Xianjia Wang

Genetic algorithm based on simplex method for solving linear-quadratic bilevel programming problem

The bilevel programming problems are useful tools for solving the hierarchy decision problems. In this paper, a genetic algorithm based on the simplex method is constructed to solve the linear-quadratic bilevel programming problem (LQBP). By use of Kuhn-Tucker conditions of the lower level programming, the LQBP is transformed into a single level programming which can be simplified to a linear programming by the chromosome according to the rule. Thus, in our proposed genetic algorithm, only the linear programming is solved by the simplex method to obtain the feasibility and fitness value of the chromosome. Finally, the feasibility of the proposed approach is demonstrated by the example.

A globally convergent algorithm for a class of bilevel nonlinear programming problem

Bilevel programming, one of the multilevel programming, is a class of optimization with hierarchical structure. This paper proposes a globally convergent algorithm for a class of bilevel nonlinear programming. In this algorithm, by use of the dual theory, the bilevel nonlinear programming is transformed into a traditional programming problem, which can be turned into a series of programming problem without constraints. So we can solve the infinite nonlinear programming in parallelism to obtain the globally convergent solution of the original bilevel nonlinear programming. And the example illustrates the feasibility and efficiency of the proposed algorithm.

A fuzzy interactive decision making algorithm for bilevel multi-followers programming with partial shared variables among followers

This paper studies a class of bilevel multi-followers programming in which there are partial shared variables among followers. A fuzzy interactive decision making approach is proposed to derive a satisfactory solution for decision makers not only considering the dominated action of the leader but also treating the ratios of satisfaction between the leader and the followers. Finally, a numerical example is illustrated to demonstrate the feasibility of the proposed approach.

A fuzzy multi-objective model for capacity allocation and pricing policy of provider in data communication service with different QoS levels

Data communication service has an important influence on e-commerce. The key challenge for the users is, ultimately, to select a suitable provider. However, in this article, we do not focus on this aspect but the viewpoint and decision-making of providers for order allocation and pricing policy when orders exceed service capacity. It is a multiple criteria decision-making problem such as profit and cancellation ratio. Meanwhile, we know realistic situations in which much of the input information is uncertain. Thus, it becomes very complex in a real-life environment. In this situation, fuzzy sets theory is the best tool for solving this problem. Our fuzzy model is formulated in such a way as to simultaneously consider the imprecision of information, price sensitive demand, stochastic variables, cancellation fee and the general membership function. For solving the problem, a new fuzzy programming is developed. Finally, a numerical example is presented to illustrate the proposed method. The results show that it is effective for determining the suitable order set and pricing policy of provider in data communication service with different quality of service (QoS) levels.

Dynamic Decision Model in Evolutionary Games Based on Reinforcement Learning

Abstract In evolutionary games, it becomes more difficult to choose optimal strategies for players because of incomplete information and bounded rationality. For bounded rational players, how to maximize the expected sum of payoffs by learning and changing strategies is an important question in evolutionary game theory. Reinforcement learning does not need a model of its environment and can be used online, it is well-suited for problems with incomplete and uncertain information. Evolutionary game theory is the subject about the decision problems of multiagent with incomplete information. In this article, reinforcement learning is introduced in evolutionary games, multiagent reinforcement learning model is constructed, and the learning algorithm is presented based on Q -learning. The results of simulation experiments show that the multiagent reinforcement learning model can be applied successfully in evolutionary games for finding the optimal strategies.

An information fusion method based on game theory

Aiming at the conflict circumstances of multi-sensor information system, the paper introduces the idea of game theory into data fusion and introduce a method to filter the conflict data based on mutual entropy to improve the reliability and accuracy of the fusion result. A function model referring to bus structure of JDL (Joint Directors of Laboratories) model is established and fusion algorithm based on game theory is brought out.

Study on risk assessment of water security of drought periods based on entropy weight methods

Purpose – The purpose of this paper is to obtain risk indicators of water security of drought periods in which the indices of reliability, resiliency, and vulnerability are integrated.Design/methodology/approach – It is not reasonable that weight coefficients of different risk indices are often determined subjectively in conventional procedures, so the entropy weight method is introduced and chosen to solve the problem. Entropy weight method can get the weight coefficients of different risk indices objectively and is valid from the case study.Findings – The feasibility and validity of entropy weight methods to determine weight coefficients of different risk indices objectively are recognized.Research limitations/implications – Accessibility and availability of data are the main limitations.Practical implications – The paper provides a more objective risk indicator of water security of drought periods for water resources managers.Originality/value – This paper determines the weight coefficients of differen...

Design, analysis and simulation of an optimal wage contract in firms

Purpose – The purpose of this paper is to analyze and solve the problem of moral hazard in firms because of asymmetry information between firms and workers and to contract upon the workers shiftless actions.Design/methodology/approach – Based on principle‐agent theory and human resource management practice, an optimal dynamic wage contract model is designed. By applying simulation technology, the dynamic wage contract model is compared to the general static wage contract model and the affects made by the optimal dynamic wage contract to workers and firms are analyzed.Findings – According to the consequences of simulation, the dynamic wage contract has better characteristics and is more practical than the static one. In the dynamic wage contract, the current action of a worker has a persistent effect on the future outcome. It is proved that the dynamic wage contract is optimal to the firm. The optimal dynamic wage contract is renegation‐proofness. It not only can incentive workers to work hard and help th...

The therapist assignment problem in home healthcare structures

A proposed mixed-integer program model for the therapist assignment problem.Effect study of the number of available time periods (ATPs) on efficiency of model.Effect study of continuity of care (COC) and patient priority (PP) on performance.A selection of at most five ATPs to each patient is optimal choice for performance.PP and COC have significant effects on the satisfaction and reassignment rate. Staff planning in Home Health Care (HHC) context is challenging due to the complexity, such as, unavailability of resources, variation in patient health conditions, and diversity of continuity of care (COC) and patients priority (PP). This necessitates the implementation of adequately effective models and intelligent systems to improve the robustness of care plans that run with limited input from the support staff. The work proposed an effective, simple, compatible and extensible model for the therapist assignment problem (TAP). The model aims at maximizing the assignment rate of demand, subject to the constraints of workload capacity limitation and available time selection clash. It helps the HHC structure managers to make proper decisions through preferred time periods (PTPs) selections and weight allocations. The analysis of the PTPs claims that the HHC structures applying the TAP model should offer a selection of at most five PTPs to each patient for the sake of effectiveness and efficiency. Following this suggestion, optimal solutions for all instances can be provided within 0.4s. The weight allocations depend on the various requirements for COC and PP. The analysis of results suggests that the HHC structures can adopt PP in the TAP model without hesitation. However, it also advises that they should pay attention on the adoption of COC, because it has a visible effect on the assignment rate of demand with the lower COC levels and the utilization rate of therapists, while slightly affecting the computational time of the TAP model and the total number of assigned demands. The work offers the HHC structures a demonstration of the core part of an effective planning system to help them make better decisions that satisfy patient demand, achieve high quality of service, and enhance efficiency.

Design of optimal double auction mechanism with multi-objectives

Abstract This paper proposes a new approach to design optimal double auction mechanism with multi-objectives. In the optimal double auction mechanism, optimality is represented as multi-objectives to maximize the expected total revenue of sellers and buyers respectively at the same time. We give representation of allocation rules and payment rules of the optimal double auction mechanism that satisfies incentive compatibility, individual rationality, market clearing, and budget-balanced restrictions. Finally, we present a numerical example to demonstrate the function of the developed optimal double auction mechanism and its efficiency.