A new bi-objective model of the urban public transportation hub network design under uncertainty

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Design of a Bi-Objective Capacitated Single-Allocation Incomplete Hub Network Considering an Elastic Demand

This paper presents a bi-objective capacitated hub arc location problem with single assignment for designing a metro network with an elastic demand. In the literature, it is widely supposed that the network created with the hub nodes is complete. In this paper, this assumption is relaxed. Moreover, in most hub location problems, the demand is assumed to be static and independent of the location of hubs. However, in real life problems, especially for locating a metro hub, the demand is dependent on the utility that is proposed by each hub. By considering the elasticity of demand, the complexity of solving the problem increases. The presented model also has the ability to compute the number of trains between each pair of two hubs. The objectives of this model are to maximize the benefits of transportation and establishing the hub facilities while minimizing the total transportation time. Furthermore, the bi-objective model is converted into a single objective one by the TH method. The significance of applicability of the developed model is demonstrated by a number of numerical experiments and some sensitivity analyses on the data inspired by the Qom monorail project. Finally, the conclusion is provided

A new bi-objective model of the urban public transportation hub network design under uncertainty

This paper presents a new bi-objective multi-modal hub location problem with multiple assignment and capacity considerations for the design of an urban public transportation network under uncertainty. Because of the high construction costs of hub links in an urban public transportation network, it is not economic to create a complete hub network. Moreover, the demand is assumed to be dependent on the utility proposed by each hub. Thus, the elasticity of the demand is considered in this paper. The presented model also has the ability to compute the number of each type of transportation vehicles between every two hubs. The objectives of this model are to maximize the benefits of transportation by establishing hub facilities and to minimize the total transportation time. Since exact values of some parameters are not known in advance, a fuzzy multi-objective programming based approach is proposed to optimally solve small-sized problems. For medium and large-sized problems, a meta-heuristic algorithm, namely multi-objective particle swarm optimization is applied and its performance is compared with results from the non-dominated sorting genetic algorithm. Our experimental results demonstrated the validity of our developed model and approaches. Moreover, an intensive sensitivity analyze study is carried out on a real-case application related to the monorail project of the holy city of Qom.Other Information Published in: Annals of Operations Research License: https://creativecommons.org/licenses/by/4.0See article on publisher's website: http://dx.doi.org/10.1007/s10479-019-03430-9</p

Comparison of pay-for-performance (P4P) programs in primary care of selected countries: a comparative study

Abstract Background Pay for performance (P4P) schemes provide financial incentives or facilities to health workers based on the achievement of predetermined performance goals. Various P4P programs have been implemented around the world. There is a question of which model is suitable for p4p implementation to achieve better results. The purpose of this study is to compare pay for performance models in different countries. Methods This is a descriptive-comparative study comparing the P4P model in selected countries in 2022. Data for each country are collected from reliable databases and are tabulated to compare their payment models. the standard framework of the P4P model is used for data analysis. Results we used the standard P4P model framework to compare pay for performance programs in the primary care sector of selected countries because this framework can demonstrate all the necessary features of payment programs, including performance domains and measures, basis for reward or penalty, nature of the reward or penalty, and data reporting. The results of this study show that although the principles of P4P are almost similar in the selected countries, the biggest difference is in the definition of performance domains and measures. Conclusions Designing an effective P4P program is very complex, and its success depends on a variety of factors, from the socioeconomic and cultural context and the healthcare goals of governments to the personal characteristics of the healthcare provider. considering these factors and the general framework of the features of P4P programs are critical to the success of the p4p design and implementation