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Automatic MILP solver configuration by learning problem similarities

ANNALS OF OPERATIONS RESEARCH 2024年第1-2期339卷 909-936页

作者： Hosny, Abdelrahman Reda, Sherief Brown Univ Dept Comp Sci Providence RI 02912 USA Brown Univ Sch Engn Providence RI 02912 USA

A large number of real-world optimization problems can be formulated as mixed integer linear Programs (MILP). MILP solvers expose numerous configuration parameters to control their internal algorithms. Solutions, and their associated costs or runtimes, are significantly affected by the choice of the configuration parameters, even when problem instances have the same number of decision variables and constraints. On one hand, using the default solver configuration leads to suboptimal solutions. On the other hand, searching and evaluating a large number of configurations for every problem instance is time-consuming and, in some cases, infeasible. In this study, we aim to predict configuration parameters for unseen problem instances that yield lower-cost solutions without the time overhead of searching-and-evaluating configurations at the solving time. Toward that goal, we first investigate the cost correlation of MILP problem instances that come from the same distribution when solved using different configurations. We show that instances that have similar costs using one solver configuration also have similar costs using another solver configuration in the same runtime environment. After that, we present a methodology based on Deep Metric Learning to learn MILP similarities that correlate with their final solutions' costs. At inference time, given a new problem instance, it is first projected into the learned metric space using the trained model, and configuration parameters are instantly predicted using previously-explored configurations from the nearest neighbor instance in the learned embedding space. Empirical results on real-world problem benchmarks show that our method predicts configuration parameters that improve solutions' costs by up to 38% compared to existing approaches.

关键词： mixed integer linear programming Algorithm configuration Metric learning Deep learning

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A novel reduced local search algorithm for optimization of digital filter coefficients

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RESULTS IN ENGINEERING 2024年 23卷

作者： Gawande, Gopal Katare, Pravin Chan, Choon Kit Pande, Milind Gohokar, Vijay Marathwada Mitra Mandals Coll Engn Fac Elect & Telecommun Engn Pune 411052 India Marathwada Mitra Mandals Coll Engn Fac Mech Engn Pune 411052 India INTI Int Univ Persiaran Perdana BBN Nilai 71800 Negeri Sembilan Malaysia INTI Int Univ Fac Engn & Quant Surveying Persiaran Perdana BBN Nilai 71800 Negeri Sembilan Malaysia MIT World Peace Univ Fac Elect & Elect Engn Pune 411038 India Marathwada Mitra Mandals Coll Engn Fac Elect Engn Pune 411052 India

Digital Filtering is an important operation in DSP. Most of the DSP applications demand low power implementations. In CMOS digital circuits, switching from logic 0 to logic 1 and vice-versa decides the dynamic power consumption. This paper proposes and develops a novel algorithm based on mixed integer linear programming to minimize the number of ones (filter cost) in filter coefficients, which in turn minimizes the switching factor of CMOS circuits and the number of additional adders needed for hardware implementation. The search space for finding the optimal value of the coefficient is reduced thereby increasing the speed of execution of the algorithm. The average reduction of 18.02 % in the number of transitions is obtained with the proposed algorithm. The suggested technique results in an 18.72 % decrease in the typical price of digital filters. Also the proposed algorithm works faster than the previously proposed algorithm used for finding the optimized coefficients.

关键词： Industrial and innovation FIR filter coefficients mixed integer linear programming Upper and lower bounds

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A mathematical programming approach for a wildfire suppression problem

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OPERATIONAL RESEARCH 2025年第1期25卷 1-27页

作者： Granda, Bibiana Vitoriano, Begona Figueira, Jose Rui Interdisciplinary Math Inst IMI HUMLOG Res Grp Plaza Ciencias 3 Madrid 28040 Spain Univ Lisbon CEGIST Inst Super Tecn P-1049001 Lisbon Portugal

Wildfires are natural recurrent events, that may be devastating if not addressed correctly. In these situations, where quick and accurate decisions are needed, Operational Research can be helpful for providing fast and robust solutions. This paper focuses on the response actions taken during the suppression stage of a wildfire. A mixed integer linear programming model is proposed to obtain a wildfire suppression strategy, including the wildfire behaviour changes induced by the solution. The selected wildfire suppression strategy is modelled in detail, pointing out which locations to control and their timing, based on available paths between them, avoiding engagement in dangerous situations. A computational study is carried out to determine the most suitable solver to provide exact solutions of the model. Also, a two-stage version of the model is proposed to deal with the multicriteria nature of the problem. A case study is also included to validate the model's applicability, which is solved using the two proposed versions of the model and an iterative approach to compare their performance.

