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23rd International Conference of the Catalan-Association-for-Artificial-Intelligence (CCIA)

作者： Espasa, Joan Coll, Jordi Miguel, Ian Villaret, Mateu Univ St Andrews Sch Comp Sci St Andrews KY16 9SX Fife Scotland Aix Marseille Univ Univ Toulon CNRS LIS Marseille France Univ Girona Dept Informat Matemat Aplicada & Estadist E-17003 Girona Spain

ISBN: (纸本)9781643682112;9781643682105

State-space planning is the de-facto search method of the automated planning community. Planning problems are typically expressed in the Planning Domain Definition Language (PDDL), where action and variable templates describe the sets of actions and variables that occur in the problem. Typically, a planner begins by generating the full set of instantiations of these templates, which in turn are used to derive useful heuristics that guide the search. Thanks to this success, there has been limited research in other directions. We explore a different approach, keeping the compact representation by directly reformulating the problem in PDDL into ESSENCE PRIME, a constraint programming language with support for distinct solving technologies including SAT and SMT. In particular, we explore two different encodings from PDDL to ESSENCE PRIME, how they represent action parameters, and their performance. The encodings are able to maintain the compactness of the PDDL representation, and while they differ slightly, they perform quite differently on various instances from the International Planning Competition.

关键词： Automated Planning constraint programming Modelling Reformulation

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Grid Scheduling Considering Energy Consumption Management and Quality of Service

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JOURNAL OF GRID COMPUTING 2022年第3期20卷 30-30页

作者： Esmaili, Samaneh Kianfar, Kamran Univ Isfahan Fac Engn Esfahan *** Iran

In this research, the grid scheduling problem has been investigated in order to maximize profit considering the dynamic voltage and frequency scaling technique, customer-centric quality of service and time-dependent energy pricing. Mixed-integer linear programming, constraint programming, a greedy heuristic algorithm along with a hybrid method of genetic algorithm and constraint programming are developed. Some techniques are proposed to improve the efficiency of the presented constraint programming model, and their effectiveness is investigated using a full factorial experiment. Parameters of the proposed hybrid algorithm have been set by Taguchi test. The hybrid meta-heuristic algorithm, with a short execution time, generates solutions of about 18% and 88% better than the best solution of the constraint programming model for large-scale problem instances. The results show that the final profit will be reduced by about 22% if the electricity prices are wrongly considered with a flat rate during the scheduling process.

关键词： Grid scheduling Time-dependent energy pricing Customer-centric quality of service DVFS constraint programming Hybrid genetic algorithm

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Optimization of the integrated problem of employee timetabling and job shop scheduling

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COMPUTERS & OPERATIONS RESEARCH 2022年 137卷 105332-105332页

作者： Frihat, Mohamed B.Hadj-Alouane, Atidel Sadfi, Cherif Univ Tunis El Manar Natl Engn Sch Tunis OASIS Lab Tunis Tunisia

This paper deals with a real manufacturing scheduling problem that is particularly encountered in the tannery industries. This problem often integrates employee timetabling and production scheduling. The employee timetabling problem is addressed in the context of skill requirements and under availability and legislative constraints. The production scheduling is considered as a re-entrant hybrid job-shop problem with time lags and sequence dependent setup times, under machine availability constraints. The objective is to minimize the labor cost, while respecting a maximum makespan and a maximum tardiness constraints. Two different models and exact resolution methods are proposed, using Mixed Integer Linear programming (MILP) and constraint programming (CP). Numerical experimentations are conducted to compare and evaluate their performances, based on randomly generated instances. The results show that the CP model is slower than the MILP model in terms of finding optimal solutions for large instances, but is more efficient in generating feasible solutions. Thus, providing a feasible initial solution to the MILP model using the CP model is a promising hybrid approach to reduce the computational time.

关键词： Employee timetabling Job-shop Mixed integer programming constraint programming

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Novel MILP and CP models for distributed hybrid flowshop scheduling problem with sequence-dependent setup times

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SWARM AND EVOLUTIONARY COMPUTATION 2022年第0期71卷

作者： Meng, Leilei Gao, Kaizhou Ren, Yaping Zhang, Biao Sang, Hongyan Chaoyong, Zhang Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau 999078 Peoples R China Jinan Univ Sch Intelligent Syst Sci & Engn Dept Ind Engn Zhuhai 519070 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Manufacturing Equipment & Te Wuhan 430074 Peoples R China

