Multiple Independent DE Optimizations to Tackle Uncertainty and Variability in Demand in Inventory Management

• URL: http://arxiv.org/abs/2309.13095v2
• Date: Mon, 9 Oct 2023 13:57:56 GMT
• Title: Multiple Independent DE Optimizations to Tackle Uncertainty and Variability in Demand in Inventory Management
• Authors: Sarit Maitra, Sukanya Kundu, Vivek Mishra
• Abstract summary: This study aims to discern the most effective strategy for minimizing inventory costs within the context of uncertain demand patterns. To find the optimal solution, the study focuses on meta-heuristic approaches and compares multiple algorithms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: To determine the effectiveness of metaheuristic Differential Evolution optimization strategy for inventory management (IM) in the context of stochastic demand, this empirical study undertakes a thorough investigation. The primary objective is to discern the most effective strategy for minimizing inventory costs within the context of uncertain demand patterns. Inventory costs refer to the expenses associated with holding and managing inventory within a business. The approach combines a continuous review of IM policies with a Monte Carlo Simulation (MCS). To find the optimal solution, the study focuses on meta-heuristic approaches and compares multiple algorithms. The outcomes reveal that the Differential Evolution (DE) algorithm outperforms its counterparts in optimizing IM. To fine-tune the parameters, the study employs the Latin Hypercube Sampling (LHS) statistical method. To determine the final solution, a method is employed in this study which combines the outcomes of multiple independent DE optimizations, each initiated with different random initial conditions. This approach introduces a novel and promising dimension to the field of inventory management, offering potential enhancements in performance and cost efficiency, especially in the presence of stochastic demand patterns.

Related papers

• Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems [61.580419063416734]
  A recent stream of structured learning approaches has improved the practical state of the art for a range of optimization problems. The key idea is to exploit the statistical distribution over instances instead of dealing with instances separately. In this article, we investigate methods that smooth the risk by perturbing the policy, which eases optimization and improves the generalization error.
  arXiv  Detail & Related papers  (2024-07-24T12:00:30Z)
• Model-Free Active Exploration in Reinforcement Learning [53.786439742572995]
  We study the problem of exploration in Reinforcement Learning and present a novel model-free solution. Our strategy is able to identify efficient policies faster than state-of-the-art exploration approaches.
  arXiv  Detail & Related papers  (2024-06-30T19:00:49Z)
• Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial Optimization [6.713974813995327]
  We present MEMENTO, an RL approach that leverages memory to improve the adaptation of neural solvers at time. We validate its effectiveness on benchmark problems, in particular Traveling Salesman and Capacitated Vehicle Routing.
  arXiv  Detail & Related papers  (2024-06-24T08:18:19Z)
• End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty [55.04219793298687]
  The Predict-Then-Forecast (PtO) paradigm in machine learning aims to maximize downstream decision quality. This paper extends the PtO methodology to optimization problems with nondifferentiable Ordered Weighted Averaging (OWA) objectives. It shows how optimization of OWA functions can be effectively integrated with parametric prediction for fair and robust optimization under uncertainty.
  arXiv  Detail & Related papers  (2024-02-12T16:33:35Z)
• Rethinking and Benchmarking Predict-then-Optimize Paradigm for Combinatorial Optimization Problems [62.25108152764568]
  Many web applications rely on solving optimization problems, such as energy cost-aware scheduling, budget allocation on web advertising, and graph matching on social networks. We consider the performance of prediction and decision-making in a unified system. We provide a comprehensive categorization of current approaches and integrate existing experimental scenarios.
  arXiv  Detail & Related papers  (2023-11-13T13:19:34Z)
• Optimizing Inventory Routing: A Decision-Focused Learning Approach using Neural Networks [0.0]
  We formulate and propose a decision-focused learning-based approach to solving real-world IRPs. This approach directly integrates inventory prediction and routing optimization within an end-to-end system potentially ensuring a robust supply chain strategy.
  arXiv  Detail & Related papers  (2023-11-02T04:05:28Z)
• Federated Conditional Stochastic Optimization [110.513884892319]
  Conditional optimization has found in a wide range of machine learning tasks, such as in-variant learning tasks, AUPRC, andAML. This paper proposes algorithms for distributed federated learning.
  arXiv  Detail & Related papers  (2023-10-04T01:47:37Z)
• Ensemble Differential Evolution with Simulation-Based Hybridization and Self-Adaptation for Inventory Management Under Uncertainty [0.0]
  This study proposes an Ensemble Differential Evolution with Simula-tion-Based Hybridization and Self-Adaptation (EDESH-SA) approach for inven-tory management (IM) under uncertainty.
  arXiv  Detail & Related papers  (2023-09-22T13:25:58Z)
• Learning to Optimize with Stochastic Dominance Constraints [103.26714928625582]
  In this paper, we develop a simple yet efficient approach for the problem of comparing uncertain quantities. We recast inner optimization in the Lagrangian as a learning problem for surrogate approximation, which bypasses apparent intractability. The proposed light-SD demonstrates superior performance on several representative problems ranging from finance to supply chain management.
  arXiv  Detail & Related papers  (2022-11-14T21:54:31Z)
• Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization [40.40632890861706]
  We consider the problem of multi-objective (MO) blackbox optimization using expensive function evaluations. We propose a novel uncertainty-aware search framework referred to as USeMO to efficiently select the sequence of inputs for evaluation.
  arXiv  Detail & Related papers  (2022-04-12T16:50:48Z)
• Math Programming based Reinforcement Learning for Multi-Echelon Inventory Management [1.9161790404101895]
  Reinforcement learning has lead to considerable break-throughs in diverse areas such as robotics, games and many others. But the application to RL in complex real-world decision making problems remains limited. These characteristics make the problem considerably harder to solve for existing RL methods that rely on enumeration techniques to solve per step action problems. We show that a properly selected discretization of the underlying uncertain distribution can yield near optimal actor policy even with very few samples from the underlying uncertainty. We find that PARL outperforms commonly used base stock by 44.7% and the best performing RL method by up to 12.1% on average
  arXiv  Detail & Related papers  (2021-12-04T01:40:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.