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Inverse Optimization for Warehouse Management 1st

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Inverse Optimization for Warehouse Management

1st International Conference on Optimization, Learning Algorithms and Applications (OL2A)

作者： Rummukainen, Hannu VTT Tech Res Ctr Finland POB 1000 Espoo 02044 Finland

ISBN: (纸本)9783030918859;9783030918842

Day-to-day operations in industry are often planned in an ad-hoc manner by managers, instead of being automated with the aid of mathematical optimization. To develop operational optimization tools, it would be useful to automatically learn management policies from data about the actual decisions made in production. The goal of this study was to investigate the suitability of inverse optimization for automating warehouse management on the basis of demonstration data. The management decisions concerned the location assignment of incoming packages, considering transport mode, classification of goods, and congestion in warehouse stocking and picking activities. A mixed-integer optimization model and a column generation procedure were formulated, and an inverse optimization method was applied to estimate an objective function from demonstration data. The estimated objective function was used in a practical rolling horizon procedure. The method was implemented and tested on real-world data from an export goods warehouse of a container port. The computational experiments indicated that the inverse optimization method, combined with the rolling horizon procedure, was able to mimic the demonstrated policy at a coarse level on the training data set and on a separate test data set, but there were substantial differences in the details of the location assignment decisions.

关键词： Multi-period storage location assignment problem Class-based storage Inverse optimization mixed-integer linear programming

A multi-objective production scheduling model and dynamic dispatching rules for unrelated parallel machines with sequence-dependent set-up times

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Decision Analytics Journal 2024年 13卷

作者： Tai, Pham Duc Kongsri, Papimol Soeurn, Prasal Buddhakulsomsiri, Jirachai School of Manufacturing Systems and Mechanical Engineering Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand

This study presents a production scheduling for unrelated parallel machines with machine and job sequence-dependent setup times. The system performance measures to minimize include makespan, total tardiness, and number of tardy jobs. The aim of the study is to develop a solution methodology that can solve the problem for the large scale. First, the problem is formulated as a mixed-integer linear programming model. The augmented ɛ-constraint method is applied to find Pareto solutions for small problem instances. The purpose is to demonstrate that Pareto solutions, which balance the trade-offs among three measures of performance, can be found for these instances. Dispatching rule-based heuristics is developed to solve large problem instances. The heuristics feature three dispatching rules that are designed to handle the dependent setup time. In addition, these rules are combined into six variants using a time-based rule-switching mechanism. The heuristics are tested with 18 problem instances, containing 244 to 298 jobs, in two demand scenarios derived from the monthly demand data from an industrial user. Under each demand scenario, a set of heuristics that provides the best performance with respect to the three measures is identified. The heuristics include combinations of the shortest completion time and due date-based rules. Finally, a multi-criteria decision-making analysis is performed to determine the conditions specified by the weight given to each measure, with which one heuristic is preferred over the others. © 2024 The Author(s)

关键词： Dispatching rules mixed-integer linear programming Multiple objectives Rule switching Sequence-dependent setup Unrelated parallel machine

A production-inventory model to optimize the operation of distributed energy resource networks in a rolling horizon

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Heliyon 2024年第21期10卷 e39900页

作者： Cortés, Pablo Escudero-Santana, Alejandro Barbadilla-Martin, Elena Guadix, José Ingeniería de Organización Escuela Técnica Superior de Ingeniería Universidad de Sevilla Camino de los Descubrimientos s/n Sevilla 41092 Spain

The recent advancements in energy production, storage, and distribution are creating unprecedented opportunities in the field. Major consumers can benefit from the implementation of distributed energy resource networks capable of generating electricity or heating from sources, often renewable ones, in close proximity to the point of use, rather than relying on centralized generation sources from power plants. In this paper, we introduce a pioneering model designed to determine the optimal set of energy commands in a distributed energy resource network, minimizing operational costs in a time horizon. Indeed, we propose an innovative mixed-integer linear programming formulation rooted in the production-inventory models commonly employed in aggregate production planning. The system integrates diverse energy generation sources, storage facilities, and demand points, encompassing both electric and heating commodities. The optimum of the model is achieved for all analyzed instances of the test library (2 scenarios-20 instances) in an exceptionally short time, outperforming other approaches previously presented in the literature. We employed the Gurobi optimizer to solve the model, obtaining rapid responses that ensure real-time decision-making and facilitate effective control of the distributed energy resource network within a three-days’ rolling horizon, as discussed in a simulated real-life application case study. Indeed, the proposed model solves in less than 1 s, enabling near-instantaneous decision-making. This swift solution time surpasses any known references in the field, effectively shifting the bottleneck in DER network operation from the decision-making process to the forecasting of demand and weather conditions. While forecasting typically requires a minimum of 15 min, our approach suggests that a reduction in this forecasting time could further enhance the control system's response time, given the model's ability to deliver optimal solutions almost immediatel

