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IEEE International Conference on Communications (ICC)

作者： Nguyen, Minh Dat Le, Long Bao Girard, Andre Univ Quebec INRS EMT Montreal PQ Canada

ISBN: (纸本)9781728171227

In this paper, we study the trajectory control and sub-channel assignment for unmanned aerial vehicle (UAV) based wireless networks with wireless backhauls. This design aims to maximize the min rate achieved by ground users (GUs) subject to their data transmission demands. To tackle the underlying mixed integer non-linear optimization problem, we use the alternating optimization approach where we iteratively optimize the sub-channel assignment and UAV trajectory control until convergence. Toward this end, we propose a heuristic algorithm to obtain a feasible solution for the sub-channel assignment sub-problem. In addition, the successive convex approximation (SCA) method is used to convexify and solve the non-convex UAV trajectory control sub-problem. Via extensive numerical studies, we illustrate the effectiveness of our proposed design for different network settings.

关键词： UAV trajectory sub-channel assignment wireless backhaul mixed integer non-linear programming

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A Cost Minimization Model for a Multi-Component Product Closed Loop Supply Chain Considering Big Data Dimensions

A Cost Minimization Model for a Multi-Component Product Clos...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Chuateco, P. D. Del Rosario, C. Y. Reyes, Y. L. Cruz, D. E. De La Salle Univ Dept Ind Engn Manila Philippines

ISBN: (纸本)9781665437714

This paper proposes a mixed integer non-linear programming cost minimization model for a generic multi-component product circulating in a closed loop supply chain system capable of Big Data Analytics (BDA). The model introduces a novel approach of operationalizing the BDA dimensions of Variety, Velocity, and Volume (3Vs) through the activities of Procurement, Demand Management, Collection, and Recovery. The results of the model were compared across 7 systems that featured different combinations of intensity levels in the 3Vs from a predetermined low, medium, and high range. It was found that across all systems with BDA, recycled raw materials were prioritized, demand was maximized, collection and recovery success was increased. Ultimately, it was found that the HMM system holds the best holistic performance across CLSC operations and cost savings.

关键词： Big Data dimensions Closed Loop Supply Chain mixed integer non-linear programming

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Multi-UAV-Assisted MEC System: Joint Association and Resource Management Framework 35

Multi-UAV-Assisted MEC System: Joint Association and Resourc...

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35th International Conference on Information Networking (ICOIN)

作者： Ei, Nway Nway Kang, Seok Won Alsenwi, Madyan Tun, Yan Kyaw Hong, Choong Seon Kyung Hee Univ Dept Comp Sci & Engn Seoul 446707 South Korea

ISBN: (纸本)9781728191010

In this paper, we study an energy-efficient multiUAV-assisted multi-access edge computing (MEC) system in which unmanned aerial vehicles (UAVs) equipped with MEC servers offer computing services to the mobile devices. In particular, the mobile devices offload a portion of their computationintensive and delay-sensitive tasks to the UAVs to minimize local computing energy consumption. However, the coupling constraints of limited energy budget at UAVs and task completion deadlines make it difficult to determine device association and the amount of task to be offloaded. Moreover, the amount of computing resources assigned to the mobile devices by each UAV might vary according to the number of associated users and the amount of task offloaded from them. Therefore, in this work, we formulate a joint device association, task assignment and computing resource allocation problem to minimize the energy consumption of mobile devices and UAVs by considering the energy budget and available computing resources at the UAVs and task completion deadline constraints. To that end, we show that the proposed optimization problem is a mixed-integer non-linear programming (MINLP) problem, which is generally a non-convex and NP-hard problem. To solve this, we first decompose the formulated problem into three subproblems which are then solved by applying an iterative block coordinate descent (BCD) algorithm. Through the extensive simulations, we verify that our proposed algorithm outperforms the other benchmark schemes, namely, random association and offloading all.

