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5th international conference on the European Electricity Market (EEM 2008), vol.2

作者： Costas G. Baslis Anastasios G. Bakirtzis Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

this paper presents a mixed-integer optimization approach to the yearly hydrothermal scheduling problem. the proposed method is applied to a hydrothermal system comprising 29 thermal units and 13 hydroplants, including 2 pumped storage plants, similar to the Greek Power System. the generation scheduling model is based on an hourly load curve. Perfect competition assumption is adopted and all thermal generators are assumed to bid their marginal cost. A large mixed-integer programming problem is formulated and implemented in GAMS. Binary variables represent thermal unit hourly commitment status. Results on an hourly basis, including thermal unit commitment and dispatch, hydroplant generation and pumping and system marginal price are presented.

关键词： Hydrothermal scheduling Mixed-integer programming Perfect competition

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Multiobjective Branch-and-Cut Method with Shared Cutting Plane Information: A Case Study on Optimizing Garbage Collection

Multiobjective Branch-and-Cut Method with Shared Cutting Pla...

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international Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Tomoki Kaho Kazutoshi Sakakibara Mikiharu Arimura Shinya Watanabe The Graduate School of Engineering Muroran Institute of Technology Muroran Japan Department of Data Science Faculty of Information Engineering Toyama Prefectural University Toyama Japan Department of Engineering Muroran Institute of Technology Muroran Japan Department of Sciences and Informatics Muroran Institute of Technology Muroran Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

this paper presents an application of the Multi-Objective Branch-and-Bound based on Decomposition (MOBB/D) method for Branch-and-Cut, combining branch-and-bound and cutting-plane methods. Traditionally, multi-objective branch-and-bound methods for multi-objective mixed-integer linear programming problems struggle with the large number of Pareto-optimal solutions. MOBB/D addresses this by decomposing multi-objective problems into single-objective subproblems using weight vectors and applying branch-and-bound to each, with information sharing between nearby subproblems. In this paper, we customize the MOBB/D method for Branch-and-Cut to achieve an efficient search by sharing cutting plane information in MOBB/D. We demonstrate our approach with a case study on garbage collection planning in Muroran City, Hokkaido, Japan. this case study combines facility location and route optimization within a multi-objective framework, balancing the burdens on residents and garbage collection workers. Numerical experiments compare execution times and solutions with and without cutting plane sharing, and visualize the impact of different objective balance preferences on route optimization. the results show substantial efficiency gains and provide practical insights for multi-objective optimization in urban planning.

关键词： Visualization Scalability Roads Urban planning Vectors Real-time systems Planning Mixed integer linear programming Computational efficiency optimization

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Learning Heuristics Approaches in Mixed integer Linear Programing

Learning Heuristics Approaches in Mixed Integer Linear Progr...

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international conference on Computers, Communications, and Systems (ICCCS)

作者： Zhilei Wang Wentao Song Yang Lei Yu Wang Naijie Gu School of Computer Science and Technology University of Science and Technology of China Hefei China School of Data Science University of Science and Technology of China Hefei China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Mixed integer Linear programming (MILP) technology is a versatile method used for formulating and solving combinatorial optimization problems. During the solving process, primal heuristics help to quickly find good feasible solutions, which can aid branch-and-bound (B&B) algorithms in reducing the primal-dual gap and pruning the B&B search tree. the heuristics employed by existing open-source solvers follow empirical rules summarized from real-world instances, enabling the solvers to achieve generally good performance but sometimes performing poorly on specific instances. In response, this paper proposes a method that uses hierarchical sequence models to learn the patterns of given instances and assigns a heuristic configuration for them. Compared to the default settings of the solver, the proposed method enables heuristic module to perform better, finding feasible solutions more quickly and reducing the primal-dual gap. Experimental results indicate that the proposed method can improve the solver's average performance on given instances by 27%.

