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This paper deals with the problem of optimising the network of a Third-Party Logistics (3PL) company, placing emphasis on the distinct characteristics of the 3PL environment. The goal is to minimise the warehousing and distribution costs for both forward and reverse product flows. A new Mixed Integer Linear Program (MILP) model is proposed to address this interesting problem. The related decisions include: selection of (a) warehouse(s) and the related product inventory quantities, (b) product unit loads transported, (c) truck combinations employed to perform the corresponding forward and reverse trips. The model was applied in a case study of a 3PL company to optimise part of its forward supply chain that comprises three warehouses, 23 suppliers and 53 customers. The results obtained were very encouraging, since overall warehousing and distribution costs were lowered by 10.8%. Sensitivity analysis indicated that additional savings may result from optimising warehousing capacities. The main contributions of this work are the inclusiveness of key aspects of the 3PL environment in the proposed model, as well as placing emphasis on warehousing costs and on management of truck capacities. A secondary contribution is the validation of the model's value through a comprehensive case study. [ABSTRACT FROM AUTHOR]

With the explosion of e-commerce, optimising parcel transportation has become increasingly important. We study the long-haul stage of parcel transportation which takes place between sorting centres and delivery depots and is performed on a two-level hierarchical network. In our case study, we describe the application framework of this industrial problem faced by a French postal company: There are two vehicle types that must be balanced over the network on a daily basis, and there are two possible sorting points for each parcel, which allows a better consolidation of parcels. These industrial constraints are formalised in the Long-Haul Parcel Transportation Problem (LHPTP). We present a Mixed Integer Linear Program (MILP) and a hierarchical algorithm with aggregation of demands which uses the MILP as a subroutine. We perform numerical experiments on large-size datasets provided by a postal company, which consist of approximately 2500 demands on a network of 225 sites. These tests enable the tuning of certain parameters resulting in a tailored heuristic for the LHPTP. Our algorithm can serve as a decision aid tool for transportation managers to build daily transportation plans, modeled on solutions produced given daily demand forecasts and can also be used to improve the network design. [ABSTRACT FROM AUTHOR]

Street networks allow people and goods to move through cities, but they are vulnerable to disasters like floods, earthquakes, and terrorist attacks. Well-planned network design can make a city more resilient and robust to such disruptions, but we still know little about worldwide patterns of vulnerability, or worldwide empirical relationships between specific design characteristics and resilience. This study quantifies and measures the vulnerability of the street networks of every urban area in the world then models the relationships between vulnerability and street network design characteristics. To do so, we simulate over 2.4 billion trips across more than 8,000 urban areas in 178 countries, while also simulating network disruption events representing floods, earthquakes, and targeted attacks. We find that disrupting high-centrality nodes severely impacts network function. All else equal, networks with higher connectivity, fewer chokepoints, or less circuity are less vulnerable to disruption's impacts. This study thus contributes a new global understanding of network design and vulnerability to the literature. We argue that these design characteristics offer high leverage points for street network resilience and robustness that planners should emphasize when designing or retrofitting urban networks.

Electric Vehicles (EVs) have a critical role in the future of sustainable transportation systems. Recently, improving the infrastructure for EVs has been a major focus of organizations around the world, such as local and federal governments, energy companies, and automotive original equipment manufacturers. In this article, we present a service network design framework to improve the infrastructure for EVs with quality of service constraints taken into consideration. We present a novel charging station location model with capacity allocation. The model maximizes the EV traffic flow captured and minimizes the average service time for drivers. The model formulation allows for assessing capacity and quality of service trade-offs, and operational insights are presented to reflect drivers' expectations. We demonstrate an application for the solution approach on a real road network and evaluate the performance based on both computational efficiency and solution quality. Our results highlight how charging times at stations affect the quality of service in the whole service system by quantifying the improvements in flow coverage while reducing waiting times. The value of faster chargers in increasing coverage and reducing the average waiting times is also highlighted in relation to a service provider's budget constraints. [ABSTRACT FROM AUTHOR]

