Tourism generates huge amounts of waste. It has been estimated that about half of the waste generated by hotels is food and garden bio-waste. This bio-waste can be used to make compost and pellets. In turn, pellets can be used as an absorbent material in composters and as an energy source. In this paper, we consider the problem of locating composting and pellet-making facilities so that the bio-waste generated by a chain of hotels can be managed at or close to the generation points. The general objective is twofold: i) to avoid waste transportation from generation to treatment points and product transportation from production to demand points, and ii) to implement a circular model in which the hotels themselves become the suppliers of the products they need (compost and pellets) by transforming the bio-waste that they generate. Any bio-waste not processed by the hotels has to be treated at private or state-run plants. A mathematical optimization model is presented to locate the facilities and allocate the waste and products. The application of the proposed location-allocation model is illustrated with an example.

The spread of infectious disease is an inherently stochastic process. As such, real time control and prediction methods present a significant challenge. For diseases which spread through direct human interaction, (e.g., transferred from infected to susceptible individuals) the contagion process can be modeled on a social-contact network where individuals are represented as nodes, and contacts between individuals are represented as links. The model presented in this paper seeks to identify the infection pattern which depicts the current state of an ongoing outbreak. This is accomplished by inferring the most likely paths of infection through a contact network under the assumption of partially available infection data. The problem is formulated as a bi-linear integer program, and heuristic solution methods are developed based on sub-problems which can be solved much more efficiently. The heuristic performance is presented for a range of randomly generated networks and different levels of information. The model results, which include the most likely set of infection spreading contacts, can be used to provide insight into future epidemic outbreak patterns, and aid in the development of intervention strategies.

The European space-economy represents a complex system with a great internal heterogeneity, intensive socioeconomic interactions and differential growth trajectories among countries and regions. The present study aims to investigate the connectivity between spatial competitiveness and resilience in Europe and seeks to design an operational framework for concerted strategies of competitive and resilient regions. To assess the linkage between resilience and competitiveness, we have developed a new measure, viz. the Resilience and Competitiveness Index (RACI) as a function of two constituent sub-indices: Resilience and Competitiveness. This approach is tested on the basis of detailed data on European regions. The empirical results from 268 EU NUTS2 regions offer a solid anchor point for the proposed operational framework for concerted development strategies of competitive and resilient regions. Our research distinguishes and proposes several systematic types of concerted regional strategies according to the performance of a region measured by Resilience and Competiveness sub-indices. A key result of the study is the design of an operational constellation for strategic regional policy evaluation, with a major added value for policy- and decision-making purposes. The use of official data from Eurostat and of standard indicators in our research assures continuity and consistency with the official Regional Competitiveness Index (RCI) classification and measurement, so that policy makers are able to compare the performance of their regions over time and to develop proper concerted strategies accordingly. The clear evidence of a connectivity between regional competitiveness and resilience may help to develop a governance approach that balances competitiveness (mainly represented by productive assets) with resilience (mainly represented by sustainability and ecological awareness) and thus to deal with the complexity in socioeconomic systems.

It is well-known that small states, because of their size, tend to be less endowed with natural resources than big ones. This makes small states vulnerable and raises the question if specific policies can be implemented to offset the drawbacks of their small size and to increase resilience. We address this question in this paper, thereby focusing on the role of connectivity - between states, organisations, parties, or otherwise - in understanding a country's vulnerability and resilience. Here 'policies' are interpreted as 'institutions' in the sense of Douglass C. North (1990), i.e. as 'humanly devised constraints that structure political, economic and social interaction'. We focus on the Caribbean area, which is characterised by a wide variety of small states, each with its own set of rules and regulations. Within this area, we concentrate on the relationship between three Dutch Caribbean islands, i.e., Aruba, Curaçao, and Sint Maarten, on the one hand, and the Netherlands, the former colonizer, on the other hand. As a first step we have measured the economic vulnerability and resilience of 17 Caribbean island states, both dependent and independent, employing the theoretical framework proposed by Lino Briguglio. The outcomes show that the three Dutch island states are performing comparatively well, although there are individual differences. We provide a first effort to explain this outcome in terms of the continuing interest of the three island states to keep their ties to the former colonizer viable. Here the presence of 'systemic interest' as shown by the stakeholders appears to be a most important variable.

