In airline industries, the aircraft maintenance cost takes up about 13% of the total operating cost. It can be reduced by a good planning. Spare parts inventories exist to serve the maintenance planning. Compared with...
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In airline industries, the aircraft maintenance cost takes up about 13% of the total operating cost. It can be reduced by a good planning. Spare parts inventories exist to serve the maintenance planning. Compared with commonly used reorder point system (ROP) and forecasting methods which only consider historical data, this paper presents two non-linear programming models which predict impending demands based on installed parts failure distribution. The optimal order time and order quantity can be found by minimizing total cost. The first basic mathematical model assumes shortage period starts from mean time to failure (MITF). An iteration method and GAMS are used to solve this model. The second improved mathematical model takes into account accurate shortage time. Due to its complexity, only GAMS is applied in solution methodology. Both models can be proved effective in cost reduction through revised numerical examples and their results. Comparisons of the two models are also discussed. (C) 2014 Elsevier Ltd. All rights reserved.
We study the following discrete facility location game. Two players, a leader and a follower, open facilities and compete to attract clients from a given market. Each player has a budget and maximizes own market share...
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ISBN:
(纸本)9781509001996
We study the following discrete facility location game. Two players, a leader and a follower, open facilities and compete to attract clients from a given market. Each player has a budget and maximizes own market share. Each client splits own demand probabilistically over all opened facilities by the gravity rule. The goal is to find the location and design of the leader facilities to maximize his market share. We present an alternating heuristic and exact method for this game. We rewrite the problem as mixed integer linear program with exponential number of constraints. In our method, we start with small subset of constraints and iteratively enlarge it until upper and lower bounds not coincide. Computational results are discussed.
Forecasting and Time series techniques are frequently used and play extremely important roles in managerial activities and decision-making processes. Holt-Winters (HW) which is one of the most popular forecasting meth...
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ISBN:
(纸本)9781479960651
Forecasting and Time series techniques are frequently used and play extremely important roles in managerial activities and decision-making processes. Holt-Winters (HW) which is one of the most popular forecasting methods is utilized in cases where data show seasonality and/or trend. The method involves the selection of several parameters for optimum prediction results. Heuristics are usually utilized for optimum parameter selection and it appears to be an open field for improvement. In this study "Spreadsheet modeling" of HW method is improved by minimizing the mean squared error (i.e. prediction error). Since the forecast error is nonlinear function;Holt-Winters parameter optimization in this study is achieved by "Excel nonlinear Solver" and "Differential Evolution Search" techniques. To increase the usability of the spreadsheet modeling advance macro programming techniques are incorporated with the developed models in Microsoft Excel.
Abstract-This article presents a non-linear programming-based model for the optimal placement of phasor measurement units. The optimal phasor measurement units placement is formulated to minimize the number of phasor ...
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Abstract-This article presents a non-linear programming-based model for the optimal placement of phasor measurement units. The optimal phasor measurement units placement is formulated to minimize the number of phasor measurement units required for full system observability and to maximize the measurement redundancy at all buses in a power system. A sequential quadratic programming algorithm is used for the solution of the proposed model. The existence of power flow and injection measurements, the limited phasor measurement units channel capacity, the lack of communication facilities in substations, and the single phasor measurement units loss are also incorporated into the initial proposed formulation. The non-linear programming model is applied to IEEE 14- and 118-bus test systems in MATLAB. The accuracy and the effectiveness of the proposed method is verified by comparing the simulation results to those obtained by a binary integer programming model also implemented in MATLAB. The comparative study shows that the proposed non-linear programming model yields the same number of phasor measurement units as the binary integer programming model. A remarkable advantage of the non-linear programming against binary integer linearprogramming is its capability to give more than one optimal solution, each one having the same minimum number of phasor measurement units (same minimum objective value), but at different locations.
This paper proposes an arlene scaling derivative-free trust region method with interior backtracking technique for bounded-constrained nonlinearprogramming. This method is designed to get a stationary point for such ...
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This paper proposes an arlene scaling derivative-free trust region method with interior backtracking technique for bounded-constrained nonlinearprogramming. This method is designed to get a stationary point for such a problem with polynomial interpolation models instead of the objective function in trust region subproblem. Combined with both trust region strategy and line search technique, at each iteration, the affine scaling derivative-free trust region subproblem generates a backtracking direction in order to obtain a new accepted interior feasible step. Global convergence and fast local convergence properties are established under some reasonable conditions. Some numerical results are also given to show the effectiveness of the proposed algorithm.
