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An Efficient Heuristic for Pooled Repair Shop Designs 7th

An Efficient Heuristic for Pooled Repair Shop Designs

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7th international conference on Operations Research and Enterprise Systems, ICORES 2018

作者： Turan, Hasan Hüseyin Pokharel, Shaligram ElMekkawy, Tarek Y. Sleptchenko, Andrei Al-Khatib, Maryam Capability Systems Centre School of Engineering and Information Technology University of New South Wales Canberra Australia Department of Mechanical and Industrial Engineering College of Engineering Qatar University Doha Qatar Department of Industrial and Systems Engineering Khalifa University of Science and Technology Abu Dhabi United Arab Emirates

ISBN: (纸本)9783030160340

An effective spare part supply system planning is essential to achieve a high capital asset availability. We investigate the design problem of a repair shop in a single echelon repairable multi-item spare parts supply system. the repair shop usually consists of several servers with different skill sets. Once a failure occurs in the system, the failed part is queued to be served by a suitable server that has the required skill. We model the repair shop as a collection of independent sub-systems, where each sub-system is responsible for repairing certain types of failed parts. the procedure of partitioning a repair shop into sub-systems is known as pooling, and the repair shop formed by the union of independent sub-systems is called a pooled repair shop. Identifying the best partition is a challenging combinatorial optimization problem. In this direction, we formulate the problem as a stochastic nonlinear integer programming model and propose a sequential solution heuristic to find the best-pooled design by considering inventory allocation and capacity level designation of the repair shop. We conduct numerical experiments to quantify the value of the pooled repair shop designs. Our analysis shows that pooled designs can yield cost reductions by 25% to 45% compared to full flexible and dedicated designs. the proposed heuristic also achieves a lower average total system cost than that generated by a Genetic Algorithm (GA)-based solution algorithm. © 2019, Springer Nature Switzerland AG.

关键词： combinatorial optimization

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Supplier Selection by Multi-Attribute combinatorial Bidding

Supplier Selection by Multi-Attribute Combinatorial Bidding

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Hawaii international conference on System Sciences

作者： Joice Cavalheiro Ribeiro Giacon Hugo T. Yoshida Yoshizaki Gilberto Montibeller Universidade de Sao Paulo London School of Economics and Political Science

ISBN: (纸本)9781479925056

Supplier selection is a challenging decision that has strategic importance for organizations. Cost is no longer the sole factor in the selection of suppliers, and the complexity of this issue arises from the interplay of several situation-specific criteria (such as total cost, CO_(2e) emissions, development time, lead time) as well as the combinatorial nature of this problem. this paper proposes an approach based on combinatorial optimization (integer linear programming) combined with multi-criteria value analysis to establish priorities and trade-offs among the defined criteria for combinatorial bidding. the approach was employed in a real-world decision, the selection of a supplier for a cosmetics packaging set for a new product line. the obtained solution is compared against standard multi-criteria optimization (without a combinatorial auction formulation) and also against single criterion optimization. the paper also reports on the challenges and advantages of applying the framework in the case study.

关键词： Mathematical model Equations optimization Analytical models Companies Adaptation models Computational modeling

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optimization of Static Patient Admission Scheduling using the Variable Neighborhood Search Method 7

Optimization of Static Patient Admission Scheduling using th...

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7th Information Systems international conference, ISICO 2023

作者： Muklason, Ahmad Elbert, Varian Premananda, I. Gusti Agung Riksakomara, Edwin Vinarti, Retno Aulia Djunaidy, Arif Jl Raya ITS Kampus ITS Sukolilo Surabaya East Java60111 Indonesia

Planning and resource management are important aspects of a company's operational sustainability. With good management, companies can achieve their targets while minimizing operational costs. the same goes for hospitals. Various challenges related to resources are experienced by hospitals, such as scheduling nurses, patient surgeries, and patient appointments. therefore, this paper aim at optimizing patient admission scheduling in order to improve hospital resource efficiency. To be more specific, patient admission scheduling, also known as the Patient Admission Scheduling Problem (PASP), is a scheduling problem that considers patient preferences and needs, bed availability, resource efficiency, and utilization. this problem is highly relevant, especially for large hospitals. the number of rooms, specialists, and facilities makes manual scheduling extremely difficult. It is due to the varied preferences, needs, and lengths of stay for patients in hospital. Various methods, both heuristic and exact, have been proposed, including the use of integer programming methods. However, for a large search space, this method requires a very long computational time. To address the PASP, this study applies the Variable Neighborhood Search (VNS) algorithm and random selection as optimization algorithms. the method was chosen because it has been proven effective to solve some combinatorial optimization problems in prior studies. Seven types of neighborhoods are implemented to find the best combinations in optimizing the PASP. the results show that the VNS algorithm outperforms the random selection algorithm, as it is able to generate 5 out of 7 solutions that are better, reducing penalties by 27.84% to 55.29%. the expected impact of this study is to increase the hospital patient satisfaction whereas in the same time minimize the operational cost. © 2023 the Authors. Published by Elsevier B.V.

