Yanfei Lan

Modeling fuzzy multi-period production planning and sourcing problem with credibility service levels

A great deal of research has been done on production planning and sourcing problems, most of which concern deterministic or stochastic demand and cost situations and single period systems. In this paper, we consider a new class of multi-period production planning and sourcing problem with credibility service levels, in which a manufacturer has a number of plants and subcontractors and has to meet the product demand according to the credibility service levels set by its customers. In the proposed problem, demands and costs are uncertain and assumed to be fuzzy variables with known possibility distributions. The objective of the problem is to minimize the total expected cost, including the expected value of the sum of the inventory holding and production cost in the planning horizon. Because the proposed problem is too complex to apply conventional optimization algorithms, we suggest an approximation approach (AA) to evaluate the objective function. After that, two algorithms are designed to solve the proposed production planning problem. The first is a PSO algorithm combining the AA, and the second is a hybrid PSO algorithm integrating the AA, neural network (NN) and PSO. Finally, one numerical example is provided to compare the effectiveness of the proposed two algorithms.

Optimizing material procurement planning problem by two-stage fuzzy programming

This paper presents a new class of two-stage fuzzy material procurement planning (MPP) models with minimum-risk criteria, in which the material demand, the spot market material unit price and the spot market material supply quantity are uncertain and assumed to be fuzzy variables with known possibility distributions. We formulate the two-stage MPP model with the objective of maximizing the credibility of the total material procurement costs less than a given allowable investment level, and the credibility can be regarded as the material procurement risk criteria in a fuzzy environment. Since the fuzzy material demand, the fuzzy spot market material unit price and the fuzzy spot market material supply quantity are usually continuous fuzzy variables with infinite supports, the proposed MPP model belongs to an infinite-dimensional optimization problem that cannot be solved directly. To avoid this difficulty, we apply an approximation approach (AA) to the proposed two-stage fuzzy MPP model, and turn it into an approximating finite-dimensional optimization one. The convergence about the objective function of the approximating two-stage MPP model to that of the original two-stage MPP one is also discussed. Since the exact analytical expression for the objective function in the approximating fuzzy MPP model is unavailable, and the approximating MPP model is a mixed-integer program that is neither linear nor convex, traditional optimization algorithms cannot be used to solve it. Therefore, we develop two heuristic algorithms to solve the approximating MPP model. The first is a particle swarm optimization (PSO) algorithm based on the AA, and the second is a hybrid PSO algorithm which based on the AA and a neural network (NN). Finally, we provide an actual optimization problem about the fuel procurement to compare the effectiveness of the designed algorithms.

An uncertain contract model for rural migrant worker's employment problems

This paper studies the implementation of the employment relationship problem between the enterprise (he) and the rural migrant worker (she) in labor market. An uncertain contract model is established to maximize the expected utility of the enterprise under incentive feasible mechanism, in which the enterprise’s assessment of the rural migrant worker’s own income at home is subjective, and characterized as an uncertain variable. The crisp equivalent model is then presented and the optimal solution for the equivalent model is obtained. The results show that if the rural migrant worker’s own income at home is higher, she is less willing to pay much effort on work, i.e., the rural migrant worker’s optimal effort level decreases with her income at home. Finally, a numerical example is presented to show the effectiveness of the model.

A bilevel fuzzy principal-agent model for optimal nonlinear taxation problems

This paper presents a bilevel fuzzy principal-agent model for optimal nonlinear taxation problems with asymmetric information, in which the government and the monopolist are the principals, the consumer is their agent. Since the assessment of the government and the monopolist about the consumer’s taste is subjective, therefore, it is reasonable to characterize this assessment as a fuzzy variable. What’s more, a bilevel fuzzy optimal nonlinear taxation model is developed with the purpose of maximizing the expected social welfare and the monopolist’s expected welfare under the incentive feasible mechanism. The equivalent model for the bilevel fuzzy optimal nonlinear taxation model is presented and Pontryagin maximum principle is adopted to obtain the necessary conditions of the solutions for the fuzzy optimal nonlinear taxation problems. Finally, one numerical example is given to illustrate the effectiveness of the proposed model, the results demonstrate that the consumer’s purchased quantity not only relates with the consumer’s taste, but also depends on the structure of the social welfare.

The impact of risk attitude in new product development under dual information asymmetry

Four classes of uncertain principal agent models are presented.The closed form expressions for the optimal wage contracts are derived.The information values of the idea and the effort are characterized.Several interesting managerial insights are provided. This paper investigates the impact of risk attitude on incentives and performances in a two-stage (research stage and development stage) new product development setting with one senior executive (she) and one project manager (he). The senior executive offers a wage contract to the project manager in the presence of dual information asymmetry including his unknown idea value of a new product in early research stage and unobservable effort to convert the idea into a product in later development stage. Due to the variability of technology and market, the subjective assessments about the idea value and the revenue generated by the product are characterized as uncertain variables. Within the framework of uncertainty theory, we first present four classes of uncertain principal agent models, and then derive their respective optimal wage contract mechanisms. We find that the structures of the senior executives optimal mechanisms depend on the project managers risk attitude. If the project manager becomes more conservative, the senior executive should set a low incentive term to avert risk. Otherwise, she should do the opposite. Moreover, we identify two values: the information value of the idea-how much the senior executive is willing to pay to acquire information regarding the project managers idea value, and the information value of the effort-how much the senior executive ensures to win when she can contract on the project managers effort. Our results show that acquiring the project managers idea information yields the highest potential when the project manager is aggressive, but in the case of contracting on his effort, the opposite appears to be true. The results also indicate that acquiring more information on an aggressive project managers idea always has higher impact on the senior executives profits than contracting on his effort. We also provide several interesting managerial insights in new product development through our analytical and simulation results.

