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This research is motivated by China's second largest flower auction market. Its existing auction logistics plan is to group the auction trolleys in a fixed order of 'same category and same grade'. The auction logistics centre cannot respond to demand fluctuations by dynamically optimising the order of participation, resulting in overall trading revenue decline. This paper proposes an integrated decision-making framework based on the theory of revenue optimisation. Through effective demand forecasting, the demand curves of different types of auction lots in multi-period are obtained. In the face of the reserved order of a limited auction trolley, order acceptance strategy is adopted to flexibly allocate auction trolleys, with the goal of revenue maximisation. Considering the impact of demand uncertainty and forecast errors on system performance, a robust control is employed. Through designing a genetic-variable neighbourhood algorithm, the solving of this large-scale practical problem can be controlled within acceptable computation time. Numerical experiments verify that the proposed approaches are superior to the traditional first-come-first-served rule. Sensitivity analyses provide significant implications for the scientific decision-making of managers, including the allocation ratio of group orders to individual orders, discounts on entry fees for group orders, and the division of auction time periods. [ABSTRACT FROM AUTHOR]

Abstract: The time control point strategy is often adopted by bus operators in China and Singapore to provide more reliable transit service. It is thus important to design a schedule, in which bus drivers should devote their efforts to catch up a scheduled arrival time at a predetermined time control point on a bus route because passengers can definitely benefit from a reliable bus route schedule. This paper first proposes a novel reliable bus route schedule design problem by taking into account the bus travel time uncertainty and the bus drivers’ schedule recovery efforts. It proceeds to develop a robust optimization model for the proposed problem, which aims to minimize the sum of the expected value of the random schedule deviation and its variability multiplied by a weighting value. A Monte Carlo simulation based solution method is subsequently designed to solve the robust optimization model. Finally, a numerical example based on a real bus route in Suzhou city of China is carried out to demonstrate the strength of the robust optimization model. We find that the optimal scheduled travel time (or slack time) depends on bus drivers’ schedule recovery behavior and on decision makers’ scheduling philosophies. [Copyright &y& Elsevier]