implementation and measurement with a vector network analyzer. The purpose of the work is to ensure the required preselector characteristics in the design process based on computer simulation.Results. The article presents the preselector design process. The preselector contains two analog switches, an analog band-pass filter, an analog high-pass filter, and a low-noise amplifier. Simulation and experimental results with their comparison are presented in the article.Conclusions. Satisfactory results were obtained. It means that Egor Gurov’s method can be applied for more complex networks design such as radio receiver preselectors.

In many countries distributed energy resources (DER) (e.g. photovoltaics, batteries, wind turbines, electric vehicles, electric heat pumps, air-conditioning units and smart domestic appliances) are part of the ‘Green Deal’ to deliver a climate neutral society. Policy roadmaps, despite providing a framework and penetration targets for DER, often lack the network planning strategies needed to transition from passive to active distribution networks. Currently, DER's dynamic performance parameters and location identification techniques are not fully standardised. In fact, it can be very ad hoc. Standardised distributed load modelling and non-intrusive load monitoring (NILM) for equipment manufacturers, installers and network operators is critical to low and medium voltage network management in order to facilitate better balancing, flexibility and electricity trading across and within the power system for mass DER deployment. The aim of this paper is to fill this load modelling and NILM knowledge gap for DERto inform the ‘Green Deal’ transition and support standardisation. In the paper, existing load modelling techniques and NILM methodologies are critically examined to inform and guide research activity, equipment development and regulator thinking, as well as network operators. Seven key findings that need urgent attention are identified to support a smooth power system reconfiguration.

Radio frequency identification (RFID) is a widespread technology used in several different industries. One of its common use cases in logistics is the automation of goods receipt. RFID gates are often deployed, to automatically detect tagged items or load carriers during their passage through the goods receipt gate. At present, however, the design of RFID gates is often based on estimations, and their commissioning is mostly approached via trial and error. Even if the RFID network planning problem is known in the literature, existing algorithms cannot be applied to the design of RFID gates due to some limitations. In this paper, we propose a new evolutionary RFID network planning algorithm to design RFID gates optimally. The objective of our algorithm is to minimise the number of antennas and to adjust their mounting heights and angles. The algorithm ensures a tag coverage of at least 99%, prevents reflections on the ground, and can be used in the future as a standard for planning and commissioning RFID-enabled goods receipt gates. To demonstrate the applicability of our algorithm, we deployed it in a case study involving logistics of the automotive sector. The results of the deployment confirm the quality of our approach, as the RFID gate optimised by the algorithm deployed 4 antennas, with a vertical coverage rate of 99.96%, an horizontal coverage rate of 89.66%, and very interesting values of other evaluation functions, namely load balance and overlapping rate.

Industrial Internet-of-Things (IIoT) applications require reliable and efficient wireless communication. Assuming dense wireless sensor networks (WSNs) operating in a harsh environment, a concept of a time-division multiple access (TDMA)-based WSN enriched with electronically steerable parasitic array radiator (ESPAR) antennas is proposed and examined in this work. The utilized antenna provides one omnidirectional and 12 directional radiation patterns that can be electronically switched by the sensor node. We introduce a relay discovery algorithm, which selects those sensor nodes with an ESPAR antenna capable to act as relay. The selection of the relay nodes is based on a certain link quality threshold that algorithm uses as input. The outcome is a reduction in the number of layers or hops with a guaranteed Quality of Service (QoS). To emphasize the physical aspect of the wireless propagation, we introduce the measured antenna radiation patterns and consider two different path-loss propagation models representing blockage-free and blockage-prone industrial environments. A number of network simulations were performed and signal-to-noise ratio (SNR) as a link quality measure was examined with respect to the network density and different measured radiation pattern settings. The main outcomes show a tradeoff between SNR per link and the percentage of nodes that can serve as relays. As a result, we propose network design guidelines that take under consideration the QoS range with respect to SNR together with an optimal number of antenna radiation patterns that should be selected as a tradeoff between latency, energy consumption, and reliability in a network.