As regards distributed hybrid flow shop scheduling with sequence-dependent setup times (DHFSP-SDST), three novel mixed-integer linear programming (MILP) models and a constraint programming (CP) model are formulated for the same-factory and different-factory environments. The three novel MILP models are based on two different modeling ideas. The existing MILP model and the three proposed MILP models are compared in detail from several aspects, such as binary decision variables, continuous decision variables, constraints, solution performance and solution time. By solving the benchmarks in existing studies, the effectiveness and superiority of the proposed MILP and CP models are proved. Experimental results show that the MILP model of sequence-based modeling idea performs best, the MILP model of adjacent sequence-based modeling idea takes the second place and the existing MILP model of position-based modeling idea performs worst. The CP model is more efficient and effective than MILP models. In addition, compared with the existing meta-heuristic algorithms (e.g., DABC and IABC), the proposed MILP models prove the optimal solutions of 37 instances and improve 17 current best solutions. The CP model solves all the 45 instances to optimality and improves 19 current best solutions for benchmarks in the existing studies

关键词： Distributed hybrid flow shop scheduling Setup time Mixed-integer linear programming constraint programming Modeling idea

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Type-2 integrated process-planning and scheduling problem: Reformulation and solution algorithms

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COMPUTERS & OPERATIONS RESEARCH 2022年 142卷 105728-105728页

作者： Naderi, Bahman Begen, Mehmet A. Zaric, Gregory S. Univ Windsor Dept Mech Automot & Mat Engn Windsor ON Canada Western Univ Ivey Business Sch London ON Canada

We study the type-2 integrated process-planning and scheduling (IPPS) problem where each job is represented by a directed network graph. To the best of our knowledge, there is only one mathematical model in the literature implementing the type-2 IPPS partially, and the solution methods available for this problem are all based on heuristics and metaheuristics. We introduce three properties that enable us to fully formulate all aspects of the type-2 IPPS problem with a mathematical programming model for the first time. To solve our model, we develop a logic-based Benders decomposition method hybridized with constraint programming. We decompose the problem into two smaller ones such that we can use the best solution technique for each one, master problem and subproblem. To enhance our solution approach, we incorporate a combinatorial relaxation of subproblem into the master problem. We evaluate our method using a well-known benchmark including 24 instances and compare its performance with six existing solution methods solving the same benchmark. We solve all the 24 instances of this benchmark to optimality where seven of these 24 instances are solved to optimality for the first time. We also generate a new set of 144 larger instances to further evaluate our solution methods and provide insights on when each method performs better.

关键词： Type-2 integrated process planning and scheduling problem Benders decomposition constraint programming Optimality cut

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GenSys: A Scalable Fixed-Point Engine for Maximal Controller Synthesis over Infinite State Spaces 2021

GenSys: A Scalable Fixed-Point Engine for Maximal Controller...

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29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)

作者： Samuel, Stanly D'Souza, Deepak Komondoor, Raghavan Indian Inst Sci Bengaluru India

ISBN: (纸本)9781450385626

The synthesis of maximally-permissive controllers in infinite-state systems has many practical applications. Such controllers directly correspond to maximal winning strategies in logically specified infinite-state two-player games. In this paper, we introduce a tool called GenSys which is a fixed-point engine for computing maximal winning strategies for players in infinite-state safety games. A key feature of GenSys is that it leverages the capabilities of existing off-the-shelf solvers to implement its fixed point engine. GenSys outperforms state-of-the-art tools in this space by a significant margin. Our tool has solved some of the challenging problems in this space, is scalable, and also synthesizes compact controllers. These controllers are comparatively small in size and easier to comprehend. GenSys is freely available for use and is available under an open-source license.

关键词： reactive synthesis fixed-points logic constraint programming

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Decentralized Coalition Structure Formation for Interdependent Tasks Allocation 33

Decentralized Coalition Structure Formation for Interdepende...

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IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Ahmadoun, Douae Bonzon, Elise Buron, Cedric Moraitis, Pavlos Saveant, Pierre Shehory, Onn Univ Paris LIPADE Paris France Thales Res & Technol Palaiseau France Argument Theory Paris Paris France Bar Ilan Univ Ramat Gan Israel

ISBN: (纸本)9781665408981

This paper addresses the problem of task allocation among multiple autonomous agents that must accomplish a complex global task. Solutions to the problem have real-world applications in defense, space, disaster management, etc. We solve this problem via agent coalition formation. Multiple coalition formation mechanisms were introduced in prior art, seldom accounting for interdependent tasks. We address this challenge. We introduce an anytime decentralized coalition formation mechanism that enables agents with complementary capabilities to form, autonomously and dynamically, feasible coalition structures that accomplish a global, composite task. The formed structures are incrementally improved via agent replacements to optimize a global utility. We analyze the complexity and show that, although the general problem is NP-hard, our mechanism provides a solution within acceptable time. We present extensive experimental results that illustrate the added value of our approach.

