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A hybrid model based on fuzzy VIKOR and the classical optimal search to detect illegal chemical warehouses

JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH PART A-TOXIC/HAZARDOUS SUBSTANCES & ENVIRONMENTAL ENGINEERING 2022年第3期57卷 229-241页

作者： Sotoudeh-Anvari, Alireza Sadi-Nezhad, Soheil Islamic Azad Univ Dept Ind Engn Sci & Res Branch Tehran Iran Univ Waterloo Dept Stat & Actuarial Sci Waterloo ON Canada

The use of chemical weapons has turned into an increasing risk for the world. In this study, a hybrid approach on the basis of fuzzy VIKOR and the optimal search model to cope with an important case study called the detection of illegal chemical warehouses is introduced. It is obvious that such illegal activities are accomplished under high secret considerations. Therefore, we have several types of ambiguity and uncertainty in this problem. First, fuzzy VIKOR is used to prioritize the suspicious warehouses based on time and cost of a search under fuzzy environment. Also the probability of existence of chemical agents in each warehouse (P-i) and the probability of detection (alpha(i)) in case materials exist in warehouse, are estimated. Next, the output of VIKOR i.e., Q is assumed as an input of optimal search model and optimal strategy for searching is achieved by solving a stochastic dynamic programming. According to this hybrid approach, we start from the location that has the maximum value of alpha P-i(i)/Q(i). Although we get benefits of fuzzy logic, VIKOR, and classical optimal search, the suggested method is easy to understand and straightforward to utilize in real-world problems. Also this model can enhance the robust nature of hybrid approach and reduces its sensitivity to the change of weights.

关键词： Illegal chemical warehouses optimal search VIKOR fuzzy sets stochastic dynamic programming

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stochastic discrete-time Nash games with constrained state estimators

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2002年第1期114卷 171-188页

作者： Kian, AR Cruz, JB Simaan, MA Ohio State Univ Dept Elect Engn Columbus OH 43210 USA Univ Pittsburgh Dept Elect Engn Pittsburgh PA USA

In this paper, we consider stochastic linear-quadratic discrete-time Nash games in which two players have access only to noise-corrupted output measurements. We assume that each player is constrained to use a linear Kalman filter-like state estimator to implement his optimal strategies. Two information structures available to the players in their state estimators are investigated. The first has access to one-step delayed output and a one-step delayed control input of the player. The second has access to the current output and a one-step delayed control input of the player. In both cases, statistics of the process and statistics of the measurements of each player are known to both players. A simple example of a two-zone energy trading system is considered to illustrate the developed Nash strategies. In this example, the Nash strategies are calculated for the two cases of unlimited and limited transmission capacity constraints.

关键词： stochastic linear-quadratic systems nonzero-sum discrete-time Nash games optimal Nash strategies estimator-controller stochastic dynamic programming Nash equilibrium

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Optimal hedging through limit orders

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stochastic MODELS 2016年第4期32卷 593-605页

作者： Agliardi, Rossella Univ Bologna Dept Math Viale Filopanti 5 I-40126 Bologna Italy

This article applies the methods of stochastic dynamic programming to a risk management problem, where an agent hedges her derivative position by submitting limit orders. Therefore, this model is the first, in the literature on optimal trading with limit orders, to handle a problem of hedging options or other derivatives. A hedging strategy is developed where both the size and the limit price of each order is optimally set.

关键词： Hedging limit orders martingales stochastic dynamic programming 91G20 91G80 G11 G13 C61

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Analysis and Improvement of Blood Collection Operations

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M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT 2019年第1期21卷 29-46页

作者： Ayer, Turgay Zhang, Can Zeng, Chenxi White, Chelsea C., III Joseph, V. Roshan Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Duke Univ Fuqua Sch Business Durham NC 27708 USA

