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Beautyscapes explores the global phenomenon of international medical travel, focusing on patient-consumers seeking cosmetic surgery outside their home country and on those who enable them to access treatment abroad, including surgeons and facilitators. It documents the journeys of those who travel for treatment abroad, as well as the nature and power relations of the IMT industry. Empirically rich and theoretically sophisticated, Beautyscapes draws on key themes of interest to students and researchers interested in globalisation and mobility to explain the nature and growing popularity of cosmetic surgery tourism. Richly illustrated with ethnographic material and with the voices of those directly involved in cosmetic surgery tourism, Beautyscapes explores cosmetic surgery journeys from Australia and China to East-Asia and from the UK to Europe and North Africa.

This paper is devoted to dynamic network flows. A phenomenon called dynamic network flow splash is studied (splash is an exceeding of flow value over maximum feasible static flow for the same network). Parallel and tree-structured network topology classes are defined. Conditions for occurring network flow splash in the parallel and tree-structured networks are discovered. A formula for computing a value of maximum dynamic network flow splash is obtained. An algorithm for calculating the formula values is presented.

We study network flow problems in an uncertain environment from the viewpoint of robust optimization. In contrast to previous work, we consider the case that the network parameters (e.g., capacities) are known and deterministic, but the network structure (e.g., nodes and arcs) is subject to uncertainty. In this paper, we study the robust and adaptive versions of the maximum flow problem and minimum cut problems in networks with node and arc failures, and establish structural and computational results. The adaptive two-stage model adjusts the solution after the realization of the failures in the network. This leads to a more flexible model and yields less conservative solutions compared to the robust model. We show that the robust maximum flow problem can be solved in polynomial time, but the robust minimum cut problem is NP-hard. We also prove that the adaptive versions are NP-hard. We further characterize the adaptive model as a two-person zero-sum game and prove the existence of an equilibrium in such games. Moreover, we consider a path-based formulation of flows in contrast to the more commonly used arc-based version of flows. This leads to a different model of robustness for maximum flows. We analyze this problem as well and develop a simple linear optimization model to obtain approximate solutions. Furthermore, we introduce the concept of adaptive maximum flows over time in networks with transit times on the arcs. Unlike the deterministic case, we show that this problem is NP-hard on series-parallel graphs even for the case that only one arc is allowed to fail. Finally, we propose heuristics based on linear optimization models that exhibit strong computational performance for large-scale instances.

The long chain has been an important concept in the design of flexible processes. This design concept, as well as other sparse designs, have been applied by the automotive and other industries as a way to increase flexibility in order to better match available capacities with variable demands. Numerous empirical studies have validated the effectiveness of these designs. However, there is little theory that explains the effectiveness of the long chain, except when the system size is large, i.e., by applying an asymptotic analysis. Our attempt in this paper is to develop a theory that explains the effectiveness of long chain designs for finite size systems. First, we uncover a fundamental property of long chains, supermodularity, that serves as an important building block in our analysis. This property is used to show that the marginal benefit, i.e., the increase in expected sales, increases as the long chain is constructed, and the largest benefit is always achieved when the chain is closed by adding the last arc to the system. Then, supermodularity is used to show that the performance of the long chain is characterized by the difference between the performances of two open chains. This characterization immediately leads to the optimality of the long chain among 2-flexibility designs. Finally, under independent and identically distributed (i.i.d.) demand, this characterization gives rise to three developments: (i) an effective algorithm to compute the performances of long chains using only matrix multiplications; (ii) a result that the gap between the fill rate of full flexibility and that of the long chain increases with system size, thus implying that the effectiveness of the long chain relative to full flexibility increases as the number of products decreases; (iii) a risk-pooling result implying that the fill rate of a long chain increases with the number of products, but this increase converges to zero exponentially fast.

We propose an approach to address data uncertainty for discrete optimization and network flow problems that allows controlling the degree of conservatism of the solution, and is computationally tractable both practically and theoretically. In particular, when both the cost coefficients and the data in the constraints of an integer programming problem are subject to uncertainty, we propose a robust integer programming problem of moderately larger size that allows controlling the degree of conservatism of the solution in terms of probabilistic bounds on constraint violation. When only the cost coefficients are subject to uncertainty and the problem is a 0-1 discrete optimization problem on n variables, then we solve the robust counterpart by solving at most n+1 instances of the original problem. Thus, the robust counterpart of a polynomially solvable 0-1 discrete optimization problem remains polynomially solvable. In particular, robust matching, spanning tree, shortest path, matroid intersection, etc. are polynomially solvable. We also show that the robust counterpart of an NP-hard [alpha]-approximable 0-1 discrete optimization problem, remains [alpha]-approximable. Finally, we propose an algorithm for robust network flows that solves the robust counterpart by solving a polynomial number of nominal minimum cost flow problems in a modified network.