关键词： Communication systems Heuristic algorithms Reinforcement learning Manuals Mixed integer linear programming Task analysis optimization

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Scheduling for last-mile meal-delivery processes 9

Scheduling for last-mile meal-delivery processes

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9th IFAC/IFIP/IFORS/IISE/INFORMS conference on Manufacturing Modelling, Management and Control (IFAC MIM)

作者： Cosmi, Matteo Nicosia, Gaia Pacifici, Andrea Univ Roma Tre Dipartimento Ingn Via Vasca Navale 79 I-00146 Rome Italy Univ Roma Tor Vergata Dipartimento Ingn Civile & Ingn Informat Via Politecn 1 I-00133 Rome Italy

We address a single machine scheduling problem arising in a last mile delivery setting for a food company. the same problem finds obvious applications also in the context of internal manufacturing logistics. A set of food orders are placed by the customers and are to be fulfilled by the company. Each order comprises a delivery point and an ideal delivery time An order is considered on time if it is delivered within a certain given time interval around the ideal delivery time. All food is prepared in a single production facility (restaurant) and immediately carried to the customers by a single courier, who may dispatch one or two different orders in a single trip. Since late deliveries correspond to canceled orders and an economic loss for the company, it is of interest to schedule orders so that the number of late orders is minimized. We model the resulting decision problem as a special single machine scheduling problem and propose different mixed integer programs to solve it. their performance is assessed through a computational study on a set of test instances derived by our real-world application. (C) 2019, IFAC (international Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Scheduling integer programming optimization Problems combinatorial Mathematics Internal Logistics Food Delivery

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Optimal Application Deployment in Mobile Edge Computing Environment

Optimal Application Deployment in Mobile Edge Computing Envi...

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IEEE international conference on Cloud Computing, CLOUD

作者： Feifei Chen Jingwen Zhou Xiaoyu Xia Hai Jin Qiang He School of Information Technology Deakin University Geelong Australia School of Computer Science and Technology Huazhong University of Science and Technology China School of Computer Science Chengdu University of Information Technology China

ISBN: (数字)9781728187808

ISBN: (纸本)9781728187815

Mobile edge computing has emerged as a new distributed computing paradigm that overcomes the limitations of traditional cloud computing. In an edge computing environment, an app vendor can hire computing and storage resources on edge servers for deploying their applications to deliver lower-latency services to their app users. Under a budget constraint, an optimal edge application deployment strategy allows an app vendor to deploy application instances on edge servers in a specific area and provide services to the most app users in the area. In this paper, we make the first attempt to tackle this edge application deployment (EAD) problem. Specifically, we formulate the EAD problem as a constrained optimization problem and prove its NP-hardness. then, we propose an optimal approach named EAD-opt to find the optimal solution of EAD based on integer programming, and an approximation approach named EAD-apx to find approximate solutions in large-scale EAD scenarios efficiently. We evaluate our approaches by conducting experiments on a widely used real-world data set and a synthetic data set with comparison against two baseline approaches. the experimental results demonstrate that our approaches can solve the EAD problem effectively and efficiently.

关键词： integer programming Cloud computing conferences Servers Distributed computing optimization Edge computing

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Maximizing Profit in Energy-Efficient Moldable Task Execution with Deadline

Maximizing Profit in Energy-Efficient Moldable Task Executio...

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Euromicro conference on Parallel, Distributed and Network-Based Processing

作者： Sebastian Litzinger Jörg Keller Christoph Kessler Faculty of Mathematics and Computer Science FernUniversität in Hagen Hagen Germany Dept. of Computer and Information Science (IDA) Linköping University Linköping Sweden

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

We consider static scheduling of parallelizable tasks onto machines with frequency scaling for the case that not all tasks can be executed prior to a deadline. We model this scenario from a HPC cluster operator's perspective. We solve the combinatorial optimization problem to maximize the operator's profit by integer linear programming and by a heuristic. We evaluate the heuristic with synthetic benchmark task sets and demonstrate that it achieves at most 20 % less profit than the solution via linear programming, so that it can be used for large task sets where the latter is not feasible anymore.

关键词： Task analysis Schedules optimization Parallel machines Program processors Runtime Radio spectrum management

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Robustness of solutions to critical node detection problems with imperfect data: a computational study

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optimization MEthODS & SOFTWARE 2017年第2期32卷 250-273页

作者： Gillen, Colin P. Veremyev, Alexander Prokopyev, Oleg A. Pasiliao, Eduardo L. Univ Pittsburgh Ind Engn 1048 Benedum Hall Pittsburgh PA 15261 USA Univ Florida Ind & Syst Engn 303 Weil Hall Gainesville FL 32611 USA US Air Force Munit Directorate Res Lab Bldg 13 Eglin AFB FL 32542 USA

A class of critical node detection problems based upon the metric of communication efficiency is considered. While both exact integer programming and heuristic centrality-based methods exist for the solution of these problems, previous work has been mostly focused on the case where perfect information about the network is available. In this paper we suppose that some level of misinformation about nodes or edges has been inflicted on the observer's perception of the network, that is, there are hidden elements or fake additional elements. An extensive computational study is conducted to ascertain whether the exact integer-programming-based solutions perform better under imperfect information than heuristic methods. For large networks, exact methods cannot produce a solution in a reasonable amount of time, hence an approximation of the exact method is also considered for such instances. the obtained approximate solutions are again compared to centrality-based heuristics under the presence of imperfect data.