Because of the importance, limited supply, and perishable nature of blood products, effective management of blood collection is critical for high-quality healthcare delivery. In this paper, working closely with the American Red Cross (ARC), we study a blood collection problem focusing on whole blood that is to be processed into cryoprecipitate (cryo), a critical blood product for controlling massive hemorrhaging. In particular, we aim to determine when and from which mobile collection sites to collect blood for cryo production, such that the weekly collection target is met while the collection costs are minimized. The cryo collection problem imposes a unique challenge: if blood collected is to be processed into cryo units, it has to be processed within eight hours after collection, while this time limit is 24 hours for most other blood products. To analyze the cryo collection problem, we first develop a mathematical program to represent and compare two different blood collection business models, namely, the status quo nonsplit model and an alternative model we propose, which splits each collection window into two intervals and allows different types of collections in the two intervals. Then, we establish several structural properties of the proposed mathematical program and develop a near-optimal solution algorithm to determine the cryo collection schedules under each collection model. Our extensive computational analyses based on real data indicated that, compared with the status quo, our proposed collection model can significantly reduce total collection costs. Based on this significant potential impact, our proposed collection model has been implemented by the ARC Douglasville manufacturing facility, the largest ARC blood manufacturing facility supplying blood to about 120 hospitals in the southern United States. Field data from postimplementation indicated that our proposed solution has resulted in (i) reducing inconsistencies in supply of cryo collections, and

关键词： blood collection cryoprecipitate humanitarian operations stochastic dynamic programming

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An Integer programming Approach for Evaluating R&D Funding Decisions With Optimal Budget Allocations

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2012年第4期59卷 679-691页

作者： Eckhause, Jeremy M. Gabriel, Steven A. Hughes, Danny R. Logist Management Inst Mclean VA 22102 USA Univ Maryland Dept Civil & Environm Engn College Pk MD 20742 USA Univ Oklahoma Dept Hlth Adm & Policy Oklahoma City OK 73019 USA

The use of real options techniques for research and development (R&D) project selection to mitigate the uncertainties has been shown to increase overall project value. While recent approaches employing stochastic dynamic programs (SDP) produce optimal solutions for many applications, this approach does not easily accommodate the inclusion of an optimal a priori budget allocation or side constraints, since their formulations are scenario specific. We formulate an integer program (IP) whose solution is equivalent to previous SDP real options models but facilitates the incorporation of these additional features and may be solved using commercial IP solvers. This IP formulation can solve what would otherwise be a nested two-level problem where the lower level problem is an SDP. We then compare the performance of the IP to that received by the SDP using a case study from the literature.

关键词： Integer programming (IP) project selection real options research and development (R&D) portfolios stochastic dynamic programming two-level problems

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Quality-Control Algorithm for Adaptive Streaming Services Over Wireless Channels

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2015年第1期9卷 50-59页

作者： Garcia, Sergio Cabrera, Julian Garcia, Narciso Univ Politecn Madrid GTI E-28040 Madrid Spain

dynamic Adaptive Streaming over HTTP (DASH) is a recent MPEG standard for IP video delivery whose aim is the convergence of existing adaptive-streaming proprietary solutions. However, it does not impose any adaptation logic for selecting the quality of the media segments requested by the client, which is crucial to cope effectively with bandwidth fluctuations, notably in wireless channels. We therefore propose a solution to this control problem through stochastic dynamic programming (SDP). This approach requires a probabilistic characterization of the system, as well as the definition of a cost function that the control strategy aims to minimize. This cost function is designed taking into account factors that may influence the quality perceived by the users. Unlike previous works, which compute control policies online by learning from experience, our algorithm solves the control problem offline, leading promptly to better results. In addition, we compared our algorithm to others during a streaming simulation and we analyzed the objective results by means of a Quality of Experience (QoE) oriented metric. Moreover, we conducted subjective tests to complete the evaluation of the performance of our algorithm. The results show that our proposal outperforms the other approaches in terms of both the QoE-oriented metric and the subjective evaluation.

关键词： Adaptive streaming DASH quality of experience stochastic dynamic programming

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The Burglar Problem with Multiple Options

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NAVAL RESEARCH LOGISTICS 2014年第5期61卷 359-364页

作者： Chen, Kai Ross, Sheldon M. Univ So Calif Dept Ind & Syst Engn Los Angeles CA 90089 USA

We consider the burglar problem in which a burglar can either retire or choose among different types of burglaries, with each type having its own success probability and reward distribution. Some general structural results are established and, in the case of exponentially distributed reward distributions, a solution technique is presented. The burglar problem's relationship to a stochastic knapsack problem with a random exponentially distributed knapsack capacity is shown. (C) 2014 Wiley Periodicals, Inc.

