Negotiating the Probabilistic Satisfaction of Temporal Logic Motion Specifications

We propose a human-supervised control synthesis method for a stochastic Dubins vehicle such that the probability of satisfying a specification given as a formula in a fragment of Probabilistic Computational Tree Logic (PCTL) over a set of environmental properties is maximized. Under some mild assumptions, we construct a finite approximation for the motion of the vehicle in the form of a tree-structured Markov Decision Process (MDP). We introduce an efficient algorithm, which exploits the tree structure of the MDP, for synthesizing a control policy that maximizes the probability of satisfaction. For the proposed PCTL fragment, we define the specification update rules that guarantee the increase (or decrease) of the satisfaction probability. We introduce an incremental algorithm for synthesizing an updated MDP control policy that reuses the initial solution. The initial specification can be updated, using the rules, until the supervisor is satisfied with both the updated specification and the corresponding satisfaction probability. We propose an offline and an online application of this method.Comment: 9 pages, 4 figures; The results in this paper were presented without proofs in IEEE/RSJ International Conference on Intelligent Robots and Systems November 3-7, 2013 at Tokyo Big Sight, Japa

Crop improvement studies based on molecular approaches in interspecific Oil palm hybrids

162 p.Oil Palm (OP) is the crop with the highest oil yield per hectare and as a result, its use has spread rapidly in tropical regions of Asia, Africa and America. The main OP plantations consist of Elaeis guineensis (Eg) species, known to produce high amounts of oil. However, in American regions this species is being affected by the ¿Pudrición de Cogollo¿ disease leading to dead palms. Therefore, OP companies started crossing this species with E. oleifera (Eo) palms which is resistant to this disease. The obtained interspecific hybrids show interesting characteristic inherited from both parents; resistance to different diseases, interesting oil quality characteristics, competitive oil production and decreased height which prolongs its useful life. However, little work has been done in the improvement of these hybrids. This thesis tries to address this gap applying different molecular approaches. First, an extensive study of an amplicon of the ¿Shell-thickness¿ (Sh) gene has been conducted on 568 Eg, Eo and hybrid genotypes. Then, with the aim to discover promising new Candidate Genes (CG) that could be exploited in further molecular assisted selection systems (MAS) a large phenotypic study of 25 production and quality traits have been performed within 198 hybrid genotypes fllowed by two Association Mapping (AM) assays. These latter have been based on targeted CG and random Restriction site associated RNA sequencing(RARSeq) approaches.Neiker teknalia Lafabril Energy & Palma Sampoerna Agr

SLIDE: A Useful Special Case of the CARDPATH Constraint

We study the CardPath constraint. This ensures a given constraint holds a number of times down a sequence of variables. We show that SLIDE, a special case of CardPath where the slid constraint must hold always, can be used to encode a wide range of sliding sequence constraints including CardPath itself. We consider how to propagate SLIDE and provide a complete propagator for CardPath. Since propagation is NP-hard in general, we identify special cases where propagation takes polynomial time. Our experiments demonstrate that using SLIDE to encode global constraints can be as efficient and effective as specialised propagators.Comment: 18th European Conference on Artificial Intelligenc

