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检索条件"任意字段=13th International Conference on Integer Programming and Combinatorial Optimization"

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A parallel Branch and Bound algorithm for solving large scale integer multiobjective problems

A parallel Branch and Bound algorithm for solving large scal...

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the international conference on Informatics and Systems (INFOS)

作者： Mahmoud M. Ismail Waiel F. Abd El-Wahed Osama abd el-raoof Faculty of Computers and Information Systems Operations Research Department Zagazig Egypt Operations Research Decision Support Faculty of Computers and Information Menoufia University Egypt

ISBN: (纸本)9781424458288;9789774033964

the multi-objective integer programming problems in large scale are considered time consuming. In the past, mathematical structures were used that can get benefits of high processing powers and parallel processing. the Branch and Bound (B&B) algorithm is one of the most used methods to solve combinatorial optimization problems. A general approach to generate all non-dominated solutions of the multi-objective integer programming (MOIP) Problem is developed. In this paper, a supervisor-master-sub-master-worker algorithm to solve large scale integer multi-objective problems that can get the benefits of mathematical structures and high processing powers has been proposed. this approach addresses several issues related to the characteristics of the algorithm itself and the properties of parallel computing systems. From the solved benchmark example this algorithm proved to provide a considerable high performance. Results show that a consistently better efficiency can be achieved in solving integer equations, providing reduction of time.

关键词： Large-scale systems Linear programming Concurrent computing Parallel processing Operations research Distributed computing optimization methods Mathematical programming Information systems Equations

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Cost optimization for the Allocation, Production, and Distribution of a Plastic Manufacturing Company Using integer Linear programming

Cost Optimization for the Allocation, Production, and Distri...

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international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Alezander Mikhail O. Galindo Elmer P. Dadios Robert Kerwin C. Billones Ira C. Valenzuela De La Salle University Manila Philippines

ISBN: (纸本)9781665401685

Optimizing the allocation of raw materials, production of raw materials to finished products, and the distribution of finished products can be quite difficult especially if the network of suppliers, production plants, and customers is vast. Linear programming (LP) can be utilized to ensure optimality. Specifically, integer linear programming (ILP) is used in this paper. An ILP model was built to determine from which suppliers and how many raw materials from these suppliers should be acquired, how many raw materials each production plant of the company should acquire, how many of the finished goods each production plant should produce, and from which production plants the finished products should be distributed to the customers. the aim of the model is to minimize total cost incurred which is composed of raw material purchase costs, production costs, and transportation costs. Data such as raw material supply, raw material costs, raw material requirements, production capabilities, production costs, raw material and finished product conversions, customer demand, and transportation costs were gathered from the plastic manufacturing company. the software MATLAB was used to determine the optimal solution. It was then determined that the total cost would be minimized from ₱23,615,400 to ₱21,940,000. through the use of ILP, the total cost was reduced by ₱1,675,400 or 7.09%. (Conversion rate: ₱50 ~ ${\$}$1).

关键词： Costs Transportation Production Companies integer linear programming Linear programming Raw materials

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A simulation-based benders' cuts generation for the joint workstation, workload and buffer allocation problem

A simulation-based benders' cuts generation for the joint wo...

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IEEE international conference on Automation Science and Engineering (CASE)

作者： Mengyi Zhang Andrea Matta Arianna Alfieri Giulia Pedrielli Department of Mechanical Engineering Politecnico di Milano Milan ITALY Department of Management Engineering Politecnico di Torino Turin ITALY School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA

the Discrete Event optimization (DEO) framework was recently proposed to formulate the simulation-optimization model of the Joint Workstation, Workload and Buffer Allocation Problem (JWWBAP) of the open flow line. However, the computational effort to solve the DEO model at optimality is quite high, because it is a mixed integer linear programming model. this work proposes a simulation cutting approach to efficiently solve the DEO model of the JWWBAP. Specifically, the DEO model is decomposed into an optimization model and a simulation model, which are the master problem and the subproblem in Benders decomposition, respectively. the optimization model is solved to find a system configuration, and the simulation model is solved to add cuts to the optimization model. An algorithm is proposed to generate cut using the simulation trajectory. Numerical analysis shows that the exact DEO model can be solved efficiently.

关键词： Workstations optimization Resource management Mathematical model Computational modeling Numerical models

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Research on Ship Crew Scheduling Algorithm Based on Improved Grey Wolf optimization

Research on Ship Crew Scheduling Algorithm Based on Improved...

