Search

This chapter builds on Chapters 2 and 4 and describes more advanced concepts, representations, procedures and graphical diagrams related to the analysis and design of heat recovery systems for both grassroots and retrofit situations. The grassroots case is extended to add more industrial realism, and to bring investment costs into the discussion. Key elements of the grassroots component are Multiple Utilities, Heat Transfer Area considerations, additional design tools beyond the Pinch Design Method, forbidden matches and an introduction to the use of optimisation for heat exchanger network design. Key elements of the retrofit part are procedures for Targeting, Design and Optimisation, where in all these stages focus is on maximum utilisation of existing equipment, making the task very different from the grassroots case. The Network Pinch concept will also be described, which as the name indicates is a feature of the Heat Exchanger Network (HEN), while the Heat Recovery Pinch is a feature of the stream data.

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.

To meet the expectation of multi-modal distributions from customers for last-mile delivery, a sustainable two-echelon E-Grocery delivery system is studied, which is motivated by the adaption of autonomous delivery robot (ADRs), parcel lockers in E-Grocery distribution industry. We formulate the multi-modal last mile system as a two-echelon location-routing problem with mixed vehicles and mixed satellites11 Intermediate depot. (2E-LRP-MVMS). A multi-objective optimization model is developed to capture the characteristics of cost components and environmental impact. The goal of the 2E-LRP-MVMS is to determine the location facilities, to optimize the number of parcels delivered to two echelons and routes at each level, also to reduce costs caused by carbon emissions. A hybrid immune algorithm is proposed, and two improved steps, vaccination and immunization are introduced in the algorithm. This research contributes to the last-mile delivery network design domain by modeling the environmental and economic cost of E-Grocery distribution adopting mixed fleets and mixed satellites.

The Routing and Spectrum Assignment (RSA) problem is NP-Hard so searching the entire problem space is not applicable. Many decomposition algorithms rely on reducing the search space in the routing space and applying heuristics algorithm in the spectrum assignment sub-problem. This is not necessarily a right solution as the ignored routing tables may lead to a better solution when they are used later as input to the Spectrum Assignment sub-problem. In this paper, we develop a new recursive decomposition approach for the RSA problem in optical networks. At the core of our approach is a new recursive branch and-bound procedure for carrying out an exhaustive search of the routing space in a scalable manner. This recursion effectively decouples the routing from the spectrum assignment part of the problem. Sequential generation of the full set of routing tables requires huge memory and very large processing time. Alternatively, our approach deploys multi-core architectures to generate the routing tables in parallel using OpenMP. Experimental results indicate that our recursive algorithm is quite efficient in searching the entire routing space for topologies representing large-scale wide area networks. Importantly, the decomposition may be more generally applied to any network design problem whose solution involves a search over both a routing and a resource allocation space. The main contributions for this paper are that we are able to generate all the search space in parallel in less than 1 min for 32-nodes network. Secondly, we are able to investigate all the routing tables, eliminate most of the search space, and select the promising routing tables that are proven to lead to a better solution in the Spectrum Assignment Sub-Problem

Road construction has a negative environmental impact on the surrounding aquatic environment, requiring the continuous monitoring of surface water quality. Here, optimization of the water quality monitoring network (WQMN) is an essential step in supporting the sustainable development of road construction projects. This study introduces Gamma Test theory (GTT) as a practical method for optimizing the WQMN of surface waters during road construction. The water quality index (WQI) was computed in 48 monitoring stations for six monitoring periods from 2017 to 2019; data was acquired from a primary monitoring network over a new highway in southern Norway. Based on the results, it is possible to reduce the number of stations by 23% in comparison with the original empirical network. The proposed method could be useful to design the monitoring networks of projects with limited construction time and budget, as well as projects lacking enough data.

Express parcel carriers offer a wide range of guaranteed delivery times in order to separate customers who value quick delivery from those that are less time but more price sensitive. To reflect the additional complexity this segmentation adds to the task of optimizing the logistics operations, we present a new model that accounts for the interplay between pricing of due times, customer decisions and the associated restrictions in the distribution process. This profit-maximizing express shipment service network design problem is solved by a heuristic solution approach that simultaneously determines the ideal set of offered delivery times, the associated pricing scheme and the load plan in order to maximize profit. High-quality solutions for realistically-sized instances are derived by a genetic-algorithm-based heuristic that exploits information of previously evaluated iterations. Using this new integrated approach, we provide insight on the potential benefit of an integrated model over sequential optimization of revenue and delivery cost. We also investigate the impact of tighter delivery due times on resource requirements and whether more granular service segmentation can be considered a profitable strategy in multimodal express parcel delivery networks.

In this paper, the thermal efficiency of the heat exchanger network of an existing crude distillation unit (CDU) was evaluated. The main goal is to determine how much energy savings can be achieved by network retrofitting. Energy losses are inevitable especially in process where equipment and heat integration network design are outdated. Pinch analysis method can help in minimizing the energy losses by retrofitting the heat exchanger grid and it is proven to be successful in many applications to predict more efficient heat exchanger networks. A base scenario case study was suggested to determine the maximum possible energy savings that can be achieved. In this scenario, the energy analyzer model in Aspen HYSYS was used to determine the optimum energy network that can accomplish maximum energy recovery with minimum capital cost. Another alternative was also evaluated using pinch analyzer tools in Aspen HYSYS. In this scenario, the existing heat exchanger network was optimized to determine how much energy can be recovered. All analysis conducted were based on the existing cooling and heating utilities in the plant. The results obtained indicate that around 67.5 MW of maximum cooling and heating utility can possibly be recovered compared to the existing utility load of 148.6 MW. This represents around 45% reduction in heat utility requirements. There is also an indication of substantial positive environmental impact in terms of how much reduction in the CO2 emissions that can be achieved reaches up to 1079 kg CO2/h for a practical and feasible option, and up to 7840 kg CO2/h for grass rooting retrofitting scenario.

