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Optimal dynamic commodity liquidation by joint spot and forward contracts

OPTIMIZATION AND ENGINEERING 2025年 1-27页

作者： Ghafouri, Behzad Moazeni, Somayeh Davison, Matt Univ Western Ontario Dept Stat & Actuarial Sci London ON Canada Stevens Inst Technol Sch Business Hoboken NJ 07030 USA

This paper studies the problem of optimal liquidation of an inventory through spot and forward markets over a finite time horizon. The decision maker can sell her inventory on the spot market or take a short position in a forward contract or both. At every time step, the maturity of the forward contract can be adjusted until the inventory is completely depleted. Considering this forward contract whose maturity can be dynamically updated, in addition to liquidation in the spot market, is one of the salient characteristics of our problem in this paper. We formulate this problem as a stochastic dynamic optimization problem, and prove that a 'partial sale', i.e. dividing the sale between the spot and forward market, is not optimal, regardless of the underlying price model. Next, we show that the optimally selected forward maturity is limited to the immediate, next, or last timestep, under certain linearity assumptions. This result can significantly reduce the computational complexity of the dynamic optimization model, by considerably reducing the search space. Next, we develop an approximate dynamic programming approach to compute an adaptive liquidation policy. The performance of the proposed solution method is numerically assessed and compared against the exact policy and other alternative policies, in terms of both achieved return and risk attributes.

关键词： approximate dynamic programming Liquidation strategy Real options Energy market Forward market

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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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Multiline Bus Bunching Control via Vehicle Substitution

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2019年 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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Recursive least-squares temporal difference learning for adaptive traffic signal control at intersection

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NEURAL COMPUTING & APPLICATIONS 2019年第2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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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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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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Logistical Planning for Electric Vehicles Under Time-Dependent Stochastic Traffic

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2019年第10期20卷 3771-3781页

作者： Bi, Xiaowen Tang, Wallace K. S. City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China

For the benefit of global environmental preservation, electric vehicles (EVs) have been gradually accepted by people in the past few years. However, the technical problem of limited drivable range and long charging duration is still a major hurdle for the popularization of EVs, especially for commercial usage. In this paper, a dynamic electric vehicle routing problem (D-EVRP) model is designed for planning the itinerary for goods delivery by the utilization of EVs in logistics industry. To reflect the real situation, the D-EVRP considers a time-dependent stochastic traffic condition and captures the discharging/charging pattern of an EV using an analytical battery model. Its aim is to minimize the overall service duration, subject to a variety of the state-of-art constraints common in EV routing problems. Furthermore, to address the D-EVRP, a hybrid rollout algorithm (HRA), which incorporates a dedicated pre-planning strategy and a rollout algorithm, is also proposed. The effectiveness of the HRA and benefits of incorporating the analytical battery model are justified by extensive simulations using the real-world D-EVRP instances.

关键词： Logistical planning electric vehicles (EVs) time-dependent stochastic traffic approximate dynamic programming

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Online H∞ control for completely unknown nonlinear systems via an identifier-critic-based ADP structure

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INTERNATIONAL JOURNAL OF CONTROL 2019年第1期92卷 100-111页

作者： Lv, Yongfeng Na, Jing Ren, Xuemei Beijing Inst Technol Sch Automat Beijing Peoples R China Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming Yunnan Peoples R China

In this paper, we propose an identifier-critic-based approximate dynamic programming (ADP) structure to online solve H infinity control problem of nonlinear continuous-time systems without knowing precise system dynamics, where the actor neural network (NN) that has been widely used in the standard ADP learning structure is avoided. We first use an identifier NN to approximate the completely unknown nonlinear system dynamics and disturbances. Then, another critic NN is proposed to approximate the solution of the induced optimal equation. The H infinity control pair is obtained by using the proposed identifier-critic ADP structure. A recently developed adaptation algorithm is used to online directly estimate the unknown NN weights simultaneously, where the convergence to the optimal solution can be rigorously guaranteed, and the stability of the closed-loop system is analysed. Thus, this new ADP scheme can improve the computational efficiency of H infinity control implementation. Finally, simulation results confirm the effectiveness of the proposed methods.

关键词： approximate dynamic programming H infinity control system identification nonlinear systems neural networks

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A two-level optimization model for elective surgery scheduling with downstream capacity constraints

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第2期276卷 602-613页

作者： Zhang, Jian Dridi, Mahjoub El Moudni, Abdellah Univ Bourgogne Franche Comte UTBM Lab Nanomed Imagerie & Therapeut Rue Thieny Mieg F-90010 Belfort France

This paper addresses an elective surgery scheduling problem involving capacity constraints for operating rooms and downstream surgical intensive care units (SICU). Considering the uncertainties in surgery durations, lengths of stay, and new arrivals of patients, we seek to provide stochastically optimized surgery schedules for surgical managers. Conventional formulations (e.g. stochastic programming) for the studied problem mainly optimize the schedule of one single decision period, without consideration of the correlations between the present and successive periods. To overcome this short-sightedness, a novel two-level optimization model is proposed in this paper: at the first level, the high-priority patients that will be scheduled are selected from the waiting list;at the second level, every selected patient is assigned to a specific surgical block. The sub-problem of the first level is modeled as a Markov decision process to reduce the expected total cost on a long-term basis;the second level is formulated as a stochastic programming problem, which optimizes the schedule over a short-term planning horizon. Intensive structural analyses are conducted for the proposed model to simplify the solution procedure. An approximate dynamic programming approach based on recursive least-squares temporal difference learning is then proposed to solve the problem. Numerical experiments are carried out to compare the proposed model with a pure stochastic programming model. The results indicate that the policy obtained from the proposed model possesses considerable advantages in reducing the total cost, shortening waiting time for patients, and improving the utilization rate of hospital facilities. (C) 2019 Elsevier B.V. All rights reserved.

关键词： OR in health services Surgery scheduling Markov decision process approximate dynamic programming Recursive least-squares temporal difference learning

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Optimization of sampling intervals for tracking control of nonlinear systems: A game theoretic approach

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NEURAL NETWORKS 2019年 114卷 78-90页

作者： Sahoo, Avimanyu Narayanan, Vignesh Oklahoma State Univ Div Engn Technol Stillwater OK 74078 USA Washington Univ Dept Elect & Syst Engn St Louis MO USA

This paper presents a near optimal adaptive event-based sampling scheme for tracking control of an affine nonlinear continuous-time system. A zero-sum game approach is proposed by introducing a novel performance index. The optimal value function, i.e., the solution to the associated Hamilton-Jacobi-Issac (HJI) equation is approximated using a functional link neural network (FLNN) with event-based aperiodic state feedback information as inputs. The saddle point approximated optimal solution is employed to design the near optimal event-based control policy and the sampling condition. An impulsive weight update scheme is designed to guarantee local ultimate boundedness of the closed-loop parameters, which is analyzed via extension of Lyapunov stability theory for the impulsive hybrid dynamical systems. Zeno-freeness of the event-sampling scheme is enforced and its effect on stability is analyzed. Finally, numerical simulation results are included to corroborate the analytical design, which shows a 48.82% reduction of feedback communication and computational load. (c) 2019 Elsevier Ltd. All rights reserved.

关键词： approximate dynamic programming Event-based control Hamilton-Jacobi-Issac equation Nonlinear tracking control Optimal control Zero-sum-game

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