Engineering simulation accelerates the development of reliable and repeatable design processes in various domains. However, the computing resource consumption is dramatically raised in the whole development processes. Making the most of these simulation data becomes more and more important in modern industrial product design. In the present study, we proposed a workflow comprised of a series of machine learning algorithms (mainly deep neuron networks) to be an alternative to the numerical simulation. We have applied the workflow to the field of dental implant design process. The process is based on a complex, time-dependent, multi-physical biomechanical theory, known as mechano-regulatory method. It has been used to evaluate the performance of dental implants and to assess the tissue recovery after the oral surgery procedures. We provided a deep learning network (DLN) with calibrated simulation data that came from different simulation conditions with experimental verification. The DLN achieves nearly exact result of simulated bone healing history around implants. The correlation of the predicted essential physical properties of surrounding bones (e.g. strain and fluid velocity) and performance indexes of implants (e.g. bone area and bone-implant contact) were greater than 0.980 and 0.947, respectively. The testing AUC values for the classification of each tissue phenotype were ranging from 0.90 to 0.99. The DLN reduced hours of simulation time to seconds. Moreover, our DLN is explainable via Deep Taylor decomposition, suggesting that the transverse fluid velocity, upper and lower parts of dental implants are the keys that influence bone healing and the distribution of tissue phenotypes the most. Many examples of commercial dental implants with designs which follow these design strategies can be found. This work demonstrates that DLN with proper network design is capable to replace complex, time-dependent, multi-physical models/theories, as well as to reveal the underlying features without prior professional knowledge.

We propose a novel hub location model that jointly eliminates some of the traditional assumptions on the structure of the network and on the discount as a result of economies of scale in an effort to better reflect real-world logistics and transportation systems. Our model extends the hub literature in various facets: instead of connecting nonhub nodes directly to hub nodes, we consider routes with stopovers; instead of connecting pairs of hubs directly, we design routes that can visit several hub nodes; rather than dimensioning pairwise connections, we dimension routes of vehicles; and rather than working with a homogeneous fleet, we use intermodal transportation. Decisions pertinent to strategic and tactical hub location and transportation network design are concurrently made through the proposed optimization scheme. An effective branch-and-cut algorithm is developed to solve realistically sized problem instances and to provide managerial insights., Science Academy of Turkey (TÜBA) BAGEP Program

The contemporary Network on Chips (NoCs) are becoming intricate in design to serve the high throughput and low latency demands of multicore platforms. The complexity level of interconnect module makes it extremely difficult to ensure the functional correctness at the presilicon verification stage. Hence, post-silicon debug is performed on NoC as a necessary step to capture the escaped network design faults. The traditional store and forward trace-based debug methods encounter the problems of large trace buffer requirement and limited availability of trace communication bandwidth. These constraints become more stringent for short-lived network faults (misroute, packet drop, etc.), which demand more frequent trace collection for their detection. In this regard, we propose WiND, which is wireless-enabled NoC for post-silicon debug. WiND is a robust NoC debug framework that optimally uses the limited trace buffer space and can efficiently speed up the trace communication. The proposed method augments wireless interfaces (WIs) on top of the baseline wired NoC for validation purposes. The wireless medium is utilized for long-range test payload communication to reduce the volume of trace. The WIs are also used for high-speed interchip trace transfer. A modified router architecture is used to enable the trace collection, and to enhance the trace communication. WiND platform is examined with several synthetic and SPLASH-2 benchmark workloads, and compared with the traditional wired platform. An overall improvement of 15%–26% on fault detection and 27%–34% on path reconstruction in the case of different faults is observed for the same trace buffer size.

Urban bicycling has been largely marginalized for decades in the global north and south. Despite a renaissance over the last two decades in academic research, political discourse, sustainability activism, and planning, cities often struggle with data quality and quantity. Digitalization has led to more and better data sources, but they still must be validated and compared with findings from conventional travel surveys. With the COVID-19 pandemic, bicycling and associated road facilities expanded, as did road crashes involving bicycles. This study utilized tens of thousands of datapoints sourced by public institutions and digital devices belonging to private companies that have spread across Berlin over the last ten years and are currently ubiquitous. What does an integrated analysis of data from these novel sources reveal for urban bicycling research, planning, and network design? We explored and visualized the relationships and spatiotemporal variations in (i) bicycling volumes and (ii) crashes, unveiling the (iii) distribution of and correlation between datasets and the city’s bikeway network at an unprecedented threshold. The findings can be useful for special interest groups and to guide future urban bicycling research, planning, and network design.