关键词： Wildfire management Wildfire suppression Optimization mixed integer linear programming

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Digital Twin-Driven Dynamic Scheduling of Flexible Manufacturing System in the Context of Smart Factory Producing Brass Accessories

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JOURNAL OF INDUSTRIAL INTEGRATION AND MANAGEMENT-INNOVATION AND ENTREPRENEURSHIP 2024年第0期 67-107页

作者： Chakroun, Ayoub Hani, Yasmina Elmhamedi, Abderrahmane Masmoudi, Faouzi Univ Paris VIII Vincennes Univ Inst Technol Lab QUARTZ EA 7393 FR-93100 Montreuil France Univ Sfax Natl Sch Engineers Sfax Lab LA2MP Route Soukra Sfax 3038 Tunisia

This study aims to deal with a dynamic scheduling problem of a real Flow Shop follow-up of an assembly process considering the specific constraints and requirements of a brass accessories manufacturing (BAM) company. We basically set up a Digital Twin-driven dynamic scheduling approach for Industry 4.0 by addressing uncertainties of machine availability. In fact, the setup of this Digital Twin (DT) is the outcome of the combination of both optimization and simulation. For the first step, we have elaborated a mixed integer linear programming (MILP) scheduling model by taking into account the dedicated requisites of our case study. Concerning simulation, a 3D simulation platform of a workshop producing brass accessories controlled by a Cyber-Physical Production System (CPPS) has been developed, in our previous work, including constraints and stochastic aspects. The simulation constraints are difficult or impossible to be modeled in the MILP model. These designs are integrated with the real workshop to construct the Digital Twin. The proposed tool enables the rescheduling of production orders based on machine disturbances and unpredictability. Validation scenarios have been designed and conducted to highlight the efficacy of the Digital Twin approach utilizing the brass accessories case study. We are confident that this represents the initial endeavor addressing the optimization of production rescheduling in a flow shop follow-up of a mixed assembly system.

关键词： Dynamic scheduling flow shop assembly process digital twin Industry 4.0 flexibility machines availability mixed integer linear programming

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Rail freight transportation service network design with mixed blocking policies: Formulation and solution methods

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JOURNAL OF RAIL TRANSPORT PLANNING & MANAGEMENT 2025年 33卷

作者： Xin, Xiaoyu Wu, Xu Wang, Jiaxi Beijing Jiaotong Univ Sch Traff & Transportat Beijing Peoples R China Zhejiang Lab Res Ctr High Efficiency Comp Syst Hangzhou Zhejiang Peoples R China

In the current China railway system, the freight transportation service network is demand category oriented and is designed by using a hierarchical approach. This simplifies the design procedure, but reduces the flexibility and optimality of the plan. To make full use of the limited railway capacity and to improve the service flexibility and quality, this paper addresses the freight service network design problem with simultaneous consideration of mixed blocking policies and mixed service classes for heterogeneous demands. A mixed integer linear programming model is developed for the problem to maximize the operator's total transportation profit. Furthermore, a two-stage decomposition framework is proposed to solve the optimization model, where both exact and heuristic algorithms are employed. Extensive computational experiments on artificial and real-world instances indicate that the proposed solution approaches can generate high-quality solutions within reasonable time frame. Moreover, the mixed blocking policies could attract more freight demands and tend to result in higher profits, compared to the single blocking policy.