关键词： critical node detection distance-based metrics graph efficiency network interdiction mixed integer programming robustness sensitivity

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A Joint Order Cost optimization Model for Multi-Item Spare Parts Manufacturing Systems Considering the Requirement of Support Probability

A Joint Order Cost Optimization Model for Multi-Item Spare P...

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international conference on Automation and Computing (ICAC)

作者： Xinhao Cui Ruiyi Yang Siyue Zhang Yiyong Xiao School of Reliability and Systems Engineering Beihang University Beijing China

Reducing the comprehensive cost of spare parts is one of the main objectives of cost-optimized design for manufacturing systems (MSs). For multi-item MSs, there are usually more variables in the composition of spare parts costs, such as purchase and inventory costs. Besides, the coupling and interaction between these variables have an impact on the final spare parts cost, forming a more complex combinatorial optimization problem. Moreover, the ordering activities of spare parts are scattered in the time window between each period, and the order quantity and timing can significantly impact the cost. To solve this problem, this paper proposes a joint ordering cost optimization model that considers the requirement of support probability. In this paper, a mixed integer linear programming model (MILP) is established for the specific problem. Finally, numerical examples are synthesized based on actual records to verify the feasibility and rationality of the model. the results also show that the proposed model significantly reduces the total spare parts costs of MSs.

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Model and combinatorial optimization Methods for Tactical Planning in Closed-Loop Supply Chains

Model and Combinatorial Optimization Methods for Tactical Pl...

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international conference on Tools with Artificial Intelligence

作者： Pierre Desport Anne Liret Frederic Lardeux Carla Di Cairano-Gilfedder David Lesaint Gilbert Owusu LERIA Univ. d'Angers Angers France BT France France BT Group plc Ipswich UK

ISBN: (纸本)9781509044603

Distribution planning in closed-loop supply chains is concerned with determining transfer and repair operations based on demand forecasts and subject to backordering, inventory, transfer and repair constraints. We present a mixed-integer programming model and a dedicated metaheuristics for this problem and show it is is NP-hard. the model is applicable to a wide range of closed-loop supply chains with different network topologies and site functions and it can also support different planning strategies by means of a weighted objective function. Comparative experiments on pseudo-random instances built on a case study in telecommunication service operations demonstrate the effectiveness and scalability of the metaheuristics. Lastly, we discuss possible extensions to address common supply chain requirements, including the ability to produce robust plans in uncertain environments.

关键词： Maintenance engineering Planning Supply chains Electronic mail Topology Linear programming

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A dynamical constraint-generating algorithm for the Single-Vehicle Cyclic Inventory-Routing Problem

A dynamical constraint-generating algorithm for the Single-V...

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16th IFAC Symposium on Information Control Problems in Manufacturing (INCOM)

作者： Lefever, Wouter Aghezzaf, El-Houssaine Hadj-Hamou, Khaled Univ Ghent Dept Ind Syst Engn & Prod Design Technol Pk 903 B-9052 Zwijnaarde Belgium Univ Lyon INSA Lyon DISP F-69621 Villeurbanne France

the Single-Vehicle Cyclic Inventory-Routing Problem (SVCIRP) is a variant of the Inventory-Routing Problem (IRP) in which the replenishment decisions of a recurring distribution plan are optimized. In this paper, we investigate the current formulation of the SVCIRP and propose a number of improvements for it. First, we introduce a new binary variable in the model to distinguish tours in which only one retailer is visited. Additionally, we formulate three new sets of valid inequalities. Because one of the sets contains an exponential number of inequalities, we develop a procedure to insert only the violated inequalities. We present an algorithm, based on this procedure, to solve the SVCIRP effectively. Our computational results show that our algorithm outperforms the other exact methods in literature. We obtain 25 new best bounds and we find 13 improved solutions. (C) 2018, IFAC (international Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： combinatorial mathematics Mathematical models integer programming Convex optimization Bounding method

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