This article proposes an algorithm to obtain an optimal local solution for the network planning process related to the optimal integration of different renewable energy sources (RES) and different Battery Energy Storage Systems (BESS) into a distribution network (DN). The algorithm provides strategic information related to investment and operation costs regarding the type of technology, location, and sizing. The mathematical formulation is based on an AC optimal power flow (OPF) to ensure the network’s minimal stability conditions. Besides, through the use of linearization and a modified version of a genetic algorithm (GA), the algorithm proposed breaks the 24 h wall, used until now in the literature, and extend it to 8760 h, which represents a much more realistic scenario to define the storage and power generation capacity of a DN in a planning context. The algorithm has been tested in a modified version of the IEEE 33-bus considering two cases of study: an off-grid case and grid-connected case, to measure the CapEx and OpEx variability, achieving to show that a grid-connected system reduces the installed capacity of DG and BESS in 37.4% and the CapEx 22.8%.

Travel demand information in the form of the origin–destination (OD) matrix plays an essential role in studying urban traffic management and network design. The present study takes a novel step toward urban traffic analysis using data mining of processed images of real-time traffic maps as a location-based data model, in which the data were analyzed by software programs such as KNIME and Python workspaces and comparing the results with the conventional traffic assignment results. Thus, we investigated a real-time OD matrix based on the trip-per-vehicle by automatic number-plate recognition (ANPR) cameras for the congestion charge zone (CCZ) of Tehran, Iran. The obtained matrix was assigned to the CCZ transportation network by the convex combination method concerning user equilibrium (UE) condition. The traffic pattern and assignment results were compared to the real traffic data gathered by ANPR, big data analysis and image processing of real-time traffic maps. Considering that the OD based on the trip-vehicle matrix was estimated for vehicle entrances-exits and found to be acceptably accurate compared to real-life conditions, it could be concluded that the UE could not find the practical assignment in 27% of cases in comparison with reality.

Coordination networks are a structurally and compositionally diverse class of network solid which are amenable to crystal engineering by exploiting the interactions and symmetry of the underlying metal centers (nodes) and organic (and/or inorganic) ligands that connect these centers (linkers). Herein, we detail how crystal engineering of coordination networks has evolved from its initial focus upon the development of design principles to its current focus upon gaining a better understanding of structure/function relationships to improve performance. The primary focus herein is the delineation of those design principles that are now well established, in particular the following: the node-and-linker approach to network design; the propensity of certain metals to form molecular building blocks or supermolecular building blocks that are robust enough to be used across a range of metals and linkers; and the utility of topological analysis to generate blueprints for families of related materials. The properties of the resulting coordination networks, and crystal engineering thereof, are addressed through case studies. This chapter may serve as an introductory guide to the state of the art of crystal engineering as it applies to coordination networks.

We present a method for bundling scenarios in a progressive hedging heuristic (PHH) applied to stochastic service network design, where the uncertain demand is represented by a finite number of scenarios. Given the number of scenario bundles, we first calculate a vector of probabilities for every scenario, which measures the association strength of a scenario to each bundle center. This membership score calculation is based on existing soft clustering algorithms such as Fuzzy C-Means (FCM) and Gaussian Mixture Models (GMM). After obtaining the probabilistic membership scores, we propose a strategy to determine the scenario-to-bundle assignment. By contrast, almost all existing scenario bundling methods such as K-Means (KM) assume before the scenario-to-bundle assignment that a scenario belongs to exactly one bundle, which is equivalent to requiring that the membership scores are Boolean values. The probabilistic membership scores bring many advantages over Boolean ones, such as the flexibility to create various degrees of overlap between scenario bundles and the capability to accommodate scenario bundles with different covariance structures. We empirically study the impacts of different degrees of overlap and covariance structures on PHH performance by comparing PHH based on FCM/GMM with that based on KM and the cover method, which represents the state-of-the-art scenario bundling algorithm for stochastic network design. The solution quality is measured against the lower bound provided by CPLEX. The experimental results show that, GMM-based PHH yields the best performance among all methods considered, achieving nearly equivalent solution quality in a fraction of the run-time of the other methods.

Habitat fragmentation is one of the key drivers of global biodiversity loss. In this context, connectivity modelling is increasingly important for effective conservation. Most previous studies on connectivity modelling are based on focal species, while fewer studies focus on models based on landscape naturalness or wildness. We propose a methodological framework for wildland network planning, which utilizes the results from wilderness mapping as input data for connectivity models. The least-cost model and circuit model are applied using Linkage Mapper and Circuitscape, with wilderness areas selected as source patches together with resistance values transformed from a wilderness quality index. Taking the Great Taihang region of China as an example, wildland networks are created and pinch-points are identified. We show that the selection of core patches and resistance surfaces have a significant impact on the resulting corridors. The wildland network could serve as an effective and efficient alternative to habitat network and supplement the protected areas network, especially when the species distribution data are lacking, or a rapid assessment is required. This framework for wildland planning could potentially be applied to fragmented landscapes across various countries to eliminate or reduce the negative impacts of habitat fragmentation on biodiversity.

