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35th Chinese Control and Decision Conference (CCDC)

作者： Zhao, Jinglin Shen, Hang Zhang, Haotian Zhao, Ziyan Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Shanghai Baosight Sofiware Ltd Co Shanghai 201900 Peoples R China Northeastern Univ Foshan Grad Sch Innovat Foshan 528311 Peoples R China

ISBN: (纸本)9798350334722

With the development of industry, manufacturing systems become more and more complex. The traditional manufacturing industry is gradually changing to the intelligent manufacturing industry, which increases the difficulty of scheduling. In order to produce a final product, its parts need to be processed in a hybrid flow shop(HFS), and then assembled according to the predefined product bill of materials. The purpose of this study is to minimize the completion time of the last product. Aiming at the problem under consideration, a constraint programming (CP) model is established and solved by a commercial solver. Then, an iterative greedy algorithm (IGA) is proposed to solve this problem. In order to verify the performance of the algorithm, this paper compares it with simulated annealing (SA) and genetic algorithms (GA). Numerical results prove the effectiveness of the proposed model and solution method.

关键词： Hybrid flow shop Complex assembly Iterative greedy algorithm constraint programming

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Nurse rostering with fatigue modelling Incorporating a validated sleep model with biological variations in nurse rostering

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HEALTH CARE MANAGEMENT SCIENCE 2023年第1期26卷 21-45页

作者： Klyve, Kjartan Kastet Senthooran, Ilankaikone Wallace, Mark Norwegian Univ Sci & Technol Dept Ind Econ & Technol Management Trondheim Norway Monash Univ Melbourne Vic Australia

We use a real Nurse Rostering Problem and a validated model of human sleep to formulate the Nurse Rostering Problem with Fatigue. The fatigue modelling includes individual biologies, thus enabling personalised schedules for every nurse. We create an approximation of the sleep model in the form of a look-up table, enabling its incorporation into nurse rostering. The problem is solved using an algorithm that combines Mixed-Integer programming and constraint programming with a Large Neighbourhood Search. A post-processing algorithm deals with errors, to produce feasible rosters minimising global fatigue. The results demonstrate the realism of protecting nurses from highly fatiguing schedules and ensuring the alertness of staff. We further demonstrate how minimally increased staffing levels enable lower fatigue, and find evidence to suggest biological complementarity among staff can be used to reduce fatigue. We also demonstrate how tailoring shifts to nurses' biology reduces the overall fatigue of the team, which means managers must grapple with the issue of fairness in rostering.

关键词： Nurse rostering Fatigue Sleep Mixed-Integer programming constraint programming Large neighbourhood search Operations research

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constraint Solving Approaches to the Business-to-Business Meeting Scheduling Problem 32

Constraint Solving Approaches to the Business-to-Business Me...

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32nd International Joint Conference on Artificial Intelligence (IJCAI)

作者： Bofill, Miquel Coll, Jordi Garcia, Marc Giraldez-Cru, Jesus Pesant, Gilles Suy, Josep Villaret, Mateu Univ Girona IMAE Dept Girona Spain CSIC Artificial Intelligence Res Inst IIIA Bellaterra Spain Univ Granada DaSCI Inst DECSAI Granada Spain Polytech Montreal Montreal PQ Canada

ISBN: (纸本)9781956792034

The B2B Meeting Scheduling Optimization Problem (B2BSP) consists of scheduling a set of meetings between given pairs of participants to an event, minimizing idle time periods in participants' schedules, while taking into account participants' availability and accommodation capacity. Therefore, it constitutes a challenging combinatorial problem in many real-world B2B events. This work presents a comparative study of several approaches to solve this problem. They are based on constraint programming (CP), Mixed Integer programming (MIP) and Maximum Satisfiability (MaxSAT). The CP approach relies on using global constraints and has been implemented in MiniZinc to be able to compare CP, Lazy Clause Generation and MIP as solving technologies in this setting. A pure MIP encoding is also presented. Finally, an alternative viewpoint is considered under MaxSAT, showing the best performance when considering some implied constraints. Experimental results on real world B2B instances, as well as on crafted ones, show that the MaxSAT approach is the one with the best performance for this problem, exhibiting better solving times, sometimes even orders of magnitude smaller than CP and MIP.

关键词： constraint programming

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Exploiting Fairness to Enhance Sensitive Attributes Reconstruction

Exploiting Fairness to Enhance Sensitive Attributes Reconstr...