Consider a portfolio containing heterogeneous risks, where the policyholders’ premiums to the insurance company might not cover the claim payments. This risk has to be taken into consideration in the premium pricing....
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Consider a portfolio containing heterogeneous risks, where the policyholders’ premiums to the insurance company might not cover the claim payments. This risk has to be taken into consideration in the premium pricing. On the other hand, the premium that the insureds pay has to be fair. This fairness is measured by the distance between the risk and the premium paid. We apply a non-linear programming formulation to find the optimal premium for each class so that the risk is below a given level and the weighted distance between the risk and the premium is minimized. We consider also the dual problem: minimizing the risk level for a given weighted distance between risks and premium.
The reliability analysis of a component or a system is an important concept in almost all engineering disciplines. In general, fuzzy sets are used to analyze the system reliability. The system reliability may be forme...
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The reliability analysis of a component or a system is an important concept in almost all engineering disciplines. In general, fuzzy sets are used to analyze the system reliability. The system reliability may be formed as linear or nonlinearprogramming with cost function in fuzzy environment. To analyze the fuzzy system reliability, the reliability of each component of the system is represented as fuzzy number in nature. In this paper, we have presented the parallel system model with fuzzy cost function to evaluate the maximum reliability subject to minimum cost using trapezoidal fuzzy number and to find out the Co-efficient of Variance for each membership function. A numerical example is given to illustrate the model for nonlinearprogramming and to evaluate the system reliability.
In this paper, we put forward a new hybrid methodology to generate forecasts of time series. Indeed, the proposed forecaster is a HWCF that integrates the following techniques: wavelet decomposition;ARIMA models;SVRs;...
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In this paper, we put forward a new hybrid methodology to generate forecasts of time series. Indeed, the proposed forecaster is a HWCF that integrates the following techniques: wavelet decomposition;ARIMA models;SVRs;wavelet combination of forecasts;and non-linear programming. Basically, the HWCF is able to capture, simultaneously, linear and non-linear auto-dependence structures exhibited by a time series, which are represented, at time t, by both the linear and non-linear combined forecasts: L-C,L-t and N-C,N-t, respectively. After obtaining the combined forecasts L-C,L-t and N-C,N-t, they are summed (i. e., L-C,L-t + N-C,N-t = yh,t), producing the hybrid forecast yh,t, for each instant t. The numerical results show that HWCF achieved relevant accuracy gains in forecasting process of the annual time series of sunspot, when comparing with other ten competitive forecasters.
This paper focuses on supplier-related decisions in a newsvendor setting. We build upon the current literature by analysing the newsvendor problem with multiple unreliable and non-identical suppliers. We also incorpor...
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This paper focuses on supplier-related decisions in a newsvendor setting. We build upon the current literature by analysing the newsvendor problem with multiple unreliable and non-identical suppliers. We also incorporate both fixed ordering costs and capacity limits for supplier selection. We develop an exact algorithm to solve the problem optimally and a heuristic algorithm to solve the problem efficiently. Through structural properties of the optimal solution and a numerical study, we provide useful managerial implications regarding optimal sourcing strategies in complex supply chains. Previous literature concludes that with multiple unreliable (independent) suppliers, cost is the order qualifier and reliability is the order winner. We found that when fixed ordering costs and supply capacities exist, this insight no longer holds. We also examine the sensitivity of the sourcing decisions to supplier capacity levels, demand uncertainty, salvage value and shortage cost. Our results show that high levels of demand uncertainty lead firms to turn to a single-sourcing strategy whereas high salvage values and high shortage cost suggest multi-sourcing strategy.
The increasing integration of larger amounts of wind energy into power systems raises important operational issues, such as the balance between power generation and demand. The pumped storage hydro (PSH) units are one...
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The increasing integration of larger amounts of wind energy into power systems raises important operational issues, such as the balance between power generation and demand. The pumped storage hydro (PSH) units are one possible solution to mitigate this problem, once they can store the excess of energy in the periods of higher generation and lower demand. However, the behavior of a PSH unit may differ considerably from the expected in terms of wind power integration when it operates in a liberalized electricity market under a price-maker context. In this regard, this paper models and computes the optimal PSH weekly scheduling in a price-taker and price-maker scenarios, either when the PSH unit operates in standalone and integrated in a portfolio of other generation assets. Results show that the price-maker standalone PSH will integrate less wind power in comparison with the price-taker situation. Moreover, when the PSH unit is integrated in a portfolio with a base load power plant, the role of the price elasticity of demand may completely change the operational profile of the PSH unit. (C) 2014 Elsevier Ltd. All rights reserved.
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