Resilience oriented network planning provides an effective solution to protect the distribution system from natural disasters by the pre-planned line hardening and backup generator allocation. In this paper, a multi-disaster-scenario based distributionally robust planning model (MDS-DRM) is proposed to hedge against two types of natural disaster-related uncertainties: random offensive resources (ORs) of various natural disasters, and random probability distribution of line outages (PDLO) that are incurred by a certain natural disaster. The OR uncertainty is represented by the defined probability-weighted scenarios with stochastic programming, and the PDLO uncertainty is modeled as the moment based ambiguity sets. Moreover, the disaster recovery strategies of network reconfiguration and microgrid formation are integrated into the pre-disaster network planning for resilience enhancement in both planning and operation stages. Then, a novel primal cut based decomposition solution method is proposed to improve the computational efficiency of the proposed model. In particular, the equivalent reformulation of the original MDS-DRM is first derived to eliminate the PDLO-related variables. Then, the reformulation problem is solved by the proposed primal cut based decomposition method and linearization techniques. Finally, Simulation results are demonstrated for IEEE 13-node, 33-node and 135-node distribution systems to validate the effectiveness of the proposed method in enhancing the disaster-induced network resilience.

© 2018 Elsevier Ltd In scientific literature two large, partly overlapping areas regarding the environmental and economical attractive removal of waste coexist: reverse logistics and waste management. Both fields study, among other topics, the flows of discarded products leaving the end consumer. This review takes an integrated point of view on reverse logistics and waste management and aims at a better integration. More specifically, it gives a concise but complete overview of the efforts already performed in the area of strategic network design in waste reverse supply chains by means of combinatorial optimization models. Its purpose is to guide interested readers and researchers directly to publications of their interest, and let them identify courses other than the well-worn paths. Among others, we explicitly refer to (1) the importance of environmental, social and performance indicators in multi-objective models, (2) the potential of incorporating the different waste reverse supply chain stakeholders into the network design model, (3) the consideration of future waste reverse supply chain developments like extended producer responsibility schemes and the circular economy and their challenges, and (4) better heuristics to deal with the increasingly complex strategic network design models. ispartof: OMEGA-THE INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE vol:91 status: Published online

This chapter examines the recent empirical evidence on peer effects in education. Such effects may arise when agents interact within a network (e.g., a classroom or school), and their existence allows education policymakers to use network design as a lever to improve academic outcomes. The existing evidence suggests that peer effects are highly context-specific. The increasing availability of more detailed datasets and the development of new estimation methods can bring more clarity to this area of research.

The advent of cheap low-powered long-range radio technologies such as LoRa that operate on an unlicensed band resulted in many real-world deployments of smart city applications. Unlike a rural and semiurban areas where we can expect long radio-frequency (RF) propagation due to strong line-of-sight component, a city that typically comprises diverse terrain environments comprising a mix of buildings, dense wooded areas, parks, and gated communities limits the radio coverage and range. This necessitates the need for proper RF network planning to ensure optimal coverage of the network in such areas with the ability to remotely manage the infrastructure equipment to make deployment and network management hassle-free. On a city scale, there is a need for an interoperable middleware that provides a single point of contact for a developer to operate devices on various network protocols such as LoRa and Zigbee. In this chapter, we describe our experiences in deploying such an interoperable long-range wide-area network and management aspects of it in a campus environment with dense foliage interspersed with buildings and shed light on some deployment insights we have obtained in order to make an optimal network deployment.

In this article applies the Harris Hawks Optimization Algorithm for optimization of the water distribution network of the Homashahr located in Iran for a period of one month (from 30 September 2018 to 30 October 2019). The utilized time-series data included water demand, reservoir storage. In this article, a model based on the Harris Hawks Optimization Algorithm (HHO) was developed for the optimization of the water distribution network. The analysis showed that the best solutions achieved by the Harris Hawks Optimization Algorithm (HHO) were 35,508 $. The results revealed that the HHO algorithm was well in the optimal design of water supply networks problem. At the end, about 12% of the optimization was done by this algorithm.•In this article applied the Harris Hawks Optimization Algorithm for optimization of the water distribution network of the Homashahr located in Iran.•The method presented in this article can be useful for managers of water and wastewater companies, water resource facilities and water distribution system managing director for optimal network design to reduce costs.•The present algorithm performs better than the other algorithms in the discussion of the optimization of water distribution networks., Graphical abstract Image, graphical abstract

This chapter is concerned with the development and implementation of advanced decision-support technologies for the design and management of supply chains. By focusing on the industrial gas business, it explores the major contributions to the bulk industrial gases and the packaged gas supply chains. This chapter discusses problems that range from strategic network design, including plant reliability, to operational problems such as energy management multisite scheduling and planning, inventory optimization, and vehicle routing. Solutions that integrate business activities, such as production and distribution, are also reviewed and discussed. Moreover, this chapter emphasizes the opportunities to develop more comprehensive optimization models that address multiscale and uncertainty issues, as well as their associated tractability challenges. Finally, it concludes by highlighting the challenges associated with the increasing availability of internal and external real-time data and the deployment and implementation of decision-support systems in an industrial setting.