关键词： Hospitals

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Cross-entropy method for K-best dependent-target data association hypothesis selection

Cross-entropy method for K-best dependent-target data associ...

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international conference on Information Fusion

作者： Shozo Mori Chee Chong Advanced Information Technologies BAE Systems Los Altos CA USA

this paper is concerned with probabilistic evaluation of multiple-frame data association hypotheses in multiple-target tracking problems, in particular, when targets are not necessarily independent a priori. Multiple-target tracking problems with dependent targets naturally arise whenever targets interact with each other, as they move in congested traffic, or as they actively coordinate their movements in other situations. this paper develops a Bayesian data association hypothesis evaluation formula for dependent targets. Because the resulting formula does not have a multiplicative or log-linear form, the best hypothesis cannot be selected by integer linear programming or multi-dimensional assignment algorithms commonly used to solve data association problems in multiple target tracking. Instead, we propose to use Reuven Rubinstein's cross-entropy method as a possible solution. A K-best hypothesis selection extension will be discussed as an application of the generalized Murty's algorithm. this paper focuses on the theoretical aspects as the first step of a solution concept development.

关键词： Target tracking Joints Uncertainty Approximation algorithms optimization

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Optimizing port yard storage at Dar es Salaam Port: a hybrid approach combining mixed-integer linear programming and genetic algorithms

Optimizing port yard storage at Dar es Salaam Port: a hybrid...

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13th international conference on Transportation and Traffic Engineering (ICTTE 2024)

作者： Majid Mohammed Kunambi Hongxing Zheng College of Transport Engineering Dalian Maritime University Dalian People's Republic of China

ISBN: (数字)9781837242931

Introduction: Efficient port yard storage is a critical challenge at Dar es Salaam Port, where limited space, diverse cargo types, and strict regulatory requirements complicate operations. Traditional optimization methods often fail to address these complexities adequately. this study presents a hybrid approach that combines Mixed-integer Linear programming (MILP) with Genetic Algorithms (GA) to optimize storage operations, aiming to improve efficiency and adaptability. Methods: the methodology involves a two-phase optimization strategy. In the first phase, a robust MILP model is developed using the PuLP library in Python to determine optimal cargo allocations based on volume, compatibility, and equipment constraints. the second phase refines these allocations using a Genetic Algorithm, facilitated by the DEAP library, to explore and optimize solutions further. this hybrid approach is designed to handle the complexities of real-world port operations by leveraging the strengths of both MILP and GA. Results: the MILP model successfully allocated cargos to storage locations, optimizing space utilization and reducing handling costs. the largest storage area, Location L5, was utilized up to 95% of its capacity, reflecting an efficient allocation strategy. the Genetic Algorithm further enhanced the initial solution, increasing overall utility by 3% and providing multiple near-optimal configurations. Sensitivity analysis revealed the system's resilience to changes in cargo volumes and handling times. Discussion: the integration of MILP and GA proved effective in optimizing port yard storage, providing both high operational efficiency and strategic flexibility. the study's findings indicate that continuous optimization and infrastructure expansion, alongside technological integration, can significantly improve port operations. the results also underscore the importance of dynamic modeling for future enhancements. Conclusion: this hybrid optimization approach combining MILP

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Disjoint cycles: Integrality gap, hardness, and approximation

Disjoint cycles: Integrality gap, hardness, and approximatio...

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11th international IPCO conference on integer programming and combinatorial optimization

作者： Salavatipour, Mohammad R. Verstraete, Jacques Department of Computing Science University of Alberta Edmonton Alta. T6G 2E8 Canada Department of Combinatorics and Optimization University of Waterloo Waterloo Ont. N2L 3G1 Canada

In the edge-disjoint cycle packing problem we are given a graph G and we have to find a largest set of edge-disjoint cycles in G. the problem of packing vertex-disjoint cycles in G is defined similarly. the best approximation algorithms for edge-disjoint cycle packing are due to Krivelevich et al. [16], where they give an O(√log n)-approximation for undirected graphs and an O(√n)-approximation for directed graphs. they also conjecture that the problem in directed case has an integrality gap of Ω(√n). No non-trivial lower bound is known for the integrality gap of this problem. Here we show that both problems of packing edge-disjoint and packing vertex-disjoint cycles in a directed graph have an integrality gap of Ω(log n/log log n). this is the first super constant lower bound for the integrality gap of these problems. We also prove that both problems are quasi-NP-hard to approximate within a factor of Ω(log1-Ε n), for any Ε > 0. For the problem of packing vertex-disjoint cycles, we give the first approximation algorithms with ratios O(log n) (for undirected graphs) and O(√n) (for directed graphs). Our algorithms work for the more general case where we have a capacity cv on every vertex v and we are seeking a largest set C of cycles such that at most cv cycles of C contain v. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Graph theory

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Test wrapper algorithm for scan chain balance based on entropy increase hybrid differential evolution

Test wrapper algorithm for scan chain balance based on entro...