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

Polymer gels stand out as emerging and promising materials in adhesives. However, flexible network and network–solvent mismatch affect their strengths and stabilities. Regulating the network architectures of polymer gels via molecular design is a promising strategy to overcome these drawbacks. Herein, a novel polymeric rigid cage (PRC) network structure design is proposed for capturing bulky vegetable oils via mechanically interlocking supramolecular interactions or steric hindrance effect to fabricate universal polymer gels‐based adhesives. By rationally designing and refining the architectures, the rigid cage prepared by 4,4′‐diphenylmethane diisocyanate (MDI) and N,N,N″,N″‐tetra(2‐hydroxypropyl) ethylenediamine (EDTP) possesses optimal encapsulation effect for epoxidized linseed oil (ELO). The resulting RCE‐50 gel exhibited robust mechanical properties (≈65 MPa) and excellent stability (high temperatures, high pressures, solvents immersion). Owing to high strength and stability of the polyurethane network, adhesion effect of high‐density amino ester groups, as well as the hydrophobic and toughening effects of ELO, the RCE‐50 gel can be used as a versatile adhesive with high adhesive strength under different conditions. This study proposes a new approach for innovating the properties of polymer gels, provides a novel idea for designing porous polymer materials, and offers fresh insights for developing versatile bio‐based adhesives. [ABSTRACT FROM AUTHOR]

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

The ability to estimate earthquake source locations, along with the appraisal of relevant uncertainties, is paramount in monitoring both natural and human-induced micro-seismicity. For this purpose, a monitoring network must be designed to minimize the location errors introduced by geometrically unbalanced networks. In this study, we first review different sources of errors relevant to the localization of seismic events, how they propagate through localization algorithms, and their impact on outcomes. We then propose a quantitative method, based on a Monte Carlo approach, to estimate the uncertainty in earthquake locations that is suited to the design, optimization, and assessment of the performance of a local seismic monitoring network. To illustrate the performance of the proposed approach, we analyzed the distribution of the localization uncertainties and their related dispersion for a highly dense grid of theoretical hypocenters in both the horizontal and vertical directions using an actual monitoring network layout. The results expand, quantitatively, the qualitative indications derived from purely geometrical parameters (azimuthal gap (AG)) and classical detectability maps. The proposed method enables the systematic design, optimization, and evaluation of local seismic monitoring networks, enhancing monitoring accuracy in areas proximal to hydrocarbon production, geothermal fields, underground natural gas storage, and other subsurface activities. This approach aids in the accurate estimation of earthquake source locations and their associated uncertainties, which are crucial for assessing and mitigating seismic risks, thereby enabling the implementation of proactive measures to minimize potential hazards. From an operational perspective, reliably estimating location accuracy is crucial for evaluating the position of seismogenic sources and assessing possible links between well activities and the onset of seismicity. [ABSTRACT FROM AUTHOR]

It is essential for network operators to guarantee required levels of availability in the network without incurring in excessive costs. We address the optimization problem of upgrading links at a given cost, to achieve end‐to‐end availability guarantees in the network, via pairs of working and backup paths. These guarantees are expressed as nonlinear path pair availability constraints which cannot be readily linearized. For this reason, most works have only considered the working path availability or disaggregated models where availability guarantees are considered separately for the working and backup paths. We show that the aggregated models which jointly consider the availabilities of the working and backup paths can provide better solutions than the disaggregated models. Moreover, we present a more efficient aggregated model based on a novel strategy to linearize the path pair availability constraints, and show that it further improves previous results. It is usual to impose that the upgraded links belong to a spanning tree subgraph. We further compare the solutions with and without this imposition and show that relaxing the tree condition can provide better solutions. [ABSTRACT FROM AUTHOR]

In this study, the design of a sustainable closed-loop supply chain network for agricultural products with the goals of minimizing the cost and emission of greenhouse gases and maximizing the response to customer demand, and creating justice-based job opportunities, simultaneously, are aimed. The intended chain is based on the fruit supply chain study, which includes fresh fruits, concentrate, and vermicompost fertilizer. A mixed-integer linear programming (MILP) model has been developed to achieve the triple bottom line. Due to the nature of the NP-hard problem, the proposed model is solved using metaheuristic approaches consisting of two renowned algorithms, NSGA-II and NRGA, and a relatively new algorithm called NSGA-III. It is worth noting that the parameters of the algorithms are adjusted to achieve the best performance in small, medium, and large-size problems exerting the Taguchi method. After comparing the results of the three algorithms based on the well-known criteria, NRGA is introduced as the superior algorithm. Ultimately, the results of sensitivity analysis indicate that appending the possibility of using vermicompost in gardens and considering several vehicles in the proposed sustainable supply chain boosts the values of economic and environmental objective functions to about 6.4 and 8.2%, respectively. [ABSTRACT FROM AUTHOR]