Standardization and technological advancements have contributed in the development of the IoT. The accessibility of ease IoT gadgets has likewise assumed a key job in facilitating IoT research, improvement, and deployment. IoT is worldview network that permits the virtual existence of physical objects throughout our life. The Internet of Things (IoT) is based on the idea of installing embedded devices in everyday objects. In the mean time, because of the low cost and high accessibility of sensor devices, wireless sensor networks (WSNs) have an extraordinary job in overspreading of IoT. To be clear, the function of such systems is completely unpredictable in terms of node heterogeneity and node failure. Continuous advancements in IoT systems have resulted in several of the new protocols designed specifically for sensor networks where energy saving is such a top priority. The routing protocols, on the other hand, have earned the most attention because they might change based on the application and network design. This study examines the most recent routing protocols for sensor networks and developing action plans for the various approaches pursued. One of the potential drawbacks in the IoT is the energy requirement. Furthermore, several directions to extend the network's life expectancy have attracted in an expanding level of attention. Recently, a number of a achievements have emerged. Designing routing protocols is one of the most encouraging of these mechanisms, as demonstrated by the significant amount of energy required for information transmission. This paper begins with a detailed description of the foundation and its associated works. In addition, this study introduces a new routing protocol to increase the energy efficiency of sensor devices in the Internet of Things. © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license( http://creativecommons.org/licenses/by-nc-nd/4.0/ ) Peer-review under responsibility of the scientific committee of the 8th International Conference on Through-Life Engineering Service – TESConf 2019.

A common feature of many networks is the presence of communities, or groups of relatively densely connected nodes with sparse connections between groups. An understanding of community structures could enable the network design for improved system performance. For electric power systems, most work in the detection of community structures (i) selects a specific algorithm to perform the detection of communities (or compares a proposed algorithm against algorithms), and (ii) focuses on topological information about the networks. The objective of this article is to provide a framework to improve the selection of appropriate community detection algorithms for a family of networks with similar structures. We propose an approach to determine the most effective community detection algorithm for a set of networks and compare which algorithms provide the most similar partitions across these networks. To illustrate the comparison of various community detection algorithms, 16 electric power systems are analyzed.

Due to the increasingly competitive nature of the global market, the capability of controlling delivery time is becoming a significant advantage for enterprises. A novel fourth-party logistics (4PL) network design problem with the objective of minimizing the overall cost under service time constraint and stochastic demand is proposed in the paper. To address this problem, a two-stage nonlinear stochastic programming model is proposed. The topological structure of the 4PL network is decided in the first stage, while the network flows are determined in the second stage. By using auxiliary variables to linearize the service time constraint and by adopting the sample average approximation (SAA) method to handle the stochastic demand, the two-stage nonlinear stochastic programming model is transformed into a mixed integer linear programming (MILP) model. To overcome the difficulties of solving the MILP model caused by a large number of demand scenarios and integer-valued decision variables, a variable separation (VS) strategy is presented to improve the dual decomposition and Lagrangian relaxation (DDLR) approach to propose a VSDDLR-SAA algorithm. Results of the numerical examples and a real-life case illustrate the effectiveness of the proposed model and VSDDLR-SAA algorithm. Comparison analysis of the 4PL network and the supply chain network shows that 4PL can deliver products within the prescribed time at a lower cost by cooperating with third-party logistics providers.

In this paper, a decision-making framework for the design of redistributed manufacturing (RdM) networks is developed. Redistributed manufacturing, a manufacturing paradigm greatly empowered by the Industry 4.0 toolset, is the shift in production towards geographically dispersed interconnected facilities. The framework is context independent, accounts for the collective impact of all decision-making levels on one another in an iterative manner, and incorporates uncertainty. The framework has been applied to a case study in the aerospace spare parts production sector. Results indicated that the RdM paradigm demonstrated considerable improvements in service level when compared with a traditional centralized counterpart, while it was not as competitive with regards to total cost. This paper contributes to the literature on model-based distributed manufacturing systems design under uncertainty, and enables informed decision-making regarding the redistribution of resources and decentralization of decision-making. The novelty of this paper is the approach employed to handle complexity, nonlinear interrelationships and uncertainty, within the domain of RdM network design. These computationally demanding attributes are handled through simulation, and only their impact is passed back to an analytical model that generates the RdM network.

