Topic
Stochastic programming
About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.
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Papers
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TL;DR: A new algorithm is described that propagates means and variances of the uncertain attributes along paths and compares partial paths that arrive at a given node within a user-specified time window and creates an effective solution set in a case study using a large network.
Abstract: We describe a method for finding nondominated paths for multiple routing objectives in networks where the routing attributes are uncertain, and the probability distributions that describe those attributes vary by time of day. This problem is particularly important in routing and scheduling of shipments of very hazardous materials. Our method extends and integrates the work of several previous authors, resulting in a new algorithm that propagates means and variances of the uncertain attributes along paths and compares partial paths that arrive at a given node within a user-specified time window. The comparison uses an approximate stochastic dominance criterion. We illustrate the effects of changing primary parameters of the algorithm using a small test network, and we show how the nondominated solution set achieved is larger than the set that would be identified if the uncertainty in routing attributes were ignored. We then demonstrate how the algorithm creates an effective solution set in a case study using a large network.
122 citations
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TL;DR: Simulation results show that the proposed real-time pricing algorithms reduce the PAR in aggregate load and help the users to reduce their energy expenses.
Abstract: In this paper, we propose a new pricing algorithm to minimize the peak-to-average ratio (PAR) in aggregate load demand. The key challenge that we seek to address is the energy provider's uncertainty about the impact of prices on users' load profiles, in particular when users are equipped with automated energy consumption scheduling (ECS) devices. We use an iterative stochastic approximation approach to design two real-time pricing algorithms based on finite-difference and simultaneous perturbation methods, respectively. We also propose the use of a system simulator unit (SSU) that employs approximate dynamic programming to simulate the operation of the ECS devices and users' price-responsiveness. Simulation results show that our proposed real-time pricing algorithms reduce the PAR in aggregate load and help the users to reduce their energy expenses.
122 citations
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TL;DR: An empirical application of discrete stochastic programming is presented, including a discussion of data requirements, matrix construction, and solution interpretation, and based on this empirical evidence, the problem-solving potential of the technique is evaluated.
Abstract: Discrete stochastic programming has been suggested as a means of solving sequential decision problems under uncertainty, but as yet little or no empirical evidence of the capabilities of this technique in solving such problems has appeared. This paper presents in some detail an empirical application of discrete stochastic programming, including a discussion of data requirements, matrix construction, and solution interpretation. Based on this empirical evidence, the problem-solving potential of the technique is evaluated.
122 citations
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TL;DR: Two classes of optimization models to maximize revenue in a restaurant (while controlling average waiting time as well as perceived fairness) that may violate the first-come-first-serve (FCFS) rule are developed.
Abstract: We develop two classes of optimization models to maximize revenue in a restaurant (while controlling average waiting time as well as perceived fairness) that may violate the first-come-first-serve (FCFS) rule. In the first class of models, we use integer programming, stochastic programming, and approximate dynamic programming methods to decide dynamically when, if at all, to seat an incoming party during the day of operation of a restaurant that does not accept reservations. In a computational study with simulated data, we show that optimization-based methods enhance revenue relative to the industry practice of FCFS by 0.11% to 2.22% for low-load factors, by 0.16% to 2.96% for medium-load factors, and by 7.65% to 13.13% for high-load factors, without increasing, and occasionally decreasing, waiting times compared to FCFS. The second class of models addresses reservations. We propose a two-step procedure: Use a stochastic gradient algorithm to decide a priori how many reservations to accept for a future time and then use approximate dynamic programming methods to decide dynamically when, if at all, to seat an incoming party during the day of operation. In a computational study involving real data from an Atlanta restaurant, the reservation model improves revenue relative to FCFS by 3.5% for low-load factors and 7.3% for high-load factors.
121 citations
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01 Oct 2018TL;DR: The results show that the proposed hybrid metaheuristic algorithms outperform the best existing techniques on the majority of case studies.
Abstract: Nowadays, operation managers usually need efficient supply chain networks including important design factors such as economic and social considerations The recent decade has seen a rapid development of controlling the uncertainty in supply chain configurations along with proposing novel solution approaches By investigating the related studies, this paper shows that most of the current studies consider the economic aspects and just a few works present the two-stage stochastic programming as well as social considerations to design a closed-loop supply chain network This motivated our attempts to consider economic and social aspects simultaneously by using the mentioned suppositions among the first studies Another main contribution of this paper is the hybridization and tuning of a number of recent algorithms to address the problem The results show that the proposed hybrid metaheuristic algorithms outperform the best existing techniques on the majority of case studies
121 citations