Due to a rapid increase in environmental impact and social concerns regarding end-of-use and end-of-life products, the need for green practice for supply chain management in turns into closed loop supply chain (CLSC) design which ultimately includes the reverse chain. The reverse chain covers a broad spectrum for optimal allocation of used products among different facility centers. This chapter proposes a typical CLSC network design in which the testing facility has been incorporated to ensure the utility of used parts. Multiproduct, multiechelon, and multiobjective scenarios have been highlighted with an exclusive emphasis on the reverse chain facility system. The proposed CLSC planning problem has been formulated as a mixed-integer linear programming problem under uncertainty. A robust ranking technique has been adopted to deal with uncertain parameters and constraints. The modified neutrosophic fuzzy programming approach with intuitionistic fuzzy preference relations among conflicting objectives has been used to solve the CLSC decision-making problem. A variety of solution sets have been generated by tuning the different feasibility degrees and weight parameters. Furthermore, the proposed CLSC planning model has been applied to a real-life case study. The results obtained inevitably outperform the optimal allocation at different echelons. Sensitivity analysis of the outcomes has been performed for ample opportunity to avail the best management scheme. Interestingly, disposable parts occur in lower quantities, which ensures a lower impact on landfills and carbon emissions during incineration from an environmental aspect. Conclusions and future research scope are discussed based on the presented work.

This chapter summarizes all relevant contributions to rail freight car fleet management problems. Classification of models and the most important characteristics are presented. Operational models consider short-term problems related to freight car inventory problems or empty and loaded freight car management problems. Tactical and strategic models cover midterm and long-term planning problems related to service network design, freight car demand modeling, and freight car fleet sizing. Besides the railway related contributions, this chapter also summarizes general as well as road and container transportation-based approaches.

Many growing mid-sized cities in the Nordic countries (Denmark, Finland, Norway, Sweden) are redesigning their public transport networks (PTN) to attract more users, while simultaneously aiming to cope with cost effectiveness pressures. Although the role of public transport (PT) in these structurally dispersed cities has been declining in the past, PT has an essential role in providing sustainable urban mobility options. With this case context in mind, this study explores the development trajectory of PT networks in 24 cities ranging from 100,000 to 500,000 inhabitants, with an aim to draw comparative lessons for PT policy formulation and implementation worldwide. In particular, research questions include evaluation of currently offered quantity and structure of PT services, as well as evaluation of planning objectives and measures. Using a mixed-methods approach, case study combines document analysis with performance measures based on PT schedule data (i.e. GTFS). Document analysis focuses on three themes related to PTN: trunk-routes, cross-routes, and network simplicity. Based on these themes five novel performance measures are calculated for the cities, enabling structural comparison between PTNs. The results indicate that PT planning has gone through a paradigm shift, changing the focus towards market-based approach. Moreover, PT overhauls have aimed for hierarchical PT network structure, with rapid-transit, high frequency trunk lines, complemented with demand-responsive services. However, despite similarities in network planning approaches and the arguments used to justify these approaches, there are a few national differences especially in the PTN implementation. In conclusion, based on this assessment, the study provides policy implications for the development of the ongoing PT planning processes in the Nordic countries.

This paper presents a mathematical programming-based model for evaluating the impact on a mixed railway network from proposals for infrastructure improvement and capacity expansion that are oriented mainly toward increasing freight transportation. The authors have applied this model to extend elements of an existing mixed railway network, perform relatively less costly actions on the network, and to enhance capacity by adding new blocking/control systems at specific locations. These aspects are usually not taken into account in models for regional planning. Rather than a model whose sole focus is on railway capacity expansion, our approach combines capacity-expansion with network design. Because the way investments generate returns to the freight transportation system is of utmost relevance for these types of problems, this model is based on the efficient frontier between investment and operating costs. The model is tested on a regional network in the Mediterranean Corridor.

The characteristics and requirements for low-power wide-area network (LPWAN) solutions vary from application to application. This chapter discusses the different key design considerations of LPWANs. It also focuses on network design and topological aspects to give an overall architectural and design framework. It also briefly describes major LPWAN technology solutions available in the marketplace and summarized as a segue to the upcoming chapters which cover the major solutions.

Machine learning (ML) is being hailed as a new direction of innovation to transform future optical communication systems. Signal processing paradigms based on ML are being considered to solve certain critical problems in optical communications that cannot be easily tackled using conventional approaches. Recent applications of ML in various aspects of optical communications and networking such as nonlinear transmission systems, network planning and performance prediction, cross-layer network optimizations for software-defined networks, and autonomous and reliable network operations have shown promising results. However, to comprehend true potential of ML in optical communications, a basic understanding of the nature of ML concepts is indispensable. In this chapter we describe mathematical foundations of several key ML methods from communication theory and signal processing perspectives and highlight the types of problems in optical communications and networking where they can be useful. We also provide an overview of existing ML applications in optical communication systems with an emphasis on physical layer.

