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Applying Algorithm Selection – a Case Study for the Generalised assignment problem

Electronic Notes in Discrete Mathematics 2018年 69卷 205-212页

作者： Degroote, Hans González-Velarde, José Luis De Causmaecker, Patrick Department of Computer Science CODeS & Imec KU Leuven Belgium Tecnologico de Monterrey Monterrey Mexico

Many combinatorial optimisation problems are NP-Hard. Yet in practice high quality solutions are often obtained by (meta)heuristics. These work well in some cases, but not in others, indicating a potential for algorithm selection. In this extended abstract is discussed how to apply algorithm selection to a combinatorial optimisation problem and which challenges arise when doing so. The generalised assignment problem is used as case study. © 2018 Elsevier B.V.

关键词： Automatic Algorithm Selection Combinatorial Optimisation Generalised assignment problem generalized assignment problem Meta-learning

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A Graph Partitioning-based Heuristic for Runtime IoT Data Placement Strategies in a Fog infrastructure 18

A Graph Partitioning-based Heuristic for Runtime IoT Data Pl...

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33rd Annual ACM Symposium on Applied Computing (ACM SAC)

作者： Naas, Mohammed Islam Lemarchand, Laurent Boukhobza, Jalil Raipin, Philippe Orange Rennes France Univ Bretagne Occidentale UMR 6285 Lab STICC F-29200 Brest France

ISBN: (纸本)9781450351911

Fog computing is a dense, heterogeneous and geographically distributed infrastructure. With the rise of IoT applications, objects may generate large amounts of data that may be processed at different locations of the Fog infrastructure. Data placement strategies have been designed to investigate the best storage location for data in order to reduce its access time for different IoT services spread over the infrastructure. Unfortunately, due to the large number of Fog nodes and the amount of data to be managed, placing data in such infrastructure is an NP-Hard problem. In this paper, we propose a divide and conquer heuristic for data placement strategies in Fog infrastructures. Our idea consists in dividing the original data placement problem into several balanced sub-problems using graph modeling and partitioning methods. Using our heuristic makes it possible to reduce the solving time by more than 450 times with less than 5% of optimality loss as compared to the exact solution (without subdivision). For a given number of partitions, our solution proved to be at least as close to the optimal as state-of-the-art solutions and 30% closer to the optimal for many workloads. In addition, our solution allowed for a better optimization in solving time as it is more flexible and scalable in terms of number of partitions.

关键词： IoT Fog Data Placement Storage Optimization generalized assignment problem Graph Partitioning

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Modeling and analysis of short-term work planning in inpatient care settings

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OPERATIONS RESEARCH FOR HEALTH CARE 2018年 19卷 14-25页

作者： Aragon, Lucy G. Cure, Laila Tiong, Ewing Bush, Rita Wichita State Univ Ind Syst & Mfg Engn 1845 Fairmount St Wichita KS 67260 USA Western Michigan Univ Ind & Entrepreneurial Engn Kalamazoo MI 49008 USA Clin Data Solut HealthTrust Nashville TN USA Pontificia Univ Catolica Peru Lima Peru

This paper uses operations research (OR) to investigate inpatient care work planning decisions from the perspective of a single healthcare provider in a hospital unit. While there has been considerable research on the modeling and analysis of healthcare delivery systems to support medium- to long-term decisions (e.g., daily operating room scheduling, weekly nurse scheduling), there is little research focusing on decisions made at the operational level by front-line providers. At this stage, resources have been allocated, personnel have been scheduled, and a set of patients with specific demands needs to be cared for in a safe, effective, efficient, timely, patient-centered, and equitable manner. It is often the responsibility of the actual providers to plan, execute, and adjust their workflow based on their knowledge and experience. This paper describes short-term work planning decisions made by front-line healthcare workers and illustrates how OR can support the analysis of such decisions. Research questions that motivate the use of OR in the design and analysis of inpatient care work are discussed. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Inpatient work design generalized assignment problem Work planning Task scheduling

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Customized bus service design for jointly optimizing passenger-to-vehicle assignment and vehicle routing

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2017年 85卷 451-475页

作者： Tong, Lu (Carol) Zhou, Leishan Liu, Jiangtao Zhou, Xuesong Beihang Univ Sch Elect & Informat Engn Beijing 100091 Peoples R China Natl Engn Lab Comprehens Transportat Big Data App Beijing 100091 Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Arizona State Univ Sch Sustainable Engn & Built Environm Tempe AZ 85287 USA

