Travel demands in remote areas are scattered and unstable. Thus, the integration of different types of transportation can be beneficial for improving the service level of transportation systems in remote areas. This s...
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Travel demands in remote areas are scattered and unstable. Thus, the integration of different types of transportation can be beneficial for improving the service level of transportation systems in remote areas. This study proposes a two-stage stochastic programming (SP) model for the integrated planning of a fixed-route-fixed-schedule bus service and dial-a-ride (DAR) transportation (DART) in remote areas. In the first stage, the route and timetable of a bus line are optimized as a set partitioning problem. In the second stage, DART is formulated as a pickup and delivery vehicle routing problem with time windows (PDVRPTW). A hybrid algorithm is developed to solve the two-stage SP problem. Case studies are presented to validate the planning model and the solution algorithm. Results show that the integration of bus and DAR services and the implementation of DART as PDVRPTW considerably reduce unserved demands and improve the service of the entire system.
This paper is concerned with the qualification management problem of parallel machines under high uncertainties in the semiconductor manufacturing industry. Product-machine qualification, or recipe-machine qualificati...
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This paper is concerned with the qualification management problem of parallel machines under high uncertainties in the semiconductor manufacturing industry. Product-machine qualification, or recipe-machine qualification, is a complicated, time-consuming process that is frequently encountered in semiconductor manufacturing. High uncertainty, a common aspect of the semiconductor manufacturing process, significantly enhances the complexity of this process. This paper mainly focuses on addressing such a complex scheduling problem by presenting a general two-stage stochastic programming formulation, which embeds uncertainty into the qualification management problem. The proposed model considers the capacity loss resulting from traditional random capacity factors, such as tool failures, and recipe-machine qualification, making it more applicable to real systems. To solve this problem, we propose a Lagrangian-relaxation-based surrogate subgradient approach. Numerical experiments indicate that this approach is capable of optimizing the problem in acceptable computation time. In addition, given that obtaining complete distribution information for random variables is unavailable in practice, a simplified approach is also developed to approximate the initial problem.
We present a brief overview of four phases of nurse planning. For the last phase, which assigns nurses to patients, a stochastic integer programming model is developed. A Benders' decomposition approach is propose...
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We present a brief overview of four phases of nurse planning. For the last phase, which assigns nurses to patients, a stochastic integer programming model is developed. A Benders' decomposition approach is proposed to solve this problem, and a greedy algorithm is employed to solve the recourse subproblem. To improve the efficiency of the algorithm, we introduce sets of valid inequalities to strengthen a relaxed master problem. Computational results are provided based upon data from Baylor Regional Medical Center in Grapevine, Texas. Finally, areas of future research are discussed.
One effective way to compensate for uncertainties is the use and management of energy storage. Therefore, a new method based on stochastic programming (SP) is proposed here, for optimal bidding of a generating company...
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One effective way to compensate for uncertainties is the use and management of energy storage. Therefore, a new method based on stochastic programming (SP) is proposed here, for optimal bidding of a generating company (GenCo) owning a compressed air energy storage (CAES) along with wind and thermal units to maximize profits. This scheduling has been presented for the GenCo's participation in day-ahead energy and spinning reserve (SR) markets and CVaR is also considered as a risk-controlling index. Firstly, the obtained results are validated by comparing with those of two previous studies. Then, the complete results of the proposed method are presented on a real power system, which indicate the capability of SP in scheduling CAES units. Furthermore, it is observed that CAES units can gain greater profits in joint energy and reserve markets due to their high ramp rates. In addition, the value of stochastic solution (VSS) is used to quantify the advantage of the stochastic method over a deterministic one, which illustrates the advantage of SP-based optimal bidding method especially for CAES and wind units and also for risk-averse GenCos. Overall, it is concluded that the stochastic method is efficient for optimal-bidding of GenCos owning CAES and wind units. (C) 2019 Elsevier Ltd. All rights reserved.
This paper deals with stochastic programming problems where the probability distribution is not explicitly known. We suppose that the probability distribution is defined by crisp or fuzzy inequalities on the probabili...
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This paper deals with stochastic programming problems where the probability distribution is not explicitly known. We suppose that the probability distribution is defined by crisp or fuzzy inequalities on the probability of the different states of nature. We formulate the problem and present a solution strategy that uses the a-cut technique in order to transform our problem into a stochastic program with linear partial information on probability distribution (SPI). The obtained SPI problem is than solved using two approaches, namely, a chance constrained approach and a recourse approach. For the recourse approach, a modified L-shaped algorithm is designed and illustrated by an example. (C) 2004 Elsevier B.V. All rights reserved.