This chapter describes the basic steps of pinch analysis for the design and optimization of a sustainable chemical process. Key elements of the chapter are the heat recovery pinch, targeting ahead of design, and a stepwise and systematic procedure for heat exchanger network design. Some extensions of pinch analysis are also provided, such as water pinch analysis and carbon emission pinch analysis. Graphical diagrams and representations of pinch analysis give the designer a good overview of the bottleneck of even the most complex processes. These tools provide insight and ease the communication between designers and engineers.

Blocking constitutes an important rail freight transport operation, by which cars with potentially different origins and destinations are grouped to be moved and handled as a single unit, yielding economies of scale. We address the tactical block-planning problem for intermodal railroads, which has been little studied so far. We propose a new block service network design model explicitly considering the specificity of intermodal rail, and which may be solved using commercial software for realistic sizes. We illustrate the performance and interest of the proposed method through an extensive case study of a major North American railroad.

In this paper, we present a state-of-the-art branch-and-cut (B&C) algorithm for the multicommodity capacitated fixed charge network design problem (MCND). This algorithm combines bounding and branching procedures inspired by the latest developments in mixed-integer programming (MIP) software tools. Several filtering methods that exploit the structure of the MCND are also developed and embedded within the B&C algorithm. These filtering methods apply inference techniques to forbid combinations of values for some variables. This can take the form of adding cuts, reducing the domains of the variables, or fixing the values of the variables. Our experiments on a large set of randomly generated instances show that an appropriate selection of filtering techniques allows the B&C algorithm to perform better than the variant of the algorithm without filtering. These experiments also show that the B&C algorithm, with or without filtering, is competitive with a state-of-the-art MIP solver.

One-quarter of Europe’s energy demand is provided by natural gas distributed through a vast pipeline network covering the whole of Europe. At a cost of 1 million Euros per kilometer the extension of the European pipeline network is already a multi-billion Euro business. Therefore, automatic planning tools that support the decision process are desired. We model the topology optimization problem in gas networks by a mixed-integer nonlinear program (MINLP). This gives rise to a so-called active transmission problem, a continuous nonlinear non-convex feasibility problem which emerges from the MINLP model by fixing all integral variables. We offer novel sufficient conditions for proving the infeasibility of this active transmission problem. These conditions can be expressed in the form of a mixed-integer program (MILP), i.e., the infeasibility of a non-convex continuous nonlinear program (NLP) can be certified by solving an MILP. This result provides an efficient pruning procedure in a branch-and-bound algorithm. Our computational results demonstrate a substantial speedup for the necessary computations.

Integrating quantitative and qualitative data is a challenge in global supply chain decision making. Decision support tools should be easier to use and focus on providing transparency and supporting human judgement. A strong emphasis on multi-objective models and trade-offs is needed. We investigate methods to solve strategic global supply chain network design problems and develop a solution framework, comprised of mixed-integer linear programming, the Analytical Hierarchy Process and the Pareto front. It is applied in a case study in the med-tech industry. Results show that the framework is easy to use for practitioners, accommodates qualitative as well as quantitative criteria and provides transparency over the entire range of efficient solutions. Managers can use it to discuss and select the configuration best fitting to their overall strategy. We believe the framework and application results can contribute to the development of more effective and user-friendly decision support tools for strategic global network design.

Electricity demand modelling is the central and integral issue for the planning and operation of power systems. Load projection provides important information for electricity network planning, and it is essential for the electricity system development. This work investigates the impact of specific economic, technical and climate characteristics on the shape of the electricity demand and introduces a methodology to project electricity demand in hourly resolution within a single framework for all countries. The method used is a multiple linear regression in terms of spectral analysis. 57 real load data profiles of diverse countries were decomposed into a set of sine functions to analyse the cyclical pattern of the data. Fourier coefficients contain information about frequencies and amplitudes in these sinusoids. The sum of various sine functions can be used to calibrate and project hourly electricity demand for any country with available input data for any year in the addressed period. The accuracy of proposed model function is represented in terms of R-squared error. The proposed model is flexible to be applied to different socio-economic scenarios based on alternative assumptions regarding both long-term trends as well as short-term projections. Post-print / Final draft

Industrial integration of solar for process heat applications with periodic heat storage and backup utility system ensures reliability and efficiency of renewable heat utilization, thus minimizing the dependence on fossil fuel and energy related emissions. Furthermore, by minimizing heat duty from backup utility, opportunities for matching solar thermal and solar heat storage could be fully explored. This study models the industrial application of solar thermal considering direct and indirect integration, periodic heat storage and backup utility. For the synthesis of the integrated design, a flowsheet superstructure is presented for multi-period heat supply network of industrial processes. The design and optimization of the integrated system is implemented and solved in GAMS with the objective of maximizing total heat output to the heat network. To demonstrate the performance of the model, the proposed solar heat network design for industrial utility retrofit is applied to an illustrative case study of preheating a cold industrial stream.

Daily industrial operations consume large quantities of energy and water but are often considered marginal to their processes. Energy is used to heat or cool water; water is frequently used in production or utility systems as steam or cooling water. This emphasizes the interconnectivity of water and energy, stipulating their simultaneous consideration to address resource use in industrial processes which requires systematic methodologies. Two main complications have been observed with in methodologies proposed in literature. As discussed in this work, these difficulties affect the overall mathematical formulation in mixed-integer nonlinear programming models; thus, resulting in a complex formulation. These difficulties have been addressed in this work by proposing a novel hyperstructure and a sequential solution strategy for the design of heat-integrated water allocation networks. Emphasis is placed on generating a set of promising solutions by addressing different performance indicators which is achieved via implementation of integer cut constraints to support decision-making for potential configurations. Applying the proposed methodology on several test cases from literature illustrated that the methodology is not only able to reach minimum total cost of the system, but also generates a set of alternative solutions exhibiting various performances with respect to other indicators.

