Entrepreneurship brings about economic innovation and job formation, and its improvement can well account for the unemployment crisis. However, many barriers either stop entrepreneurs from entering the market, or lead their business to failure after entering. These barriers have been sparsely and case dependently reported in the literature, but, to the best of our knowledge, no studies have been yet designated to investigate the general barriers to entrepreneurship. This paper tries to bridge this gap by reviewing the most relevant and available literature to elicit the major general barriers to entrepreneurship. Eleven general barriers to entrepreneurship are identified and supported by the related literature. Since these barriers are not independent and unconnected, but interrelated and interactive, understanding the interactions among them can help decision makers in determining appropriate overcoming measures. In order to model these interactions this paper utilizes interpretive structural modeling (ISM) which has shown to be an efficient approach for analyzing systematic interactions among barriers. We distinct barriers into two groups of inside and outside barriers and with the support of the ISM-based model, we show that inside barriers are dependent on outside barriers. Corrupted and unsupportive business environment then, shows to be the major driving barrier to entrepreneurship.

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This paper introduces the Steiner Pollution-Routing Problem (SPRP) as a realistic variant of the PRP that can take into account the real operating conditions of urban freight distribution. The SPRP is a multi-objective, time and load dependent, fleet size and mix PRP, with time windows, flexible departure times, and multi-trips on congested urban road networks, that aims at minimising three objective functions pertaining to (i) vehicle hiring cost, (ii) total amount of fuel consumed, and (iii) total makespan (duration) of the routes. The paper focuses on a key complication arising from emissions minimisation in a time and load dependent setting, corresponding to the identification of the full set of the eligible road-paths between consecutive truck visits a priori, and to tackle the issue proposes new combinatorial results leading to the development of an exact Path Elimination Procedure (PEP). A PEP-based mixed integer programming model is further developed for the SPRP and embedded within an efficient mathematical programming technique to generate the full set of the non-dominated points on the Pareto frontier of the SPRP. The proposed model considers truck instantaneous Acceleration/Deceleration (A/D) rates in the fuel consumption estimation, and to address the possible lack of such data at the planning stage, a new model for the construction of reliable synthetic spatiotemporal driving cycles from available macroscopic traffic speed data is introduced. Several analyses are conducted to demonstrate the added value of the proposed approach, exhibit the trade-off between the business and environmental objectives on the Pareto front of the SPRP, show the benefits of using multiple trips, and verify the reliability of the proposed model for the generation of driving cycles. A real road network based on the Chicago's arterial streets is also used for further experimentation with the proposed PEP algorithm.