Network Design Cost Research Articles

Impact of Superconductor Technologies on Network Design and Energy Cost- a Case Study in Libya

Several studies and investigations haveattempted to introduce a new design for the conventionaldistribution networks. This proposed design aims to offer a reduction in substation installation cost and in power losses by eliminating some voltage levels in networks. Sine, this is difficult to achieve with conventional distribution networks, when the conventional cables and transformers are used, where the impedance of conventional equipment is high. Due to High-Temperature Superconductor (HTS) materials provide extremely low impedance to AC when cooled to the boiling point of liquid nitrogen (77K), the interest in using HTS materials to produce power cables and transformers is increased. The present paper investigates the practical impacts of installing HTS cables and transformers and seeks to introduce a future network design which leads to the best implementation for superconducting networks, thus, a drop in the substation installation cost and energy losses cost by eliminating some voltage levels in the traditional networks.

Designing a resilient medical laboratory service network under disruption and uncertainty: A case study

Health service networks play a key role in ensuring the health of society. Disruptive and operational risks are constantly affecting the goals of healthcare supply chain and service networks. Therefore, adopting a suitable approach to deal with risks is critical in the mentioned networks. This study proposes a mathematical model for designing an integrated resilient medical laboratory service network and the supply chain of laboratory consumables under multiple facility disruptions to minimize the total network design costs. A robust possibilistic optimization approach is employed to deal with the uncertainty of input parameters. Then, a case study is presented to extract the results and evaluate the proposed model. Regarding the results, the integration of the laboratory service network and laboratory consumables supply chain significantly reduces costs and improves the service process in the long run. Also, adopting more efficient resiliency strategies will increase the cost savings of the entire network.

The potential of lake-source district heating and cooling for European buildings

Lake-source thermal district networks can efficiently supply heating and cooling to buildings and thus save energy and CO2 emissions. However, it remains unclear to which degree they are a sustainable alternative at a larger geographical scale. An evaluation of the potential of developing technically and economically feasible lake-source district systems in Europe was performed in this study, with an integrated spatial explicit techno-economic assessment that accounts for different boundary conditions, such as electricity price, CO2 price and climate change. The feasibility of covering building energy demand near lakes was found to be particularly sensitive to the relationship between capital costs from network design and operational costs from heat pumps, associated electricity consumption and CO2 emissions. Results suggest a European techno-economic potential of 1.9 TWh/y considering only direct cooling and 11.3 TWh/y if thermal networks supply both direct heating and cooling by heat pumps. Respective electricity savings are 0.36 TWh/y and 0.78 TWh/y. An estimated 17% of the cooling demand near European lakes can thus be covered by viable cooling-only lake-source systems. For combined systems, the viable potential is estimated to be 7% of the total combined heating and cooling demand. Lake-source district systems are found to be particularly promising for Italy, Germany, Turkey and Switzerland. The integration of lake-source thermal networks should rarely lead to severe lake water temperature alteration and therefore not limit the techno-economic potential. The introduced methodology allows for a combined evaluation of technological, ecological and economic boundary conditions for using lakes as a source for district heating and cooling. Thereby, a more realistic estimation of their potential implementation becomes possible, enabling informed energy planning for central or decentral system configurations.

Supply chain network design considering customer psychological behavior-a 4PL perspective

From the perspective of Fourth Party Logistics, a novel supply chain network design problem considering customer psychological behavior is proposed in this paper. First, we calculate the minimum cost of the supply chain network design in Fourth Party Logistics when the demand of customers is fully satisfied. In the shortage of investment in the process of supply chain network design, the demand of customers can be partially satisfied. Then, customer psychological behavior is considered to maximize the value function of customer satisfaction under cost constraint. After introducing the definitions of the psychological reference point and the service level for customers based on the prospect theory, we formulate a non-linear integer programming model for Fourth Party Logistics network design problem. Since the objective function of the proposed model is non-linear, an approximation linearization method is introduced to adjust the proposed model to be an equivalent linear model. Numerical experiments are designed to justify the effectiveness of the proposed method. Through a thorough analysis on customer psychological behavior, it can be seen that customers tend to be more risk-averse for gains than risk-seeking for losses in Fourth Party Logistics supply chain network design. Moreover, valuable investment insights are recapitulated. This proposed method provides an effective tool for a Fourth Party Logistics provider to give reasonable suggestions to an investor in Fourth Party Logistics supply chain network design. Furthermore, we extend the basic model of 4PL supply chain network design considering customer satisfaction with multiple commodities and provid a solution method based on greedy adding heuristic to further reduce the calculation time for large scale cases.

