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Production and changeover control policies of a class of failure prone buffered flow-shops

PRODUCTION PLANNING & CONTROL 2009年第8期20卷 785-800页

作者： Hajji, Adnene Gharbi, Ali Kenne, Jean-Pierre Hidehiko, Yamamoto Univ Quebec Dept Automated Prod Engn LCCSP Lab Ecole Technol Super Quebec City PQ Canada Univ Quebec Dept Mech Engn LITP Lab Ecole Technol Super Quebec City PQ Canada Gifu Univ Fac Engn Dept Human & Informat Syst Gifu 50111 Japan

This article deals with a stochastic optimal control problem for a class of buffered multi-parts flow-shops manufacturing system. The involved machines are subject to random breakdowns and repairs. The flow-shop under consideration is not completely flexible and hence requires setup time and cost in order to switch the production from a part type to another, this changeover is carried on the whole line. Our objective is to find the production plan and the sequence of setups that minimise the cost function, which penalises inventories/backlogs and setups. A continuous dynamic programming formulation of the problem is presented. Then, a numerical scheme is adopted to solve the obtained optimality conditions equations for a two buffered serial machines two parts case. A complete heuristic policy, based on the numerical observations which describe the optimal policies in system states, is developed. It will be shown that the obtained policy is a combination of a KANBAN/CONWIP and a modified hedging corridor policy. Moreover, based on our observations and existent research studies extension to cover more complex flow-shops is henceforth possible. The robustness of such a policy is illustrated through sensitivity analysis.

关键词： production and setup control buffered flow-shops stochastic dynamic programming numerical methods production control real-time scheduling

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Integrated production-delivery control policy for an unreliable manufacturing system and multiple retailers

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2022年 245卷

作者： Dhahri, Akrem Gharbi, Ali Ouhimmou, Mustapha Univ Quebec Syst Engn Dept Prod Syst Design & Control Lab Ecole Technol Super 1100 Notre Dame St West Montreal PQ H3C 1K3 Canada CIRRELT Interuniv Res Ctr Enterprise Networks Log Montreal PQ Canada

Production control policies have been studied in the literature for different types of unreliable manufacturing systems. But only few have been addressed to the unreliable manufacturer-retailer systems. We consider the integrated production and delivery control for an unreliable manufacturing system and multiple retailers. The retailers, supplied by the manufacturer through a common warehouse, are located in different sites and face different demand rates. We determine the optimal joint production and delivery control policy with the objective of minimizing the total cost, which comprises holding/backlog in addition to transportation costs. The stochastic optimal control and the impulse control theory are used to develop the optimal control policy (policy 1). The production is controlled by a hedging point policy and the delivery policy is governed by a state dependent economic order quantity. The optimal parameters of the proposed policy are determined by adopting a simulation-based optimization approach. A sensitivity analysis is conducted to verify the robustness of the results. An improved policy (policy 2) involving priority rules for delivery is proposed using a simulation-based approach. We conduct a comparative study of the new policies (policy 1 and 2) and the most relevant policy found in the literature (policy 3), and the results show that the new proposed improved policy (policy 2) outperforms others.

关键词： Unreliable manufacturing system Feedback production planning Economic order quantity Vendor managed inventory One-vendor multiple-buyer system Simulation stochastic dynamic programming

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Allocating resources via price management systems: a dynamic programming-based approach

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INTERNATIONAL JOURNAL OF CONTROL 2021年第8期94卷 2123-2143页

作者： Forootani, Ali Liuzza, Davide Tipaldi, Massimo Glielmo, Luigi Univ Sannio Dept Engn Piazza Roma 21 I-82100 Benevento Italy ENEA Fus & Nucl Safety Dept Frascati Rome Italy

In this paper, a novel model for price management systems in resource allocation problems is proposed. stochastic customer requests for resource allocations and releases are modelled as constrained parallel Birth-Death Processes (BDP). We address both instant (i.e. the customer requires a resource to be allocated immediately) and advance (i.e. the customer books a resource for future use) reservation requests, the latter with both bounded and unbounded time interval options. Algorithms based on dynamic programming (DP) principles are proposed for the calculation of suitable price profiles. At the core of such algorithms, there is the resolution of stochastic optimisation problems. In particular, the maximisation of the expected total revenue is formulated via a constrained stochastic dynamic programming (SDP) approach, which becomes time-variant in case of advance reservation requests. Approximate dynamic programming (ADP) techniques are adopted in case of large state spaces. Simulations are performed to show the effectiveness of the proposed models and the related algorithms.