Few would argue that maximising the performance of the many flow networks (FNs) that operate for the benefit of our society and the economy is anything but essential. Through seeking to mitigate the risks posed by different asset failure modes, maintenance is critical to minimising disruptions and maximising resilience. Repairable flow network (RFN) optimisation and reliability centred maintenance (RCM) are both used to support asset related decisions in FNs but independently; meaning, attempts to maximise FN performance using RCM are likely to result in suboptimal outcomes. There is limited work bringing RFN optimisation and RCM together to support evaluating maintenance decisions in the context of holistic network level performance (measured in terms of profitability) and in a way that considers the complex structural and topological relationships that exist between process components, equipment components, and failure modes. Hence, this paper addresses this by developing the complex repairable flow network (CRFN) modelling framework with the goal of ensuring RFN optimisation integrates complex process and equipment (including failure modes) component topologies, such that it operates in alignment with the needs of RCM as part of maximising network flow in terms of gross profitability. This was done through the creation of a novel and transdisciplinary multi-layered network-based approach that integrates information from what were termed the facility, process, maintainable item, and failure mode levels. Furthermore, through running simulation experiments on an example CRFN, it is demonstrated that the CRFN modelling framework can be used to evaluate the impact different maintenance strategies have on maximising network flow in terms of gross profitability.

In the face of diverse chronic pressures and increased factor mobility, the resilience of urban internal network structures has become a cutting-edge research topic. This study utilizes 2019 mobile signaling big data to construct employment and recreational flow networks among 101 townships and streets within Nanjing City. Based on the characteristics of these network structures, the resilience of the network structure is measured from the perspectives of density, symmetry, and transmissibility through interruption simulation techniques. The results show that the intensity of population mobility within Nanjing presents a general decay from the central urban area to the outer layers. In the employment scenario, cross-river population mobility is more frequent, while in the recreational scenario, the natural barrier effect of the Yangtze River is prominent. Due to the concentration of employment centers and high spatial heterogeneity, the employment flow network exhibits greater vulnerability to sudden shocks. Townships and streets with weighted degree values ranking around 60 and 80 are of great importance for maintaining the normal operation of both employment and recreational flow networks. Strengthening the construction of resilient parks and village planning within resilient cities can enhance the risk resistance of employment and recreational flow networks.

The problem of group ranking, also known as rank aggregation, has been studied in contexts varying from sports, to multicriteria decision making, to machine learning, to ranking Web pages, and to behavioral issues. The dynamics of the group aggregation of individual decisions has been a subject of central importance in decision theory. We present here a new paradigm using an optimization framework that addresses major shortcomings that exist in current models of group ranking. Moreover, the framework provides a specific performance measure for the quality of the aggregate ranking as per its deviations from the individual decision-makers’ rankings. The new model for the group-ranking problem presented here is based on rankings provided with intensity—that is, the degree of preference is quantified. The model allows for flexibility in decision protocols and can take into consideration imprecise beliefs, less than full confidence in some of the rankings, and differentiating between the expertise of the reviewers. Our approach relaxes frequently made assumptions of: certain beliefs in pairwise rankings; homogeneity implying equal expertise of all decision makers with respect to all evaluations; and full list requirement according to which each decision maker evaluates and ranks all objects. The option of preserving the ranks in certain subsets is also addressed in the model here. Significantly, our model is a natural extension and generalization of existing models, yet it is solvable in polynomial time. The group-rankings models are linked to network flow techniques.

We develop a network-flow approach for characterizing interim-allocation rules that can be implemented by ex post allocations. Our method can be used to characterize feasible interim allocations in general multi-unit auctions where agents face capacity constraints, both ceilings and floors. Applications include a variety of settings of practical interest, ranging from individual and group-specific capacity constraints, set-aside sale, partnership dissolution, and government license reallocation.

The complex and iterative workflow for designing the secondary air system (SAS) of a gas turbine engine still largely depends on human expertise and hence requires long lead times and incurs high design time-cost. This paper proposes an automated methodology to generate the whole-engine SAS flow network model from the engine geometry model and presents a convenient and inter-operable framework of the secondary air system modeller. The SAS modeller transforms the SAS cavities and flow paths into a 1D flow network model composed of nodes and links. The novel, object-oriented pre-processor embedded in the SAS modeller automatically assembles the conservation equations for all flow nodes and the loss correlations for all links. The twin-level, hierarchical SAS solver then solves the conservation equations of mass, momentum and energy supplemented with the correlations in the loss model library. The modelling swiftness, mathematical robustness and numerical stability of the present methodology are demonstrated through the results obtained from IP compressor rotor drum flow network model.