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Multi-Objective Optimization based Robust Sensor Network Design

IFAC Proceedings Volumes 2010年第5期43卷 79-84页

作者： Prakash R. Kotecha Mani Bhushan Ravindra D. Gudi Department of Chemical Engineering Indian Institute of Technology Bombay Powai Mumbai 400 076 (Tel:91-22-25767204)

In this article, we propose an explicit integer optimization formulation for the design of reliable and robust (to uncertainty in reliability data) sensor networks. The robustness is achieved by incorporating simultaneous occurrence of different kinds of uncertainty in the failure rate data in the optimization formulation. We show the use of constraint programming to solve these combinatorial problems to global optimality and also evaluate the globally optimal pareto front between robustness and cost of these sensor networks. Such tradeoffs help the designer in making informed choices for the selection of sensor networks. The applicability of the proposed work has been demonstrated on a case study taken from literature.

关键词： Multi-objective optimization sensor network design constraint programming robust design uncertainty

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Overload-Checking and Edge-Finding for Robust Cumulative Scheduling

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INFORMS JOURNAL ON COMPUTING 2023年第6期35卷 1419-1438页

作者： Fahimi, Hamed Quimper, Claude-Guy Shahid Chamran Univ Ahvaz Fac Math Sci & Comp Dept Comp Sci Ahvaz *** Iran Univ Laval Fac Sci & Genie Dept Informat & Genie Logiciel Quebec City PQ G1V 0A6 Canada

Scheduling frameworks are not necessarily stable. The aim is to introduce schedules resistant to disruptions such as when resources become unavailable, the supply chain for them breaks down, etc. A schedule is robust if it absorbs some level of unforeseen events when at most a certain number of activities are delayed. Taking advantage of constraint programming, we present two new filtering algorithms for a constraint that models cumulative scheduling problems in robust contexts where up to r out of n tasks can be concurrently delayed while keeping the schedule valid. We adapt the overload-checking and edge-finding filtering rules for this framework. We show that our robust versions of these algorithms run in & UTheta;(r2nlog(n)) and O(r2znlog(n)), respectively, where z denotes the number of distinct capacities of all tasks. This achievement implies that the complexities of the state-of-the-art algorithms for these techniques are invariable when r is constant. Experiments illustrate that our algorithms scale, with respect to n and r. As a practical application, the experimental results on a special case of crane assignment problem also verify a stronger filtering for these methods in terms of backtrack numbers as well as computation times when used in conjunction with time tabling. Finally, in order to show that our CP-based algorithms improve to solve a robust scheduling problem, we make a comparison against temporal protection as an external robust scheduling approach.

关键词： constraint programming constraint propagation filtering algorithms robust cumulative scheduling uncertainty data structure

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Configurable Domain Objects for Resource Modelling in Treatment Scheduling

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2015年第5期25卷 871-885页

作者： Schneeweiss, Denny Hofstedt, Petra Brandenburg Univ Technol Cottbus Senftenberg Programming Languages & Compiler Res Grp D-03046 Cottbus Germany

This paper presents the concept and realization of configurable resource models as extension of a treatment scheduling system for users in the medical sector. Our approach aims to ease the handling of automated treatment scheduling by domain experts without the immediate assistance of IT-experts. Configurable resource models were integrated into our automated treatment planning system for medical facilities and support a user-friendly configuration of resources such as specialized treatment rooms, medical devices, or medical staff. In our approach, treatment process models are defined in the BPMN workflow-language by the domain experts. The new concept of configurable resource models allows the end-user to interactively describe the available resources in their environment. These descriptions (i.e. configurable resource objects or CDOs) can then be linked to activities specified in the treatment models. Together, CDOs and the BPMN treatment models are automatically transformed into CSPs, i.e. mathematical descriptions which can be solved by constraint solvers, thus yielding optimal treatment plans.

关键词： BPMN constraint programming resource optimization planning scheduling

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Web service selection and composition based on uncertain quality of service

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第1期34卷

作者： Zeyneb Yasmina, Remaci Fethallah, Hadjila Fadoua, Lahfa Abou Baker Belkaid Univ LRIT Lab Dept Comp Sci Tilimsen Algeria

Web services are becoming a major utility for accomplishing complex tasks over the Internet. In practice, the end-users usually search for Web service compositions that best meet the quality of service (QoS) requirements (i.e., QoS global constraints). Since the number of services is constantly increasing and their respective QoS is inherently uncertain (due to environmental conditions), the task of selecting optimal compositions becomes more challenging. To tackle this problem, we propose a heuristic based on majority judgment that allows for reducing the search space. In addition, we perform a constraint programming search to select the Top K compositions that fulfill the QoS global constraints. The experimental results demonstrate the high performance of our approach.