关键词： Price management systems resource allocation problems stochastic dynamic programming Markov decision process approximate dynamic programming

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A SELECTIVE IMPROVEMENT TECHNIQUE FOR FASTENING NEURO-dynamic programming IN WATER RESOURCE NETWORK MANAGEMENT

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IFAC Proceedings Volumes 2005年第1期38卷 7-12页

作者： A. Castelletti D. de Rigo A.E. Rizzoli R. Soncini-Sessa E. Weber Dipartimento di Elettronica e Informazione Politecnico di Milano Italy IDSIA Manno Switzerland

An approach to the integrated water resources management based on Neuro-dynamic programming (NDP) with an improved technique for fastening its Artificial Neural Network (ANN) training phase will be presented. When dealing with networks of water resources, stochastic dynamic programming provides an effective solution methodology but suffers from the so-called “curse of dimensionality”, that rapidly leads to the problem intractability. NDP can sensibly mitigate this drawback by approximating the solution with ANNs. However in the real world applications NDP shows to be considerably slowed just by this ANN training phase. To overcome this limit a new training architecture (SIEVE: Selective Improvement by Evolutionary Variance Extinction) has been developed. In this paper this new approach is theoretically introduced and some preliminary results obtained on a real world case study are presented.

关键词： Integrated Water Resources Management stochastic dynamic programming Neuro-dynamic programming Evolutionary algorithm

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Vehicle-Based Trajectory Specification in Presence of Traffic Lights with stochastic Switching Times

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IFAC-PapersOnLine 2021年第2期54卷 298-305页

作者： P. Typaldos V. Volakakis M. Papageorgiou I. Papamichail Dynamic Systems & Simulation Laboratory School of Production Engineering & Management Technical University of Crete 73100 Chania Greece

Vehicle-based GLOSA (Green Light Optimal Speed Advisory) systems use information about the next switching time of the traffic lights to calculate fuel-efficient position and velocity profiles for connected vehicles, according to their current state (position and speed). A stochastic optimal control problem was recently proposed to address the GLOSA problem in cases where the next switching time is decided in real time and is therefore uncertain in advance. The corresponding numerical solution via SDP (stochastic dynamic programming) calls for substantial computational time (few minutes), which excludes problem solution in the vehicle’s computer in real time. This work considers the same stochastic problem of optimal trajectory specification for vehicles approaching a signalized junction with traffic signals operated in real-time (adaptive) mode, due to which the next switching time is stochastic. However, a modified version of dynamic programming, known as Discrete Differential dynamic programming (DDDP), is used for numerical solution of the stochastic optimal control problem. It is demonstrated, based on a realistic example, that the DDDP algorithm achieves results equivalent to those obtained with the ordinary SDP algorithm, albeit with significantly better performance in terms of computational time. Specifically, the solution is typically obtained in around 1 CPUs, which is real-time feasible and would allow for the DDDP calculations to be executed in the vehicle’s on-board computer.

关键词： GLOSA stochastic dynamic programming Discrete Differential dynamic programming (DDDP) Traffic Light Advisory Speed Advisory

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APPROXIMATE dynamic programming STRATEGY FOR DUAL ADAPTIVE CONTROL

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IFAC Proceedings Volumes 2005年第1期38卷 459-464页

作者： Jong Min Lee Jay H. Lee School of Chemical and Biomolecular Engineering Georgia Institute of Technology Atlanta GA 30332 USA

An approximate dynamic programming (ADP) strategy for a dual adaptive control problem is presented. An optimal control policy of a dual adaptive control problem can be derived by solving a stochastic dynamic programming problem, which is computationally intractable using conventional solution methods that involve sampling of a complete hyperstate space. To solve the problem in a computationally amenable manner, we perform closed-loop simulations with different control policies to generate a data set that defines a subset of a hyperstate within which the Bellman equation is iterated. A local approximator with a penalty function is designed for estimation of cost-to-go values over the continuous hyperstate space. An integrating process with an unknown gain is used for illustration.

关键词： approximate dynamic programming dual control adaptive control stochastic dynamic programming

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Assessing opportunity and relocation costs of marine protected areas using a behavioural model of longline fleet dynamics

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FISH AND FISHERIES 2012年第2期13卷 139-157页

作者： Dowling, Natalie A. Wilcox, Chris Mangel, Marc Pascoe, Sean CSIRO Marine & Atmospher Res Pelag Fisheries & Ecosyst Hobart Tas 7000 Australia Univ Calif Santa Cruz Dept Appl Math & Stat MS E 2 Ctr Stock Assessment Res Santa Cruz CA 95064 USA CSIRO Marine & Atmospher Res Ecosci Precinct Dutton Pk Qld 4102 Australia Univ Bergen Dept Biol Bergen Norway

Increasing use of spatial management tools in fisheries requires an understanding of fleet response, and in particular to where displaced fishing effort is likely to move. We develop a state-dependent decision-making model to address the spatial allocation of effort in an Australian tuna longline fishery. We assume that fishers have an economic objective in deciding where to fish, but that decisions in any period are also influenced by the remaining quota held at the time of the decision. Key features of the model include endogenous price dynamics, a moving stock and a competitive pool of different vessel types operating from different port locations. We utilize this model to illustrate fleet responses to marine reserves and limits on fishing effort. The results illustrate that the model framework provides advantages over statistically based models in that decisions made in response to the imposition of a reserve are not consistent with a proportional reallocation of effort. Rather, the stochastic dynamic model yielded an overall profit level of similar to 4% higher relative to scenarios with no reserve. Incorporating the opportunity cost of a quota into the model resulted in an optimal utilization of effort, in which effort was concentrated in time periods and locations yielding maximized profit. Under a low level of effort relative to the season length, the model indicated an overall profit level 43% greater than the highest obtained when the same level of effort was applied solely within any given quarter of the season.