Emerging transportation network services, such as customized buses, hold the promise of expanding overall traveler accessibility in congested metropolitan areas. A number of internet-based customized bus services have been planned and deployed for major origin-destination (OD) pairs to/from inner cities with limited physical road infrastructure. In this research, we aim to develop a joint optimization model for addressing a number of practical challenges for providing flexible public transportation services. First, how to maintain minimum loading rate requirements and increase the number of customers per bus for the bus operators to reach long-term profitability. Second, how to optimize detailed bus routing and timetabling plans to satisfy a wide range of specific user constraints, such as passengers' pickup and delivery locations with preferred time windows, through flexible decision for matching passengers to bus routes. From a space-time network modeling perspective, this paper develops a multi-commodity network flow based optimization model to formulate a customized bus service network design problem so as to optimize the utilization of the vehicle capacity while satisfying individual demand requests defined through space-time windows. We further develop a solution algorithm based on the Lagrangian decomposition for the primal problem and a space-time prism based method to reduce the solution search space. Case studies using both the illustrative and real-world large-scale transportation networks are conducted to demonstrate the effectiveness of the proposed algorithm and its sensitivity under different practical operating conditions.

关键词： Customized buses Space-time network Vehicle routing problem generalized assignment problem Ridesharing

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iFogStor: an IoT Data Placement Strategy for Fog Infrastructure 1

iFogStor: an IoT Data Placement Strategy for Fog Infrastruct...

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1st IEEE International Conference on Fog and Edge Computing (ICFEC)

作者： Naas, Mohammed Islam Parvedy, Philippe Raipin Boukhobza, Jalil Lemarchand, Laurent Orange Rennes France Univ Bretagne Occidentale Lab STICC UMR 6285 F-29200 Brest France

ISBN: (纸本)9781509030477

Internet of Things (IoT) will be one of the driving application for digital data generation in the next years as more than 50 billions of objects will be connected by 2020. IoT data can be processed and used by different devices spread all over the network. The traditional way of centralizing data processing in the Cloud can hardly scale because it cannot satisfy many of the latency critical IoT applications. In addition, it generates a too high network traffic when the number of objects and services increase. Fog infrastructure provides a beginning of an answer to such an issue. In this paper, we present a data placement strategy for Fog infrastructures called iFogStor. The objective of iFogStor is to take profit of the heterogeneity and location of Fog nodes to reduce the overall latency of storing and retrieving data in a Fog. We formulated the data placement problem as a generalized assignment problem (GAP) and proposed two ways to solve it: 1) an exact solution using integer programming and 2) a heuristic one based on geographical zoning to reduce the solving time. Both solutions proved very good performance as they reduced the latency by more than 86% as compared to a Cloud based solution and by 60% as compared to a naive Fog solution. Using geographical zoning heuristic can allow solving problems with large number of Fog nodes efficiently and in a couple of seconds making iFogStor feasible in runtime and scalable.

关键词： Internet of Things Fog Data Placement Storage Optimization generalized assignment problem

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The 0-1 Multiple Knapsack problem

The 0-1 Multiple Knapsack Problem

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作者： Hayat Abdullah Shamakhai Laurentian University

学位级别：硕士

In operation research, the Multiple Knapsack problem (MKP) is classified as a combinatorial optimization problem. It is a particular case of the generalized assignment problem. The MKP has been applied to many applications in naval as well as financial management. There are several methods to solve the Knapsack problem (KP) and Multiple Knapsack problem (MKP); in particular the Bound and Bound Algorithm (B&B). The bound and bound method is a modification of the Branch and Bound Algorithm which is defined as a particular tree-search technique for the integer linear programming. It has been used to obtain an optimal solution. In this research, we provide a new approach called the Adapted Transportation Algorithm (ATA) to solve the KP and MKP. The solution results of these methods are presented in this thesis. The Adapted Transportation Algorithm is applied to solve the Multiple Knapsack problem where the unit profit of the items is dependent on the knapsack. In addition, we will show the link between the Multiple Knapsack problem (MKP) and the multiple assignment problem (MAP). These results open a new field of research in order to solve KP and MKP by using the algorithms developed in transportation.

