A fuzzy reinforcement learning algorithm for inventory control in supply chains

Zarandi, M. H. Fazel; Moosavi, Seyed Vahid; Zarinbal, Marzieh

doi:10.1007/s00170-012-4195-z

Cited by 11 publications

(3 citation statements)

References 27 publications

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“…rough a methodology based on reinforcement learning and numerical study, they show their approach can outperform the newsvendor. Zarandi et al [29] presented a flexible fuzzy reinforcement learning algorithm where the value function is approximated by a fuzzy rule-based system and considered the problem of a fuzzy agent (supplier), that is, how to determine the amount of orders for each retailers based on their utility for supplier when its supply capacity is limited. Finally, the effectiveness of their proposed algorithm is proved by a simulation.…”

Section: Inventory Control With Reinforcementmentioning

confidence: 99%

Solving a Joint Pricing and Inventory Control Problem for Perishables via Deep Reinforcement Learning

Wang

Gan

et al. 2021

Complexity

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We study a joint pricing and inventory control problem for perishables with positive lead time in a finite horizon periodic-review system. Unlike most studies considering a continuous density function of demand, in our paper the customer demand depends on the price of current period and arrives according to a homogeneous Poisson process. We consider both backlogging and lost-sales cases, and our goal is to find a simultaneously ordering and pricing policy to maximize the expected discounted profit over the planning horizon. When there is no fixed ordering cost involved, we design a deep reinforcement learning algorithm to obtain a near-optimal ordering policy and show that there are some monotonicity properties in the learned policy. We also show that our deep reinforcement learning algorithm achieves a better performance than tabular-based Q-learning algorithms. When a fixed ordering cost is involved, we show that our deep reinforcement learning algorithm is effective and efficient, under which the problem of “curse of dimension” is circumvented.

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Section: Inventory Control With Reinforcementmentioning

confidence: 99%

Solving a Joint Pricing and Inventory Control Problem for Perishables via Deep Reinforcement Learning

Wang

Gan

et al. 2021

Complexity

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“…Fuzzy Q learning algorithms where fuzzy inference is used to calculate Q function values that evaluate state-action pairs proposed [6][7][8][9][10][11][12][13][14][15][16]. Glorennec [6] proposed two reinforcement-based learning algorithms, one is Fuzzy Q-learning and the other is Dynamical Fuzzy Q-learning discovering the best rules among a given randomly generated rule set.…”

Section: Introductionmentioning

confidence: 99%

“…The main differences between Fuzzy Q learning algorithms in the literature are on the method of constructing fuzzy rules and the place of fuzzy rules. Zarandi, Moosavi and Zarinbal [16] developed fuzzy reinforcement learning algorithm, in which, both structure and parameters of value function are optimized through reinforcement signal. Neuro-fuzzy system is a type of system characterized by a fuzzy system where fuzzy sets and fuzzy rules are adjusted by neural networks using inputoutput pattern.…”

Section: Introductionmentioning

confidence: 99%

A reinforcement learning algorithm with fuzzy approximation for semi Markov decision problems

Kula

Ocaktan

2015

Journal of Intelligent &Amp; Fuzzy Systems

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Real life stochastic problems are generally large-scale, difficult to model, and therefore, suffer from the curses of dimensionality. Such problems cannot be solved by classical optimization methods. This paper presents a reinforcement learning algorithm using a fuzzy inference system, ANFIS to find an approximate solution for semi Markov decision problems (SMDPs). The performance of the developed algorithm is measured and compared to a classical reinforcement algorithm, SMART in a numerical example. Our numerical examples show that the developed algorithm converges significantly faster as the problem size increases and the average cost calculated by the algorithm gets closer to that of SMART as number of epochs used in the developed algorithm is increased. a 1 1,0,0,0 0,1,0,0 1,1,0,0 0,2,0,0 0,0,1,0 0,3,0,0 0,1,1,0 0,4,0,0 0,2,1,0 0,0,0,1 a 2 2,0,0,0 3,0,0,0 2,1,0,0 1,2,0,0 1,0,1,0 1,3,0,0 1,1,1,0 1,4,0,0 0,0,2,0 a 3 4,0,0,0 3,1,0,0 2,2,0,0 2,0,1,0 2,3,0,0 2,1,1,0 0,5,0,0 a 4 5,0,0,0 4,1,0,0 3,2,0,0 3,0,1,0 3,3,0,0 1,2,1,0 a 5 6,0,0,0 5,1,0,0 4,2,0,0 4,0,1,0 2,4,0,0 a 6 7,0,0,0 6,1,0,0 5,2,0,0 3,1,1,0 a 7 8,0,0,0 7,1,0,0 4,3,0,0 a 8 9,0,0,0 5,0,1,0 a 9 6,2,0,0 a 10 8,1,0,0 a 11 10,0,0,0 Fig. 3. Average cost per unit time of the policies determined by the algorithms (Problem 1).

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