Towards Defining Reasonable Minimum Composition Thresholds – Impacts of Variable CO2 Stream Compositions on Transport, Injection and Storage

The collaborative project “Impacts of impurities in CO2 streams captured from different emitters in a regional cluster on transport, injection and storage (CLUSTER)” aimed to set up recommendations on how to define “reasonable minimum composition thresholds” that CO2 streams should meet when accessing CO2 transport pipeline networks. Within CLUSTER, we investigated potential impacts of CO2 streams with different and temporally variable compositions and mass flow rates along the whole CCS chain. Investigations included, amongst others, impacts on: • corrosion of pipeline steel, • pipeline network design and related transport costs, • alteration of well bore cements, • pressure development and rock integrity, • geochemical reactions, and • petrophysical and geomechanical rock properties. All investigations are based on a generic CCS chain scenario. In this scenario, CO2 streams are captured from a spatial cluster of eleven emitters and collected in a regional pipeline network. Emitters comprise seven fossil fuel-fired power plants equipped with different capture technologies, two cement plants, one refinery and one integrated iron and steel plant. In total, 19.78 Mio t CO2 (including impurities) are captured in the emitter cluster annually. The combined CO2 stream is transported in a trunk line with a length of 400 km (100 km of these offshore) and is injected into five generic storage structures. The storage reservoirs are saline aquifers of the Buntsandstein. The investigations revealed beneficial and deteriorating impacts of different impurities and combinations thereof. Overall, no fundamental technical obstacles for transporting, injecting and storing CO2 streams of the modelled variable compositions and mass flow rates were observed. Based on the results, the CLUSTER project team recommends not to define “minimum composition thresholds” for CO2 streams as strict threshold values for each individual impurity in the stream. Instead, CO2 stream compositions and variabilities for specific CCS projects should be constrained with regard to a set of parameters including i) the overall CO2 content, ii) maximum contents of relevant impurities or elements, iii) acceptable variability of CO2 stream composition, and iv) impurity combinations to be avoided.

Підхід щодо розміщення реклоузерів в розподільних мережах

Today, the electricity supplier is not able to declare the possible level of reliability of electricity supply, and the consumer simply does not have the opportunity to buy such “reliable” electricity. In such conditions, a differentiated approach to tariff formation is inevitable, which has been implemented in practice today, albeit in its infancy. Further improvement of the tariff-forming mechanism is impossible without creating a "flexible" dependence of the tariff on the indicators of the consumer's power supply reliability. Reliability indicators, in turn, are based, on the one hand, on the methods and approaches for their determination, and on the other, on statistically reliable data on the reliability of the elements that make up the equipment in the "generation-consumer" chain. Localization by automatic sectioning points at the design stage in the context of RAB tariff setting (incentive tariff setting) ensures the target nature of reducing SAIDI (index of average duration of outages in the system) and, as a result, assessing the possibility of achieving the target ENS (estimated volume of non-supplied electrical energy) significantly reduces the cost of network design and reconfiguration. In this regard, it is necessary to revise the state and topology of problem networks, including the operated partitioning hardware; to define support services in the energy company; be trained in software (for example, TELARM) as a design tool; to create the standards of the company (group of companies), taking into account the experience of colleagues from Moldova, the Security Council, the Baltic States, far abroad. As part of a scientific search, an approach was proposed, which is the basis for calculations to determine the optimal installation locations for automatic sectioning points for an average air line, as well as a parallel-serial air line with "targeted" automatic reserve input, corresponding to the optimal SAIDI value. The results obtained are presented in the framework of a joint technical meeting of leading specialists of operating enterprises, the customer and the staff of the Department of Power Supply named after A. V.M. Sinkova NULES of Ukraine.

