Nowadays, the sourcing problem has become more challenging for supply chain members. Different types of sourcing for different market conditions are presented in the literature. In this paper, an option contract, as a...
详细信息
Nowadays, the sourcing problem has become more challenging for supply chain members. Different types of sourcing for different market conditions are presented in the literature. In this paper, an option contract, as an efficient tool for sourcing, is developed in a multi-period setting in which the price and demand follow two stochastic processes. The sourcing decision is analyzed from a risk neutral and a risk averse decision-maker point of view. This paper applies the stochastic programming approach to model the presented option contract based on price and demand uncertainties. Next, using CVaR as a coherent risk measure, the effects of risk on sourcing problem are studied. By numerical example, using the presented efficient frontier, the simulation results of our developed models show that the decision maker can make a trade-off between risk and cost associated with the sourcing problem. The paper also performs a sensitivity analysis in order to demonstrate the effects of change in cost parameters on the results of our option model. (C) 2014 Sharif University of Technology. All rights reserved.
Consideration was given to the a priori formulation of the multistage problem of stochastic programming with a quantile criterion which is reducible to the two-stage problem. Equivalence of the two-stage problems with...
详细信息
Consideration was given to the a priori formulation of the multistage problem of stochastic programming with a quantile criterion which is reducible to the two-stage problem. Equivalence of the two-stage problems with the quantile criterion in the a priori and a posteriori formulations was proved for the general case. The a posteriori formulation of the two-stage problem was in turn reduced to the equivalent problem of mixed integer linear programming. An example was considered.
Large corporations fund their capital and operational expenses by issuing bonds with a variety of indexations, denominations, maturities and amortization schedules. We propose a multistage linear stochastic programmin...
详细信息
Large corporations fund their capital and operational expenses by issuing bonds with a variety of indexations, denominations, maturities and amortization schedules. We propose a multistage linear stochastic programming model that optimizes bond issuance by minimizing the mean funding cost while keeping leverage under control and insolvency risk at an acceptable level. The funding requirements are determined by a fixed investment schedule with uncertain cash flows. Candidate bonds are described in a detailed and realistic manner. A specific scenario tree structure guarantees computational tractability even for long horizon problems. Based on a simplified example, we present a sensitivity analysis of the first stage solution and the stochastic efficient frontier of the mean-risk trade-off. A realistic exercise stresses the importance of controlling leverage. Based on the proposed model, a financial planning tool has been implemented and deployed for Brazilian oil company Petrobras. (C) 2014 Elsevier B.V. All rights reserved.
The stochastic transportation problem involves in many areas such as production scheduling, facility location, resource allocation, logistics management. Constructing an operable solving method has important theoretic...
详细信息
The stochastic transportation problem involves in many areas such as production scheduling, facility location, resource allocation, logistics management. Constructing an operable solving method has important theoretical and practical value. In this paper, we first analyze the characteristic and deficiencies of the existing stochastic programming methods, such as higher computation complexity. We then give the concept of reliability coefficient and a quasi-linear processing pattern based on expectation and variance. We further analyze the relationship between reliability coefficient and reliability degree, also give the selecting strategy of reliability coefficient. Based on that, we establish a quasi-linear programming model for stochastic transportation problem, and we analyze its performance by a case-based example. The results indicate that this model has good interpretability and operability. It can effectively solve the transportation problem under complex stochastic environment or with incomplete information. (C) 2013 Elsevier Inc. All rights reserved.
In this paper, we study the relationship between maximum principle (MP) and dynamic programming principle (DPP) for forward-backward control system under consistent convex expectation dominated by G-expectation. Under...
详细信息
The performance of an engine is one of the major concerns in the automotive industry. Increased emission restrictions on motor vehicles have led to the necessity of more accurate control on the engine performance. The...
详细信息
The performance of an engine is one of the major concerns in the automotive industry. Increased emission restrictions on motor vehicles have led to the necessity of more accurate control on the engine performance. The objective of this study is to optimize the injection and ignition system in an automobile internal-combustion engine. In this paper the engine torque, the fuel consumption and the hydrocarbon emissions are considered as the main performance metrics of the engine, and a mathematical model is proposed on the basis of response surface methodology and two-stage stochastic programming. The proposed model encompasses desirability functions with a maximum-minimum operator to convert several responses into a single response and also considers the probabilistic pattern of regression coefficients by some random scenarios. To illustrate application of the proposed approach, it was applied in a real case in an automotive company. The results of this test represent a reasonable performance of the proposed approach.