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international conference on Electronic Measurement and Instruments, ICEMI

作者： Deng Libao Zhang Baoquan Wang Sha Qiao Liyan School of Information and Electrical Engineering Harbin Institute of Technology at Weihai Weihai China Dept. of Automatic Test and Control Harbin Institute of Technology Harbin China

A well cores reused-based wrapper design is an important approach to minimize SOC test application time and test costs. the combinatorial optimization problem of core wrapper design has been proven to be a NP-hard problem. In this paper, a wrapper scan chain balance algorithm with entropy increase hybrid discrete differential evolution (EIHDE) is proposed to solve the core wrapper problem, which is inspired by thermodynamic system principle of entropy increase and outstanding global searching ability of Differential Evolution (DE). the proposed approach develops a cooperative mutation strategy based on entropy increase for the problem to preserving its interesting search mechanism for discrete domains. In the proposed model, two cooperative encode modes of individuals are introduced for standard differential mutation and the cooperative entropy increase mutation: integer encode mode and binary encode mode. EIHDE controls the search space by differential mutation, and search for superior individual in local space by entropy increase mutation. the combination of two kinds of mutation operations promotes the optimization ability considerably and achieves a better tradeoff between exploitation and exploration. the experimental results of the ITC'02 SOC test benchmarks show that EIHDE can achieve more balanced results compared with other algorithms.

关键词： IP networks Algorithm design and analysis Entropy optimization Sociology Statistics Binary codes

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optimization in the construction of nearly cardinal and nearly symmetric wavelets

Optimization in the construction of nearly cardinal and near...

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international conference on Sampling theory and Applications (SampTA)

作者： Neil D. Dizon Jeffrey A. Hogan Joseph D. Lakey School of Maths and Phys Sci University of Newcastle Callaghan NSW Australia Department of Mathematical Sciences New Mexico State University Las Cruces NM USA

ISBN: (数字)9781728137414

ISBN: (纸本)9781728137421

We present two approaches to the construction of scaling functions and wavelets that generate nearly cardinal and nearly symmetric wavelets on the line. the first approach casts wavelet construction as an optimization problem by imposing constraints on the integer samples of the scaling function and its associated wavelet and with an objective function that minimizes deviation from cardinality or symmetry. the second method is an extension of the feasibility approach by Franklin, Hogan, and Tam to allow for symmetry by considering variables generated from uniform samples of the quadrature mirror filter, and is solved via the Douglas-Rachford algorithm.

关键词： Multiresolution analysis optimization Wavelet transforms Linear programming Mirrors

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Performance Comparison of Unbounded Knapsack Problem Formulations 7

Performance Comparison of Unbounded Knapsack Problem Formula...

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7th international conference on Computational Linguistics and Intelligent Systems. Volume III: Intelligent Systems Workshop, CoLInS 2023

作者： Pichugina, Oksana Matsiy, Olha Skob, Yuriy University of Toronto 27 King's College Circle TorontoM5S 1A1 Canada National Aerospace University "Kharkiv Aviation Institute" 17 Chkalova Street Kharkiv61070 Ukraine V. N. Karazin Kharkiv National University 4 Svobody Sq. Kharkiv61022 Ukraine

this study investigates various linear discrete formulations of the unbounded knapsack problem, including binary and bounded forms. Two variations of binary knapsack problem reformulations are examined, each with distinct linear constraints. Gurobi and CPLEX solvers are used to compare the performance of these models. the computational experiments are conducted on randomly generated instances of sizes 10-100. the results revealed that, on average, Gurobi was 20% faster than CPLEX. integer, bounded, and 0/1 knapsack problem formulations had comparable mean run times. Incorporating specific constraints in the 0/1 formulation yielded superior results compared to the basic 0/1 knapsack problem model. Overall, this research contributes to understanding efficient and effective formulations for solving the unbounded knapsack problem and choosing a better solver. © 2023 Copyright for this paper by its authors.

关键词： combinatorial optimization

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Mathematical and economic extrapolations on the model for optimal microgrid operation and its solution

Mathematical and economic extrapolations on the model for op...

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international conference on Electrical Machines, Drives and Power Systems (ELMA)

作者： Vesselina Trashlieva Electric Power Systems Department Technical University of Sofia Sofia Bulgaria

Mixed-integer linear programming problems represent a large group of models that are widely constructed for optimization problems in Electric Power Systems Operation. An economical and financial analysis is obligatory for the obtained solution as being a part of the effective system planning and operation. In this paper a formal approach to such analysis is given having a combinatorial problem with binary variables designing the different states in the microgrid. the numerical example includes a detailed post-solution analysis made with Matlab® and LPSolve.

关键词： Microgrids optimization Load modeling Sensitivity analysis Upper bound Production

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