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Multi-sourcing under supply uncertainty and Buyer's risk aversion

EURO JOURNAL ON COMPUTATIONAL OPTIMIZATION 2021年 9卷

作者： Chintapalli, Prashant Western Univ Ivey Business Sch London ON N6A 3K7 Canada

We address the combined problem of supplier (or vendor) selection and ordering decision when a buyer can choose to procure from multiple suppliers whose yields are uncertain and potentially correlated. We model this problem as a stochastic program with recourse in which the buyer purchases from the suppliers in the first period and, if needed, chooses to purchase from the spot market or from the suppliers with excess supply, whichever is beneficial, in the second period in order to meet the target procurement quantity. We solve the above problem using sample average approximation (SAA) technique that enables us to solve the problem easily in practice. We compare the performance of our solution with the certainty equivalent problem, which is practiced widely and which we use as the benchmark, to evaluate the efficacy of our approach. Next, we extend our model to incorpo-rate buyer's risk aversion with respect to the quantity procured. We reformulate the multi-sourcing problem as a mixed integer linear program (MILP) and adopt a statistical approach to account for buyer's risk aversion. Thus, we design a simple computational technique that provides an optimal sourcing policy from a set of suppliers when each supplier's yield is uncertain with a generic probability distribution.

关键词： Purchasing Supplier/vendor selection Supply/yield uncertainty approximate dynamic programming Stochastic programming with recourse

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Economic Dispatch of an Integrated Microgrid Based on the dynamic Process of CCGT Plant

Economic Dispatch of an Integrated Microgrid Based on the Dy...

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第33届中国控制与决策会议

作者： Zhiyi Lin Chunyue Song Jun Zhao Chao Yang Huan Yin College of Control Science and Engineering Zhejiang University

Intra-day economic dispatch of an integrated microgrid is a fundamental requirement to integrate distributed *** dynamic energy flows in cogeneration units present challenges to the energy management of the *** this paper,a novel approximate dynamic programming(ADP) approach is proposed to solve this problem based on value function approximation,which is distinct with the consideration of the dynamic process constraints of the combined-cycle gas turbine(CCGT) ***,we mathematically formulate the multi-time periods decision problem as a finite-horizon Markov decision *** deal with the thermodynamic process,an augmented state vector of CCGT is ***,the proposed VFA-ADP algorithm is employed to derive the near-optimal real-time operation *** addition,to guarantee the monotonicity of piecewise linear function,we apply the SPAR algorithm in the update *** validate the effectiveness of the proposed method,we conduct experiments with comparisons to some traditional optimization *** results indicate that our proposed ADP method achieves better performance on the economic dispatch of the microgrid.

关键词： Microgrid dynamic Process Combined-Cycle Gas Turbine approximate dynamic programming

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Anticipation versus reactive reoptimization for dynamic vehicle routing with stochastic requests

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NETWORKS 2019年第3期73卷 277-291页

作者： Ulmer, Marlin W. Tech Univ Carolo Wilhelmina Braunschweig Carl Friedrich Gauss Fak Braunschweig Germany

Due to new business models and technological advances, dynamic vehicle routing is gaining increasing interest. Especially solving dynamic vehicle routing problems with stochastic customer requests becomes increasingly important, for example, in e-commerce and same-day delivery. Solving these problems is challenging, because it requires optimization along two dimensions. First, as a reaction to new customer requests, current routing plans need to be reoptimized. Second, potential future requests need to be anticipated in current decision making. Decisions need to be derived in real-time. The limited time often prohibits extensive optimization in both dimensions and the question arises how to utilize the limited calculation time effectively. In this paper, we analyze the merits of reactive route reoptimization and anticipation for a dynamic vehicle routing problem with stochastic requests. To this end, we compare an existing method from each dimension as well a policy allowing for a tunable combination of the two approaches. We show how the appropriate optimization combination is strongly connected to the degree of dynamism, the percentage of unknown requests. We also show that our combination does not provide significant benefit compared to the respectively best optimization dimension.