Integrating Tier-1 module suppliers in car sequencing problem

[EN] The objective of this study is to develop a car assembly sequence that is mutually agreed between car manufacturers and Tier-1 module suppliers such that overall modular supply chain efficiency is improved. In the literature so far, only constraints of car manufacturers have been considered in the car sequencing problem. However, an assembly sequence from car manufacturer imposes a module assembly sequence on Tier-1 module suppliers since their assembly activities are synchronous and in sequence with assembly line of that car manufacturer. An imposed assembly sequence defines a certain demand rate for Tier-1 module suppliers and has significant impacts on operational cost of these suppliers which ultimately affects the overall modular supply chain efficiency. In this paper, a heuristic approach has been introduced to generate a supplier cognizant car sequence which does not only provide better operational conditions for Tier-1 module suppliers, but also satisfies constraints of the car manufacturer.Jung, E. (2021). Integrating Tier-1 module suppliers in car sequencing problem. International Journal of Production Management and Engineering. 9(2):113-123. https://doi.org/10.4995/ijpme.2021.14985OJS11312392Benoist, T., Gardi, F., Megel, R., Nouioua, K. 2011. LocalSolver 1.x: a black-box local-search solver for 0-1 programming. 4OR - A Quarterly Journal of Operations Research, 9(299). https://doi.org/10.1007/s10288-011-0165-9Boysen, N., Fliedner, M., Scholl, A. 2009. Sequencing mixed-model assembly lines: survey, classification and model critique. European Journal of Operational Research, 192, 349-373. https://doi.org/10.1016/j.ejor.2007.09.013Doran, D. 2002. Manufacturing for synchronous supply: a case study of Ikeda Hoover Ltd. Integrated Manufacturing Systems, 13(1), 18-24. https://doi.org/10.1108/09576060210411477Drexl, A., Kimms, A. 2001. Sequencing JIT mixed-model assembly lines under station-load and part-usage constraints. Management Science, 47,(3), 480-491. https://doi.org/10.1287/mnsc.47.3.480.9777Estellon, B., Gardi, F. 2006. Car sequencing is NP-hard: a short proof. Journal of the Operational Research Society, 64, 1503-1504. https://doi.org/10.1057/jors.2011.165Estellon, B., Gardi, F., Nouioua, K. 2006. Large neighborhood improvements for solving car sequencing problems. RAIRO - Operations Research, 40(4), 355-379. https://doi.org/10.1051/ro:2007003Estellon, B., Gardi, F., Nouioua, K. 2008. Two local search approaches for solving real-life car sequencing problems. European Journal of Operational Research, 191(3), 928-944. https://doi.org/10.1016/j.ejor.2007.04.043Fredriksson, P., Gadde, L.E. 2005. Flexibility & rigidity in customization and build-to-order production. Science Direct Industrial Marketing Management, 34, 695-705. https://doi.org/10.1016/j.indmarman.2005.05.010Gagne, C., Gravel, M., Price, W. 2006. Solving real car sequencing problems with ant colony optimization. European Journal of Operational Research, 174(3), 1427-1448. https://doi.org/10.1016/j.ejor.2005.02.063Gottlieb, J., Puchta, M., Solnon., C. 2003. A study of greedy, local search and ant colony optimization approaches for car sequencing problems. In Applications of Evolutionary Computing, Lecture Notes in Computer Science, 2611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36605-9_23Hellingrath, B. 2008. Key principles of flexible production and logistics networks. Build to Order: The Road to the 5-Day Car, Springer-Verlag, London, 177-180. https://doi.org/10.1007/978-1-84800-225-8_10Larsson, A. 2002. The development and regional significance of the automotive industry: supplier parks in Western Europe. International Journal of Urban and Regional Research, 26(4), 767-784. https://doi.org/10.1111/1468-2427.00417Monden, Y. 1998. Toyota production systems: an integrated approach to just-in-time, 3rd edition. Industrial Engineering & Management Press, NorcossNiemann, J., Seisenberger, S., Schlegel, A., Putz, M. 2019. Development of a method to increase flexibility and changeability of supply contracts in the automotive industry. 52nd CIRP Conference on Manufacturing Systems, Ljubljana, Slovenia, June 12-14. https://doi.org/10.1016/j.procir.2019.03.045Parrello, B.D., Kabat, W.C., Wos, L. 1986. Job-shop scheduling using automated reasoning: a case study of the car-sequencing problem. Journal of Automated Reasoning, 2(1), 1-42. https://doi.org/10.1007/BF00246021Regin, J.C., Puget, J.F. 1997. A filtering algorithm for global sequencing constraints. In: Smolka G. (eds) Principles and Practice of Constraint Programming-CP97. Lecture Notes in Computer Science, 1330. Springer, Heidelberg. https://doi.org/10.1007/BFb0017428Solnon, C., Cung, V.D., Nguyen A., Artigues, C. 2008. The car sequencing problem: overview of state-of-the-art methods and industrial casestudy of the ROADEEF'2005 challenge problem. European Journal of Operational Research, 191, 912-927. https://doi.org/10.1016/j.ejor.2007.04.03

An Ontological formalization of the planning task

In this paper we propose a generic task ontology, which formalizes the space of planning problems. Although planning is one of the oldest researched areas in Artificial Intelligence and attempts have been made in the past at developing task ontologies for planning, these formalizations suffer from serious limitations: they do not exhibit the required level of formalization and precision and they usually fail to include some of the key concepts required for specifying planning problems. In con-trast with earlier proposals, our task ontology formalizes the nature of the planning task independently of any planning paradigm, specific domains, or applications and provides a fine-grained, precise and comprehensive characterization of the space of planning problems. Finally, in addition to producing a formal specification we have also operationalized the ontology into a set of executable definitions, which provide a concrete reusable resource for knowledge acquisition and system development in planning applications

Theories, models, simulations: a computational challenge

In this talk I would like to illustrate with examples taken from Quantum Field Theory and Biophysics how an intelligent exploitation of the unprecedented power of today's computers could led not only to the solution of pivotal problems in the theory of Strong Interactions, but also to the emergence of new lines of interdisciplinary research, while at the same time pushing the limits of modeling to the realm of living systems.Comment: 19 pages, 1 figure, conference pape

Modeling and Solution Methodologies for Mixed-Model Sequencing in Automobile Industry