关键词： constraint programming majority judgment uncertain quality of service Web service Web service selection

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The patient bed assignment problem solved by autonomous bat algorithm

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APPLIED SOFT COMPUTING 2019年 81卷 105484-000页

作者： Taramasco, Carla Olivares, Rodrigo Munoz, Roberto Soto, Ricardo Villar, Matias de Albuquerque, Victor Hugo C. Univ Vaparaiso Valparaiso Chile Pontificia Univ Catolica Vaparaiso Valparaiso Chile Univ Fortaleza Grad Program Appl Informat Fortaleza Ceara Brazil

The patient bed assignment problem consists of managing, in the best possible way, a set of beds with particular features and assigning them to a set of patients with special requirements. This assignment problem can be seen an optimization problem, of which the intended aims are usually to minimize the number of internal movements within a unit and to maximize bed usage according to the levels of criticality of the patients, among others. The usual approaches for solving this problem follow a traditional model based on the constraint programming paradigm, mainly using hard constraints. However, in real-life problems, constraints that should ideally be satisfied are often violated. In this paper, we present a new model for the patient bed assignment problem based on the minimum sum of unsatisfied constraints. This technique enables the consideration of soft constraints in the potential solutions that exhibit the best performance. The aim is to find the assignment that minimizes a weighted sum of the unsatisfied constraints. To this end, we use an autonomous binary version of the bat algorithm, which is an optimization technique inspired by the bio-sonar behaviour of microbats, to find the best set of potential solutions without requiring any expert user knowledge to achieve an efficient solution process. To validate our proposal, we use our model to solve problem instances based on data from several hospitals, and we perform a detailed comparative statistical analysis with a traditional constraint programming solver and several well-known optimization algorithms, including the classic bat algorithm. Promising results show that our approach is capable of efficiently solving 30 instances with decreased solution times. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Patient bed assignment problem constraint programming constraint optimization problem Dynamic weighted model Autonomous bat optimization

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Metaheuristics with restart and learning mechanisms for the no-idle flowshop scheduling problem with makespan criterion

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COMPUTERS & OPERATIONS RESEARCH 2022年 138卷 105616-105616页

作者： Oztop, Hande Tasgetiren, M. Fatih Kandiller, Levent Pan, Quan-Ke Izmir Demokrasi Univ Dept Ind Engn Izmir Turkey Yasar Univ Dept Int Logist Management Izmir Turkey Yasar Univ Dept Ind Engn Izmir Turkey Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Shandong Peoples R China

The no-idle permutation flowshop scheduling problem (NIPFSP) extends the well-known permutation flowshop scheduling problem, where idle time is not allowed on the machines. This study proposes a new mixed-integer linear programming (MILP) model and a new constraint programming (CP) model for the NIPFSP with makespan criterion. To the best of our knowledge, this study presents a CP model for the NIPFSP for the first time in the literature. We also compare the performance of the proposed MILP and CP models with a well-known MILP model from the literature. Since the studied problem is NP-hard, we also develop a new iterated greedy algorithm with restart and learning mechanisms (IG_RL) and a new iterated local search with restart and learning mechanisms (ILS_RL) as metaheuristics for the problem. In the proposed algorithms, all the parameters are determined by a learning mechanism in a self-adaptive way. Furthermore, a restart mechanism is employed in the proposed IG_RL and ILS_RL algorithms to guarantee the variety of the initial solutions and to assist the algorithm in avoiding the local optima. A variable neighborhood descent procedure is also embedded in the proposed algorithms. We use two well-known benchmark sets, i.e., VRF and Ruiz benchmark suites, to evaluate the performance of proposed solution methods. For almost half of the 240 small VRF instances, optimal results are reported by the MILP and CP models, whereas time-limited model results are reported for the rest. The results on small instances show that the proposed MILP and CP models outperform the MILP model from literature, where the CP model performs better than both MILP models. We compare the performance of the proposed IG_RL and ILS_RL algorithms with the state-of-the-art metaheuristics from the literature on both large VRF instances and Ruiz benchmark instances. The computational results show the effectiveness and superiority of the proposed ILS_RL and IG_RL algorithms for solving the NIPFSP. Primar

关键词： No-idle flowshop scheduling Makespan Iterated greedy algorithm Iterated local search Mathematical modeling constraint programming

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A Logic-Based Benders Decomposition Approach for Mapping Applications on Heterogeneous Multicore Platforms

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ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS 2016年第1期15卷 19-19页

作者： Emeretlis, Andreas Theodoridis, George Alefragis, Panayiotis Voros, Nikolaos Univ Patras Elect & Comp Engn Dept GR-26110 Patras Greece Technol Educ Inst Western Greece Embedded Syst Design & Applicat Lab Comp & Informat Engn Dept Patras Greece