关键词： anticipation approximate dynamic programming degree of dynamism dynamic vehicle routing mixed-integer programming reoptimization stochastic requests

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Generalized maximum entropy estimation

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JOURNAL OF MACHINE LEARNING RESEARCH 2019年第1期20卷 1-29页

作者： Sutter, Tobias Sutter, David Esfahani, Peyman Mohajerin Lygeros, John Ecole Polytech Fed Lausanne Risk Analyt & Optimizat Chair Lausanne Switzerland Swiss Fed Inst Technol Inst Theoret Phys Zurich Switzerland Delft Univ Technol Delft Ctr Syst & Control Delft Netherlands Swiss Fed Inst Technol Dept Elect Engn & Informat Technol Zurich Switzerland

We consider the problem of estimating a probability distribution that maximizes the entropy while satisfying a finite number of moment constraints, possibly corrupted by noise. Based on duality of convex programming, we present a novel approximation scheme using a smoothed fast gradient method that is equipped with explicit bounds on the approximation error. We further demonstrate how the presented scheme can be used for approximating the chemical master equation through the zero-information moment closure method, and for an approximate dynamic programming approach in the context of constrained Markov decision processes with uncountable state and action spaces.

关键词： Entropy maximization convex optimization relative entropy minimization fast gradient method approximate dynamic programming

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Planning horizons based proactive rescheduling for stochastic resource-constrained project scheduling problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第1期273卷 58-66页

作者： Brcic, Mario Katic, Marija Hlupic, Nikica Univ Zagreb Fac Elect Engn & Comp Unska 3 Zagreb 10000 Croatia Birkbeck Univ London Malet St London WC1E 7HX England

Parties that collaborate on projects need to synchronize their efforts. For this reason they seek a decreased rescheduling variability of the time arrangements. Proactive-reactive scheduling is important in such situations. It predominantly achieves synchronization through a shared baseline schedule and deviation penalties. As the latter currently introduce an unrealistically high level of inflexibility, the solution methods never proactively update the baseline schedule. We propose threshold-based cost functions for the deviation penalties to enable a more realistic modeling of aspects of project collaboration. These functions introduce a greater degree of flexibility through the notion of planning horizons for the activities. This results in the possibility of profitable proactive changes to the baseline schedule. We present two metaheuristic approaches for the case of stochastic durations: rollout-based and iterative policy search. Both these approaches use such opportunities to achieve substantial cost-performance improvements in comparison to the best existing method. This enhancement comes at the price of an increased computational burden and the greater complexity of the solution space. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Project scheduling Proactive scheduling Rollout policies approximate dynamic programming Stochastic RCPSP

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A Risk-Averse Stochastic dynamic programming Approach to Energy Hub Optimal Dispatch

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IEEE TRANSACTIONS ON POWER SYSTEMS 2019年第3期34卷 2169-2178页

作者： Moazeni, Somayeh Miragha, Amir H. Defourny, Boris Stevens Inst Technol Sch Business Hoboken NJ 07030 USA Nanyang Technol Univ ERI N Energy Res Inst Singapore 637141 Singapore Lehigh Univ Dept Ind & Syst Engn Bethlehem PA 18015 USA

This paper studies the optimal operation problem of an energy hub with multiple energy sources to serve stochastic electricity and heat loads in the presence of uncertain prices as well as operational constraints, such as minimum uptime and downtime requirements. Price and demand uncertainties are modeled by stochastic processes. The goal is to minimize some risk functional of the energy hub operational cost. A stochastic dynamic optimization formulation is introduced for the problem. An approximate dynamic programming framework, based on cost function approximation, is proposed to obtain dynamic dispatch policies. The approach enables a risk-sensitive energy hub operator to consider a non-differentiable risk measure and various constraints. The performance of the approach for the energy hub dispatch problem and characteristics of the storage levels are numerically investigated.

关键词： Energy hub stochastic optimization approximate dynamic programming risk

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Recursive least-squares temporal difference learning for adaptive traffic signal control at intersection

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NEURAL COMPUTING & APPLICATIONS 2019年第2-Sup期31卷 1013-1028页

作者： Yin, Biao Dridi, Mahjoub El Moudni, Abdellah UPEM Ecole Ponts ParisTech LVMT City Mobil Transport Lab IFSTTAR F-77455 Champs Sur Marne France Univ Technol Belfort Montbeliard NIT O2S F-90000 Belfort France

This paper presents a new method to solve the scheduling problem of adaptive traffic signal control at intersection. The method involves recursive least-squares temporal difference (RLS-TD(lambda)) learning that is integrated into approximate dynamic programming. The learning mechanism of RLS-TD(lambda) is to make an adaptation of linear function approximation by updating its parameters based on environmental feedback. This study investigates the method implementation after modeling a traffic dynamic system at intersection in discrete time. In the model, different traffic control schemes regarding signal phase sequence are considered, especially the defined adaptive phase sequence (APS). By simulating traffic scenarios, RLS-TD(lambda) is superior to TD(lambda) for updating functional parameters in the approximation, and APS outperforms other conventional control schemes on reducing traffic delay. By comparing with other traffic signal control algorithms, the proposed algorithm yields satisfying results in terms of traffic delay and computation time.