关键词： Distributed energy resource Inventory mixed-integer linear programming Production Renewable energy source Rolling horizon

ON INTERRELATIONS BETWEEN GRAPH COMPLEXES

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arXiv 2023年

作者： Merkulov, Sergei A. Department of Mathematics Luxembourg University Maison du Nombre 6 Avenue de la Fonte Esch-Sur-AlzetteL-4364 Luxembourg

We study Maxim Kontsevich’s graph complex GCd for any integer d as well as its oriented and targeted versions, and show new short proofs of the theorems due to Thomas Willwacher and Marko Živković which establish isomorphisms of their cohomology groups. A new result relating the cohomology of the sourced-targeted graph complex in dimension d+1 with the direct sum of two copies of the cohomology group of Maxim Kontsevich’s graph complex GCd in dimension d is obtained. We introduce a new graph complex spanned by purely trivalent graphs and show that its cohomology is isomorphic to H•(GCd). Copyright © 2023, The Authors. All rights reserved.

关键词： mixed-integer linear programming

A novel dual-decomposition method for non-convex two-stage stochastic mixed-integer quadratically constrained quadratic problems

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arXiv 2023年

作者： Belyak, Nikita Oliveira, Fabricio Department of Mathematics and Systems Analysis Aalto University Espoo Finland

We propose the novel p-branch-and-bound method for solving two-stage stochastic programming problems whose deterministic equivalents are represented by non-convex mixed-integer quadratically constrained quadratic programming (MIQCQP) models. The precision of the solution generated by the p-branch-and-bound method can be arbitrarily adjusted by altering the value of the precision factor p. The proposed method combines two key techniques. The first one, named p-Lagrangian decomposition, generates a mixed-integer relaxation of a dual problem with a separable structure for a primal non-convex MIQCQP problem. The second one is a version of the classical dual decomposition approach that is applied to solve the Lagrangian dual problem and ensures that integrality and non-anticipativity conditions are met once the optimal solution is obtained. This paper also presents a comparative analysis of the p-branch-and-bound method efficiency considering two alternative solution methods for the dual problems as a subroutine. These are the proximal bundle method and Frank-Wolfe progressive hedging. The latter algorithm relies on the interpolation of linearisation steps similar to those taken in the Frank-Wolfe method as an inner loop in the classic progressive hedging. The p-branch-and-bound method’s efficiency was tested on randomly generated instances and demonstrated superior performance over commercial solver Gurobi. © 2023, CC BY.

关键词： mixed-integer linear programming

Pre-trained mixed integer Optimization through Multi-variable Cardinality Branching

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arXiv 2023年

作者： Chen, Yanguang Gao, Wenzhi Ge, Dongdong Ye, Yinyu Shanghai University of Finance and Economics China Stanford University United States Shanghai Jiaotong University China

In this paper, we propose a Pre-trained mixed integer Optimization framework (PreMIO) that accelerates online mixed integer program (MIP) solving with offline datasets and machine learning models. Our method is based on a data-driven multi-variable cardinality branching procedure that splits the MIP feasible region using hyperplanes chosen by the concentration inequalities. Unlike most previous ML+MIP approaches that either require complicated implementation or suffer from a lack of theoretical justification, our method is simple, flexible, provable and explainable. Numerical experiments on both classical OR benchmark datasets and real-life instances validate the efficiency of our proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： mixed-integer linear programming

FairWASP: Fast and Optimal Fair Wasserstein Pre-processing

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arXiv 2023年

作者： Xiong, Zikai Dalmasso, Niccolò Mishler, Alan Potluru, Vamsi K. Balch, Tucker Veloso, Manuela Massachusetts Institute of Technology United States J.P. Morgan AI Research New York United States

