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Reducing energy consumption and promoting sustainable mobility solutions, including public transport (PT), are increasingly becoming key objectives for policymakers worldwide. Energy saving dispatching optimization for bus rapid transit (BRT) is one of the most efficient strategies for reducing traffic congestion and energy conservation. The purpose of this paper is to address the BRT dispatching problem while taking into account the association between the vehicle type, the waiting time of passengers and the energy consumption of vehicles. This paper presents a mechanical model to describe the level of energy used in different vehicles based on engine universal characteristics considering the characteristics of the vehicle, engine, road, and driving type. The load factor and the passenger average waiting time are used to estimate the quality of service. Furthermore, in order to determine the vehicle scheduling scheme, a multi-objective energy saving dispatching optimization model of BRT is developed aiming to minimize the waiting time of passengers and energy consumption of vehicles. Moreover, a two-phase algorithm is employed in order to solve this multi-objective model. The results show that the designed algorithm is valid for solving the dispatching optimization model of BRT, and the energy consumption and passenger waiting time can be reduced by using an appropriate dispatching scheme. INDEX TERMS BRT dispatching, energy consumption, multiple types of vehicles, multi-objective, niched genetic algorithm.

Purpose – Critical links in traffic networks are those who should be better protected because their removal has a significant impact on the whole network. So, the purpose of this paper is to identify the critical links of traffic networks. Design/methodology/approach – This paper proposes the definition of the critical link for an urban traffic network and establishes mathematical model for determining critical link considering the travellers’ heterogeneous risk-taking behavior. Moreover, in order to improve the computational efficiency, the impact area of a link is quantified, a partial network scan algorithm for identifying the critical link based on the impact area is put forward and the efficient paths-based assignment algorithm is adopted. Findings – The proposed algorithm can significantly reduce the search space for determining the most critical links in traffic network. Numerical results also demonstrate that the structure of efficient paths has significant impact on identifying the critical links. Originality/value – This paper identifies the critical links by using a bi-level programming approach and proposes a partial network scan algorithm for identifying critical links accounting for travellers’ heterogeneous risk-taking behavior.

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The diversity of products and fierce competition make the stability and production cost of manufacturing industry more important. So, the purpose of this paper is to deal with the multi-product aggregate production planning (APP) problem considering stability in the workforce and total production costs, and propose an efficient algorithm. Taking into account the relationship of raw materials, inventory cost and product demand, a multi-objective programming model for multi-product APP problem is established to minimize total production costs and instability in the work force. To improve the efficiency of the algorithm, the feasible region of the planned production and the number of workers in each period are determined and a local search algorithm is used to improve the search ability. Based on the analysis of the feasible range, a genetic algorithm is designed to solve the model combined with the local search algorithm. For analyzing the effect of this algorithm, the information entropy strategy, NSGA-II strategy and multi-population strategy are compared and analyzed with examples, and the simulation results show that the model is feasible, and the NSGA-II algorithm based on the local search has a better performance in the multi-objective APP problem.