The development of efficient methods for mapping applications on heterogeneous multicore platforms is a key issue in the field of embedded systems. In this article, a novel approach based on the Logic-Based Benders decomposition principle is introduced for mapping complex applications on these platforms, aiming at optimizing their execution time. To provide optimal solutions for this problem in a short time, a new hybrid model that combines Integer Linear programming (ILP) and constraint programming (CP) models is introduced. Also, to reduce the complexity of the model and its solution time, a set of novel techniques for generating additional constraints called Benders cuts is proposed. An extensive set of experiments has been performed in which synthetic applications described by Directed Acyclic Graphs (DAGs) were mapped to a number of heterogeneous multicore platforms. Moreover, experiments with DAGs that correspond to two real-life applications have also been performed. Based on the experimental results, it is proven that the proposed approach outperforms the pure ILP model in terms of the solution time and quality of the solution. Specifically, the proposed approach is able to find an optimal solution within a time limit of 2 hours in the vast majority of performed experiments, while the pure ILP model fails. Also, for the cases where both methods fail to find an optimal solution within the time limit, the solution of the proposed approach is systematically better than the solution of the ILP model.

关键词： Performance Algorithms Task scheduling multicore embedded platforms integer linear programming constraint programming Benders decomposition

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constraint-based Diversification of JOP Gadgets

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JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH 2021年 72卷 1471-1505页

作者： Tsoupidi, Rodothea Myrsini Lozano, Roberto Castaneda Baudry, Benoit KTH Royal Inst Technol Stockholm Sweden Univ Edinburgh Edinburgh Midlothian Scotland

Modern software deployment process produces software that is uniform, and hence vulnerable to large-scale code-reuse attacks, such as Jump-Oriented programming (JOP) attacks. Compiler-based diversification improves the resilience and security of software systems by automatically generating different assembly code versions of a given program. Existing techniques are efficient but do not have a precise control over the quality, such as the code size or speed, of the generated code variants. This paper introduces Diversity by Construction (DivCon), a constraint-based compiler approach to software diversification. Unlike previous approaches, DivCon allows users to control and adjust the conflicting goals of diversity and code quality. A key enabler is the use of Large Neighborhood Search (LNS) to generate highly diverse assembly code efficiently. For larger problems, we propose a combination of LNS with a structural decomposition of the problem. To further improve the diversification efficiency of DivCon against JOP attacks, we propose an application-specific distance measure tailored to the characteristics of JOP attacks. We evaluate DivCon with 20 functions from a popular benchmark suite for embedded systems. These experiments show that DivCon's combination of LNS and our application-specific distance measure generates binary programs that are highly resilient against JOP attacks (they share between 0.15% to 8% of JOP gadgets) with an optimality gap of <= 10%. Our results confirm that there is a trade-off between the quality of each assembly code version and the diversity of the entire pool of versions. In particular, the experiments show that DivCon is able to generate binary programs that share a very small number of gadgets, while delivering near-optimal code. For constraint programming researchers and practitioners, this paper demonstrates that LNS is a valuable technique for finding diverse solutions. For security researchers and software engineers, DivCon extend

关键词： constraint programming

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A Matheuristic Approach for Solving the Dynamic Facility Layout Problem

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Procedia Computer Science 2017年 108卷 1374-1383页

作者： Sadan Kulturel-Konak Penn State Berks Reading PA USA

The Dynamic Facility Layout Problem (DFLP) is designing a facility over a multi-period planning horizon where the interdepartmental material flows change from one period to the next one due to changes in product demands. The DFLP is used while designing manufacturing and logistics facilities over multiple planning periods; however, it is a very challenging nonlinear optimization problem. In this paper, a zone-based block layout is used to design manufacturing and logistics facilities over multiple planning periods. A zone-based block layout inherently includes possible aisle structures, which can easily be adapted to different material handling systems. The unequal area DFLP is modeled and solved using a zone-based structure where the dimensions of the departments are decision variables, and the departments are assigned to flexible zones with a pre-structured positioning. A matheuristic approach, which combines concepts from Tabu Search (TS) and mathematical programming, is proposed to solve the zone-based DFLP on the continuous plane with unequal area departments. The TS determines the relative locations of departments and their assignments to zones while their exact locations and shapes are calculated by the mathematical programming. Numerical results for a set of test problems from the literature showed that our proposed matheuristic approach is promising.

关键词： constraint programming Matheuristics Tabu Search Facility Layout Zones

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A LOCAL SEARCH ALGORITHM FOR THE INVENTORY MATCHING PROBLEM IN A STEEL COMPANY

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IFAC Proceedings Volumes 2006年第3期39卷 359-364页

作者： Birger Raa Wim Van der Stricht Department of Industrial Management Ghent University Technologiepark 903 9052 Zwijnaarde Systems and Models Sidmar (Arcelor Group) J. Kennedylaan 51 9042 Gent Belgium

The problem studied is the selection of slabs from a slab yard for the hot rolling program in a steel company, with the objective of minimizing and balancing the incurred workload on the slab yard. A local search algorithm in a constraint programming environment is developed. This local search has two iterative phases: descent into and escape from local optima. Computational experiments show savings of 40 percent and more compared to the currently used greedy search.

关键词： Local Search constraint programming Hot Rolling Schedule Inventory Matching

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