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1st IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)

作者： Ferry, Julien Aivodji, Ulrich Gambs, Sebastien Huguet, Marie-Jose Siala, Mohamed Univ Toulouse CNRS LAAS Toulouse France Ecole Technol Superieure Montreal PQ Canada Univ Quebec Montreal Montreal PQ Canada Univ Toulouse CNRS LAAS INSA Toulouse France

ISBN: (纸本)9781665462990

In recent years, a growing body of work has emerged on how to learn machine learning models under fairness constraints, often expressed with respect to some sensitive attributes. In this work, we consider the setting in which an adversary has black-box access to a target model and show that information about this model's fairness can be exploited by the adversary to enhance his reconstruction of the sensitive attributes of the training data. More precisely, we propose a generic reconstruction correction method, which takes as input an initial guess made by the adversary and corrects it to comply with some user-defined constraints (such as the fairness information) while minimizing the changes in the adversary's guess. The proposed method is agnostic to the type of target model, the fairness-aware learning method as well as the auxiliary knowledge of the adversary. To assess the applicability of our approach, we have conducted a thorough experimental evaluation on two state-of-the-art fair learning methods, using four different fairness metrics with a wide range of tolerances and with three datasets of diverse sizes and sensitive attributes. The experimental results demonstrate the effectiveness of the proposed approach to improve the reconstruction of the sensitive attributes of the training set.

关键词： Reconstruction attack privacy fairness machine learning constraint programming

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Time allocation and long term scheduling of ESO telescopes at La Silla Paranal Observatory 10

Time allocation and long term scheduling of ESO telescopes a...

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Observatory Operations: Strategies, Processes, and Systems X 2024

作者： Rejkuba, M. Hainaut, O.R. Bierwirth, T. Pruemm, M. Weiss, A. European Southern Observatory Karl-Schwarzschild-Strasse 2 Garching b. München85748 Germany Fev Etamax GmbH Germany

ISBN: (纸本)9781510675193

The preparation of the long-term telescope schedule follows the submission and scientific review of new proposals. At the European Southern Observatory (ESO) this process entails scheduling the scientific proposals according to their scientific merit and available observing resources, as well as scheduling technical, maintenance, and commissioning activities for all operational telescopes of the La Silla Paranal Observatory. After the recent overhaul of the phase one proposal submission software, ESO started the development of a new telescopes time allocation and scheduling tool. The new tool makes the scheduling process more efficient while optimizing the use of observing facilities. Besides scheduling activities and allocating time according to scientific merit, available resources, operational and programmatic needs, the tool will enable simultaneous scheduling of multiple telescopes to appropriately account for dependencies between them. The implementation of this new Time Allocation tool opens the possibility for dynamic re-scheduling of the telescopes, which is a pre-requisite to implementing a yearly "Call for Proposals"along with the "Fast Track Channel"at ESO. © 2024 SPIE.

关键词： constraint programming

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Binary Search-Based Methods for Solving constraint Hierarchies over Finite Domains 35

Binary Search-Based Methods for Solving Constraint Hierarchi...

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35th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Hosobe, Hiroshi Satoh, Ken Hosei Univ Fac Comp & Informat Sci Tokyo Japan Natl Inst Informat Principles Informat Res Div Tokyo Japan

ISBN: (纸本)9798350342734

constraint programming is a powerful tool for modeling and solving various problems. Especially, soft constraints are useful since they enable the treatment of over- and under-constrained real-world problems by relaxing conflicting constraints and introducing default constraints. constraint hierarchies provide a soft constraint framework that introduces hierarchical preferences called strengths. In a constraint hierarchy, constraints are associated with strengths such as required, strong, medium, and weak, and a solution is obtained to maximally satisfy stronger constraints in the sense of a given solution criterion. In this paper, we propose three methods based on binary search for solving constraint hierarchies over finite domains by using a criterion called unsatisfied-count-better. Our methods solve constraint hierarchies by encoding them into ordinary constraint satisfaction problems and repeatedly solving the encoded problems with an external solver. We also present the implementations of our methods and the results of the experiment that we conducted to evaluate them.

关键词： constraint programming constraint solver soft constraint constraint hierarchy

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Cutting the Cake into Crumbs: Verifying Envy-Free Cake-Cutting Protocols Using Bounded Integer Arithmetic 26th

Cutting the Cake into Crumbs: Verifying Envy-Free Cake-Cutti...

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26th International Symposium on Practical Aspects of Declarative Languages (PADL)

作者： Lester, Martin Mariusz Univ Reading Reading Berks England

ISBN: (纸本)9783031520372;9783031520389

Fair division protocols specify how to split a continuous resource (conventionally represented by a cake) between multiple agents with different preferences. Envy-free protocols ensure no agent prefers any other agent's allocation to his own. These protocols are complex and manual proofs of their correctness may contain errors. Recently, Bertram and others [5] developed the DSL Slice for describing these protocols and showed how verification of envy-freeness can be reduced to SMT instances in the theory of quantified non-linear real arithmetic. This theory is decidable, but the decision procedure is slow, both in theory and in practice. We prove that, under reasonable assumptions about the primitive operations used in the protocol, counterexamples to envy-freeness can always be found with bounded integer arithmetic. Building on this result, we construct an embedded DSL for describing cake-cutting protocols in declarative-style C. Using the bounded model-checker CBMC, we reduce verifying envy-freeness of a protocol to checking unsatisfiability of pure SAT instances. This leads to a substantial reduction in verification time when the protocol is unfair.

