On computing optimal policies in perishable inventory control using value iteration

Hendrix, E.M.T.; Ortega, Gloria; Haijema, R.; Buisman, Marjolein E.; Garcı́a, Inmaculada

doi:10.1002/cmm4.1027

Cited by 7 publications

(23 citation statements)

References 11 publications

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“…The iterative procedure of Algorithm 1 stops whenever span(V , W ) is smaller than a pre-specified value , which indicates the accuracy in estimating π [8,9].…”

Section: Value Iteration To Reach the Optimal Policymentioning

confidence: 99%

A CUDA approach to compute perishable inventory control policies using value iteration

2018

Self Cite

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Dynamic programming (DP) approaches, in particular value iteration, is often seen as a method to derive optimal policies in inventory management. The challenge in this approach is to deal with an increasing state space when handling realistic problems. As a large part of world food production is thrown out due to its perishable character, a motivation exists to have a good look at order policies in retail. Recently, investigation has been introduced to consider substitution of one product by another, when one is out of stock. Taking this tendency into account in a policy requires an increasing state space. Therefore, we investigate the potential of using GPU platforms in order to derive optimal policies when the number of products taken into account simultaneously is increasing. First results show the potential of the GPU approach to accelerate computation in value iteration for DP.

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“…The iterative procedure of Algorithm 1 stops whenever span(V , W ) is smaller than a pre-specified value , which indicates the accuracy in estimating π [8,9].…”

Section: Value Iteration To Reach the Optimal Policymentioning

confidence: 99%

A CUDA approach to compute perishable inventory control policies using value iteration

2018

Self Cite

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“…More recently, despite advances in computational power, researchers have stated that value iteration remains infeasible or impractical when the maximum useful life of the product is longer than two or three periods or when additional complexities (e.g. substitution between products or random remaining useful life on arrival) are introduced (De Moor et al 2022;Hendrix et al 2019;Mirjalili 2022) The prevalent view in the literature about the scale of problems for which value iteration is feasible appears to neglect the recent developments in graphical processing unit (GPU) hardware, and in software libraries that make it possible for researchers and practitioners to take advantage of GPU capabilities without detailed knowledge of GPU hardware and GPU-specific programming approaches. GPUs were developed for rendering computer graphics, which requires the same operations to be efficiently applied to many inputs in parallel.…”

Section: Introductionmentioning

confidence: 99%

Going faster to see further: GPU-accelerated value iteration and simulation for perishable inventory control using JAX

Farrington¹,

Li²,

Wong³

et al. 2023

Preprint

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Value iteration can find the optimal replenishment policy for a perishable inventory problem, but is computationally demanding due to the large state spaces that are required to represent the age profile of stock. The parallel processing capabilities of modern GPUs can reduce the wall time required to run value iteration by updating many states simultaneously. The adoption of GPU-accelerated approaches has been limited in operational research relative to other fields like machine learning, in which new software frameworks have made GPU programming widely accessible. We used the Python library JAX to implement value iteration and simulators of the underlying Markov decision processes in a high-level API, and relied on this library's function transformations and compiler to efficiently utilize GPU hardware. Our method can extend use of value iteration to settings that were previously considered infeasible or impractical. We demonstrate this on example scenarios from three recent studies which include problems with over 16 million states and additional problem features, such as substitution between products, that increase computational complexity. We compare the performance of the optimal replenishment policies to heuristic policies, fitted using simulation optimization in JAX which allowed the parallel evaluation of multiple candidate policy parameters on thousands of simulated years. The heuristic policies gave a maximum optimality gap of 2.49%. Our general approach may be applicable to a wide range of problems in operational research that would benefit from large-scale parallel computation on consumer-grade GPU hardware.

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“…In view of the fluctuations of these processes, the implementation of Lean Warehousing tools is developed, which aim to optimize existing warehouse operations and seek alternatives for waste reduction [6].…”

Section: Introductionmentioning

confidence: 99%

Implementation of engineering techniques for reducing waste in Warehousing: A case study in a Peruvian Food Company

Chumpitaz-Martínez¹,

Sánchez-Sotelo²,

Leon-Chavarri³

2022

Proceedings of the 2nd LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development (LEIRD 202

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Companies belonging to the food sector are exposed to a problem related to the loss of products during the production and commercialization processes, which mainly causes an increase in the amount of waste generated. The objective of this research is the reduction of waste generated during warehouse operations in a food company, which presents an increase in its waste ratio equivalent to 233% compared to previous years. The implementation of Lean Warehousing tools was developed, specifically Kaizen Teian and Kanban Pull, together with the Slotting warehouse strategy considering the useful life of the products as a criterion.With the development of these tools, the food waste ratio is expected to be reduced to a value close to the industry standard, corresponding to 0.25%.

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On computing optimal policies in perishable inventory control using value iteration

Cited by 7 publications

References 11 publications

A CUDA approach to compute perishable inventory control policies using value iteration

A CUDA approach to compute perishable inventory control policies using value iteration

Going faster to see further: GPU-accelerated value iteration and simulation for perishable inventory control using JAX

Implementation of engineering techniques for reducing waste in Warehousing: A case study in a Peruvian Food Company

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