The global competitive environment leads companies to consider how to produce high-quality products at a lower cost. Mixed-model assembly lines are often designed such that average station work satisfies the time allocated to each station, but some models with work-intensive options require more than the allocated time. Sequencing varying models in a mixed-model assembly line, mixed-model sequencing (MMS), is a short-term decision problem that has the objective of preventing line stoppage resulting from a station work overload. Accordingly, a good allocation of models is necessary to avoid work overload. The car sequencing problem (CSP) is a specific version of the MMS that minimizes work overload by controlling the sequence of models. In order to do that, CSP restricts the number of work-intensive options by applying capacity rules. Consequently, the objective is to find the sequence with the minimum number of capacity rule violations. In this dissertation, we provide exact and heuristic solution approaches to solve different variants of MMS and CSP. First, we provide five improved lower bounds for benchmark CSP instances by solving problems optimally with a subset of options. We present four local search metaheuristics adapting efficient transformation operators to solve CSP. The computational experiments show that the Adaptive Local Search provides a significant advantage by not requiring tuning on the operator weights due to its adaptive control mechanism. Additionally, we propose a two-stage stochastic program for the mixed-model sequencing (MMS) problem with stochastic product failures, and provide improvements to the second-stage problem. To tackle the exponential number of scenarios, we employ the sample average approximation approach and two solution methodologies. On one hand, we develop an L-shaped decomposition-based algorithm, where the computational experiments show its superiority over solving the deterministic equivalent formulation with an off-the-shelf solver. We also provide a tabu search algorithm in addition to a greedy heuristic to tackle case study instances inspired by our car manufacturer partner. Numerical experiments show that the proposed solution methodologies generate high-quality solutions by utilizing a sample of scenarios. Particularly, a robust sequence that is generated by considering car failures can decrease the expected work overload by more than 20\% for both small- and large-sized instances. To the best of our knowledge, this is the first study that considers stochastic failures of products in MMS. Moreover, we propose a two-stage stochastic program and formulation improvements for a mixed-model sequencing problem with stochastic product failures and integrated reinsertion process. We present a bi-objective evolutionary optimization algorithm, a two-stage bi-objective local search algorithm, and a hybrid local search integrated evolutionary optimization algorithm to tackle the proposed problem. Numerical experiments over a case study show that while the hybrid algorithm provides a better exploration of the Pareto front representation and more reliable solutions in terms of waiting time of failed vehicles, the local search algorithm provides more reliable solutions in terms of work overload objective. Finally, dynamic reinsertion simulations are executed over industry-inspired instances to assess the quality of the solutions. The results show that integrating the reinsertion process in addition to considering vehicle failures can keep reducing the work overload by around 20\% while significantly decreasing the waiting time of the failed vehicles

Proceedings of the 2022 XCSP3 Competition

This document represents the proceedings of the 2022 XCSP3 Competition. The results of this competition of constraint solvers were presented at FLOC (Federated Logic Conference) 2022 Olympic Games, held in Haifa, Israel from 31th July 2022 to 7th August, 2022.Comment: arXiv admin note: text overlap with arXiv:1901.0183

Automated medical scheduling : fairness and quality

Dans cette thèse, nous étudions les façons de tenir compte de la qualité et de l’équité dans les algorithmes de confection automatique d’horaires de travail. Nous découpons ce problème en deux parties. La modélisation d’un problème d’horaires permet de créer des horaires plus rapidement qu’un humain peut le faire manuellement, puisqu’un ordinateur peut évaluer plusieurs horaires simultanément et donc prendre des décisions en moins de temps. La première partie du problème étudié consiste à améliorer la qualité des horaires en encodant des contraintes et des préférences à l’aide de modèles mathématiques. De plus, puisque la création est plus rapide à l’aide d’un ordinateur, il est plus facile pour un ordinateur de trouver l’horaire ayant la meilleure qualité lorsque les règles et préférences sont clairement définies. Toutefois, déterminer les règles et préférences d’un groupe de personne n’est pas une tâche facile. Ces individus ont souvent de la difficulté à exprimer formellement leurs besoins et leurs préférences. Par conséquent, la création d’un bon modèle mathématique peut prendre beaucoup de temps, et cela même pour un expert en création d’horaires de travail. C’est pourquoi la deuxième partie de cette thèse concerne la réduction du temps de modélisation à l’aide d’algorithmes capable d’apprendre un modèle mathématique à partir de solutions données comme par exemple, dans notre cas, des horaires de travail.In this thesis, we study the ways to take quality and fairness into account in the algorithms of automatic creation of work schedules. We separate this problem into two subproblems. The modeling of a scheduling problem allows a faster creation of schedules than what a human can produce manually. A computer can generate and evaluate multiple schedules at a time and therefore make decisions in less time. This first part of the studied problem consists in improving the quality of medical schedules by encoding constraints and preferences using mathematical models. Moreover, since the creation is faster, it is easier for a computer to find the schedule with the highest quality when the rules and the preferences are clearly defined. However, determining the rules and preferences of a group of people is not an easy task. Those individuals often have difficulties formally expressing their requirements and preferences. Therefore, the creation a good mathematical model might take a long time, even for a scheduling expert. This is why the second part of this thesis concerns the reduction of modeling time using algorithms able to learn mathematical models from given solutions, in our case schedules