Yina Li

Pricing and promised delivery lead time decisions with a risk-averse agent

This paper studies how a risk-averse firm determines promised delivery lead time and price in a circumstance where the market demand depends not only on the selling price but also on the promised delivery lead time. Two scenarios, one in which the price and promised delivery lead time are independent and the other in which the price is promised delivery lead time sensitive, are analysed. Under conditional value-at-risk criterion, we examine how the risk aversion of decision-makers influences the optimal selection of price, promised delivery lead time and the overall utility of a risk-averse decision-maker. Moreover, by conducting comprehensive sensitivity analyses, we investigate the interaction effects of the decision-maker’s risk aversion and different parameters, including the price and lead time elasticity indexes, the inventory costs, as well as the capacity parameters on the optimal decisions and utilities, so as to offer managerial implications for practitioners under time-based competition environment.

Power system dispatch with wind power integrated using mean-variance model and group search optimizer

This paper presents the mean-variance (MV) model to solve power system dispatch problems with wind power integrated. In the MV model, the profit and the risk are taken into account simultaneously under the uncertain wind power (speed) environment. To describe this uncertain environment, the Monte Carlo (MC) simulation method is used to sample uncertain wind speeds. The optimization algorithm, group search optimizer (GSO), then optimize the MV model by introducing the risk tolerance parameter. The simulation is conducted based on the IEEE 30-bus power system, and the results demonstrate the applicability of the proposed model into power system dispatch problems with wind power integrated.

Optimal power flow using group search optimizer with intraspecific competition and lévy walk

This paper presents an enhanced group search optimizer (GSO), group search optimizer with intraspecific competition and lévy walk (GSOICLW), to solve the optimal power flow (OPF) problem. GSOICLW s a more biologically realistic algorithm and performs better balance between global and local searching than GSO n hat intraspecific competition IC) and lévy walk (LW) are introduced o GSO. GSOICLW is tested or the OPF problem on the IEEE 30-bus power system, with green house gases emission constraint considered. Simulation results demonstrate the accuracy and reliability of the proposed algorithm, compared with other evolutionary algorithms EAs).

Optimal introduction time decision for holiday products with uncertain market demand

In this paper, we investigate how retailers with different risk preferences determine the optimal introduction time and order quantity for holiday products to maximise performance in environments characterised by uncertain market demand. Specifically, both the market demand and the accuracy of sales forecasts are assumed to be affected by the timing of the introduction, which in turn affects the order quantity and subsequent performance. The results suggest that as the level of uncertainty in the market demand increases, as the retailer’s power in the industry increases and as the coefficient of elasticity of the competitive demand over introduction time is larger, the retailer prefers to set a later introduction time and to order smaller quantities, to reduce the risk of overstocking. Moreover, a risk-averse retailer will set a later introduction time and order smaller quantities than a risk-neutral retailer. Indeed, the more risk-averse the retailer is, the later will be the introduction time and the smaller the quantity ordered. The managerial implications are presented for decision-makers with different risk preferences regarding the interconnections between important factors and introduction time and inventory decisions for holiday products in environments characterised by uncertain market demand.