关键词： coalition formation multi-agent systems task allocation task interdependence constraint programming

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Exact stochastic constraint optimisation with applications in network analysis

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ARTIFICIAL INTELLIGENCE 2022年 304卷 103650-103650页

作者： Latour, Anna L. D. Babaki, Behrouz Fokkinga, Daniel Anastacio, Marie H. Hoos, Holger H. Nijssen, Siegfried Leiden Univ LIACS POB 9512 NL-2300 RA Leiden Netherlands Polytech Montreal Montreal PQ H3T 1J4 Canada Univ British Columbia Vancouver BC V6T 1Z4 Canada UCLouvain ICTEAM Pl St Barbe 2Bte L5-02-01 B-1348 Louvain La Neuve Belgium

We present an extensive study of methods for exactly solving stochastic constraint (optimisation) problems (SCPs) in network analysis. These problems are prevalent in science, governance and industry. The first method we study is generic and decomposes stochastic constraints into a multitude of smaller local constraints that are solved using a constraint programming (CP) or mixed-integer programming (MIP) solver. However, many SCPs are formulated on probability distributions with a monotonic property, meaning that adding a positive decision to a partial solution to the problem cannot cause a decrease in solution quality. The second method is specifically designed for solving global stochastic constraints on monotonic probability distributions (SCMDs) in CP. Both methods use knowledge compilation to obtain a decision diagram encoding of the relevant probability distributions, where we focus on ordered binary decision diagrams (OBDDs). We discuss theoretical advantages and disadvantages of these methods and evaluate them experimentally. We observed that global approaches to solving SCMDs outperform decomposition approaches from CP, and perform complementarily to MIPbased decomposition approaches, while scaling much more favourably with instance size. Both methods have many alternative design choices, as both knowledge compilation and constraint solvers are used in a single pipeline. To identify which configurations work best, we apply programming by optimisation. Specifically, we show how an automated algorithm configurator can be used to find optimised configurations of our pipeline. After configuration, our global SCMD solving pipeline outperforms its closest competitor (a MIPbased decomposition pipeline) on all test sets we considered by up to two orders of magnitude in terms of PAR10 scores. (c) 2021 The Author(s). Published by Elsevier B.V.

关键词： constraint programming Probabilistic inference Stochastic constraints Ordered binary decision diagrams Monotonic probability distributions Global constraints Automated algorithm configuration Probabilistic networks

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Physician Scheduling During a Pandemic 18th

Physician Scheduling During a Pandemic

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18th International Conference on the Integration of constraint programming, Artificial Intelligence, and Operations Research (CPAIOR)

作者： Geibinger, Tobias Kletzander, Lucas Krainz, Matthias Mischek, Florian Musliu, Nysret Winter, Felix TU Wien Christian Doppler Lab Artificial Intelligence & O DBAI Vienna Austria Med Univ Vienna St Anna Childrens Hosp Vienna Austria

ISBN: (纸本)9783030782306;9783030782290

At the beginning of the pandemic last year some hospitals had to change their physician schedules to take into account infection risks and potential quarantines for personnel. This was especially important for hospitals that care for high-risk patients, like the St. Anna Children's Hospital in Vienna, which is a tertiary care center for pediatric oncology. It was very important to develop solving methods for this complex problem in short time. We relied on constraint solving technology which proved to be very useful in such critical situations. In this paper we present a constraint model that includes the variety of requirements that are needed to ensure day-to-day operations as well as the additional constraints imposed by the pandemic situation. We introduce an innovative set of grouping constraints to partition the staff, with the intention to easily isolate a small group in case of an infection. The produced schedules also keep part of the staff as backup to replace personnel in quarantine. In our case study, we evaluate and compare our proposed model on several state-of-the-art solvers. Our approach could successfully produce a high-quality schedule for the considered real-world planning scenario, also compared to solutions found by human planners with considerable effort.

关键词： Physician scheduling COVID-19 constraint programming

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Interval constraint-Based Mutation Testing of Numerical Specifications 2021

Interval Constraint-Based Mutation Testing of Numerical Spec...

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30th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)

作者： Jeangoudoux, Clothilde Darulova, Eva Lauter, Christoph MPI SWS Saarbrucken Germany Univ Alaska Anchorage Anchorage AK USA

ISBN: (纸本)9781450384599

Mutation testing is an established approach for checking whether code satisfies a code-independent functional specification, and for evaluating whether a test set is adequate. Current mutation testing approaches, however, do not account for accuracy requirements that appear with numerical specifications implemented in floating-point arithmetic code, but which are a frequent part of safety-critical software. We present Magneto, an instantiation of mutation testing that fully automatically generates a test set from a real-valued specification. The generated tests check numerical code for accuracy, robustness and functional behavior bugs. Our technique is based on formulating test case and oracle generation as a constraint satisfaction problem over interval domains, which soundly bounds errors, but is nonetheless efficient. We evaluate Magneto on a standard floating-point benchmark set and find that it outperforms a random testing baseline for producing useful adequate test sets.

关键词： mutation testing constraint programming functional specification floating-point arithmetic interval arithmetic

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