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 and amount of MWMs increases day by day. We present Medical Waste Chain Network Design (MWMCND) 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 make to increase the cost function.

District heating and cooling systems play an important role in future energy infrastructure by accessing local energy potentials, reducing temperature levels and exploiting synergies between energy systems. Therefore, a transition to next generation heating and cooling networks is needed, which can be realized by multi-level design, bidirectional usage of production and consumption or combined heating and cooling systems. This will increase the complexity of network design which may be hard to analyze and assess without the support of a system simulation. The following paper presents the dhcSim Modelica library which can be used to model complex district heating and cooling networks within a multi-level network structure. The library was developed as extension to the Modelica Buildings library and Modelica Standard library and provides a clear and easy to use model structure. This structure distinguishes between two-, four and multi-port-models which allows the modeling of highly complex multi-level DHC systems. The library provides models for both district heating and cooling and can be easily adapted to individual user cases. To illustrate the implementation of the library and how it can be used, a case study of a three-level district heating network is presented. Using the network simulation of the introduced example, it could be shown that the supply temperature requirements of some consumers could not be achieved by the proposed network and thus further action is needed.

An orderly and effective vaccination campaign is essential in combating the global COVID-19 pandemic. As one of the pioneers, the U.S. Center for Disease Control proposes a phased plan to promote the vaccination process. This plan starts with vaccinating the high-priority population in Phase I, then turns to the remainder of the public in Phase II, and ends with a scale-back network in Phase III. The phased plan not only provides a sense of hope to impacted communities that this global pandemic can be defeated, but can serve as a template for other countries. To enhance this plan, this paper develops a generalizable framework for designing a hub-and-spoke vaccination dispensing network to achieve the goals in the Phase 2, which aims to expand the vaccination coverage for the general public. We introduce a new coverage index to measure the priority of different potential dispensing sites based on geo-data and develop an optimization model for network design. The hub-and-spoke network enhances the accessibility of the vaccines to various communities and helps to overcome the challenges related to ultra-cold storage facility shortage. A case study of Middlesex County in New Jersey is presented to demonstrate the application of the framework and provide insights for the Phase 2. Results from the baseline scenario show that increasing the driving time limit from 10 minutes to 25 minutes can improve the total coverage index from 40.8 to 55.9. Additionally, we explore how the changes of parameters impact the network design and discuss potential solutions for some special cases. When we allow 4 outreach nodes per hub, all potential 45 outreach points can be covered in the vaccination network within a 20-minute drive, and the total coverage index reaches its maximum value of 58.3.

A digital microfluidic biochip (DMFB) enables the miniaturization of immunoassays, point-of-care clinical diagnostics, DNA sequencing, and other laboratory procedures in biochemistry. A recent generation of biochips uses a micro-electrode-dot-array (MEDA) architecture, which provides fine-grained control of droplets and seamlessly integrates microelectronics and microfluidics using CMOS technology and a TSMC fabrication process. To ensure that bioassays are carried out on MEDA biochips efficiently, high-level synthesis algorithms have recently been proposed. However, as in the case of conventional DMFBs, microelectrodes are likely to fail when they are heavily utilized, and previous methods fail to consider reliability issues. In this article, we first present a new microelectrode cell (MC) design such that the droplet-sensing operation can be enabled/disabled for individual MCs. Next, “partial update” and “partial sensing” operations are presented based on an IEEE Std. 1687 IJTAG network design. Finally, wear-leveling synthesis method is proposed to ensure uniform utilization of MCs on MEDA. A comprehensive set of simulation results demonstrate the effectiveness of the proposed hardware design and design automation methods.

We address the tactical-planning problem for an extended two-tiered City Logistics system. This more realistic problem setting, compared to the literature, integrates inbound and outbound demands, different transportation modes combining traditional, road-based, carriers with modes and vehicles of mass transport, such as light and regular rail. Aside from the assignment of customers to consolidation distribution centers and satellites, we manage a number of major resources, such as the multiple satellite capacity measures and the structure, allocation, and size of the heterogeneous fleets. We propose a scheduled service network design formulation for the tactical planning of such extended systems, and develop an efficient Benders decomposition algorithm, which includes a tailored partial decomposition technique for deterministic mixed-integer linear-programming formulations. The results of extensive numerical experiments show the efficiency of the proposed solution method, as well as the benefits of integrating several demand types and multimodal transportation networks into a single formulation.