Optimal design of water distribution networks using whale optimization algorithm

ABSTRACTA new optimization technique called Whale Optimization Algorithm (WOA) is applied for the least cost design of pipe networks. The rounded search agents of the algorithm were considered to cope with the discrete nature of pipe diameters as the decision variables in the optimization process. In addition, a new performance indicator based on both number of generations and the minimum number of evaluations is proposed to measure the performance of the optimization technique. The algorithm is applied to three pipe networks and the results are compared to that obtained by optimizing the networks using different optimization techniques and proved that WOA is a promising optimization model since it provided the least cost network design when compared to most of the optimization techniques available in the literature. However, the values of the performance indicator suggested that further research is recommended for more refinement of the algorithm for faster convergence towards the optimal solution.

Supply chain network design under the risk of uncertain disruptions

Facility disruptions in the supply chain often lead to catastrophic consequences, although they occur rarely. The low frequency and non-repeatability of disruptive events also make it impossible to estimate the disruption probability accurately. Therefore, we construct an uncertain programming model to design the three-echelon supply chain network with the disruption risk, in which disruptions are considered as uncertain events. Under the constraint of satisfying customer demands, the model optimises the selection of retailers with uncertain disruptions and the assignment of customers and retailers, in order to minimise the expected total cost of network design. In addition, we simplify the proposed model by analysing its properties and further linearise the simplified model. A Lagrangian relaxation algorithm for the linearised model and a genetic algorithm for the simplified model are developed to solve medium-scale problems and large-scale problems, respectively. Finally, we illustrate the effectiveness of proposed models and algorithms through several numerical examples.

Strategic optimization of wheat supply chain network under uncertainty: a real case study

Today, wheat and its by-products are considered the most important food grain source for humans across the world. Accordingly, integrally investigating the wheat supply chain is of great importance in strategic decisions. In this respect, this paper addresses a real case study of wheat supply chain in Iran as well as the entities involved it. Presenting a new mathematical model, the total cost of the wheat supply chain network design is optimized. The proposed model integrates collection, production, inventory, and distribution echelons of the wheat supply chain, simultaneously. The inherent uncertainty in supply, demand, related costs, and climate changing result in the different quality of wheat which make it challenging to design and manage an optimal structure for the wheat supply chain network. Hence, the role of uncertainty in the mathematical optimization model is highlighted, and then, a robust approach is utilized to tackle the inevitable uncertainty of parameters. The proposed robust model not only manage to overcome the complexity of uncertainty but also outperform the deterministic model. It shows the proposed robust model is more effective than deterministic one that can be applied to make robust strategic and tactical decisions for the wheat supply chain. Moreover, the sensitivity analysis of influential parameters is conducted. Finally, according to the obtained results as well as sensitivity analysis, some managerial insights are provided.

Minimum cost network design in strategic alliances

Strategic alliances are established between firms to improve their competitiveness in markets and generally appear in the form of joint ventures. Such collaborative efforts require centralized planning, and the survival of the alliance largely depends on the success of joint planning processes. In this regard, we investigate the opportunities that centralized collaboration can offer to firms when designing their service networks. Apart from the classical fixed and variable costs associated with the network design, we also consider transaction costs induced by the formation of the alliance, which can broadly be defined as cost components related to the coordination and monitoring of the people, efforts and resources. We concentrate on bilateral alliances and develop alternative models for solving their associated network design problem. We also adopt a state-of-the-art heuristic to solve large-scale instances. Our findings confirm that accounting for the transaction cost in network design is vital for the alliance. These transaction costs can be high enough to even render the collaboration unattractive. Hence, careful data collection and model treatment are required before deciding whether to form an alliance.

Shaped Power Pattern Synthesis With Minimization of Dynamic Range Ratio

The problem of synthesizing array patterns while reducing dynamic range ratio (DRR) of complex excitations is of great importance in practical applications since it enables active arrays to be more attractive by appropriately controlling the mutual coupling between the neighboring elements, as well as reducing the cost and complexity of the feeding network design. Unlike the commonly adopted DRR control technique, this paper focuses on minimizing the DRR of the excitations while synthesizing the anticipated array pattern, which results in a new nonconvex and nonlinear optimization problem because of its fractional objective function and nonconvex constraints. By introducing auxiliary variables, we derive an equivalent optimization problem that transforms the fractional objective function into a linear one, decompose the original optimization problem into several subproblems in each iteration, and simplify them as either single-variable quadratic unconstrained optimization or least-squares problems that are solved efficiently in each iteration. Numerical results with different radiation requirements are shown to demonstrate the effectiveness of the proposed scheme.