关键词： Adaptive traffic signal control Recursive least-squares temporal difference approximate dynamic programming Adaptive phase sequence

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Multiline Bus Bunching Control via Vehicle Substitution

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2019年第Aug.期126卷 68-86页

作者： Petit, Antoine Lei, Chao Ouyang, Yanfeng Univ Illinois Dept Civil & Environm Engn4 Urbana IL 61801 USA

Traditional bus bunching control methods (e.g., adding slack to schedules, adapting cruising speed), in one way or another, trade commercial speed for better system stability and, as a result, may impose the burden of additional travel time on passengers. Recently, a dynamic bus substitution strategy, where standby buses are dispatched to take over service from late/early buses, was proposed as an attempt to enhance system reliability without sacrificing too much passenger experience. This paper further studies this substitution strategy in the context of multiple bus lines under either time-independent or time varying settings. In the latter scenario, the fleet of standby buses can be dynamically utilized to save on opportunity costs. We model the agency's substitution decisions and retired bus repositioning decisions as a stochastic dynamic program so as to obtain the optimal policy that minimizes the system-wide costs. Numerical results show that the dynamic substitution strategy can benefit from the "economies of scale" by pooling the standby fleet across lines, and there are also benefits from dynamic fleet management when transit demand varies over time. Numerical examples are presented to illustrate the applicability and advantage of the proposed strategy. The substitution strategy not only holds the promise to outperform traditional holding methods in terms of reducing passenger costs, they also can be used to complement other methods to better control very unstable systems. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Bus bunching Transit reliability approximate dynamic programming Multiline control

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Symbolic Optimal Control

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2019年第6期64卷 2224-2239页

作者： Reissig, Gunther Rungger, Matthias Bundeswehr Univ Munich Dept Aerosp Engn Chair Control Engn D-85577 Munich Germany Tech Univ Munich Dept Elect & Comp Engn Hybrid Control Syst Grp D-80333 Munich Germany

We present novel results on the solution of a class of leavable, undiscounted optimal control problems in the minimax sense for nonlinear, continuous-state, discrete-time plants. The problem class includes entry(exit-)time problems as well as minimum-time, pursuite-vasion, and reach-avoid games as special cases. We utilize auxiliary optimal control problems ("abstractions") to compute both upper bounds of the value function, i.e., of the achievable closed-loop performance, and symbolic feedback controllers realizing those bounds. The abstractions are obtained from discretizing the problem data, and we prove that the computed bounds and the performance of the symbolic controllers converge to the value function as the discretization parameters approach zero. In particular, if the optimal control problem is solvable on some compact subset of the state space, and if the discretization parameters are sufficiently small, then we obtain a symbolic feedback controller solving the problem on that subset. These results do not assume the continuity of the value function or any problem data, and they fully apply in the presence of hard state and control constraints.

关键词： approximate dynamic programming difference inclusion discrete abstraction nonlinear system optimal control symbolic control

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Resource allocation in congested queueing systems with time-varying demand: An application to airport operations

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第2期276卷 566-581页

作者： Shone, Rob Glazebrook, Kevin Zografos, Konstantinos G. Univ Lancaster Dept Management Sci Sch Management Ctr Transport & Logist CENTRAL Lancaster England

Motivated by the need to develop time-efficient methods for minimizing operational delays at severely congested airports, we consider a problem involving the distribution of a common resource between two sources of time-varying demand. We formulate this as a dynamic program in which the objective is based on second moments of stochastic queue lengths and show that, for sufficiently high volumes of demand, optimal values can be well-approximated by quadratic functions of the system state. We identify conditions which enable the strong performance of myopic policies and develop approaches to the design of heuristic policies by means of approximate dynamic programming (ADP) methods. Numerical experiments suggest that our ADP-based heuristics, which require very little computational effort, are able to improve substantially upon the performances of more naive decision-making policies, particularly if exogenous system parameters vary considerably as functions of time. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Queueing Optimization approximate dynamic programming Airport operations Aviation

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