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

Reverse logistics network design is a complex decision-making problem that involves the reuse, repair, remanufacturing, and recycling of end-of-life (EOL) under the tradeoff among conflicting objectives. The cutting-edge technologies in Industry 4.0 are now leading to an unprecedented and dynamic transformation of reverse logistics systems, which, however, further complicates the initial network design. In this paper, a two-level decision-support framework combined with both optimization and dynamic simulation is proposed to balance the cost, environmental impact, and service level in smart and sustainable reverse logistics network design under a dynamically evolving and stochastic environment. The results of a real-world case study in Norway show that the method can better support robust strategic decisions, eliminate dominated/near-dominated solutions, and yield holistic performance analyses considering smart reverse logistics transformation. The proposed two-level decision-support framework can better analyze the impact of the technology transformation of Industry 4.0 on reverse logistics systems, while it also provides a fundamental structure for digital reverse logistics twin. [ABSTRACT FROM AUTHOR]

The network design problem with vulnerability constraints and probabilistic edge reliability (NDPVC‐PER) is an extension of the NDPVC obtained by additionally considering edge reliability. We consider the design of a telecommunication network in which every origin‐destination pair is connected by a hop‐constrained primal path, and by a hop‐constrained backup path when certain edges in the network fail. The edge failures occur with respect to their reliability, and the network is designed by considering a minimum reliability level. Therefore, a hop‐constrained backup path must be built by considering all simultaneous edge failures that have a certain probability of realization. While there exist models to solve the NDPVC without enumerating all edge subsets, edge reliability cannot be dealt with by applying the techniques applied to the NDPVC. Therefore, we develop models based on a new concept of resilient length‐bounded cuts, and solve the NDPVC‐PER without edge set enumerations. We perform extensive testing of the model to determine the best performing settings, and demonstrate the computational efficiency of the developed model. Our findings on these instances show that, in the dataset considered in this study, increasing the reliability level from 90% to 95% increases the average cost only by 12.4%, while increasing it from 95% to 99% level yields a cost increase of 93.9%. [ABSTRACT FROM AUTHOR]

Background: This work is part of the regional research project PIPER—Intelligent Platform for the Optimization of Recycling Operations, which aims to develop a network design model for waste collection in the metropolitan city of Rome, Italy. The goal is to enhance the strategic planning of treatment and disposal facilities by incorporating transshipment locations to improve the efficiency of waste collection. The motivation for this study stems from the stringent targets set by the European Green Deal to achieve near-zero emissions and the critical role of waste management in realizing circular economy practices. Methods: The problem is formulated as a mixed integer linear program (MILP) that includes constraints on vehicle allocation to shifts and driver requirements. An additional feature of the model is its ability to account for vehicle maintenance issues when deployed on consecutive shifts. Results: The model was tested against a real-world case study in Rome and demonstrated significant potential savings, with a reduction in total traveled distance exceeding 10%. The mathematical model was also used for evaluating strategic scenarios in a "what-if" analysis, allowing the municipal collection company to assess location options for depots, waste treatment facilities, and transshipment points, as well as to optimize the fleet composition and driver allocation during work shifts. Conclusions: This study presents a robust tool for strategic planning in waste collection operations, highlighting the benefits of using transshipment locations to improve efficiency. The findings indicate substantial potential for cost savings and operational improvements, making the model a valuable asset for municipal waste management planning. [ABSTRACT FROM AUTHOR]