Fixed telecom network utilities critically rely on subscriber demand forecasts from diverse socio-economic classes, having very little focus on micro scale. This study aims to spatially forecast the broadband demand at neighborhood level in urbanized areas. GIS techniques are applied through decomposition approach for geographical divisions and classification of tenancies and subscribers into land use categories. Moreover, univariate demand of each land use class was forecasted using Exponential Moving Average at sub-zone for further weighted distribution into neighborhood units. The land use composition was found diverse with mixed land use clusters. The model spatially graded the aggregated demand at neighborhood level with adequate accuracy. This approach is quite useful in dealing with heterogeneous spatio-temporal demand patterns and support decision-makers in taking strategic decisions regarding network planning and business operations.

The emerging IEEE 802.11ah is a promising communication standard for large-scale networks particularly the Internet of Things (IoT). The single-channel-based centralized channel access mechanism employed in 802.11ah does not scale well in such networks and leads to poor data reception quality. In this paper, we propose a multi-band sectorization and dynamic load balancing scheme for improving scalability. These features facilitate multi-hop communication more efficiently and enhance network capacity. Traffic congestion issues prevailing around the access point node due to the large volume of uplink traffic is mitigated by allowing simultaneous transmission using multiple orthogonal channels and sectors. Simulation and analytical results establish the essence of the novel protocol by showing significant improvements in terms of throughput and average packet delay over the existing schemes. The proposed network architecture improves throughput and delay performance up to 150% and 100% respectively compared to the relevant schemes.

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.

With the continuous improvement of the expressway logistics network, the location-routing problems (LRP) have become the obstacle to be overcome in the development of related industries. Based on the needs of modernization, in the era of the Internet of Things, classic traffic path planning algorithms can no longer meet the increasingly diverse needs, and related research results are not ideal. To reduce logistics costs and meet customer needs, this paper studies transportation route planning models and algorithms based on Internet of Things technology and particle swarm optimization. Firstly, the LRP model of expressway logistics network planning analyzes the achievement of goals, lists the assumptions, and builds the LRP model of expressway logistics network planning based on the mathematical model of path planning. Then the model is optimized and solved based on the particle swarm optimization algorithm. In order to verify the effectiveness and feasibility of the algorithm, MATLAB is used to simulate the algorithm. Finally, the LRP particle swarm optimization model of highway logistics network planning is put into the actual distribution work of a logistics company to analyze the change of distribution cost and investigate the related satisfaction. Experimental data show that the improved particle swarm optimization algorithm in this paper begins to converge in the 100th generation, the shortest running time is 57s, and the value of the objective function fluctuates slightly around 880. This shows that the model algorithm in this paper has strong search ability and stability. In the simulation experiment, the optimal objective function value of the model is 1001 yuan, which can be used to formulate the optimal distribution scheme. In the actual distribution work, the total cost of distribution before and after the application of the model was 12176.99 yuan and 9978.4 yuan, the fuel consumption cost decreased by 2097.23 yuan, and the penalty cost decreased by 85%. In the satisfaction survey, the satisfaction of all people was 9 points or above, with an average score of 9.42 points. This shows that the LRP particle swarm optimization model of expressway logistics network planning based on the Internet of Things technology can effectively save distribution costs and improve satisfaction.

Recent advances in networking and computing technologies have led to an increased interest in considering computation and communication in a joint and distributed mode according to the computing in the network paradigm. Furthermore, the emergence of the sixth generation (6G) networks is profiling the ever more challenging applications' requirements imposed by the new era service classes. In order to guarantee efficient implementation and availability to the upcoming applications, characterized by stringent quality of experience requirements, an accurate network design and delay analysis is becoming mandatory to pursue efficient 6G network dimensioning and enable computing in the network-based applications. Toward this goal, this article proposes a suitable end-to-end delay performance analysis and reliability evaluation in the case of a 6G network offering virtual reality services, which is considered one of the most challenging technologies for the coming new era service classes. More in depth, the aim of the article is the formulation of the stochastic end-to-end delay bound by applying supermartingale envelopes in order to allow accurate VR reliability prediction in relation to the number of users linked to the same computing node with a specified service profile. The goodness and effectiveness of the proposed approach is validated by providing comparison with simulation results and analytical predictions derived by resorting to the use of the classical Markov queueing theory.