关键词： Rail freight transportation Network design Blocking policy mixed integer linear programming Lagrangian relaxation

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Ensemble Provably Robust Learn-to-Optimize Approach for Security-Constrained Unit Commitment

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IEEE TRANSACTIONS ON POWER SYSTEMS 2023年第6期38卷 5073-5087页

作者： Sang, Linwei Xu, Yinliang Sun, Hongbin Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen 518055 Peoples R China Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China

Security-constrained unit commitment (SCUC) is the basis for power systems and markets operation, which is solved periodically via mixed-integer programming (MIP) with limited input data changes to historical solved instances. This paper proposes an ensemble provably robust learn-to-optimize approach (EPR-L2O) for transforming the MIP-based SCUC into the tractable convex problem with predictable solving time by identifying the integer part of MIP, denoted by unit status integer solution. It formulates the SCUC model for unit status integer solution generation, prunes the generated integer solutions, clusters the units based on their operation status, and proposes the provably robust learning approach to training the unit status integer solution identifiers, which are provably robust against the input adversarial perturbation. After learning, the integer solutions from different identifiers are assembled to identify the integer variables of SCUC. Case study verifies that: 1) EPR-L2O achieves 6.91, 19.71, and 22.14 times acceleration in the 30-bus, 118-bus, and 300-bus systems than the Branch-and-Bound;2) compared to cross-entropy-based learning, EPR-L2O reduces the MIP gap by 0.031%, 0.21%, and 0.0084% in the 30-bus, 118-bus, and 300-bus systems, indicating its operation economy and robustness.

关键词： Predictive models Optimization Ensemble learning mixed integer linear programming Security-constrained unit commitment ensemble learning provably robust learning learn-to-optimize

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A matheuristic for flexible job shop scheduling problem with lot-streaming and machine reconfigurations

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2023年第19期61卷 6565-6588页

作者： Fan, Jiaxin Zhang, Chunjiang Shen, Weiming Gao, Liang Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan Peoples R China

Multi-variety and small-batch production mode enables manufacturing industries to expeditiously satisfy customers' personalised demands, where a large amount of identical jobs can be split into several sublots, and be processed by reconfigurable machines with multiple machining technics. However, such highly flexible manufacturing environments bring some intractable problems to the production scheduling. Mathematical programming and meta-heuristic methods become less efficient when a scheduling problem contains both discrete and continuous optimisation attributes. Therefore, matheuristic, which combines advantages of the two methodologies, is regarded as a promising solution. This paper investigates a flexible job shop scheduling problem with lot-streaming and machine reconfigurations (FJSP-LSMR) for the total weighted tardiness minimisation. First, a monolithic mixed integer linear programming (MILP) model is established for the FJSP-LSMR. Afterwards, a matheuristic method with a variable neighbourhood search component (MH-VNS) is developed to address the problem. The MH-VNS adopts the classical genetic algorithm (GA) as the framework, and introduces two MILP-based lot-streaming optimisation strategies, LSO1 and LSO2, to improve lot-sizing plans with varying degrees. Four groups of instances are extended from the well-known Fdata benchmark to evaluate the performance of proposed MILP model, LSO1 and LSO2 components, and MH-VNS. Numerical experimental results suggest that LSO1 and LSO2 are efficient in different scenarios, and the proposed MH-VNS can well balance the solution quality and computational costs for reasonably integrating the GA- and MILP-based local search strategies. In addition, a complicated FJSP-LSMR case is abstracted from a real-world shop floor for processing large-sized structural parts to further validate the MH-VNS.

关键词： Flexible job shop scheduling lot-streaming machine reconfiguration matheuristic genetic algorithm mixed integer linear programming

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Design of a future hydrogen supply chain: A multi-objective model for Turkey

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2023年第31期48卷 11775-11789页

作者： Erdogan, Ahmet Gecici, Ebru Guler, Mehmet Guray Yildiz Tech Univ Fac Mech Engn Dept Ind Engn Istanbul Turkiye