The ability to estimate earthquake source locations, along with the appraisal of relevant uncertainties, is paramount in monitoring both natural and human-induced micro-seismicity. For this purpose, a monitoring network must be designed to minimize the location errors introduced by geometrically unbalanced networks. In this study, we first review different sources of errors relevant to the localization of seismic events, how they propagate through localization algorithms, and their impact on outcomes. We then propose a quantitative method, based on a Monte Carlo approach, to estimate the uncertainty in earthquake locations that is suited to the design, optimization, and assessment of the performance of a local seismic monitoring network. To illustrate the performance of the proposed approach, we analyzed the distribution of the localization uncertainties and their related dispersion for a highly dense grid of theoretical hypocenters in both the horizontal and vertical directions using an actual monitoring network layout. The results expand, quantitatively, the qualitative indications derived from purely geometrical parameters (azimuthal gap (AG)) and classical detectability maps. The proposed method enables the systematic design, optimization, and evaluation of local seismic monitoring networks, enhancing monitoring accuracy in areas proximal to hydrocarbon production, geothermal fields, underground natural gas storage, and other subsurface activities. This approach aids in the accurate estimation of earthquake source locations and their associated uncertainties, which are crucial for assessing and mitigating seismic risks, thereby enabling the implementation of proactive measures to minimize potential hazards. From an operational perspective, reliably estimating location accuracy is crucial for evaluating the position of seismogenic sources and assessing possible links between well activities and the onset of seismicity.

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

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

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

Upgrading the connections of an existing network is common practice in telecommunication and electric grid networks. In most applications such as low-voltage grids, upgrading is preferable compared to building a new network from scratch given the limited resources available. When the structure of the underlying network is required to be a tree, this leads to a novel variant of the Constrained Bottleneck Spanning Tree Problem (CBST) that takes into account edge weight variability. There are only two existing polynomial-time algorithms that can tackle this problem after transforming the network into a CBST instance in linear time. Through a computational study, we highlight the strengths and weaknesses of these approaches. By combining the strengths of these algorithms, namely progressive network reduction and iterative bound alteration, we developed a polynomial-time algorithm that outperforms both existing algorithms in terms of computation speed. Beyond solving the practical problem at hand, this paper lays the foundations for future research into what we term the Constrained Bottleneck Spanning Tree Problem with Upgrades (CBSTU). [ABSTRACT FROM AUTHOR]

The current study focuses on the critical role of efficient cold supply chain logistics (CSCL) within the beef meat supply chain (SC), ensuring the timely delivery of premium products. Despite its significance, substantial food loss and waste (FLW) in CSCL pose multifaceted challenges across economic, social, and environmental dimensions. This comprehensive literature review aims to identify state-of-the-art CSCL for reducing food waste, key research themes, and their potential roles in red meat waste reduction, as well as identify future research directions. It explores four pivotal themes—management, sustainability, network design (ND), and new information technologies (IT)—each contributing uniquely to mitigating FLW. A review of papers published in the last two decades reveals management as the predominant theme, followed by sustainability, ND, and IT. The study underscores the interconnectedness of these themes and highlights gaps in current research, particularly the need for multi-objective optimisation models. These models should integrate uncertainties, sustainability dimensions, and technological advancements, while aligning strategic, tactical, and operational decisions to enhance CSCL sustainability and reduce FLW in the beef meat industry. This review informs stakeholders—researchers, policymakers, practitioners, the government, and the public—about emerging trends and opportunities in addressing food waste, thereby fostering more efficient and sustainable CSCL practices. [ABSTRACT FROM AUTHOR]

Palestine is water-stressed and prone to possible shocks in water supply. However, about one-third to one-half of the delivered water in the Palestinian territories is lost in the distribution network, highlighting the necessity of a cost-effective supply policy that also minimizes corrosion and health-related risks by achieving a maximum level of water quality. There must be a decision-making system to help managers review and measure the status quo and develop optimal allocation decisions to resolve the potential problems with supply and quality as much as possible. This research presents optimal resource allocation for the drinking water supply system (DWSS) to minimize the unit cost of supply in the system and the chloride concentration of the water supplied simultaneously. A bespoke biobjective mixed-integer linear programming (BOMILP) model was developed. The weighted sum method (WSM) based on the positive ideal solution (PIS) approach is utilized to tackle the biobjectiveness of the model in a Pareto sense. The model was implemented and validated using a case study of Gaza. The results suggest that the share of desalinated water resources in the supply network will increase by about 50% in the 2030–2035 period. Furthermore, we found that the unit cost of supply is highly sensitive to a decrease in resource capacities. Finally, the average quality of water supplied can deteriorate rapidly if demand surges. The model can successfully handle the complexity of effective utilization of resources and yield optimal decisions for policymakers. [ABSTRACT FROM AUTHOR]