Cloud or Centralized Radio Access Networks (C-RANs) are expected to be widely deployed under 5G in order to support the anticipated increasedtraffic demands and reduce costs. Under C-RAN, the radio el- ements (e.g., eNB or gNB in 5G) are split into a basic radio part (Distributed Unit, DU),and a pool-able base band processing part (Central Unit, CU). This functional split results in high bandwidth and delay constrained traffic flowsbetween DUs and CUs (referred to as fronthaul), calling for the deployment of a specialized network to accommodate them or for integrating themwith the rest of the flows (referred to as backhaul) over the existing infrastructure. This work studies the next generation of transport networks, which aims at integrating fronthaul and backhaul traffic over the same transport stratum. An optimization framework for routing and resourceplacement is developed, taking into account delay, capacity and path constraints, maximizing the degree of DU deployment while minimizing thesupporting CUs. The frame- work and the developed heuristics (to reduce the computational complexity) are validated and applied to both small andlarge- scale (production-level) networks. They can be useful to network operators for both network planning as well as network operation adjustingtheir (virtualized) infrastructure dynamically. This work was supported by H2020 European Research Coun- cil project “5G-TRANSFORMER: 5G Mobile Transport Platform for Verticals” (grant no. 761536 ), EU H2020 5G-Coral Project (grant no. 761586) and by the Madrid Community through the BRADE project (P2013/ICE-2958 (BRADE-CM))

This chapter introduces scenario-based methods for tackling the uncertainties of renewable generation in the electricity distribution network planning problem (NPP). Two scenarios generation methods are introduced here. The one is improved moment-matching (IMM) method, another is Taguchi's orthogonal array (OA) method. IMM method aims to generate the typical scenarios to capture the stochastic features of renewable power, including mean, variance, skewness, kurtosis, and correlation between multiple wind farms. While OA method aims to generate the extreme scenarios. Based on the typical scenarios, a robust distribution NPP is presented and formulated. The solution of the problem is robust against all the scenarios that represent the stochastic features of renewable generation power. Several test systems are used to compare the planning results of IMM method and OA method. The simulation results show that the MM method has better performance than the OA method in terms of the trade-off between robustness and economy.

The International Maritime Organization (IMO) has agreed in 2018 on a reduction of total greenhouse gas (GHG) emissions from international shipping, which should lower the total annual CO2 emissions by at least 50% by 2050 compared to 2008. Although extensive studies exist on the impact of international shipping at a global scale, few tools and empirical papers are available to assess the progress made so far in the shift towards carbon clean maritime supply chains. The paper identifies the key factors affecting CO2 emissions by container ships based on extant literature and business insights and measures how much has been already achieved in the past decade by offering multi-trade comparisons of the situations in 2007 and 2016. The paper concludes to a general decrease in annual CO2 emissions estimated at 33% since 2007. Two factors explain this decrease: first, the CO2 fuel efficiency in grams per TEU-km ( -53%) due to the general decrease in speed and change in technology, and second, the decrease from changes in network design leading to less distance travelled ( - 21%). One factor counterbalances these positive effects, i.e. the increase in the total deployed fleet capacity, partly due to an increase in the number and average size of vessels ( + 81%). Finally, the paper discusses how these findings could be used by shippers and logistics service providers when designing cargo routing solutions in a supply chain setting based on their carbon efficiency.

We consider a network flow problem, where the outgoing flow is reduced by a certain percentage in each node. Given a maximum amount of flow that can leave the source node, the aim is to find a solution that maximizes the amount of flow which arrives at the sink. Starting from this basic model, we include two new, additional aspects: On the one hand, we are able to reduce the loss at some of the nodes; on the other hand, the exact loss values are not known, but may come from a discrete uncertainty set of exponential size. Applications for problems of this type can be found in evacuation planning, where one would like to improve the safety of nodes such that the number of evacuees reaching safety is maximized. We formulate the resulting robust flow problem with losses and improvability as a two-stage mixed-integer program with uncertain recourse for finitely many scenarios and present an iterative scenario-generation procedure that avoids the inclusion of all scenarios from the beginning as well as several heuristic solution methods. In a computational study using both randomly generated instances and realistic data based on the city of Nice, France, we compare our solution algorithms.

This paper addresses two variants of the routing and wavelength assignment problem arising in the context of optical transport networks. In both variants, we address the case where the physical coverage of the fiber network is such that regenerators, to be placed on intermediate nodes of the lightpaths, have to be used to reach full connectivity between network nodes. In the first variant, we aim to minimize the solution cost given by the sum of the costs of all electrical–optical–electrical converter components. In the second variant, among all minimum cost solutions, we aim to optimize the network load balancing by minimizing the highest assigned wavelength. For each problem variant, we start by defining a basic integer linear programming compact formulation. Then, we improve each formulation using variable reformulation and variable elimination techniques. Finally, we present computational results showing that the reformulated formulations with variable elimination let us obtain provable optimal solutions for problem instances of relevant size within reasonable runtimes.