For small farmers wishing to sell their products under the “local agriculture” marketing concept, connecting with consumers can be challenging. One approach to mitigating this disconnect between where production occurs and where consumers reside is through a network of regional consolidation points. In this study, we utilize optimization models to assist the Missouri Coalition of Environment (MCE) in helping farmers from Missouri and Illinois route products from their farms to a central hub in St. Louis. The aim of this study was to minimize the ton-miles traveled by farmers and MCE vehicles in delivering agricultural products from farms to regional hubs to the central hub. Given historical data about variability of plant and animal production in the Greater Plains region, a robust optimization approach was incorporated to increase the likelihood that the network can accommodate uncertainty in agricultural production. GAMS/CPLEX was used to solve the model under different configurations and identify potential locations for regional hubs. Computational testing determined that the capacity of hubs plays a key role in the optimal assignments: given the assumed model constraint that farmers can travel only to their nearest regional hub, solutions may sacrifice a better objective function value in order to accommodate farmers’ travel requirements.

Parametric models of video quality are designed for service and network planning as well as video quality monitoring. They are widely applied in a broad range of applications especially when video streams are encrypted or even unavailable at all. Designing metrics in these models remains challenge due to limited available information. In this paper, a spatio-temporal resolution-adaptive parametric (STRAP) model is proposed to evaluate the quality of video streaming services considering the spatial and temporal resolutions. This work serves as a follow-up study for ITU Rec. P.1203.1 that we were previously involved. The relationship between the content complexity and the spatial and temporal resolutions are analyzed and incorporated into the proposed model. Moreover, the effect of video up/down-scaling in display devices on the perceived video quality is further taken into consideration. Experimental results showed that the proposed model can be used as a reliable indicator for video streaming providers to improve their services performance.

Network design problems, such as flexibility design, are ubiquitous in modern marketplaces where firms constantly innovate new ways to match supply and demand. We develop a primal-dual based approach to analyze the flexibility design problem, i.e., allocating subsets of limited resources to each of multiple demand classes, and establish that the problem possesses a novel structural property. The property, which we call cover modularity, can be interpreted as an approximate form of submodularity, in the sense that local changes in the objective function can be used to bound global changes. We use this structure to analyze a class of greedy heuristics and establish the first constant factor approximation guarantee for solving the general flexibility design problem. Further, we identify a significant practical byproduct of our primal-dual analysis: the dual solutions we construct can be used as surrogates to guide the heuristics, leading to order of magnitude gains in computational efficiency, without loss of optimization performance. Finally, we extend our analysis by demonstrating the presence of cover modularity in a general class of linear programming formulations, indicating applicability of our approach to a wide range of network design problems distinct from flexibility design.

A new channel assignment algorithm, called the Viterbi-like algorithm (VLA), is proposed to solve the channel assignment problem in cellular radio networks. The basic idea of the proposed algorithm is step-by-step (sequential) channel assignment with the objectives of minimum bandwidth required at every step, subject to adjacent channel and cochannel separation constraints. The VLA provides the benefits of minimum required bandwidth, stability of solution, and fast execution time. The performance of the VLA is evaluated by computer simulation, applied first to 19 benchmark problems on channel assignment and then applied to study cellular radio network performance. Results from computer simulation studies show that bandwidth requirements by VLA closely match or are sometimes better than those of the existing channel assignment algorithms. Furthermore, it is found that execution of VLA is approximately two times faster than the local search algorithm—the existing channel assignment algorithm with the least bandwidth requirements. The combined advantages of minimum required bandwidth, stability of solution, and fast execution time make the VLA a useful candidate for cellular radio network planning.

Natural surveillance strategy in public areas can be established with high crowd engagement. Consideration of the type of pathway layout of green roof would determine the rate of public engagement, thus creating a safe environment for people to use the facility. Application of Space Syntax allows the analysis in determining pathway design layout with the highest rate of traffic engagement. Comparing a similar number of nodes and axial lines shows that axial line with more intersecting points creates more possibility of human interaction, higher crowd engagement thus functioning as natural surveillance in green roof setting. Keywords: green roof; natural surveillance; space syntax; building simulation eISSN: 2398-4287© 2021. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians), and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v6i17.2803