关键词： Multi access edge computing unmanned aerial vehicle mixed integer non-linear programming block coordinate descent

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Exact global optimization of frame structures for additive manufacturing

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2022年第3期65卷 97-97页

作者： Toragay, Oguz Silva, Daniel F. Vinel, Alexander Shamsaei, Nima Auburn Univ Ind & Syst Engn Dept Auburn AL 36849 USA Auburn Univ Mech Engn Dept Auburn AL 36849 USA

We consider the problem of designing lightweight load-bearing frame structures with additive manufacturability constraints. Specifically, we focus on mathematical programming approaches to finding exact globally optimal solutions, given a pre-specified discrete ground structure and continuous design element dimensions. We take advantage of stiffness matrix decomposition techniques and expand on some of the existing modeling approaches, including exact mixed-integer nonlinear programming and its mixed-integer linear programming restrictions. We propose a (non-convex) quadratic formulation using semi-continuous variables, motivated by recent progress in state-of-the-art quadratic solvers, and demonstrate how some additive-specific restrictions can be incorporated into mathematical optimization. While we show with numerical experiments that the proposed methods significantly reduce the required solution time for finding global optima compared to other formulations, we also observe that even with these new techniques and advanced computational resources, discrete modeling of frame structures remains a tremendously challenging problem.

关键词： Discrete topology optimization Additive manufacturing Frame structures mixed integer non-linear programming mixed integer quadratically constrained programming mixed integer linear programming

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A two-stage Genetic Algorithm for joint coordination of spare parts inventory and planned maintenance under uncertain failures

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APPLIED SOFT COMPUTING 2022年 130卷

作者： Yu, Vincent F. Salsabila, Nabila Yuraisyah Siswanto, Nurhadi Kuo, Po-Hsun Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan Natl Taiwan Univ Sci & Technol Ctr Cyber Phys Syst Innovat Taipei 106 Taiwan Inst Teknol Sepuluh Nopember Dept Ind & Syst Engn Surabaya 60111 Indonesia

The main problem of spare parts management is to maintain the minimal requirement of stock keeping units kept. This research proposes a joint optimization model of multi-item multi-period spare parts inventory management and planned maintenance under uncertain failures in order to balance inventory cost and spare parts availability. This paper presents a mixed integer non-linear programming formulation of the inventory optimization model under a minimum and maximum inventory policy with stock review intervals. Some studies in the literature have considered aggregating spare parts inventory management as they assume that it will reduce the ordering cost. We consider both independent and aggregate spare parts inventory policies and then combine the formulation with the predictive maintenance interval, which is a replacement action due to uncertain failures under predefined distribution. Furthermore, a novel two-stage Genetic Algorithm is proposed as a sim-heuristic approach to deal with the non-linearity, combinations, and stochasticity of the problem and solve large-scale instances. In the end, we perform a computational study on some instances and a real-world case study to demonstrate the proposed approach's effectiveness and efficiency. The computational study shows that the independent policy results in lower cost than the aggregate policy, and the proposed Genetic Algorithm performs efficiently for large-scale problems.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Inventory management Planned maintenance Stochastic programming mixed integer non-linear programming Genetic algorithm Sim-heuristics

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imPhy: Imputing Phylogenetic Trees with Missing Information Using Mathematical programming

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020年第4期17卷 1222-1230页

作者： Yasui, Niko Vogiatzis, Chrysafis Yoshida, Ruriko Fukumizu, Kenji Univ Alberta Dept Comp Sci Edmonton AB T6G 2R3 Canada North Carolina A&T State Univ Dept Ind & Syst Engn Greensboro NC 27406 USA Naval Postgrad Sch Dept Operat Res Monterey CA 93943 USA Inst Stat Math 10-3 Midoricho Tachikawa Tokyo 1908562 Japan Inst Stat Math Res Ctr Stat Machine Learning 10-3 Midoricho Tachikawa Tokyo 1908562 Japan Grad Univ Adv Studies 10-3 Midoricho Tachikawa Tokyo 1908562 Japan