关键词： Fishery fleet dynamics location choice model spatial fisheries management state-dependent model stochastic dynamic programming tuna longline fishery

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A dynamic optimisation model for the behaviour of tunas at ocean fronts

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FISHERIES OCEANOGRAPHY 2000年第4期9卷 328-342页

作者： Kirby, DS Fiksen, O Hart, PJB Univ Leicester Dept Biol Fisheries Res Grp Leicester LE1 7RH Leics England Univ Leicester Dept Phys Earth Observ Sci Grp Leicester LE1 7RH Leics England Natl Inst Water & Atmospher Res Wellington New Zealand Univ Bergen Dept Fisheries & Marine Biol N-5020 Bergen Norway

We present a model that simulates the foraging behaviour of tunas in the vicinity of ocean fronts. stochastic dynamic programming is used to determine optimal habitat choice and swimming speed in relation to environmental variables (water temperature and clarity) and prey characteristics (abundance and energy density). By incorporating submodels for obligate physiological processes (gastric evacuation, standard and active metabolic costs) and sensory systems (visual feeding efficiency), we have integrated into a single fitness-based model many of the factors that might explain the aggregation of tunas at ocean fronts. The modelling technique describes fitness landscapes for all combinations of states, and makes explicit, testable predictions about time- and state-dependent behaviour. Enhanced levels of searching activity when hungry and towards the end of the day are an important feature of the optimal behaviour predicted. We consider the model to be particularly representative of the behaviour of the warm-a ater tunas or Neothunnus (e.g. skipjack, Katsuwonus pelamis, and yellowfin, Thunnus albacares) and for surface-dwelling temperate tunas (e.g. young albacore, Thunnus alalunga), which are often observed to aggregate near fronts. For the bluefin group (i.e. older albacore;northern and southern bluefin, Thunnus thynnus and Thunnus maccoyii), for which extended vertical migrations are a significant and as yet unexplained component of behaviour, the model is able to reproduce observed behaviour by adopting the lower optimal temperature and standard metabolic rate of albacore. The model cannot explain why physiological differences exist between and within the different tuna species, but it does show how differences in susceptibility to thermal stress will permit different behaviour.

关键词： behaviour fronts physiology stochastic dynamic programming tunas

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Optimal eradication: when to stop looking for an invasive plant

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ECOLOGY LETTERS 2006年第7期9卷 759-766页

作者： Regan, TJ McCarthy, MA Baxter, PWJ Panetta, FD Possingham, HP Univ Queensland Ctr Ecol Sch Integrat Biol St Lucia Qld 4072 Australia Univ Melbourne Australian Res Ctr Urban Ecol Royal Bot Gardens Melbourne cl Sch Bot Melbourne Vic 3010 Australia Univ Melbourne Sch Bot Melbourne Vic 3010 Australia Dept Nat Resources & Mines Sherwood Qld 4075 Australia CRC Australian Weed Management Glen Osmond SA 5064 Australia

The notion of being sure that you have completely eradicated an invasive species is fanciful because of imperfect detection and persistent seed banks. Eradication is commonly declared either on an ad hoc basis, on notions of seed bank longevity, or on setting arbitrary thresholds of 1% or 5% confidence that the species is not present. Rather than declaring eradication at some arbitrary level of confidence, we take an economic approach in which we stop looking when the expected costs outweigh the expected benefits. We develop theory that determines the number of years of absent surveys required to minimize the net expected cost. Given detection of a species is imperfect, the optimal stopping time is a trade-off between the cost of continued surveying and the cost of escape and damage if eradication is declared too soon. A simple rule of thumb compares well to the exact optimal solution using stochastic dynamic programming. Application of the approach to the eradication programme of Helenium amarum reveals that the actual stopping time was a precautionary one given the ranges for each parameter.

关键词： decision theory detectability economic costs eradication invasive plants stochastic dynamic programming rule of thumb weed

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A nonlinear stochastic growth model on discrete time domains

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JOURNAL OF DIFFERENCE EQUATIONS AND APPLICATIONS 2016年第11期22卷 1732-1746页

作者： Atici, Ferhan M. Cheng, Gang Lebedinsky, Alex Western Kentucky Univ Dept Math Bowling Green KY 42101 USA Western Kentucky Univ Dept Econ Bowling Green KY 42101 USA

In this paper, we introduce nonlinear stochastic dynamic problems on discrete time domains where events may occur at unevenly spaced time points. We define Euler equation and transversality condition for the problem. We prove that the Euler equation and the transversality condition are sufficient for the existence of the optimal solution. Next we generalize discrete time Cagan type rational expectation model to multivariate case. As an application of the main results, we obtain an explicit solution to a log-linearized nonlinear stochastic growth model.

关键词： Optimization stochastic dynamic programming rational expectations

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