We study a new network design model combining ring and tree structures under capacity constraints. The solution topology of this capacitated ring tree problem (CRTP) is based on ring trees which are the union of trees and 1-trees. The objective is the minimization of edge costs but could also incorporate other types of measures. This overall problem generalizes prominent capacitated vehicle routing and Steiner tree problem variants. Two customer types have to be connected to a distributor ensuring single and double node connectivity, respectively, while installing optional Steiner nodes. The number of ring trees and the number of customers supplied by such a single structure are bounded. After embedding this combinatorial optimization model in existing network design concepts, we develop a mathematical formulation and introduce several valid inequalities for the CRTP that are separated in our exact algorithm. Additionally, we use local search techniques to tighten the obtained upper bounds. For a set of literature-derived instances we consider various reliability scenarios and present computational results.

A modal shift from road transport towards inland water or rail transport could reduce the total Green House Gas emissions and societal impact associated with Municipal Solid Waste management. However, this shift will take place only if demonstrated to be at least cost-neutral for the decision makers. In this paper we examine the feasibility of using multimodal truck and inland water transport, instead of truck transport, for shipping separated household waste in bulk from collection centres to waste treatment facilities. We present a dynamic tactical planning model that minimises the sum of transportation costs, external environmental and societal costs. The Municipal Solid Waste Service Network Design Problem allocates Municipal Solid Waste volumes to transport modes and determines transportation frequencies over a planning horizon. This generic model is applied to a real-life case in Flanders, the northern region of Belgium. Computational results show that multimodal truck and inland water transportation can compete with truck transport by avoiding or reducing transhipments and using barge convoys.

Work and heat exchanger networks have recently drawn increasing attention due to their paramount importance in achieving energy savings. In this work, we introduce a new optimization model for the cost-effective synthesis and energy integration of work and heat exchanger networks considering unclassified process streams (i.e., streams whose classification as hot or cold streams cannot be defined a priori). Our innovative modelling approach combines mathematical programming techniques and the pinch location method to obtain an optimal network design with minimal cost, while adjusting pressure and temperature levels of unclassified streams. We propose disjunctive operators for the selection of pressure manipulation equipment, and streams identity classification depending on energy requirements and process operating conditions. In addition, our approach addresses previous shortcomings by eliminating the need for: (i) assigning a specific route of pressure manipulation; and, (ii) classifying streams as low or high-pressure streams; which provides further flexibility to the system. Our methodology is also able to effectively deal with variable inlet and outlet streams temperatures to reach specific optimization goals. The model is solved to global optimality through the minimization of the process total annualized cost. Besides improved computational performance, results from energy analyses reveal that streams classification during process optimization can be greatly advantageous for both subambient and above-ambient applications. In the liquefied natural gas process, it reduces up to 89% the energy demand when compared to literature records. The authors are grateful for the financial support provided by the European Regional Development Fund (FEDER) through the Regional Operational Programme CENTRO 2020, within the scope of the project grant No. CENTRO-01-0145-FEDER-000006. The financial support provided by the Spanish «Ministerio de Economía, Industria y Competitividad, Gobierno de España», under project grant No. CTQ2016-77968-C3-2-P (AEI/FEDER, UE) is also gratefully acknowledged.

The file attached to this record is the author's final peer reviewed version. Traffic Related Air Pollution (TRAP) studies are usually investigated using different categories such as air pollution exposure for health impacts, urban transportation network design to mitigate pollution, environmental impacts of pollution, etc. All of these subfields often rely on a robust air pollution model, which also necessitates an accurate prediction of future pollutants. As is widely accepted by the heath authorities, TRAP is considered to be the major health issue in urban areas, and it is difficult to keep pollution at harmless levels if the time sequenced dynamic pollution and traffic parameters are not identified and modelled efficiently. In our work here, artificial intelligence techniques, such as Bayesian Networks with an optimized configuration, are used to deliver a probabilistic traffic data analysis and predictive modelling for air pollution (SO2, NO2 and CO) at very local scale of an urban region with up to 85% accuracy. The main challenge for traditional data analysis is a lack of capability to reveal the hidden links between distant data attributes (e.g. pollution sources, dynamic traffic parameters, etc.), whereas some subtle effects of these parameters or events may play an important role in pollution on a long-term basis. This study focuses on the optimisation of Bayesian Networks to unveil hidden links and to increase the prediction accuracy of TRAP considering its further association with a predictive GIS system

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.

Most hub location studies in the literature use a complete-network structure as an input in developing optimization models. This starting point is not necessarily from assuming that the underlying real-world network (e.g., physical network such as road and rail networks) on which the hub system will operate is complete. It is implicitly or explicitly assumed that a complete-network structure is constructed from the shortest-path lengths between origin-destination pairs on the underlying real-world network through a shortest-path algorithm. Thus, the network structure used as an input in most models is a complete network with the distances satisfying the triangle inequality. Even though this approach has gained acceptance, not using the real-world network and its associated data structure directly in the models may result in several computational and modeling disadvantages. More importantly, there are cases in which the shortest path is not preferred or the triangle inequality is not satisfied. In this regard, we take a new direction and define the p-hub median problem directly on non-complete networks that are representative of many real-world networks. The proposed problem setting and the modeling approach allow several basic assumptions about hub location problems to be relaxed and provides flexibility in modeling several characteristics of real-life hub networks. The proposed models do not require any specific cost and network structure and allow to use the real-world network and its asociated data structure directly. The models can be used with the complete networks as well. We also develop a heuristic based on the proposed modeling aproach and present computational studies. This research was supported by the Scientific and Technological Research Council of Turkey ( TÜBİTAK Grant No: 114M363 ). The authors are grateful to anonymous referees for providing constructive feedback that has helped improve in major ways the presentation of the material in the paper. Appendix A

The first part of this chapter presents a literature review of the approaches for the planning and design of hydrogen infrastructure including optimization methods, geographical information system (GIS)-based approaches, and evaluation plans toward the transition to hydrogen infrastructure. While the second part presents the modeling and control of hydrogen in a network of green hydrogen refueling stations.