关键词： generalized assignment problem assignment problem Knapsack problem Multiple Knapsack problem Branch and Bound Algorithm Bound and Bound Algorithm Transportation problem Multiple assignment problem Adapted Transportation problem Vogel Approximation Method Group Role assignment problem

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Bees algorithm for generalized assignment problem

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APPLIED MATHEMATICS AND COMPUTATION 2010年第11期215卷 3782-3795页

作者： Ozbakir, Lale Baykasoglu, Adil Tapkan, Pinar Gaziantep Univ Dept Ind Engn Gaziantep Turkey Erciyes Univ Dept Ind Engn Kayseri Turkey

Bees algorithm (BA) is a new member of meta-heuristics. BA tries to model natural behavior of honey bees in food foraging. Honey bees use several mechanisms like waggle dance to optimally locate food sources and to search new ones. This makes them a good candidate for developing new algorithms for solving optimization problems. In this paper a brief review of BA is first given, afterwards development of a BA for solving generalized assignment problems (GAP) with an ejection chain neighborhood mechanism is presented. GAP is a NP-hard problem. Many meta-heuristic algorithms were proposed for its solution. So far BA is generally applied to continuous optimization. In order to investigate the performance of BA on a complex integer optimization problem, an attempt is made in this paper. An extensive computational study is carried out and the results are compared with several algorithms from the literature. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Meta-heuristics Bees algorithm generalized assignment problem Ejection chain Integer programming Swarm intelligence

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A computational study of exact knapsack separation for the generalized assignment problem

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2010年第3期45卷 543-555页

作者： Avella, Pasquale Boccia, Maurizio Vasilyev, Igor Russian Acad Sci Inst Syst Dynam & Control Theory Siberian Branch Irkutsk 664033 Russia Univ Sannio Res Ctr Software Technol I-82100 Benevento Italy

The generalized assignment problem is a well-known NP-hard combinatorial optimization problem which consists of minimizing the assignment costs of a set of jobs to a set of machines satisfying capacity constraints. Most of the existing algorithms are of a Branch-and-Price type, with lower bounds computed through Dantzig-Wolfe reformulation and column generation. In this paper we propose a cutting plane algorithm working in the space of the variables of the basic formulation, whose core is an exact separation procedure for the knapsack polytopes induced by the capacity constraints. We show that an efficient implementation of the exact separation procedure allows to deal with large-scale instances and to solve to optimality several previously unsolved instances.

关键词： generalized assignment problem Cutting plane algorithm Exact separation

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A hybrid tabu search/branch & bound approach to solving the generalized assignment problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2010年第2期207卷 566-578页

作者： Woodcock, Andrew J. Wilson, John M. Univ Nottingham Sch Business Nottingham NG8 1BB England Univ Loughborough Loughborough LE11 3TU Leics England Aberystwyth Univ Aberystwyth SY23 2DD Dyfed Wales

A new approach for solving the generalized assignment problem (GAP) is proposed that combines the exact branch & bound approach with the heuristic strategy of tabu search (TS) to produce a hybrid algorithm for solving GAP. The algorithm described uses commercial software to solve sub-problems generated by the TS guiding strategy. The TS approach makes use of the concept of referent domain optimisation and introduces novel add/drop strategies. In addition, the linear programming relaxation of GAP that forms part of the branch & bound approach is itself helpful in suggesting which variables might take binary values. Computational results on benchmark test instances are presented and compared with results obtained by the standard branch & bound approach and also several other heuristic approaches from the literature. The results show the new algorithm performs competitively against the alternatives and is able to find some new best solutions for several benchmark instances. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Integer programming Tabu search Branch and bound generalized assignment problem

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Performance analysis of swarm optimization approaches for the generalized assignment problem in multi-target tracking applications

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2010年第6期18卷 1059-1076页

作者： Bozdogan, Ali Onder Yilmaz, Asim Egemen Efe, Murat Ankara Univ Fac Engn Dept Elect Engn TR-06100 Ankara Turkey

The aim of this study is to investigate the suitability of selected swarm optimization algorithms to the generalized assignment problem as encountered in multi-target tracking applications. For this purpose, we have tested variants of particle swarm optimization and ant colony optimization algorithms to solve the 2D generalized assignment problem with simulated dense and sparse measurement/track matrices and compared their performance to that of the auction algorithm. We observed that, although with some modification swarm optimization algorithms provide improvement in terms of speed, they still fall behind the auction algorithm in finding the optimum solution to the problem. Among the investigated colony optimization approaches, the particle swarm optimization algorithm using the proposed 1-opt local search was found to perform better than other modifications. On the other hand, it is assessed that swarm optimization algorithms might be powerful tools for multiple hypothesis target tracking applications at noisy environments, since within single execution they provide a set of numerous good solutions to the assignment problem.

关键词： generalized assignment problem ant colony optimization particle swarm optimization data association target tracking

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