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Information Technology,Big Data and Artificial Intelligence (ICIBA), international conference on IEEE international conference on

作者： Lin Tang Wenwei Ying Meng Huang Yu Zou Unit 91977 of the PLA Beijing China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

Crew scheduling in civilian ships is a combinatorial optimization problem with various constraints. Traditional methods struggle with large-scale scheduling, while classic algorithms often fail to balance performance and practicality. this paper presents an Improved Grey Wolf optimization (IGWO) algorithm. We establish a mixed-integer programming model that considers hard and soft constraints to maximize crew-position matching. the IGWO algorithm enhances the standard Grey Wolf optimization (GWO) framework by integrating strategies like heuristic initialization, adaptive encirclement search, elite leader selection, and constraint violation repair. Simulation on random and real-world datasets show that IGWO outperforms manual methods, rule-based heuristics, and the standard GWO algorithm in objective function value, constraint satisfaction, and employee satisfaction. this research advances heuristic optimization in combinatorial problems.

关键词： Technological innovation Heuristic algorithms Manuals programming Maintenance engineering Dynamic scheduling Marine vehicles optimization Standards Tuning

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Data-based predictive optimization for by product gas system in steel industry

Data-based predictive optimization for by product gas system...

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IEEE international conference on Automation Science and Engineering (CASE)

作者： Jun Zhao Chunyang Sheng Wei Wang Witold Pedrycz Quanli Liu School of Control Science and Engineering Dalian University of Technology Dalian China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

In light of significant complexity of the byproduct gas system in steel industry (which limits an ability to establish its physics-based model), this study proposes a data-based predictive optimization (DPO) method to carry out real-time adjusting for the gas system. Two stages of the method, namely the prediction modeling and real-time optimization, are involved. At the prediction stage, the states of the optimized objectives, the consumption of the outsourcing natural gas and oil, the power generation and the tank levels, are forecasted based on a proposed mixed Gaussian kernel-based prediction intervals (PIs) construction model. the Jacobian matrix of this model is represented by a kernel matrix through derivation, which greatly facilitates the subsequent calculation. At the second stage, a rolling optimization based on a mathematical programming technique involving continuous and integer decision-making variables is developed via the prediction intervals.

关键词： optimization Jacobian matrices Predictive models Real-time systems Kernel Job shop scheduling Computational modeling

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Data fitting using solutions of differential equations: Fractional-order model versus integer-order model

Data fitting using solutions of differential equations: Frac...

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international Carpathian Control conference (ICCC)

作者： Tomas Skovranek Igor Podlubny Ivo Petras Dagmar Bednarova Institute of Control and Informatization of Production Processes BERG Faculty Technical University of Kosice Kosice Slovakia

the present paper deals with using different mathematical tools and approaches for the data fitting. A comparison of two types of mathematical models, which solutions are used to fit experimental data is done, where the error-of-fit and the computation time are taken as the fitting benchmarks. Both types of the defined models consist of differential equations, one uses fractional, the other integer orders of differentiation. the first advantage of the fractional-order models is the fact that fractional-order differential equation (FDE) has one degree of freedom more (the order of differentiation lies in the interval (0,1)), whereas the integer-order differential equation has the order of differentiation constant, equal to 1. the other advantage besides the “freedom in order” is that FDEs provide a powerful instrument for description of memory and hereditary properties of systems in comparison to the integer-order models, where such effects are neglected or difficult to incorporate. the first aspect of this work consist in choosing a suitable optimization method for finding the parameters of the defined models based on minimizing chosen fitting criterion. the Medium-scale optimization, using the tools of sequential quadratic programming, Quasi-Newton and line-search algorithms (performed by the MATLAB function fmincon) is compared with the evolutionary - genetic algorithm (performed by the MATLAB function ga). the second aspect lies in using two different approaches in the formulation of the optimization criterion. For the time domain identification the classical least squares method (LSM) and so the sum of vertical offsets will be used. the state-space identification will use the total least squares method (TLSM or the so-called orthogonal distance fitting criterion - ODF), which uses the sum of orthogonal (perpendicular) distances between the experimental points and the fitting curve. Several examples are presented in the form of figures. the efficiency of computat

关键词： Mathematical model Data models Computational modeling optimization Time domain analysis Genetic algorithms Differential equations

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Development of threshold Algorithms for a Location Problem with Elastic Demand 11th

Development of Threshold Algorithms for a Location Problem w...

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11th international conference on Large-Scale Scientific Computations (LSSC)

作者： Levanova, Tatyana Gnusarev, Alexander Sobolev Inst Math Omsk Branch Pevtsova Str 13 Omsk 644043 Russia Dostoevsky Omsk State Univ Prospekt Mira 55A Omsk 644077 Russia

ISBN: (纸本)9783319734415;9783319734408

this work is devoted to development of threshold algorithms for one static probabilistic competitive facility location and design problem in the following formulation. New Company plans to enter the market and to locate new facilities with different design scenarios. Clients of each point choose to use the facilities of Company or its competitors depending on their attractiveness and distance. the aim of the new Company is to capture the greatest number of customers thus serving the largest share of the demand. this share for the Company is elastic and depends on clients' decisions. We offer three types of threshold algorithms: Simulated annealing, threshold improvement and Iterative improvement. Experimental tuning of parameters of algorithms was carried out. A comparative analysis of the algorithms, depending on the nature and value of the threshold on special test examples up to 300 locations is carried out. the results of numerical experiments are discussed.