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

Rapid and effective blood carbon monoxide (CO) assessment is of great importance, especially in estimating CO-related morbidity and instituting effective preventive measures. The conventional detection methods using CO breath analysis lack sensitivity, while collecting biological fluid samples for CO level measurement is prone to external contamination and expensive for frequent use. This study proposes a one-dimensional convolutional neural network (1D-CNN) consisting of three stacked biconvolutional layers for binary classification of blood CO status using the diffuse reflectance spectroscopy technique. Iterative particle swarm optimization (PSO) has efficiently found the best network parameters to learn important features from the reflectance spectroscopy data. The findings showed good testing accuracy, specificity, and precision of 92.9%, 90%, and 89.7%, respectively, and a high sensitivity of 96.3% in determining abnormal blood CO among smokers using the proposed CNN network. Comparisons with eight existing machine learning and deep learning models revealed the proposed method’s effectiveness in classifying blood CO status while reducing computing time by 8-13 folds. The findings of this work provide new insights that are valuable for researchers in neural network design automation, healthcare management, and skin-related research, specifically for application in nondestructive evaluation and clinical decision-making. [ABSTRACT FROM AUTHOR]

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

We consider the following node-capacitated network design problem. The input is an undirected graph, a set of demands, uniform node capacity, and arbitrary node costs. The goal is to find a minimum node-cost subgraph that supports all demands concurrently subject to the node capacities. We consider both single- and multicommodity demands and provide the first polylogarithmic approximation guarantees. For single-commodity demands (i.e., all request pairs have the same sink node), we obtain an O(log²n) approximation to the cost with an O(log³n) factor violation in node capacities. For multicommodity demands, we obtain an O(log4n) approximation to the cost with an O(log10n) factor violation in node capacities. We use a variety of techniques, including single-sink confluent flows, low-load set cover, random sampling, and cut-sparsification. We also develop new techniques for clustering multicommodity demands into (nearly) node-disjoint clusters, which may be of independent interest. Moreover, this network design problem has applications to energy-efficient virtual circuit routing. In this setting, there is a network of routers that are speed scalable and that may be shut down when idle. We assume the standard model for power: the power consumed by a router with load (speed) s is \sigma + s\alpha, where \sigma is the static power and the exponent \alpha > 1. We obtain the first polylogarithmic approximation algorithms for this problem when speed-scaling occurs on nodes of a network. [ABSTRACT FROM AUTHOR]

In response to the ever-increasing pursuit of competitiveness among organizations in today's global business landscape, the subject of supply chain management has become a vital domain encompassing a wide range of sectors and industries across the economy. The growing concern about sustainable development has prompted public and private supply chain players to incorporate the three pillars of sustainability, namely, economic, environmental, and social, into the design of their supply chain networks. This study reviews and examines the content of 102 relevant papers to discuss the mathematical models, modeling approaches, and solutions that have been explored in the existing literature on forward sustainable supply chain network design. This paper also investigates the sustainability elements and supply chain network peculiarities including design factors and decision levels. In this review, several limitations in the current literature on sustainable supply chain network design optimization models are highlighted. According to the analysis, it was found that a better understanding of the industry and its sustainability requirements and priorities is essential for designing sustainable supply chain networks that are tailored to the needs of a specific industry rather than achieving general sustainability objectives. In addition, integrating strategic, tactical, and operational decision levels in the design of supply chain networks is critical for evaluating their impact on each other in terms of sustainability. More sophisticated mathematical solution methods for dealing with real-life scenarios including nonlinearity and uncertainty sources are required. The paper concludes with new prospects of research to promote a better integration of sustainability into supply chain networks. [ABSTRACT FROM AUTHOR]