关键词： fair division constraint programming declarative C

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Satellite Communications Resilience - Service Restoration and Retainment

Satellite Communications Resilience - Service Restoration an...

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41st IEEE Military Communications Conference (IEEE MILCOM) - Communications Supporting Military Operations in a Contested Environment

作者： Gobbi, Richard L. Schaefer, Elsa Jacobs, Jabril Street, Dow LinQuest Corp Chesapeake VA 23320 USA LinQuest Corp Herndon VA USA LinQuest Corp Los Angeles CA USA LinQuest Corp San Francisco CA USA

ISBN: (纸本)9798350321814

A network modeling and resilience quantification approach is described for multiple-beam, geosynchronous-orbit satellite communications (SATCOM) systems operating in the millimeter wave (similar to 20-300 GHz) spectral region providing frequency reuse and dual polarization coverage of user terminals (UTs) and gateways. Focus is on the UT service restoration process in face of jamming attacks on multiple beams, including the retainment of service to collaterally affected UTs. The combinatorial optimization approach used to determine alternative pointing of UTs to beams on the attacked satellite, or to beams on satellites in adjacent orbital slots, is a Multiple Knapsack Problem (MKP) formulation. The cascading effect of jamming attacks on connected UTs removed by one or more satellite hops from jammed UTs is addressed and related to the graph theoretical component structure of the UT network;this forms an analytical basis for addressing overall network disruption due to jamming attacks. The MKP formulation is extended to include the case of multiple service provider ( SP) sharing of the satellite spectrum, enabling assessment of the effect on competitive SPs due to cooperation in the form of the sharing of beam capacity. The MKP is solved using a constraint programming (CP) technique which allows latitude in the choice of objective functions.

关键词： Resilience SATCOM resilience Satellite system threats Integer linear programming Multiple knapsack problems constraint programming Graph theory and SATCOM systems

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Predicting the Optimal Period for Cyclic Hoist Scheduling Problems 1

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20th International Conference on the Integration of constraint programming, Artificial Intelligence, and Operations Research (CPAIOR)

作者： Efthymiou, Nikolaos Yorke-Smith, Neil Delft Univ Technol STAR Lab Delft Netherlands

ISBN: (数字)9783031332715

ISBN: (纸本)9783031332708;9783031332715

Since combinatorial scheduling problems are usually NP-hard, this paper investigates whether machine learning (ML) can accelerate exact solving of a problem instance. We adopt supervised learning on a corpus of problem instances, to acquire a function that predicts the optimal makespan for a given instance. The learned predictor is invariant to the instance size as it uses statistics of instance attributes. We provide this prediction to a solving algorithm in the form of bounds on the objective function. Specifically, this approach is applied to the well-studied Cyclic Hoist Scheduling Problem (CHSP). The goal for a CHSP instance is to find a feasible schedule for a hoist which moves objects between tanks with minimal cyclic period. Taking an existing constraint programming (CP) model for this problem, and an exact CP-SAT solver, we implement a Deep Neural Network, a Random Forest and a Gradient Boosting Tree in order to predict the optimal period p. Experimental results find that, first, ML models (in particular DNNs), can be good predictors of the optimal p;and, second, providing tight bounds for p around the predicted value to an exact solver significantly reduces the solving time without compromising the optimality of the solutions.

关键词： combinatorial optimisation supervised learning cyclic hoist scheduling constraint programming

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Parallel Batch Processing for the Coating Problem 33

Parallel Batch Processing for the Coating Problem

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33rd International Conference on Automated Planning and Scheduling, ICAPS 2023

作者： Horn, Matthias Demirovic, Emir Yorke-Smith, Neil Algorithmics Group Delft University of Technology Netherlands

ISBN: (纸本)1577358813

We solve a challenging scheduling problem with parallel batch processing and two-dimensional shelf strip packing constraints that arises in the tool coating field. Tools are assembled on so-called planetaries (batches) before they are loaded into coating machines to get coated. The assembling is not trivial and must fulfil specific constraints, which we refer to as shelf strip packing constraints. Further, each tool is associated with a starting time window s.t. tools can only be put on the same planetary if their time window overlap. The objective is to minimise the makespan and the number of required planetaries. Since the problem naturally decomposes into scheduling and packing parts, we tackle the problem with a two-phase logic-based Benders decomposition approach. The master problem assigns items to batches. The first phase solves as subproblem the packing problem by checking if the assignment is feasible, whereas the second phase solves the scheduling subproblem. The approach is compared with a monolithic mixed integer linear programming approach as well as a monolithic constraint programming approach. Experimental evaluation shows that our proposed approach outperforms the state-of-the-art benchmarks by solving more instances to optimality in a shorter time. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： constraint programming

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