Showing papers by "Yanfei Lan published in 2016"

Joint operations algorithm for large-scale global optimization

Abstract: Graphical abstractDisplay Omitted HighlightsWe propose a novel meta-heuristic algorithm called joint operations algorithm.Joint operations algorithm contains offensive, defensive and regroup operations.We compare JOA with six algorithms on 20 functions and four real-life problems.The experimental results show that JOA has the best overall performance. Large-scale global optimization (LSGO) is a very important but thorny task in optimization domain, which widely exists in management and engineering problems. In order to strengthen the effectiveness of meta-heuristic algorithms when handling LSGO problems, we propose a novel meta-heuristic algorithm, which is inspired by the joint operations strategy of multiple military units and called joint operations algorithm (JOA). The overall framework of the proposed algorithm involves three main operations: offensive, defensive and regroup operations. In JOA, offensive operations and defensive operations are used to balance the exploration ability and exploitation ability, and regroup operations is applied to alleviate the problem of premature convergence. To evaluate the performance of the proposed algorithm, we compare JOA with six excellent meta-heuristic algorithms on twenty LSGO benchmark functions of IEEE CEC 2010 special session and four real-life problems. The experimental results show that JOA performs steadily, and it has the best overall performance among the seven compared algorithms.

Incentive contract design in project management with serial tasks and uncertain completion times

Abstract: This article investigates an incentive contract design problem for a project manager who operates a project consisting of multiple tasks performed sequentially by different subcontractors in which all task completion times are uncertain and described by fuzzy variables. On the basis of an expected value criterion and a critical value criterion, two classes of fuzzy bilevel programming models are developed. In the case where the uncertain task completion times are mutually independent, each model can first be decomposed into multiple equivalent sub-models by taking advantage of the structural characteristics, and then a two-step optimization method is employed to derive the optimal incentive contract in each sub-model. In a more general case where the uncertain task completion times are correlative, the approximation approach (AA) technique is adopted first in order to evaluate the objective functions involving fuzzy parameters, which are usually difficult to convert into their crisp equivalents. Then, an ...

An uncertain wage contract model for risk-averse worker under bilateral moral hazard

Abstract: This paper investigates a wage mechanism design problem faced by a risk neutral firm (he) who employs a risk averse worker (she) to sell products for him. The effort levels of both the firm and the worker are unobservable to each other, which results in bilateral moral hazard. The firm offers a wage contract menu to the worker with the objective of maximizing his expected profit. The results show that the firm will provide the same wage contract to the worker when the worker's effort is observable regardless of the market condition being full or private information. The optimal wage contract is related to the worker's risk averse level when the bilateral moral hazard exists. The information values of the worker's effort and the market condition are studied, respectively. The results show that the firm benefits from the worker's observable effort under full information and only when the sales uncertainty is sufficiently low, can the firm profit from that under private information. Moreover, only if the cost coefficient of the firm's effort is sufficiently high, the firm can benefit from full information in the scenario when the worker's effort is unobservable.

Impact of price cap regulation on supply chain contracting between two monopolists

Abstract: This paper considers a supply chain with an unregulated upstream monopolist (she) supplying a kind of products to a regulated downstream monopolist (he). The upstream monopolist's production efficiency, which represents her type, is only privately known to herself. When the downstream monopolist trades with the upstream monopolist, his pricing discretion is constrained by price cap regulation (PCR). We model this problem as a game of adverse selection with the price cap constraint. In this model, the downstream monopolist offers a menu of contracts, each of which consists of two parameters: the transfer payment and the retail price. We show that private information can weaken PCR's impact on the optimal contract, and PCR can dampen the effects of private information. We also shed light on the influences of private information and PCR on the optimal contract, the downstream monopolist's profit, the upstream monopolist's profit, the consumers' surplus and the social total welfare, respectively. Finally, a numerical example is given to illustrate the proposed results.