There is a growing attention to circular economy through researchers, enterprises and governments. The idea of circular economy is to give no harm to environment while using material till the end of its life and keeping it as much as possible in the cycle. From this point of view, to keep the material in cycle might be possible with a well-designed logistics infrastructure. In this study, the small and medium size enterprises (SMEs) that make sales through e-commerce and the way of handling returns are brought forward. A case study is created for footware industry in Istanbul and a circular supply chain network design is proposed. Afterwards, a linear programming is applied to minimize cost. This study gives a clear and simple solution for SMEs to minimize the cost for handling returns while keeping them in the cycle. [ABSTRACT FROM AUTHOR]

In this paper we propose some extensions of the minimum labelling spanning tree problem. The main focus is on the minimum labelling Steiner tree problem: given a graph G with a color (label) assigned to each edge, and a subset Q of the nodes of G (basic vertices), we look for a connected subgraph of G with the minimum number of different colors covering all the basic vertices. The problem has several applications in telecommunication networks, electric networks, multimodal transportation networks, among others, where one aims to ensure connectivity by means of homogeneous connections. Numerical results for several metaheuristics to solve the problem are presented. [ABSTRACT FROM AUTHOR]

Telecommunications networks are facing increasing demand for Internet services. Therefore, the problem of telecommunications network design with the objective to maximize service data flows and provide fair treatment of all services is very up-to-date. In this application, the so-called maxmin fair (MMF) solution concept is widely used to formulate the resource allocation scheme. It assumes that the worst service performance is maximized and the solution is additionally regularized with the lexicographic maximization of the second worst performance, the third one, etc. In this paper we discuss solution algorithms for MMF problems related to telecommunications network design. Due to lexicographic maximization of ordered quantities, the MMF solution concept cannot be tackled by the standard optimization model (mathematical programme). However, one can formulate a sequential lexicographic optimization procedure. The basic procedure is applicable only for convex models, thus it allows to deal with basic design problems but fails if practical discrete restrictions commonly arriving in telecommunications network design are to be taken into account. Then, however, alternative sequential approaches allowing to solve non-convex MMF problems can be used. [ABSTRACT FROM AUTHOR]

Supply chain management includes strategic, tactical, and operational decisions for long, medium, and short-term planning. Strategic decisions, such as network design, and operational decisions, such as last-mile routing, have mutual implications. Therefore, modelling them separately can lead to sub-optimal solutions. The integrated modelling of these decisions has been addressed as a location routing problem (LRP). This paper aims to identify the solution strategies and methods to solve the LRP, as well as related challenges and research opportunities based on a critical literature review. The findings reveal that 46% of the reviewed publications have adopted a multistage modelling approach to address the LRP, sequentially tackling strategic and operational decisions. Moreover, in addition to the challenge of modelling diverse decision levels, the LRP models need to incorporate variables such as time windows, delivery failure rates, demand density, etc. Five research opportunities are proposed: i) modelling the first and last mile with a strategic approach when making strategic network decisions, ii) integrating environmental and social objectives into the modelling framework, iii) applying the solution methods and algorithms to complex real-world cases, iv) exploring competitive and cooperative models in LRP, and v) evaluating the use of emerging technologies. [ABSTRACT FROM AUTHOR]

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

This research suggests a Robust and Risk-Averse Medical Waste Chain Network Design by considering Viability requirements (RRMWCNDV). The aim is to locate a waste management facility that minimizes waste and promotes the recycling of materials like metal and plastic, contributing to environmental benefits. The proposed RRMWCNDV aims to be viable, robust and risk-averse. A two-stage robust stochastic programming model was utilized to develop this framework. It incorporates risk by employing the Weighted Value at Risk (WVaR) approach for the first time. The study reveals that incorporating risk and robustness scenarios results in a lower cost function. The degree of conservatism in decision-making can be adjusted between 0% and 100%, increasing the cost function. The confidence level in WVaR indicates risk aversion, with an increase in the cost function with a 4% increase. The agility coefficient, which indicates the percentage of waste demand production from HC transferred to another facility, also affects the cost function and population risk. A decrease in the sustainability coefficient results in a 53% rise in the cost function and a 12.82% increase in population risk. The model demonstrates NP-hard characteristics and becomes exponentially complex for larger scales. [ABSTRACT FROM AUTHOR]