This paper develops a multistage stochastic programming to optimally solve the distribution problem of refined products. The stochastic model relies on a time series analysis, as well as on a scenario tree analysis, i...
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This paper develops a multistage stochastic programming to optimally solve the distribution problem of refined products. The stochastic model relies on a time series analysis, as well as on a scenario tree analysis, in order to effectively deal and represent uncertainty in oil price and demand. The ARIMA methodology is explored to study the time series of the random parameters aiming to provide their future outcomes, which are then used in the scenario-based approach. As the designed methodology leads to a large scale optimization problem, a scenario reduction approach is employed to compress the problem size and improve its computational performance. A real-world example motivates the case study, based on the downstream oil supply chain of mainland Portugal, which is used to validate the applicability of the stochastic model. The results explicitly indicate the performance of the designed approach in tackling large and complex problems, where uncertainty is present. (C) 2017 Elsevier Ltd. All rights reserved.
The planning of offshore oil or gas field infrastructure under uncertainty is addressed in this article. The main uncertainties considered are in the initial maximum oil or gas flowrate, the recoverable oil or gas vol...
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The planning of offshore oil or gas field infrastructure under uncertainty is addressed in this article. The main uncertainties considered are in the initial maximum oil or gas flowrate, the recoverable oil or gas volume, and the water breakthrough time of the reservoir, which are represented by discrete distributions. Furthermore, it is assumed that these uncertainties are not immediately realized, but are gradually revealed as a function of design and operation decisions. To account for these decision-dependent uncertainties, we propose a multistage stochastic programming model that captures the complex economic objectives and nonlinear reservoir behavior and simultaneously optimizes the investment and operating decisions over the entire planning horizon. The proposed solution algorithm relies on a duality-based branch-and-bound method involving subproblems as nonconvex mixed-integer nonlinear programs. Several examples involving nonlinear reservoir models are presented to illustrate the application of the proposed method.
In this paper the maintenance scheduling problem is cast in a stochastic framework. Then, a stochastic programming model with recourse is developed for the problem. The model is a stochastic version of Robert and Escu...
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In this paper the maintenance scheduling problem is cast in a stochastic framework. Then, a stochastic programming model with recourse is developed for the problem. The model is a stochastic version of Robert and Escudero model for scheduling maintenance personnel. An illustrative example is given to demonstrate the utility of the model, and the Value of the stochastic solution is calculated and it showed about 10% improvement over the deterministic formulation of the maintenance scheduling problem. (C) 1999 Elsevier Science Inc. All rights reserved.
Motivated by real challenges on energy management faced by industrial firms, we propose a novel way to reduce production costs by including the pricing of electricity in a multi-product lot-sizing problem. In incentiv...
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Motivated by real challenges on energy management faced by industrial firms, we propose a novel way to reduce production costs by including the pricing of electricity in a multi-product lot-sizing problem. In incentive-based programs, when electric utilities face power consumption peaks, they request electricity-consuming firms to curtail their electric load, rewarding the industrial firms with incentives if they comply with the curtailment requests. Otherwise, industrial firms must pay financial penalties for an excessive electricity consumption. A two-stage stochastic formulation is presented to cover the case where a manufacturer wants to satisfy any curtailment request. A chance-constrained formulation is also proposed, and its relevance in practice is discussed. Finally, computational studies are conducted to compare mathematical models and highlight critical parameters and show potential savings when subscribing incentive-based programs. We show that the setup cost ratio, the capacity utilisation rate, the number of products and the timing of curtailment requests are critical parameters for manufacturers.
stochastic programming brings together models of optimum resource allocation and models of randomness to create a robust decision-making framework. The models of randomness with their finite, discrete realisations are...
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stochastic programming brings together models of optimum resource allocation and models of randomness to create a robust decision-making framework. The models of randomness with their finite, discrete realisations are called scenario generators. In this paper, we investigate the role of such a tool within the context of a combined information and decision support system. We explain how two well-developed modelling paradigms, decision models and simulation models can be combined to create "business analytics" which is based on ex-ante decision and ex-post evaluation. We also examine how these models can be integrated with data marts of analytic organisational data and decision data. Recent developments in on-line analytical processing (OLAP) tools and multidimensional data viewing are taken into consideration. We finally introduce illustrative examples of optimisation, simulation models and results analysis to explain our multifaceted view of modelling. In this paper, our main objective is to explain to the information systems (IS) community how advanced models and their software realisations can be integrated with advanced IS and DSS tools. (c) 2006 Elsevier B.V. All rights reserved.
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