In view of costly nature of water treatment processes, minimization of freshwater usage as well as wastewater generation in process systems is of great environmental and economic importance. Significant effects are achieved by optimization of water network, which is a network involving both water-using processes and regeneration/treatment units. Ultimately, zero discharge, i.e., closed water circuit, can be reached. The means to reach substantial reduction of freshwater consumption by water network optimization are formulated as: (1) water reuse in water-using processes and (2) application of regeneration processes by using redistributed water treatment. Traditional water network is illustrated where freshwater is supplied to processes in parallel with central water treatment station. Example of water reuse network is given. The concept of water network in a single industrial site is extended to industrial complexes of industrial parks and urban water networks. Mathematical models are formulated for water network problem. An overview of approaches in the literature of water network design is presented. The approaches are classified into two broad groups: (1) insight-based methods and (2) optimization-based methods. Similarly to HEN design methods, existing approaches to water networks include pinch concept as the most useful. The water pinch targeting method for water-using processes of mass transfer type are described by a simple example. The wastewater composite curve plot gives the minimum treatment flow rate and concentrations of the contaminant. It also shows the designer which streams require treatment and which streams can be left untreated. Examples are presented that concern design of water network with reuse and regeneration. They are able to cope with various goal functions, multiple contaminants, and various technological constraints.

Heat exchanger network (HEN) is one of the major technique of energy saving. We have proposed a HEN design system using pinch technology and multiagent. In this system, the maximum heat recovery is derived from pinch technology. And then, an initial HEN can be obtained according to the AreaTargeting method in order to meet the maximum heat recovery. The initial HEN has complexity and a number of heat exchangers due to the AreaTargeting method. Therefore it is necessary to simplify the HEN without deterioration of the energy saving efficiency. In our system, simplification strategy is defined to prescribe the priority order of procedure and/or the part of HEN. The number and variety of resulted HENs increase along with the number of strategies. It is useful if a large number of HENs with wide variety is obtained because the preferable HEN must be different due to the process objectives or conditions. We implemented several HEN design agents (HENDAs) with different simplification strategies. In this study, we introduced several improvements in simplification strategy of HENDAs using strategy switching mechanism. Several HENDAs have their own strategy, and then they exchange the HENs on the way—that is “strategy switching” from the standpoint of a HEN. We investigated the effect of improvements in the simplification strategies and strategy switching mechanism.

The theory of combinatorial designs has recently been used in order to build switch-centric data centre networks incorporating a large number of servers, in comparison with the popular Fat-Tree data centre network. We clarify and extend these results and prove that in these data centre networks: there are pairwise link-disjoint paths joining all the servers adjacent to some switch with all the servers adjacent to any other switch; and there are pairwise link-disjoint paths from all the servers adjacent to some switch to any identically-sized collection of target servers where these target servers need not be adjacent to the same switch. In both cases, we always control the path lengths. Our constructions and analysis are undertaken on bipartite graphs with the applications to data centre networks being easily derived. Our results show the potential of the application of results and methodologies from combinatorics to data centre network design.

Segmenting large supply chains into lean and agile segments has become a powerful strategy allowing companies to manage different market demands effectively. A current stream of research into supply chain segmentation proposes demand volume and variability as the key segmentation criteria. This literature adequately justifies these criteria and analyses the benefits of segmentation. However, current work fails to provide approaches for allocating products to segments which go beyond simple rules of thumb, such as 80-20 Pareto rules. We propose a joint network and safety stock optimisation model which optimally allocates Stock Keeping Units (SKUs) to segments. We use this model, populated both with synthetic data and data from a real case study and demonstrate that this approach significantly improves cost when compared to using simple rules of thumb alone.

We focus on optimization models involving individual chance constraints, in which only the right-hand side vector is random with a finite distribution. A recently introduced class of such models treats the reliability levels / risk tolerances associated with the chance constraints as decision variables and trades off the actual cost / return against the cost of the selected reliability levels in the objective function. Leveraging recent methodological advances for modeling and solving chance-constrained linear programs with fixed reliability levels, we develop strong mixed-integer programming formulations for this new variant with variable reliability levels. In addition, we introduce an alternate cost function type associated with the risk tolerances which requires capturing the value-at-risk (VaR) associated with a variable reliability level. We accomplish this task via a new integer linear programming representation of VaR. Our computational study illustrates the effectiveness of our mathematical programming formulations. We also apply the proposed modeling approach to a new stochastic last mile relief network design problem and provide numerical results for a case study based on the real-world data from the 2011 Van earthquake in Turkey. (C) 2018 Elsevier Ltd. All rights reserved.