Multisource Hyperspectral and LiDAR Data Fusion for Urban Land-Use Mapping based on a Modified Two-Branch Convolutional Neural Network

Accurate urban land-use mapping is a challenging task in the remote-sensing field. With the availability of diverse remote sensors, synthetic use and integration of multisource data provides an opportunity for improving urban land-use classification accuracy. Neural networks for Deep Learning have achieved very promising results in computer-vision tasks, such as image classification and object detection. However, the problem of designing an effective deep-learning model for the fusion of multisource remote-sensing data still remains. To tackle this issue, this paper proposes a modified two-branch convolutional neural network for the adaptive fusion of hyperspectral imagery (HSI) and Light Detection and Ranging (LiDAR) data. Specifically, the proposed model consists of a HSI branch and a LiDAR branch, sharing the same network structure to reduce the time cost of network design. A residual block is utilized in each branch to extract hierarchical, parallel, and multiscale features. An adaptive-feature fusion module is proposed to integrate HSI and LiDAR features in a more reasonable and natural way (based on “Squeeze-and-Excitation Networks”). Experiments indicate that the proposed two-branch network shows good performance, with an overall accuracy of almost 92%. Compared with single-source data, the introduction of multisource data improves accuracy by at least 8%. The adaptive fusion model can also increase classification accuracy by more than 3% when compared with the feature-stacking method (simple concatenation). The results demonstrate that the proposed network can effectively extract and fuse features for a better urban land-use mapping accuracy.

Part commonality effects on integrated network design and inventory models for low-demand service parts logistics systems

Low demand logistics systems with response time based service levels such as service parts supply chains exhibit strong interaction between network design and part stocking decisions motivating integrated models making such decisions simultaneously. Extending the earlier models of single product, single part systems, we formulate a multi-product service parts supply chain design problem with part commonality. In particular, our model captures (1) multiple multi-part products with part commonality across products, (2) system-wide, product-specific, time-based service requirements, and (3) stochastic demand satisfied by facilities operating with one-for-one replenishment inventory policy. We then linearize this naturally non-linear integer program via approximation of fill rate function and introduction of new variables to handle quadratic terms in the constraints. We finally conduct a numerical study to quantify the effects of part commonality on network design and inventory decisions and costs using this linear integer reformulation. Our results show that dedicated parts (ignoring commonality) can lead to significantly suboptimal decisions, not only on stock levels but also on network design (location and demand allocation) decisions. Furthermore, our tests with relatively larger networks show that considering part commonality, even in a sequential approach of deciding network design first and part stocking decisions next, significantly improves the total costs of network design and inventory.

Schedule-Based Integrated Intercity Bus Line Planning via Branch-and-Cut

This work addresses integrated line planning for intercity bus lines, which differs in several respects from line planning in public transit. Passengers in intercity transportation decide on specific timetabled services to get to their destination. This is a contrast to an urban setting with higher frequencies, where it is generally sufficient to choose a line. Furthermore, intercity bus transportation in deregulated markets is usually characterized by fierce competition within and across modes. Customers are highly sensitive to price, time of day, duration, convenient access to stations, and service quality. Hence, bus line operators need to decide thoroughly on every single timetabled service they offer to manage the cost and revenue consequences of network design and timetable. We provide a schedule-based modeling approach integrating aspects of dynamic demand, network planning, and timetabling. For a given line corridor, locations of potential stations and ideal service times are determined simultaneously. We analyze the performance of our branch-and-cut solution approach using data from a German intercity bus carrier operating in a newly deregulated and quickly developing market. Moreover, we show that the integrated and schedule-based line planning often produces insightful new results that differ significantly from conventional approaches.

A method of network topology optimization design considering application process characteristic

Communication networks are designed to meet the usage requirements of users for various network applications. The current studies of network topology optimization design mainly considered network traffic, which is the result of network application operation, but not a design element of communication networks. A network application is a procedure of the usage of services by users with some demanded performance requirements, and has obvious process characteristic. In this paper, we first propose a method to optimize the design of communication network topology considering the application process characteristic. Taking the minimum network delay as objective, and the cost of network design and network connective reliability as constraints, an optimization model of network topology design is formulated, and the optimal solution of network topology design is searched by Genetic Algorithm (GA). Furthermore, we investigate the influence of network topology parameter on network delay under the background of multiple process-oriented applications, which can guide the generation of initial population and then improve the efficiency of GA. Numerical simulations show the effectiveness and validity of our proposed method. Network topology optimization design considering applications can improve the reliability of applications, and provide guidance for network builders in the early stage of network design, which is of great significance in engineering practices.