关键词： Discrete optimization integer programming Location problems threshold algorithms Simulated annealing

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Review of Quantum(-Inspired) optimization Methods for System Reliability Problems 16

Review of Quantum(-Inspired) Optimization Methods for System...

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16th international conference on Probabilistic Safety Assessment and Management, PSAM 2022

作者： Araújo, Lavínia Maria Mendes Lins, Isis Didier Figueroa, Diego Andres Aichele Maior, Caio Bezerra Souto das Chagas Moura, Márcio Droguett, Enrique López Universidade Federal de Pernambuco Recife Brazil Technology Center Universidade Federal de Pernambuco Caruaru Brazil University of California Los Angeles United States

Many industrial systems demand equipment with high levels of reliability. Companies and academia have been developing, over the years, mathematical methods and advancing engineering techniques to assist in the maintenance of active and reliable systems. there are different optimization problems in this context, highlighting (1) the redundancy allocation problem (RAP), (2) the reliability allocation problem, and (3) the reliability-redundancy allocation problem (RRAP). Many solving methods have already been applied to these problems, e.g., dynamic, linear, integer, and nonlinear programming, as well as classical metaheuristics based on evolutionary algorithms, such as the Genetic Algorithm (GA). Either way, these methods are modeled according to the specificities of the systems. However, these approaches can be very computationally expensive depending on the problem instances. Meanwhile, quantum computing has gained ground for combinatorial optimization problems. It is expected that problems with a high level of complexity can be solved more efficiently using these new methods than classical ones. optimization methods have attempted to improve their efficiency by adding quantum concepts, as is the case of Quantum-inspired Evolutionary Algorithms (QEA). the QEA has a better diversity and convergence rate than other EAs because it uses qubit representation instead of numerical, binary, or symbolic representations. In this context, this paper aims to develop a systematic review of the literature through keyword filtering, article reading, and bibliometric analysis on applying purely quantum and quantum-inspired methods in system reliability optimization problems, specifically in RAP, the reliability allocation problem, and the RRAP. Our goal is to identify quantum-based techniques' advantages, limitations, and potential in such a context and suggest a research plan based on the observed literature gaps. We observed few studies in the context, especially in classical qua

关键词： Nonlinear programming

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optimization model for a microgrid with fuel cell vehicles

Optimization model for a microgrid with fuel cell vehicles

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IEEE international conference on Networking, Sensing and Control

作者： Kazuhiko Shinoda Esther Park Lee Masaru Nakano Zofia Lukszo Faculty of System Design and Management Keio University Kanagawa Japan Faculty of Technology University of Technology Delft The Netherlands

ISBN: (纸本)9781467399760

Vehicle-to-Grid and a microgrid are emerging concepts which are expected to replace the conventional energy and transportation systems with more efficient and flexible ones. there have been research on the integration of these technologies, but a microgrid with fuel cell vehicles (FCVs) have hardly been studied at the moment in spite of the several technical advantages of FCVs as generating units. this paper therefore presents the mathematical model of a microgrid which includes FCVs, on-site hydrogen stations, solar photovoltaic systems and a wind turbine. the optimal scheduling of hydrogen production, hydrogen refueling to FCVs and electricity supply from FCVs which minimizes the power imported from the main grid is obtained by solving a mixed integer linear programming problem. the computation results of a test case indicate that the model can be used for identifying a bottleneck in the energy flow of a microgrid. the presented model can be extended by including important factors to consider for further research on the integration of microgrids and FCVs.

关键词： Microgrids Hydrogen optimization Mathematical model Wind turbines Production Vehicles

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Search strategies for hybrid search spaces

Search strategies for hybrid search spaces

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international conference on Tools for Artificial Intelligence (ICTAI)

作者： C. Gomes B. Selman Department of Computer Science Cornell University Ithaca NY USA

Recently, there has been much interest in enhancing purely combinatorial formalisms with numerical information. For example, planning formalisms can be enriched by taking resource constraints and probabilistic information into account. the mixed integer programming (MIP) paradigm from operations research provides a natural tool for solving optimization problems that combine such numeric and non-numeric information. the MIP approach relies heavily on linear program relaxations and branch-and-bound search. this is in contrast with depth-first or iterative deepening strategies generally used in AI. We provide a detailed characterization of the structure of the underlying search spaces as explored by these search strategies. Our analysis indicates that the traditional approach of identifying dominating search strategies for a given problem domain is inadequate. We show that much can be gained from combining search strategies for solving hard MIP problems, thereby leveraging the strength of different search strategies regarding both the combinatorial and numeric components of the problem.

关键词： Artificial intelligence Linear programming Read only memory Constraint optimization Computer science Vehicles combinatorial mathematics Operations research Ear Relaxation methods

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