Article
Management
Xuwei Qin, Zhong-Zhong Jiang, Minghe Sun, Liang Tang, Xiaoran Liu
Summary: This study proposes an adaptive multi-phase basestock policy for managing repairable spare parts provisioning to support performance-based service under multiregional fleet expansions. By considering nonstationary demands and profit-centric model, a greedy algorithm is developed to solve the problem effectively.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Yan Zhao, Qianwang Deng, Like Zhang, Weifeng Han, Fengyuan Li
Summary: Integrated scheduling of production and distribution is important for cost saving and customer satisfaction. This paper proposes an optimal spare parts production scheduling model in a flowshop environment, with the objective of maximizing equipment operational utility on the customer side and introducing a speed adjustment strategy to guide equipment operation and maintenance.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Mustafa Hekimoglu, A. Gurhan Kok, Mustafa Sahin
Summary: This study develops an advanced stockout risk estimation system for repairable spare parts, using statistical data to estimate future stockout risks, and proposes a repairable inventory control system including repair expediting, inspection, and condemnation processes. The suggested method outperforms heuristic approaches in empirical tests, achieving high accuracy rates and suggesting savings of up to 8%.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Review
Engineering, Industrial
Alessandra Cantini, Mirco Peron, Filippo De Carlo, Fabio Sgarbossa
Summary: Configuring supply chains is crucial for the success of spare parts retailers. This paper introduces a new methodology called SP-LACE, which reviews the configuration of spare parts supply chains and evaluates their economic benefits. The results indicate that SP-LACE provides economic benefits and ensures high service levels, overcoming the limitations of existing literature methodology.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Mathematics
Gia-Shie Liu, Kuo-Ping Lin
Summary: This study proposes an availability optimization decision support design system for repairable n-stage mixed systems. The system employs different combinations of subsystems, such as parallel, standby, and k-out-of-q, connected in series configuration. Several optimization methods are proposed to determine the appropriate system configuration design and minimize the total system cost. The results show that the proposed system can effectively save cost and meet high-level availability requirements compared to single component series systems.
Article
Computer Science, Interdisciplinary Applications
Saba Sareminia, