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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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A Server Reassignment Algorithm for DHT Load Balance and the Effect of Heterogeneity

A Server Reassignment Algorithm for DHT Load Balance and the...

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IEEE Global Telecommunications Conference (GLOBECOM 06)

作者： Tsai, Kun-Cheng Chen, Chyouhwa Inst Informat Ind Networks & Multimedia Inst Taipei Taiwan Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781424403561

Application layer peer to peer (P2P) networks are considered to be the most important development for next generation Internet infra-structure. For these systems to be effective, load balancing among the peers is critical. Early structured P2P systems rely on the randomness of object ID generated with a consistent hash function to avoid the load imbalance issue, and has been known to result in an imbalance factor in the number of items stored at a node, even in the case of a homogeneous system where all nodes have the same capacity. We propose to address the problem systematically using an optimization based approach, and show that the problem of rearranging loads among the peers can be cast in the framework of a generalized assignment problem. The dual space local search (DSLS) heuristic algorithm is proposed, which minimizes the load movement involved in an efficient manner. We demonstrate the superior performance of our proposal in general, and its advantages over previous strategies. We also explore other important issues vital to the performance for the virtual server framework, such as the performance ramification of peer registration strategies. Significantly, we characterize systematically the effect of heterogeneity on load balancing algorithm performance, and the conditions in which heterogeneity may be easy or hard to deal with.

关键词： Structured Peer-to-Peer System Load Balance Distributed Hash Table generalized assignment problem Local Search

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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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An efficient algorithm for solving two multi-object generalized assignment problems and its application

Huazhong Ligong Daxue Xuebao/Journal Huazhong (Central China...

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Huazhong Ligong Daxue Xuebao/Journal Huazhong (Central China) University of Science and Technology 2001年第1期29卷 70-72页

作者： Song, Y. Chen, M. Zhang, S. Dept. of Control Sci. and Eng. HUST Wuhan 430074 China

Two multi-object generalized assignment problems (MOGAP) are discussed and their multi-object integer linear programming models are presented respectively. By combining the fuzzy theory with Hungary algorithm applied to solving the conventional assignment problem, Fuzzy Hungary Algorithm for solving the MOGAP is proposed. The application in the ordinance material supply illustrates the method.

关键词： generalized assignment problem Hungary algorithm Membership grade Multi-object

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Revenue maximization and ex-post budget constraints 15

Revenue maximization and ex-post budget constraints

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16th ACM Conference on Economics and Computation, EC 2015

作者： Daskalakis, Constantinos Devanur, Nikhil R. Weinberg, S. Matthew M.I.T United States Microsoft Research United States Princeton University United States

ISBN: (纸本)9781450334105

We consider the problem of a revenue-maximizing seller with m items for sale to n additive bidders with hard budget constraints, assuming that the seller has some prior distribution over bidder values and budgets. The prior may be correlated across items and budgets of the same bidder, but is assumed independent across bidders. We target mechanisms that are Bayesian Incentive Compatible, but that are ex-post Individually Rational and ex-post budget respecting. Virtually no such mechanisms are known that satisfy all these conditions and guarantee any revenue approximation, even with just a single item. We provide a computationally efficient mechanism that is a 3-approximation with respect to all BIC, ex-post IR, and ex-post budget respecting mechanisms. Note that the problem is NP-hard to approximate better than a factor of 16=15, even in the case where the prior is a point mass [Chakrabarty and Goel 2010]. We further characterize the optimal mechanism in this setting, showing that it can be interpreted as a distribution over virtual welfare maximizers. We prove our results by making use of a black-box reduction from mechanism to algorithm design developed by [Cai et al. 2013]. Our main technical contribution is a computationally efficient 3-approximation algorithm for the algorithmic problem that results by an application of their framework to this problem. The algorithmic problem has a mixed-sign objective and is NP-hard to optimize exactly, so it is surprising that a computationally efficient approximation is possible at all. In the case of a single item (m = 1), the algorithmic problem can be solved exactly via exhaustive search, leading to a computationally efficient exact algorithm and a stronger characterization of the optimal mechanism as a distribution over virtual value maximizers.

关键词： generalized assignment problem virtual welfare budget constraints revenue optimization

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Optimizing the U. S. Marine Corps' Selective Reenlisted Bonus program for the career force retention

Optimizing the U. S. Marine Corps' Selective Reenlisted Bonu...

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作者： Robbins, Kent A. NAVAL POSTGRADUATE SCHOOL

学位级别：硕士

The Marine Corps uses its Selective Reenlisted Bonus (SRB) Program to influence Marines to reenlist for a designated term into certain Military Occupational Specialties (MOSs) in order to reach planned manpower goals. The bonus amount is determined by selecting an "SRB multiplier" for each combination of MOS and Zone ("MOSZ"). ("Zone" corresponds to length of service.) A higher multiplier means a larger bonus and leads to a higher percentage of Marines reenlisting. That percentage, predicted by an existing forecasting model, is assumed exact here. The "SRB multiplier model" assigns multipliers to minimize a sum of weighted squared deviations from MOSZ targets subject to a budget constraint. This model is implemented as a generalized assignment problem, and solved approximately on a personal computer using Lagrangian relaxation and a secondary heuristic. (The algorithm is programmed in Visual Basic for Applications and has an Excel interface.) Data for FY04 shows 491 bonus-eligible MOSZs. With up to 11 possible multiplier values, this yields a model with 5,401 0-1 variables and 491 constraints. A solution within 0.0018% of optimality is reached in 1.4 seconds on 1.58 GHz personal computer. Standard integer-programming software verifies the correctness of the solution.

关键词： Selective Reenlistment Bonus Multiplier generalized assignment problem Lagrangian Relaxation

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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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Determination of Selective reenlistment Bonus multipliers in the United States Marine Corps.

Determination of Selective reenlistment Bonus multipliers in...

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作者： DeWolfe, Dean D. Naval Postgraduate School

学位级别：硕士

Selective Reenlistment Bonuses (SRBs) are offered to improve retention in designated military occupational specialties (MOSs) for specified years-of- service intervals (zones). The amount of the bonus is set by assigning an "SRB Multiplier" for each MOS and zone combination (cell). Determination of multipliers is modeled as a nonlinear knapsack problem which is then linearized to a generalized assignment problem. The objective is to minimize the sum over all cells of a weighted squared deviation from the reenlistment target in each cell. Lagrangian relaxation provides lower bounds and feasible solutions. The best feasible solution is improved using a greedy heuristic to apportion unexpended funds. A FORTRAN 77 computer program implements the procedure. Data for FY86 yields a 0-1 integer program with 4895 binary variables and 980 constraints. A solution within.01% of optimality is obtained on an IBM 3033AP in 1.7 seconds and on an IBM PC in about four minutes.

关键词： Math Programming Integer Programming Knapsack problem Selective Reenlistment Bonus Lagrangian Relaxation generalized assignment problem

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Interference-aware feedback based iterative Hungarian approach to allocate multiple channels to multiple D2D pairs to maximize underlay D2D network capacity

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2024年第9期37卷 e5772-e5772页

作者： Sengar, Aditya Singh Gangopadhyay, Ranjan Debnath, Soumitra LNM Inst Informat Technol Dept Elect & Commun Engn Jaipur Rajasthan India LNM Inst Informat Technol Jaipur 302031 Rajasthan India

The spectral efficiency of a cellular network can be increased significantly by allowing spatial reuse of its spectrum by an underlay device-to-device (D2D) network. In an underlay D2D network, devices in close vicinity are allowed to establish low-power direct links with little to no involvement of the base station. In order to increase the spectral efficiency and the number of devices with channel access, multiple D2D pairs may transmit in each cellular channel. Additionally, each pair can be allowed to utilize multiple channels to transmit so as to maximize the D2D network capacity. This multiple-pair multiple-channel (MPMC) strategy is quite appealing but is limited by the resultant additional aggregate interference and the inherent complexity, hence necessitating the need for a fast and reliable channel allocation scheme. This work proposes a polynomial-time iterative Hungarian assignment with feedback (IHAF) algorithm for multiple channel allocations amongst multiple D2D pairs that increases the D2D network capacity manifold while maintaining the desired minimum capacity for each cellular user. Underlay D2D networks increase the capacity of cellular networks, provided the uplink channels are allocated to the underlay users in an interference-aware manner. In the presented work, a novel polynomial-time iterative Hungarian assignment with Feedback algorithm has been proposed which enables multiple D2D pairs to coexist on multiple channels while keeping the interference to the cellular users under a threshold, enabling manifold increase in the D2D network capacity and increased resilience due to multiple assigned channels to each D2D pair. image

关键词： device-to-device generalized assignment problem Hungarian algorithm multiple-pair multiple-channel allocation

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An intelligent stochastic optimization approach for stochastic order allocation problems with high-dimensional order uncertainties

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年 167卷

作者： Zhang, Zhenzhong Guo, Chunxiang Ruan, Wenjie Wang, Wei Zhou, Peng Sichuan Univ Business Sch Chengdu 610065 Peoples R China Natl Univ Singapore Ind Syst Engn & Management Singapore 117576 Singapore Hohai Univ Coll Harbor Coastal & Offshore Engn Nanjing 210098 Peoples R China

Effective order allocation has a great impact on improving the efficiency of supply chain operations. This paper investigates a high-dimensional stochastic order allocation problem that assigns uncertain customer orders to heterogeneous parallel machines. Considering the arrival uncertainty of customer orders in the real-world production environment, a mathematical model of the investigated problem is established with the objective of maximizing the expected profit of order processing. A novel intelligent stochastic optimization approach is proposed by combining a modified adaptive large neighborhood search algorithm with a scenario generation technique. In this approach, the modified adaptive large neighborhood search algorithm suitable for the generalized allocation problem is proposed to seek the best solutions, while the scenario generation technique is used to generate the scenarios needed to evaluate the solutions of the high-dimensional stochastic optimization problem. Experimental results demonstrate that the proposed approach outperforms the compared stochastic optimization method in terms of optimization efficiency and optimization ability.

关键词： Order uncertainty Order assignment generalized assignment problem High-dimensional random variable Scenario generation

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