This paper highlights the relevance of connectivity and its architecture as a general conceptual framework which underlies and integrates the concepts of network vulnerability, complexity, and resilience. In particular, it will be pointed out that connectivity architecture can be considered an explicit key element for network vulnerability and shock propagation. While the relevance of the various connectivity configurations is not clearly emphasised in the dynamic complexity models of the space-economy, it appears to play a primary role in network analysis. In this regard, the emerging recognition of connectivity architecture in relation to hubs ‒ and hierarchies of hubs ‒ in a complex network will help the enhancement of network resilience. The paper develops as follows. First, the notion of network vulnerability, which refers not only to the phenomenon of shocks, but also to the propagation of shocks in a network, will be examined. Here it appears that modelling vulnerability and shock propagation, also jointly with cascading disaster models, is strongly based on connectivity issues. The question is: How can conventional (complex) system dynamic modelling, as well as network modelling, take into account these shocks and connectivity dynamics from the methodological viewpoint? A review in this respect shows how connectivity is a 'hidden' element in these complexity models, for example, in chaos or (dynamic) competition models, where interaction parameter values might lead to vulnerable domains and chaotic behaviour. On the contrary, connectivity and its various topologies have a distinct, primary role in network analysis. The issue of network resilience appears therefore to be the 'response' to vulnerability and chaos, calling for robustness and stability of the network in the presence of shocks and disruptions. Resilience analysis refers to the speed at which a network returns to its equilibrium after a shock, as well as to the perturbations/shocks that can be absorbed before the network is induced into some other equilibrium (adaptivity). Connectivity is relevant here, but not often considered in spatial economics. In order to reach a unified methodological framework, attention will finally be paid to a complementary analysis of the (dynamic) concepts of vulnerability and resilience. In this light, chaos models/properties might be seen in a positive perspective, since small changes can lead to uncertain and unstable effects, but also, thanks to connectivity, to new equilibria which are not necessarily negative. Thus, the architecture of connectivity, in its interdisciplinary insights, can be considered as a fundamental (and analytical) approach for identifying vulnerability and resilience patterns in complex networks.

To better deploy the landside rapid transit network for large airports, this study proposes a multi-objective transit network design model to maximize passenger demand coverage, reduce passenger travel time and minimize operational cost simultaneously. This model is formulated as an equivalent integer programming problem by predefining the transportation corridors and passengers' OD pairs. A branch-and-cut algorithm is proposed to find a non-inferior solution set. We also conduct trade-off analysis between efficiency, effectiveness and equity under each deployment strategy using the modified Gini coefficient method. The effectiveness of the proposed model and solution algorithm are tested with rapid transit network of the Beijing Capital International Airport. Results show that among the three common network topologies, including star, tree and finger, the passenger demand coverage and travel time reduction per unit cost under star topology outperform the other two topologies. As for finger topology, the performances of the passenger demand coverage and travel time reduction are the best among the three, but the cost is the poorest. In addition, the trade-off analysis shows that the solution whose objective is to maximize passenger demand coverage has a higher efficiency and a lower unit cost than the solution whose objective is to reduce travel time. However, the latter has a higher level of equity, especially for the medium and low-cost solutions. The proposed method in this study can help the decision makers to design effective landside rapid transit networks for large airports to improve the service level.

This paper investigates rumor transmission over online social networks, such as those via Facebook or Twitter, where users liberally generate visible content to their followers, and the attractiveness of rumors varies over time and gives rise to opposition such as counter-rumors. All users in social media platforms are modeled as nodes in one of five compartments of a directed random graph: susceptible, hesitating, infected, mitigated, and recovered (SHIMR). The system is expressed with edge-based formulation and the transition dynamics are derived as a system of ordinary differential equations. We further allow individuals to decide whether to share, or disregard, or debunk the rumor so as to balance the potential gain and loss. This decision process is formulated as a game, and the condition to achieve mixed Nash equilibrium is derived. The system dynamics under equilibrium are solved and verified based on simulation results. A series of parametric analyses are conducted to investigate the factors that affect the transmission process. Insights are drawn from these results to help social media platforms design proper control strategies that can enhance the robustness of the online community against rumors.