One of the important alternatives to conventional fossil fuel vehicles in the transportation sector is hydrogen fuel cell vehicle (HFCV) technology. One of the most significant obstacles to the widespread use of these vehicles is the hydrogen supply chain network (HSC) infrastructure. In the design of this network, the harm caused by the network to the environment and the security risks that may arise are as important as the associated cost of building it. In this study, an HSC design that will minimize cost, carbon emission and security risk for Turkey is proposed. The problem is modeled using a mixed integer linear programming (MILP). Five different optimization cases are studied when the penetration rate of HFCV is 25%. In the first three cases, the objectives are independently optimized. The multi-objective optimization is addressed in Case 4 and Case 5. Case 4 is solved with epsilon constraint method by employing the results of the first three cases. The most balanced solution found is 88%, 10% and 2% away from the best cost, carbon emission and risk values, respectively. It is observed that the proposed solution has a decentralized network structure where steam methane reforming (SMR) and electrolysis (ELE) production plants are established. In Case 5, the weighted sum method (another multi-objective optimization method) is used and those which gave the closest results to that of the epsilon constraint method are chosen as the associated weights of three objectives. Using these weights, 10 different demand scenarios are studied. It is observed that the HSC has a decentralized structure under almost all demand scenarios. (c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Hydrogen supply chain Multi-objective optimization mixed integer linear programming

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The multiple traveling salesman problem in presence of drone- and robot-supported packet stations

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期305卷 630-643页

作者： Kloster, Konstantin Moeini, Mahdi Vigo, Daniele Wendt, Oliver Tech Univ Kaiserslautern Chair Business Informat Syst & Operat Res BISOR D-67663 Kaiserslautern Germany Univ Bologna Dept Elect Elect & Informat Engn G Marconi I-40136 Bologna Italy

In this paper, we introduce the multiple Traveling Salesman Problem with Drone Stations (mTSP-DS), which is an extension to the classical multiple Traveling Salesman Problem (mTSP). In the mTSP-DS, we have a depot, a set of trucks, and some packet stations that host a given number of autonomous vehicles (drones or robots). The trucks start their mission from the depot and can supply some packet stations, which can then launch and operate drones/robots to serve customers. The goal is to serve all customers either by truck or by drones/robots while minimizing the makespan. We formulate the mTSP-DS as a mixed integer linear programming (MILP) model to solve small instances. To address larger instances, we first introduce two variants of a decomposition-based matheuristic. Afterwards, we suggest a third approach that is based on populating a solution pool with several restarts of an iterated local search metaheuristic, which is followed by determining the best combination of tours using a set-partitioning model. To verify the performance of our algorithms, we conducted extensive computational experiments. According to the numerical results, we observe that the use of drone stations leads to considerable savings in delivery time compared to traditional mTSP solutions. Furthermore, we investigated the energy consumption of trucks and drones. Indeed, depending on the energy consumption coefficients of trucks and drones as well as on the distance covered by drones, the mTSP-DS can also achieve energy savings in comparison to mTSP solutions.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Logistics Vehicle routing Drone deliveries mixed integer linear programming Heuristics

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Algorithmic Symplectic Packing

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EXPERIMENTAL MATHEMATICS 2024年第2期33卷 175-192页

作者： Fischer, Greta Gutt, Jean Junger, Michael Univ Cologne Dept Math & Comp Sci Cologne Germany Univ Toulouse Inst Math Toulouse France INU Champollion Albi France

In this article we explore a symplectic packing problem where the targets and domains are 2n-dimensional symplectic manifolds. We work in the context where the manifolds have first homology group equal to Z(n) , and we require the embeddings to induce isomorphisms between first homology groups. In this case, Miller Maley, Mastrangeli, and Traynor showed that the problem can be reduced to a combinatorial optimization problem, namely packing certain allowable simplices into a given standard simplex. They designed a computer program and presented computational results. In particular, they determined the simplex packing widths in dimension four for up to k = 12 simplices, along with lower bounds for higher values of k. We present a modified algorithmic approach that allows us to determine the k-simplex packing widths for up to k = 13 simplices in dimension four and up to k = 8 simplices in dimension six. Moreover, our approach determines all simplex-multisets that allow for optimal packings.

关键词： Symplectic packings symplectic capacities combinatorial optimization mixed integer linear programming semidefinite programming

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