Advances in modern genomics have allowed researchers to apply phylogenetic analyses on a genome-wide scale. While large volumes of genomic data can be generated cheaply and quickly, data missingness is a non-trivial and somewhat expected problem. Since the available information is often incomplete for a given set of genetic loci and individual organisms, a large proportion of trees that depict the evolutionary history of a single genetic locus, called gene trees, fail to contain all individuals. Data incompleteness causes difficulties in data collection, information extraction, and gene tree inference. Furthermore, identifying outlying gene trees, which can represent horizontal gene transfers, gene duplications, or hybridizations, is difficult when data is missing from the gene trees. The typical approach is to remove all individuals with missing data from the gene trees, and focus the analysis on individuals whose information is fully available - a huge loss of information. In this work, we propose and design an optimization-based imputation approach to infer the missing distances between leaves in a set of gene trees via a mixed integer non-linear programming model. We also present a new research pipeline, imPhy, that can (i) simulate a set of gene trees with leaves randomly missing in each tree, (ii) impute the missing pairwise distances in each gene tree, (iii) reconstruct the gene trees using the Neighbor Joining (NJ) and Unweighted Pair Group Method with Arithmetic Mean (UPGMA) methods, and (iv) analyze and report the efficiency of the reconstruction. To impute the missing leaves, we employ our newly proposed non-linear programming framework, and demonstrate its capability in reconstructing gene trees with incomplete information in both simulated and empirical datasets. In the empirical datasets apicomplexa and lungfish, our imputation has very small normalized mean square errors, even in the extreme case where 50 percent of the individuals in each gene tree a

关键词： Gene trees missing information mixed integer non-linear programming

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Joint Data Compression and Computation Offloading in Hierarchical Fog-Cloud Systems

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2020年第1期19卷 293-309页

作者： Ti Ti Nguyen Vu Nguyen Ha Long Bao Le Schober, Robert Univ Quebec INRS Montreal PQ H5A 1K6 Canada Ecole Polytech Montreal Montreal PQ H3T 1J4 Canada Friedrich Alexander Univ Erlangen Nurnberg Inst Digital Commun D-91058 Erlangen Germany

Data compression (DC) has the potential to significantly improve the computation offloading performance in hierarchical fog-cloud systems. However, it remains unknown how to optimally determine the compression ratio jointly with the computation offloading decisions and the resource allocation. This optimization problem is studied in this paper where we aim to minimize the maximum weighted energy and service delay cost (WEDC) of all users. First, we consider a scenario where DC is performed only at the mobile users. We prove that the optimal offloading decisions have a threshold structure. Moreover, a novel three-step approach employing convexification techniques is developed to optimize the compression ratios and the resource allocation. Then, we address the more general design where DC is performed at both the mobile users and the fog server. We propose three algorithms to overcome the strong coupling between the offloading decisions and the resource allocation. Numerical results show that the proposed optimal algorithm for DC at only the mobile users can reduce the WEDC by up to 65 compared to computation offloading strategies that do not leverage DC or use sub-optimal optimization approaches. The proposed algorithms with additional DC at the fog server lead to a further reduction of the WEDC.

关键词： Servers Resource management Wireless communication Delays Optimization Computational modeling Data compression Fog computing resource allocation computation offloading hierarchical fog cloud data compression energy saving latency mixed integer non-linear programming

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Grid-Specific Co-Optimization of Incentive for Generation Planning in Power Systems With Renewable Energy Sources

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2020年第2期11卷 947-957页

作者： Parizy, Ehsan Saeidpour Choi, Seungdeog Bahrami, Hamid Reza Univ Akron Dept Elect & Comp Engn Akron OH 44325 USA Case Western Reserve Univ Dept Elect Engn & Comp Sci Cleveland OH 44106 USA Mississippi State Univ Dept Elect & Comp Engn Starkville MS 39762 USA

Enhancement in efficiency of renewable energy sources, raised concern about greenhouse gas emission, significant power losses within transmission and distribution networks, unpredictable and unreliable fossil fuel cost, and expansion of reliable communication infrastructure offered by smart grids have all contributed to an increasing interest in deployment of renewable energy sources in energy mix. However, the widespread utilization of renewable energy sources in an open energy market without proper grid-based incentives for aimed contribution levels is uncertain. This article proposes a co-optimization algorithm to find the minimum incentives that result in the desired level of renewable energy source penetration in energy supply chain and optimal distribution of power sources in the electric power grid. The algorithm first finds the proper incentive based on the power-flow losses, then it uses a novel optimization approach to solve a mixed integer non-linear programming optimization of generation planning to minimize the energy cost. Unlike other incentive design methodologies, the proposed algorithm is not only supply specific, but also it depends on the demand and the structures of the grid. The method finds an exclusively designed set of incentives for electric energy supplies that do not emit greenhouse gases or heat to the atmosphere.