The Design of Transparent Optical Networks Minimizing the Impact of Critical Nodes

For a given fiber network and a given set of client demands, the transparent optical network design problem is the task of assigning routing paths and wavelengths for a set of lightpaths able to groom all client demands. We address this design problem minimizing the impact of a given set of critical nodes. The problem is tackled in two steps: first, we minimize the demand that is disrupted by the simultaneous failure of all critical nodes; second, we minimize the network design cost guaranteeing that the minimum disrupted demand is met. We present MILP models for each step, together with valid inequalities strengthening both models. For the second step, an efficient hybrid heuristic is also proposed.

Integrated Supply Chain Management via Randomized Rounding

We consider the supply chain problem of minimizing ordering, distribution, and inventory holding costs of a supply chain formed by a set of warehouses and retailers over a finite time horizon, which we call the production and distribution problem. This is a common generalization of the classical metric facility location problem and joint replenishment problem that coordinates the network design and inventory management decisions in an integrated manner. This coordination can represent significant economy for many applications, where network design and operational costs are normally considered separately. This problem is considered when the instances satisfy assumptions such as metric space of warehouse and retailer locations, and monotonic increasing inventory holding costs. In this work, we give a 2.77-approximation based on the randomized rounding of the natural mixed-integer programming relaxation. Also, we give a 5-approximation for the case that objective function includes retailer ordering setup costs.

Exact solution of hub network design problems with profits

This paper studies hub network design problems with profits. They consider a profit-oriented objective that measure the tradeoff between the revenue due to served commodities and the overall network design and transportation costs. An exact algorithmic framework is proposed for two variants of this class of problems, where a sophisticated Lagrangian function that exploits the structure of the problems is used to efficiently obtain bounds at the nodes of an enumeration tree. In addition, reduction tests and partial enumerations are used to considerably reduce the size of the problems and thus help decrease the computational effort. Numerical results on a set of benchmark instances with up to 100 nodes confirm the efficiency of the proposed algorithmic framework. The proposed methodology can be used as a tool to solve more complex variants of this class of problems as well as other discrete location and network design problems involving servicing decisions.

A multimodal logistics service network design with time windows and environmental concerns.

The design of a multimodal logistics service network with customer service time windows and environmental costs is an important and challenging issue. Accordingly, this work established a model to minimize the total cost of multimodal logistics service network design with time windows and environmental concerns. The proposed model incorporates CO2 emission costs to determine the optimal transportation mode combinations and investment selections for transfer nodes, which consider transport cost, transport time, carbon emission, and logistics service time window constraints. Furthermore, genetic and heuristic algorithms are proposed to set up the abovementioned optimal model. A numerical example is provided to validate the model and the abovementioned two algorithms. Then, comparisons of the performance of the two algorithms are provided. Finally, this work investigates the effects of the logistics service time windows and CO2 emission taxes on the optimal solution. Several important management insights are obtained.

Single‐commodity stochastic network design under demand and topological uncertainties with insufficient data

AbstractStochastic network design is fundamental to transportation and logistic problems in practice, yet faces new modeling and computational challenges resulted from heterogeneous sources of uncertainties and their unknown distributions given limited data. In this article, we design arcs in a network to optimize the cost of single‐commodity flows under random demand and arc disruptions. We minimize the network design cost plus cost associated with network performance under uncertainty evaluated by two schemes. The first scheme restricts demand and arc capacities in budgeted uncertainty sets and minimizes the worst‐case cost of supply generation and network flows for any possible realizations. The second scheme generates a finite set of samples from statistical information (e.g., moments) of data and minimizes the expected cost of supplies and flows, for which we bound the worst‐case cost using budgeted uncertainty sets. We develop cutting‐plane algorithms for solving the mixed‐integer nonlinear programming reformulations of the problem under the two schemes. We compare the computational efficacy of different approaches and analyze the results by testing diverse instances of random and real‐world networks. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 154–173, 2017