In mixed-model assembly lines, smooth operation of the assembly line depends on adherence to the scheduled sequence. However, during production process, this sequence is altered both intentionally and uninstentionally...
详细信息
In mixed-model assembly lines, smooth operation of the assembly line depends on adherence to the scheduled sequence. However, during production process, this sequence is altered both intentionally and uninstentionally. A major source of unintentional sequence alteration in automobile plants is the paint defects. A post-paint resequencing buffer, located before the final assembly is used to restore the altered sequence. Restoring the altered sequence back to the scheduled sequence requires three distinct operations in this buffer: Changing the positions (i.e. resequencing) of vehicles, inserting spare vehicles in between difficult models and replacing spare vehicles with paint defective vehicles. We develop a two-stage stochastic model to determine the optimal number of spare vehicles from each model-colour type to be placed into the Automated Storage and Retrieval System resequencing buffer that maximises the scheduled sequence achievement ratio (SSAR). The model contributes to the literature by explicitly considering above three distinct operations and random nature of paint defect occurrences. We use sample average approximation algorithm to solve the model. We provide managerial insights on how paint entrance sequence, defect rate and buffer size affect the SSAR. A value of stochastic solution shows that the model significantly outperforms its deterministic counterpart.
Resource portfolio planning optimization is crucial to high-tech manufacturing industries. One of the most important characteristics of such a problem is intensive investment and risk in demands. In this study, a nonl...
详细信息
Resource portfolio planning optimization is crucial to high-tech manufacturing industries. One of the most important characteristics of such a problem is intensive investment and risk in demands. In this study, a nonlinear stochastic optimization model is developed to maximize the expected profit under demand uncertainty. For solution efficiency, a stochastic programming-based genetic algorithm (SPGA) is proposed to determine a profitable capacity planning and task allocation plan. The algorithm improves a conventional two-stage stochastic programming by integrating a genetic algorithm into a stochastic sampling procedure to solve this large-scale nonlinear stochastic optimization on a real-time basis. Finally, the tradeoff between profits and risks is evaluated under different settings of algorithmic and hedging parameters. Experimental results have shown that the proposed algorithm can solve the problem efficiently. (C) 2006 Elsevier B.V. All rights reserved.
This study presents a stochastic mixed-integer linear programming model for the aircraft sequencing and scheduling problem. The proposed model aims to minimise the average fuel consumption per aircraft in the Terminal...
详细信息
This study presents a stochastic mixed-integer linear programming model for the aircraft sequencing and scheduling problem. The proposed model aims to minimise the average fuel consumption per aircraft in the Terminal Manoeuvring Area while considering uncertain flight durations for each flight. The tabu search algorithm was selected to solve the problem. The stochastic solution and deterministic solution results were compared to show the benefits of the stochastic solution. The average sample approximation technique was applied to this problem, and enhancement rates of the average fuel consumption per aircraft were 8.78% and 9.11% comparing the deterministic approach
Testing is a crucial step in new product development in many industrial sectors, from microelectronics to the automotive industry. In the pharmaceutical sector, specifically, candidate drugs have to undergo clinical t...
详细信息
Testing is a crucial step in new product development in many industrial sectors, from microelectronics to the automotive industry. In the pharmaceutical sector, specifically, candidate drugs have to undergo clinical trials, a process that takes 2-4 years and costs hundreds of millions of dollars. In this paper we are concerned with the scheduling of clinical trials and the planning of the resources necessary to carry these trials out. We present a stochastic programming (SP) framework that addresses the two problems simultaneously. To address large problems we develop a number of results and methods. First, we exploit the structure of the problem to reduce the number of pairs of scenarios for which non-anticipativity has to be enforced, and the number of binary variables. Second, we develop a finite-horizon approximation that allows us to formulate problems using fewer stages without compromising the quality of the solution. Third, we take advantage of the sequential nature of the testing process to develop a smaller but tighter mixed-integer programming (MIP) formulation;we show that a relaxation of this formulation can be used to obtain feasible and most often optimal solutions over the stages of interest. Finally, we develop a rolling-horizon-based approach, where the decisions of the relaxed problem are used over few early periods determining how and when uncertainty will be realized, and a new problem is formulated and solved as we move forward in time. (C) 2008 Elsevier Ltd. All rights reserved.
We discuss the incorporation of risk measures into multistage stochastic programs. While much attention has been recently devoted in the literature to this type of model, it appears that there is no consensus on the b...
