A Case Study on Optimization of Warehouses
- URL: http://arxiv.org/abs/2112.12058v1
- Date: Tue, 23 Nov 2021 07:22:57 GMT
- Title: A Case Study on Optimization of Warehouses
- Authors: Veronika Lesch, Patrick B.M. M\"uller, Moritz Kr\"amer, Samuel Kounev,
Christian Krupitzer
- Abstract summary: In warehouses, order picking is known to be the most labor-intensive and costly task in which the employees account for a large part of the warehouse performance.
In this work, we aim at optimizing the storage assignment and the order picking problem within mezzanine warehouse with regards to their reciprocal influence.
- Score: 2.2101681534594237
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In warehouses, order picking is known to be the most labor-intensive and
costly task in which the employees account for a large part of the warehouse
performance. Hence, many approaches exist, that optimize the order picking
process based on diverse economic criteria. However, most of these approaches
focus on a single economic objective at once and disregard ergonomic criteria
in their optimization. Further, the influence of the placement of the items to
be picked is underestimated and accordingly, too little attention is paid to
the interdependence of these two problems. In this work, we aim at optimizing
the storage assignment and the order picking problem within mezzanine warehouse
with regards to their reciprocal influence. We propose a customized version of
the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for optimizing the
storage assignment problem as well as an Ant Colony Optimization (ACO)
algorithm for optimizing the order picking problem. Both algorithms incorporate
multiple economic and ergonomic constraints simultaneously. Furthermore, the
algorithms incorporate knowledge about the interdependence between both
problems, aiming to improve the overall warehouse performance. Our evaluation
results show that our proposed algorithms return better storage assignments and
order pick routes compared to commonly used techniques for the following
quality indicators for comparing Pareto fronts: Coverage, Generational
Distance, Euclidian Distance, Pareto Front Size, and Inverted Generational
Distance. Additionally, the evaluation regarding the interaction of both
algorithms shows a better performance when combining both proposed algorithms.
Related papers
- Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial Optimization [52.80408805368928]
We introduce a novel greedy-style subset selection algorithm for batch acquisition.
Our experiments on the red fluorescent proteins show that our proposed method achieves the baseline performance in 1.69x fewer queries.
arXiv Detail & Related papers (2024-06-21T05:57:08Z) - On Constructing Algorithm Portfolios in Algorithm Selection for Computationally Expensive Black-box Optimization in the Fixed-budget Setting [0.0]
This paper argues the importance of considering the number of function evaluations used in the sampling phase when constructing algorithm portfolios.
The results show that algorithm portfolios constructed by our approach perform significantly better than those by the previous approach.
arXiv Detail & Related papers (2024-05-13T03:31:13Z) - Data-Efficient Interactive Multi-Objective Optimization Using ParEGO [6.042269506496206]
Multi-objective optimization seeks to identify a set of non-dominated solutions that provide optimal trade-offs among competing objectives.
In practical applications, decision-makers (DMs) will select a single solution that aligns with their preferences to be implemented.
We propose two novel algorithms that efficiently locate the most preferred region of the Pareto front in expensive-to-evaluate problems.
arXiv Detail & Related papers (2024-01-12T15:55:51Z) - Accelerating Cutting-Plane Algorithms via Reinforcement Learning
Surrogates [49.84541884653309]
A current standard approach to solving convex discrete optimization problems is the use of cutting-plane algorithms.
Despite the existence of a number of general-purpose cut-generating algorithms, large-scale discrete optimization problems continue to suffer from intractability.
We propose a method for accelerating cutting-plane algorithms via reinforcement learning.
arXiv Detail & Related papers (2023-07-17T20:11:56Z) - A Study of Scalarisation Techniques for Multi-Objective QUBO Solving [0.0]
Quantum and quantum-inspired optimisation algorithms have shown promising performance when applied to academic benchmarks as well as real-world problems.
However, QUBO solvers are single objective solvers. To make them more efficient at solving problems with multiple objectives, a decision on how to convert such multi-objective problems to single-objective problems need to be made.
arXiv Detail & Related papers (2022-10-20T14:54:37Z) - Tree ensemble kernels for Bayesian optimization with known constraints
over mixed-feature spaces [54.58348769621782]
Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search.
Two well-known challenges in using tree ensembles for black-box optimization are (i) effectively quantifying model uncertainty for exploration and (ii) optimizing over the piece-wise constant acquisition function.
Our framework performs as well as state-of-the-art methods for unconstrained black-box optimization over continuous/discrete features and outperforms competing methods for problems combining mixed-variable feature spaces and known input constraints.
arXiv Detail & Related papers (2022-07-02T16:59:37Z) - Multi-objective robust optimization using adaptive surrogate models for
problems with mixed continuous-categorical parameters [0.0]
Robust design optimization is traditionally considered when uncertainties are mainly affecting the objective function.
The resulting nested optimization problem may be solved using a general-purpose solver, herein the non-dominated sorting genetic algorithm (NSGA-II)
The proposed approach consists of sequentially carrying out NSGA-II while using an adaptively built Kriging model to estimate the quantiles.
arXiv Detail & Related papers (2022-03-03T20:23:18Z) - Outlier-Robust Sparse Estimation via Non-Convex Optimization [73.18654719887205]
We explore the connection between high-dimensional statistics and non-robust optimization in the presence of sparsity constraints.
We develop novel and simple optimization formulations for these problems.
As a corollary, we obtain that any first-order method that efficiently converges to station yields an efficient algorithm for these tasks.
arXiv Detail & Related papers (2021-09-23T17:38:24Z) - Generalization in portfolio-based algorithm selection [97.74604695303285]
We provide the first provable guarantees for portfolio-based algorithm selection.
We show that if the portfolio is large, overfitting is inevitable, even with an extremely simple algorithm selector.
arXiv Detail & Related papers (2020-12-24T16:33:17Z) - Bilevel Optimization: Convergence Analysis and Enhanced Design [63.64636047748605]
Bilevel optimization is a tool for many machine learning problems.
We propose a novel stoc-efficientgradient estimator named stoc-BiO.
arXiv Detail & Related papers (2020-10-15T18:09:48Z)
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.