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...
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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.
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...
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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...
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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.
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...
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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.
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...
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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
In many real-world problems, quality of the available information is generally poor and uncertainties presented as multiple formats may exist in various system components. In this study, a dual-interval fuzzy stochast...
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In many real-world problems, quality of the available information is generally poor and uncertainties presented as multiple formats may exist in various system components. In this study, a dual-interval fuzzy stochastic programming (DIFSP) method is developed for tackling uncertainties presented as dual intervals and random variables within a multistage context. The developed method is applied to a case study of long-term planning of a municipal solid waste-management system. It has been demonstrated that DIFSP has advantages in addressing the dynamic, interactive, and uncertain characteristics. Moreover, it can reflect dynamics in terms of both waste-flow allocation and facility-capacity expansion through constructing a multilayer scenario tree. With the aid of an interactive algorithm woven with a vertex analysis, solutions for waste-flow allocation and facility-capacity expansion under various uncertainty levels in decision making may be illuminated in support of solid waste-management system planning in urban regions. They can help decision makers to identify desired waste-management policies with minimized system cost and maximized environmental efficiency.
We consider in this paper the synthesis of process networks with time-varying uncertain yields in which investment in pilot plants can be considered to reduce uncertainty of the yields. We formulate this problem as a ...
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We consider in this paper the synthesis of process networks with time-varying uncertain yields in which investment in pilot plants can be considered to reduce uncertainty of the yields. We formulate this problem as a multistage stochastic program with decision dependent elements where investment strategies are considered to reduce uncertainty, and time-varying distributions are used to describe uncertainty. We propose a new mixed-integer/disjunctive programming model which is reformulated as a mixed-integer linear program. Since the model can only be solved through an LP-based branch and bound for smaller instances, we propose a duality-based branch and bound algorithm for solving larger problems. Two numerical examples are presented to illustrate the application of the proposed method. (C) 2007 Elsevier Ltd. All rights reserved.
We propose a two-stage stochastic integer programming model for the winner determination problem (WDP) in combinatorial auctions to hedge the shipper's risk under shipment uncertainty. The shipper allows bids on c...
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We propose a two-stage stochastic integer programming model for the winner determination problem (WDP) in combinatorial auctions to hedge the shipper's risk under shipment uncertainty. The shipper allows bids on combinations of lanes and solves the WDP to determine which carriers are to be awarded lanes. In addition, many other important comprehensive business side constraints are included in the model. We demonstrate the value of the stochastic solution over one obtained by a deterministic model based on using average shipment volumes. Computational results are given that indicate that moderately sized realistic instances can be solved by commercial branch and bound solvers in reasonable time. (C) 2009 Elsevier Ltd. All rights reserved.
We consider stochastic programs with risk measures in the objective and study stability properties as well as decomposition structures. Thereby we place emphasis on dynamic models, i.e., multistage stochastic programs...
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We consider stochastic programs with risk measures in the objective and study stability properties as well as decomposition structures. Thereby we place emphasis on dynamic models, i.e., multistage stochastic programs with multiperiod risk measures. In this context, we define the class of polyhedral risk measures such that stochastic programs with risk measures taken from this class have favorable properties. Polyhedral risk measures are defined as optimal values of certain linear stochastic programs where the arguments of the risk measure appear on the right-hand side of the dynamic constraints. Dual representations for polyhedral risk measures are derived and used to deduce criteria for convexity and coherence. As examples of polyhedral risk measures we propose multiperiod extensions of the Conditional-Value-at-Risk.
Due to the increased number and intensity of wildfires, the need for solutions that minimize the impact of fire are needed. Fuel treatment is one of the methods used to mitigate the effects of fire at a certain area. ...
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Due to the increased number and intensity of wildfires, the need for solutions that minimize the impact of fire are needed. Fuel treatment is one of the methods used to mitigate the effects of fire at a certain area. In this thesis, a two-stage stochastic programming model for fuel treatment management is constructed. The model optimizes the selection of areas for fuel treatment under budget and man- hour constraints. The process makes use of simulation tools like PHYGROW, which mimics the growth of vegetation after treatment, and FARSITE, which simulates the behavior of fire. The model minimizes the costs of fuel treatment as well as the potential losses when fire occurs. Texas Wildfire Risk Assessment Model (TWRA) used by Texas Forest Service (TFS) is used to quantify risk at each area. The model is applied at TX 12, which is a fire planning unit under the administration of TFS. Results show that the total of the expenditures on fuel treatment and the expected impact justify the efforts of fuel treatment.
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