详细信息
We discuss the incorporation of risk measures into multistage stochastic programs. While much attention has been recently devoted in the literature to this type of model, it appears that there is no consensus on the best way to accomplish that goal. In this paper, we discuss pros and cons of some of the existing approaches. A key notion that must be considered in the analysis is that of consistency, which roughly speaking means that decisions made today should agree with the planning made yesterday for the scenario that actually occurred. Several definitions of consistency have been proposed in the literature, with various levels of rigor;we provide our own definition and give conditions for a multi-period risk measure to be consistent. A popular way to ensure consistency is to nest the one-step risk measures calculated in each stage, but such an approach has drawbacks from the algorithmic viewpoint. We discuss a class of risk measures which we call expected conditional risk measures that address those shortcomings. We illustrate the ideas set forth in the paper with numerical results for a pension fund problem in which a company acts as the sponsor of the fund and the participants' plan is defined-benefit. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.
In this paper, a new type of wind turbine that is called INVELOX has been used. INVELOX has many advantages such as six times more power generation than previous types, work at low speed, inconsiderable maintenance an...
详细信息
In this paper, a new type of wind turbine that is called INVELOX has been used. INVELOX has many advantages such as six times more power generation than previous types, work at low speed, inconsiderable maintenance and investment costs, and reduce the environmental effects of previous wind turbines. Moreover, other renewable and nonrenewable generators are used in the energy management and scheduling of the microgrid. The test case is a microgrid with selling and buying energy capability in which the cost and pollution are considered as the objective functions. In the following, Uncertainties of wind speed, solar radiation and electrical-thermal loads are investigated and a multi-objective stochastic mixed integer linear programming is solved in the first scenario. Then, in the second scenario, the effects of fuel cost uncertainty on generation units and objective functions have been studied. The Epsilon constraints method and fuzzy satisfying are utilized to solve the problem and choose the best solution, respectively. By using of INVELOX turbines, total cost and pollution of the microgrid in both deterministic and stochastic planning are reduced from 192.68 $ to 97.23 $ and 249.28 $ to 126.38 $, as well 3334.76 Kg to 3302.7 and 3925.63 to 3910.2 Kg respectively. (c) 2019 Elsevier Ltd. All rights reserved.
This paper provides a technique based on stochastic programming to optimally solve the electricity procurement problem faced by a large consumer. Supply sources include bilateral contracts, a limited amount of self-pr...
详细信息
This paper provides a technique based on stochastic programming to optimally solve the electricity procurement problem faced by a large consumer. Supply sources include bilateral contracts, a limited amount of self-production and the pool.. Risk aversion is explicitly modeled using the conditional value-at-risk methodology. Results from a realistic case study are provided and analyzed.
We argue that stochastic programming provides a powerful framework to tune and analyze the performance limits of controllers. In particular, stochastic programming formulations can be used to identify controller setti...
详细信息
We argue that stochastic programming provides a powerful framework to tune and analyze the performance limits of controllers. In particular, stochastic programming formulations can be used to identify controller settings that remain robust across diverse scenarios (disturbances, set-points, and modeling errors) observed in real-time operations. We also discuss how to use historical data and sampling techniques to construct operational scenarios and inference analysis techniques to provide statistical guarantees on limiting controller performance. Under the proposed framework, it is also possible to use risk metrics to handle extreme (rare) events and stochastic dominance concepts to conduct systematic benchmarking studies. We provide numerical studies to illustrate the concepts and to demonstrate that modern modeling and local/ global optimization tools can tackle large-scale applications. The proposed work also opens the door to data-based controller tuning strategies that can be implemented in real-time operations. (C) 2017 American Institute of Chemical Engineers
To handle the product configuration problem with uncertain supply and demand, stochastic programming approach is applied to formulate the problem as a stochastic mixed-integer programming model. Carbon emission is fur...
详细信息
To handle the product configuration problem with uncertain supply and demand, stochastic programming approach is applied to formulate the problem as a stochastic mixed-integer programming model. Carbon emission is further integrated into the deployed stochastic model under four different carbon emission regulations. Benders decomposition algorithm is utilized to solve the stochastic model. Computational studies show that the Benders decomposition method can solve large-scale stochastic programming problems with faster convergence rate than commercial solver CPLEX does. The results from the numerical experimental analysis demonstrate the impacts of carbon emission regulations on product configuration decisions.
The periodic selection of new product development (NPD) projects is a crucial operational decision. The main goals of start-up companies in NPD are to attain a reliable return level and deliver this return level fast....
详细信息
The periodic selection of new product development (NPD) projects is a crucial operational decision. The main goals of start-up companies in NPD are to attain a reliable return level and deliver this return level fast. Achieving these goals is complicated because of uncertainties in projects' returns and durations. We develop new disjunctive stochastic programming models that capture the above-mentioned NPD goals. The first stochastic model is static, representing the traditional waterfall product development process, whereas the second one is dynamic, representing the agile product development process. We design a reformulation method and a decomposition algorithm to solve a problem encountered by a U.S.-based software start-up company. Our results indicate counter-intuitively that high reliability in attaining a targeted return may be achieved by investing in projects with a longer development time and higher risk. Furthermore, we show that if the capability to make dynamic decisions is overlooked while available, the time to attain the targeted return is overestimated.
暂无评论