The location of multiple cross-docking centers (CDCs) and vehicle routing scheduling are two crucial choices to be made in strategic/tactical and operational decision levels for logistics companies. The choices lead t...
详细信息
The location of multiple cross-docking centers (CDCs) and vehicle routing scheduling are two crucial choices to be made in strategic/tactical and operational decision levels for logistics companies. The choices lead to more realistic problem under uncertainty by covering the decision levels in cross-docking distribution networks. This paper introduces two novel deterministic mixed-integer linear programming (MILP) models that are integrated for the location of CDCs and the scheduling of vehicle routing problem with multiple CDCs. Moreover, this paper proposes a hybrid fuzzy possibilistic-stochastic programming solution approach in attempting to incorporate two kinds of uncertainties into mathematical programming models. The proposed solving approach can explicitly tackle uncertainties and complexities by transforming the mathematical model with uncertain information into a deterministic model. m' imprecise constraints are converted into 2Rm' precise inclusive constraints that agree with R alpha-cut levels, along with the concept of feasibility degree in the objective functions based on expected interval and expected value of fuzzy numbers. Finally, several test problems are generated to appraise the applicability and suitability of the proposed new two-phase MILP model that is solved by the developed hybrid solution approach involving a variety of uncertainties and complexities. (C) 2013 Elsevier Inc. All rights reserved.
This paper provides necessary and sufficient optimality conditions for abstract-constrained mathematical programming problems in locally convex spaces under new qualification conditions. Our approach exploits the geom...
详细信息
This paper provides necessary and sufficient optimality conditions for abstract-constrained mathematical programming problems in locally convex spaces under new qualification conditions. Our approach exploits the geometrical properties of certain mappings, in particular their structure as difference of convex functions, and uses techniques of generalized differentiation (subdifferential and coderivative). It turns out that these tools can be used fruitfully out of the scope of Asplund spaces. Applications to infinite, stochastic and semi-definite programming are developed in separate sections.
We introduce a variant of Multicut Decomposition Algorithms, called CuSMuDA (Cut Selection for Multicut Decomposition Algorithms), for solving multistage stochastic linear programs that incorporates a class of cut sel...
详细信息
We introduce a variant of Multicut Decomposition Algorithms, called CuSMuDA (Cut Selection for Multicut Decomposition Algorithms), for solving multistage stochastic linear programs that incorporates a class of cut selection strategies to choose the most relevant cuts of the approximate recourse functions. This class contains Level 1 (Philpott et al. in J Comput Appl Math 290:196-208, 2015) and Limited Memory Level 1 (Guigues in Eur J Oper Res 258:47-57, 2017) cut selection strategies, initially introduced for respectively stochastic Dual Dynamic programming and Dual Dynamic programming. We prove the almost sure convergence of the method in a finite number of iterations and obtain as a by-product the almost sure convergence in a finite number of iterations of stochastic Dual Dynamic programming combined with our class of cut selection strategies. We compare the performance of Multicut Decomposition Algorithms, stochastic Dual Dynamic programming, and their variants with cut selection (using Level 1 and Limited Memory Level 1) on several instances of a portfolio problem. On these experiments, in general, stochastic Dual Dynamic programming is quicker (i.e., satisfies the stopping criterion quicker) than Multicut Decomposition Algorithms and cut selection allows us to decrease the computational bulk with Limited Memory Level 1 being more efficient (sometimes much more) than Level 1.
This paper extends the single-item single-stocking location nonstationary stochastic inventory problem to relax the assumption of independent demand. We present a mathematical programming-based solu-tion method built ...
详细信息
This paper extends the single-item single-stocking location nonstationary stochastic inventory problem to relax the assumption of independent demand. We present a mathematical programming-based solu-tion method built upon an existing piecewise linear approximation strategy under the receding horizon control framework. Our method can be implemented by leveraging off-the-shelf mixed-integer linear pro-gramming solvers. It can tackle demand under various assumptions: the multivariate normal distribution, a collection of time-series processes, and the Martingale Model of Forecast Evolution. We compare against exact solutions obtained via stochastic dynamic programming to demonstrate that our method leads to near-optimal plans.(c) 2022 Elsevier B.V. All rights reserved.
暂无评论