关键词： Power systems Renewable energy sources Optimization Power generation Planning Device-to-device communication Generators Incentive design renewable energy sources and grid integration economic impact of sustainable energy sources generation expansion planning mixed integer non-linear programming

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Decentralized Greedy-Based Algorithm for Smart Energy Management in Plug-in Electric Vehicle Energy Distribution Systems

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IEEE ACCESS 2020年 8卷 75666-75681页

作者： Mehrabi, Abbas Nunna, H. S. V. S. Kumar Dadlani, Aresh Moon, Seungpil Kim, Kiseon Nottingham Trent Univ Dept Comp & Technol Nottingham NG11 8NS England Nazarbayev Univ Dept Elect & Comp Engn Astana 010000 Kazakhstan Korea Elect Power Res Inst KEPRI Deajeon 34056 South Korea Gwangju Inst Sci & Technol Sch Elect Engn & Comp Sci Gwangju 61005 South Korea

Variations in electricity tariffs arising due to stochastic demand loads on the power grids have stimulated research in finding optimal charging/discharging scheduling solutions for electric vehicles (EVs). Most of the current EV scheduling solutions are either centralized, which suffer from low reliability and high complexity, while existing decentralized solutions do not facilitate the efficient scheduling of on-move EVs in large-scale networks considering a smart energy distribution system. Motivated by smart cities applications, we consider in this paper the optimal scheduling of EVs in a geographically large-scale smart energy distribution system where EVs have the flexibility of charging/discharging at spatially-deployed smart charging stations (CSs) operated by individual aggregators. In such a scenario, we define the social welfare maximization problem as the total profit of both supply and demand sides in the form of a mixed integer non-linear programming (MINLP) model. Due to the intractability, we then propose an online decentralized algorithm with low complexity which utilizes effective heuristics to forward each EV to the most profitable CS in a smart manner. Results of simulations on the IEEE 37 bus distribution network verify that the proposed algorithm improves the social welfare by about 30% on average with respect to an alternative scheduling strategy under the equal participation of EVs in charging and discharging operations. Considering the best-case performance where only EV profit maximization is concerned, our solution also achieves upto 20% improvement in flatting the final electricity load. Furthermore, the results reveal the existence of an optimal number of CSs and an optimal vehicle-to-grid penetration threshold for which the overall profit can be maximized. Our findings serve as guidelines for V2G system designers in smart city scenarios to plan a cost-effective strategy for large-scale EVs distributed energy management.

关键词： Vehicle-to-grid Cascading style sheets Optimal scheduling Batteries Scheduling Electric vehicle-to-grid (V2G) distributed energy management mixed integer non-linear programming greedy-based algorithm smart cities

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Vehicle routing problem in a kanban controlled supply chain system considering cross-docking strategy

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OPERATIONAL RESEARCH 2020年第4期20卷 2397-2425页

作者： Hasani Goodarzi, Asefeh Zegordi, Seyed Hessameddin Tarbiat Modares Univ Fac Ind & Syst Engn POB 14115-143 Tehran Iran

This paper considers an inventory-routing problem in a distribution network in which kanban is used as a means to implement just-in-time strategy. There are a set of part suppliers and cross-docks in this network, which provide parts for an assembly plant. A novel mixed integer non-linear programming formulation is developed for the problem in which the optimum number of kanbans is determined considering inventory and transportation cost. In the other word, this research aims at reducing the level of inventory and transportation cost in a kanban controlled supply chain system, in which the number of kanbans is to be determined optimally considering demand from suppliers and geographical distribution of them. A fleet of vehicles is applied to transport kanbans from suppliers to the assembly plant via two transportation strategies: direct shipment and shipment through cross-docks (indirect shipment). In the second strategy, it is possible to have routes between suppliers. The proposed problem is NP-hard based on literature, thus a memetic algorithm is introduced to solve it. Solving several examples reveals that the solving method significantly outperforms GAMS/CPLEX in reducing objective value and computational time.

关键词： Just-in-time Kanban Cross-docking Direct shipment mixed integer non-linear programming

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