Recent years have seen a surge of machine learning approaches aimed at reducing disparities in model outputs across different subgroups. In many settings, training data may be used in multiple downstream applications by different users, which means it may be most effective to intervene on the training data itself. In this work, we present FairWASP, a novel pre-processing approach designed to reduce disparities in classification datasets without modifying the original data. FairWASP returns sample-level weights such that the reweighted dataset minimizes the Wasserstein distance to the original dataset while satisfying (an empirical version of) demographic parity, a popular fairness criterion. We show theoretically that integer weights are optimal, which means our method can be equivalently understood as duplicating or eliminating samples. FairWASP can therefore be used to construct datasets which can be fed into any classification method, not just methods which accept sample weights. Our work is based on reformulating the pre-processing task as a large-scale mixed-integer program (MIP), for which we propose a highly efficient algorithm based on the cutting plane method. Experiments demonstrate that our proposed optimization algorithm significantly outperforms state-of-the-art commercial solvers in solving both the MIP and its linear program relaxation. Further experiments highlight the competitive performance of FairWASP in reducing disparities while preserving accuracy in downstream classification settings. Copyright © 2023, The Authors. All rights reserved.

关键词： mixed-integer linear programming

Peer-to-peer energy trading among smart homes considering responsive demand and interactive visual interface for monitoring

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2022年 29卷

作者： Gorgulu, Hamza Topcuoglu, Yusuf Yaldiz, Abdurahman Gokcek, Tayfur Ates, Yavuz Erdinc, Ozan Yildiz Tech Univ Dept Elect Engn Istanbul Turkey

Increasing demand due to the technological advancements and population expansion poses some issues in terms of environmental considerations and establishment of a sustainable market structure. In this context, the role of Peer-to-Peer (P2P) energy trading in system operation is examined in this paper under different case studies with four prosumer models which have different Energy Storage Systems (ESS), Photovoltaic (PV) systems and responsive demand such as Electric Vehicle (EV), and economic analysis is performed using the developed interface. An active market structure is created with an energy management system where energy can be bought and sold both through the P2P market operator and the distribution system operator, depending on the pricing in different time periods for household models. Through the use of mathematical modeling based on MILP, an optimal solution with the lowest possible cost is achieved amongst the parties. In this study, the amount of traded energy is observed according to both households and time indices with different domestic models and developed an optimization technique. The realization of bidirectional energy transfer of EVs, the importance of ESS, PV system and different domestic cases in terms of P2P energy trading is also examined. Additionally, the system's benefits are demonstrated in advance to participants by verifying and visualizing the data via a system that accepts these cases as input and output. (C) 2021 Published by Elsevier Ltd.

关键词： Peer-to-peer energy trading Responsive demand mixed-integer linear programming Visual interface Optimization

Optimal deployment of virtual network functions for securingtelecommunication networks against distributed denial of service attacks: Arobust optimization approach

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COMPUTERS & OPERATIONS RESEARCH 2022年 146卷

作者： Gicquel, Celine Vanier, Sonia Papadimitriou, Alexandros Univ Paris Saclay Lab Interdisciplinaire Sci Numer Gif Sur Yvette France Univ Paris 01 Lab Informat Ecole Polytech Gif Sur Yvette France Orange Labs Prod & Serv Meylan France

Distributed Denial of Service (DDoS) cyberattacks represent a major security risk for network operators andinternet service providers. They thus need to invest in security solutions to protect their network against DDoSattacks. The present work focuses on deploying a network function virtualization based architecture to securea network against an on-going DDoS attack. We assume that the target, sources and volume of the attack havebeen identified. However, due to 5G network slicing, the exact routing of the illegitimate flow in the networkis not known by the internet service provider. We seek to determine the optimal number and locations ofvirtual network functions in order to remove all the illegitimate traffic while minimizing the total cost of theactivated virtual network functions. We propose a robust optimization framework to solve this problem. Theuncertain input parameters correspond to the amount of illegitimate flow on each path connecting an attacksource to the target and can take values within a predefined uncertainty set. In order to solve this robustoptimization problem, we develop an adversarial approach in which the adversarial sub-problem is solvedby a Branch & Price algorithm. The results of our computational experiments, carried out on medium-sizerandomly generated instances, show that the proposed solution approach is able to provide optimal solutionswithin short computation times.

关键词： Telecommunication networks Cybersecurity Distributed denial of service Network function virtualization Robust optimization Adversarial approach mixed-integer linear programming Branch & price Column generation