Joint operations algorithm for large-scale global optimization

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.

A fuzzy supply chain contract problem with pricing and warranty

This paper analyzes a supply chain contract problem combining pricing with warranty under incomplete information, in which the suppliers product quality is usually unobservable and has a vagueness boundary to the buyer, it is reasonable to be characterized as a fuzzy variable. There are two important decisions of the buyer: the pricing decision and the warranty decision. Thus, a pricing and warranty contract model is developed with the purpose of maximizing the buyers expected payoff under incentive feasible scheme. The analysis method is mainly decomposing the buyers problem into an implementation problem and an optimization problem. The results demonstrate that, if purchasing quantity and product quality are complementary, the buyers second-best purchasing quantity will be less than the first-best one; if substitutable, the opposite is true. Moreover, in order to demonstrate the superiority/novelty of the proposed model, two degenerated contracts, i.e., the pricing contract and the warranty contract are discussed, respectively, and the advantage of the combined pricing and warranty contract is also given. Finally, one numerical example is given to illustrate the applicability of the proposed model.

Fuzzy two-stage material procurement planning problem

Material procurement planning (MPP) deals with the problem that purchasing the right quantity of material from the right supplier at the right time, a purchaser can reduce the material procurement costs via a reasonable MPP model. In order to handle the MPP problem in a fuzzy environment, this paper presents a new class of two-stage fuzzy MPP models, in which the material demand, the spot market material unit price and the spot market material supply quantity are assumed to be fuzzy variables with known possibility distributions. In addition, the procurement decisions are divided into two groups. Some procurement decisions, called first-stage decisions, must be taken before knowing the the particular values taken by the fuzzy variables; while some other decisions, called second-stage decisions, can be taken after the realizations of the fuzzy variables are known. The objective of the proposed fuzzy MPP model is to minimize the expected material procurement costs over the two stages. On other hand, since the fuzzy material demand, the fuzzy spot market material unit price and the fuzzy spot market material supply quantity are usually continuous fuzzy variables with infinite supports, the proposed MPP model belongs to an infinite-dimensional optimization problem whose objective function cannot be computed exactly. To avoid this difficulty, we suggest an approximation approach (AA) to evaluating the objective function, and turn the original MPP model into an approximating finite-dimensional one. To show the credibility of the AA, the convergence about the objective function of the approximating MPP model to that of the original MPP one is discussed. Since the exact analytical expression for the objective function in the approximating fuzzy MPP model is unavailable, and the approximating MPP model is a mixed-integer program that is neither linear nor convex, the traditional optimization algorithms cannot be used to solve it. Therefore, we design an AA-based particle swarm optimization to solve the approximating two-stage fuzzy MPP model. Finally, we apply the two-stage MPP model to an actual fuel procurement problem, and demonstrate the effectiveness of the designed algorithm via numerical experiments.

An inspection-based price rebate and effort contract model with incomplete information

Three strategies, named inspection, price rebate and effort, are employed.The second-best inspection ratio is the same as the first-best one.The second-best effort is less than the first-best one.The second-best effort is larger than first-best in the case of substitute.It is opposite in the case of complement. This paper considers a supply chain contract design problem, in which a buyer purchases a batch of products from a supplier and then sells it to consumers. The product quality with a continuous type is the suppliers private information and cannot be observed by the buyer. Furthermore, three strategies, named inspection, price rebate and effort, are simultaneously employed in the contract so as to incentivize the supplier to improve his product quality. An inspection-based price rebate and effort contract model is developed with the purpose of maximizing the buyers expected payoff. The optimal solution demonstrates that the second-best inspection ratio is the same as the first-best one. With respect to the suppliers optimal effort level, if the suppliers effort level and his product quality are substitutable, then his second-best effort level will be larger than the first-best one, while in the case of complement, it is smaller than the first-best level.

Monitoring mechanisms in new product development with risk-averse project manager

It is necessary for one senior executive (she) to monitor her project manager (he) who conducts early research stage followed by a later development stage in new product development. In this paper, we analyze two monitoring mechanisms: (1) the idea information-based monitoring (IM) mechanism wherein the senior executive engages one supervisor to monitor the project manager’s idea information; (2) the effort-based monitoring (EM) mechanism wherein the senior executive engages another supervisor to monitor the project manager’s effort. Within the framework of uncertainty theory, we first present two classes of bilevel uncertain principal-agent monitoring models, and then derive their respective optimal incentive contracts. We find that the senior executive should set the incentive term as high as possible to motivate each supervisor to monitor the project manager’s idea information and effort no matter how much the design idea value is. We also find that EM mechanism can always dominate IM mechanism when the monitoring costs are equal. Moreover, comparing with a no monitoring scenario, we identify two values of monitoring: the value of monitoring idea information and the value of monitoring effort. Our results show that adopting IM and EM mechanisms can improve the senior executive’s profits obtained in the no monitoring scenario when the revenue uncertainty is sufficiently low. The results also indicate that the value of monitoring idea information decreases as the risk aversion level of the project manager improves, while the value of monitoring effort shows the opposite feature.