关键词： stochastic dynamic programming burglar problem knapsack problem

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Conservation decision-making in large state spaces

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ECOLOGICAL MODELLING 2010年第21期221卷 2531-2536页

作者： Nicol, Samuel C. Chades, Ladine Linke, Simon Possingham, Hugh P. Univ Queensland Spatial Ecol Lab St Lucia Qld 4072 Australia CSIRO Sustainable Ecosyst St Lucia Qld 4067 Australia Griffith Univ Australian Rivers Inst Nathan Qld 4111 Australia

When looking for the best course of management decisions to efficiently conserve metapopulation systems, a classic approach in the ecology literature is to model the optimisation problem as a Markov decision process and find an optimal control policy using exact stochastic dynamic programming techniques. stochastic dynamic programming is an iterative procedure that seeks to optimise a value function at each timestep by evaluating the benefits of each of the actions in each state of the system defined in the Markov decision process. Although stochastic dynamic programming methods provide an optimal solution to conservation management questions in a stochastic world, their applicability in metapopulation problems has always been limited by the so-called curse of dimensionality. The curse of dimensionality is the problem that adding new state variables inevitably results in much larger (often exponential) increases in the size of the state space, which can make solving superficially small problems impossible. The high computational requirements of stochastic dynamic programming methods mean that only simple metapopulation management problems can be analysed. In this paper we overcome the complexity burden of exact stochastic dynamic programming methods and present the benefits of an on-line sparse sampling algorithm proposed by Kearns, Mansour and Ng (2002). The algorithm is particularly attractive for problems with large state spaces as the running time is independent of the size of the state space of the problem. This appealing improvement is achieved at a cost: the solutions found are no longer guaranteed to be optimal. We apply the algorithm of Kearns et al. (2002) to a hypothetical fish metapopulation problem where the management objective is to maximise the number of occupied patches over the management time horizon. Our model has multiple management options to combat the threats of water abstraction and waterhole sedimentation. We compare the performance of the

关键词： Markov decision processes stochastic dynamic programming Metapopulation Optimal management On-line sparse sampling algorithm

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Fixed versus flexible production systems: A real options analysis

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2008年第1期188卷 169-184页

作者： Fontes, Dalila B. M. M. Univ Porto Fac Econ LIACC P-4200464 Oporto Portugal

In this work, we address investment decisions in production systems by using real options. As is standard in literature, the stochastic variable is assumed to be normally distributed and then approximated by a binomial distribution, resulting in a binomial lattice. The methodology establishes a discrete-valued lattice of possible future values of the underlying stochastic variable (demand in our case) and then, computes the project value. We have developed and implemented stochastic dynamic programming models both for fixed and flexible capacity systems. In the former case, we consider three standard options: the option to postpone investment, the option to abandon investment, and the option to temporarily shut-down production. For the latter case, we introduce the option of corrective action, in terms of production capacity, that the management can take during the project by considering the existence of one of the following: (i) a capacity expansion option;(ii) a capacity contraction option;or (iii) an option considering both expansion and contraction. The full flexible capacity model, where both the contraction and expansion options exist, leads, as expected, to a better project predicted value and thus, investment policy. However, we have also found that the capacity strategy obtained from the flexible capacity model, when applied to specific demand data series, often does not lead to a better investment decision. This might seem surprising, at first, but it can be explained by the inaccuracy of the binomial model. The binomial model tends to undervalue future decreases in the stochastic variable (demand), while at the same time tending to overvalue an increase in future demand values. (C) 2007 Elsevier B.V. All rights reserved.

关键词： fixed and flexible capacity investments real options stochastic dynamic programming binomial lattice investment analysis

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Modelling and solving resource allocation problems via a dynamic programming approach

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INTERNATIONAL JOURNAL OF CONTROL 2021年第6期94卷 1544-1555页

作者： Forootani, Ali Tipaldi, Massimo Zarch, Majid Ghaniee Liuzza, Davide Glielmo, Luigi Univ Sannio Dept Engn I-82100 Benevento Italy Univ Sci & Technol Dept Elect Engn Tehran Iran

In this paper, resource allocation problems are formulated via a set of parallel birth-death processes (BDP). This way, we can model the fact that resources can be allocated to customers at different prices, and that customers can hold them as long as they like. More specifically, a discretisation approach is applied to model resource allocation problems as a set of discrete-time BDPs, which are then integrated into one Markov decision process. The stochastic dynamics of the resulting system are also investigated. As a result, revenue management becomes a stochastic decision-making problem, where price managers can propose suitable prices to the allocation requests such that the maximum expected total revenue is obtained at the end of a predefined finite time horizon. stochastic dynamic programming is employed to solve the related optimisation problem with the support of an ad-hoc Matlab-based application. Several simulations are performed to prove the effectiveness of the proposed model and the optimisation approach.

关键词： Resource allocation problems stochastic dynamic programming Markov chain Markov decision process birth-death process

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