The Ring Star Problem consists in selecting a subset of nodes called hubs including the depot and linking them with a cycle, the remaining nodes being connected to exactly one hub, at minimum cost. We study a survivable variant of the Ring Star Problem where at most one node in a given subset of so‐called uncertain nodes can fail if selected as a hub. We model this problem as an Integer Linear Program (ILP), that is also addressed with a Branch‐and‐Benders‐cut decomposition. The Benders subproblem is turned into a linear program with the addition of new inequalities that are shown to be facet‐defining, and several enhancements to both the ILP and Branch‐and‐Benders‐cut algorithm are also presented. Both approaches are compared on the basis of extensive numerical experiments that bring the following conclusions. First, the survivable variant is shown to be much harder than the original Ring Star Problem, and the extra cost induced by survivability is significant. Second, the ILP formulation tends to produce tighter lower bounds but memory issues are frequent for large instances. Finally, the Branch‐and‐Benders‐cut algorithm returns feasible solutions that are often of better quality than those produced by ILP, and is less frequently subjected to memory issues on the considered set of instances. [ABSTRACT FROM AUTHOR]

Because of the long planning periods and their long life cycle, railway infrastructure has to be outlined long ahead. At the present, the infrastructure is designed while only little about the intended operation is known. Hence, the timetable and the operation are adjusted to the infrastructure. Since space, time and money for extension measures of railway infrastructure are limited, each modification has to be done carefully and long lasting and should be appropriate for the future unknown demand. To take this into account, we present the robust network design problem for railway infrastructure under capacity constraints and uncertain timetables. Here, we plan the required expansion measures for an uncertain long‐term timetable. We show that this problem is NP‐hard even when restricted to bipartite graphs and very simple timetables and present easier solvable special cases. This problem corresponds to the fixed‐charge network design problem where the expansion costs are minimized such that the timetable is conductible. We model this problem by an integer linear program using time expanded networks. To incorporate the uncertainty of the future timetable, we use a scenario‐based approach. We define scenarios with individual departure and arrival times and optional trains. The network is then optimized such that a given percentage of the scenarios can be operated while minimizing the expansion costs and potential penalty costs for not scheduled optional trains. [ABSTRACT FROM AUTHOR]

Energy conservation is crucial in wireless ad hoc sensor network design to increase network lifetime. Since communication consumes a major part of the energy used by a sensor node, efficient communication is important. Topology control aims at achieving more efficient communication by dropping links and reducing interference among simultaneous transmissions by adjusting the nodes’ transmission power. Since dropping links make a network more susceptible to node failure, a fundamental problem in wireless sensor networks is to find a communication graph with minimum interference and minimum power assignment aiming at an induced topology that can satisfy fault-tolerant properties. In this paper, we examine and propose linear integer programming formulations and a hybrid meta-heuristic GRASP/VNS (Greedy Randomized Adaptive Search Procedure/Variable Neighborhood Search) to determine the transmission power of each node while maintaining a fault-tolerant network and simultaneously minimize the interference and the total power consumption. Optimal biconnected topologies for moderately sized networks with minimum interference and minimum power are obtained using a commercial solver. We report computational simulations comparing the integer programming formulations and the GRASP/VNS, and evaluate the effectiveness of three meta-heuristics in terms of the tradeoffs between computation time and solution quality. We show that the proposed meta-heuristics are able to find good solutions for sensor networks with up to 400 nodes and that the GRASP/VNS was able to systematically find the best lower bounds and optimal solutions.

Indonesia experiences a consistent annual escalation in plastic production, primarily attributed to the high demands from numerous industries. As a result of this escalated production rate, more significant quantities of plastic waste continue to be produced each year. In this regard, it is essential to comprehend that uncontrolled plastic waste generates harmful substances for humans and the environment. A reverse logistics network was introduced and developed to decrease the damaging effects of this waste on the environment. The Indonesian plastic waste reverse logistics system encountered some uncertainties due to limited data availability, showing significant fluctuations. To address these uncertainties, this study proposed a robust optimization model for the management of plastic waste within the reverse logistics system in Jakarta. The results showed that the model could accurately identify optimal facility locations and determine the exact quantity to transport between facilities while considering social, economic, and environmental factors. The results also showed that the proposed model minimized cost by 332 million USD, reduced gas emissions to 626 million m3 (ca. 1.2 billion kgCO2), and maximized labor by 611 thousand people.