Scheduling of home health care services based on multi-agent systems

Home Health Care (HHC) services are growing worldwide and, usually, the home care visits are manually planned, being a time and effort consuming task that leads to a non optimized solution. The use of some optimization techniques can significantly improve the quality of the scheduling solutions, but lacks the achievement of solutions that face the fast reaction to condition changes. In such stochastic and very volatile environments, the fast re-scheduling is crucial to maintain the system in operation. Taking advantage of the inherent distributed and intelligent characteristics of Multi-agent Systems (MAS), this paper introduces a methodology that combines the optimization features provided by centralized scheduling algorithms, e.g. genetic algorithms, with the responsiveness features provided by MAS solutions. The proposed approach was codified in Matlab and NetLogo and applied to a real-world HHC case study. The experimental results showed a significant improvement in the quality of scheduling solutions, as well as in the responsiveness to achieve those solutions.info:eu-repo/semantics/publishedVersio

Using emission functions in modeling environmentally sustainable traffic assignment policies

Transport systems play a crucial role for sustainable development, and hence, sustainable urban transportation has recently become a major research area. Most of the existing studies propose evaluation methods that use simulation tools to assess the sustainability of different transportation policies. Although there are some recent studies, considering the sustainability dimension and the resulting policies through mathematical programming models is still an open research area. In this study, we focus on controlling the gas emissions for the environmental sustainability and propose several mathematical programming models that incorporate the measurements of gas emissions over a traffic network. We define emission functions in terms of the traffic flow so that the accumulated emission amounts can be modeled accurately, particularly in case of congestion. Using these emission functions, we introduce alternate objective functions and develop optimization models under various policies which are based on the well-known toll pricing and capacity enhancement. The proposed models both reflect the route choice decisions of the network users and the decisions of the transportation managers that aim at making the transport systems more sustainable through the policies of interest. We conduct a computational study on a well-known testing network and present numerical results to evaluate the proposed alternate models. We conclude that simultaneously applying the toll pricing and capacity enhancement policies is in general more effective in serving the travel demand and reducing the emission amounts compared to implementing these policies individually

Disaster Response in Turkey : Conditions Promoting Cross-Sectoral Collaboration and Implications for Effectiveness

Local and civil society can play decisive roles in disaster response. Yet, the disaster management literature is unclear regarding the conditions that enable cross-sectoral collaboration. Using a collaborative governance framework and 44 semi-structured interviews, this study investigates how trust, pre-existing relations, interdependence, knowledge, and resources affect cross-sectoral collaboration during disaster response in Turkey. The results illustrate how these factors interact with system context factors, like political compatibility, to facilitate or hinder cross-sectoral collaboration. The study concludes that cross-sectoral collaboration is no panacea for successful disaster response but empirical examples suggest that cross-sectoral collaboration can contribute to reducing suboptimal disaster response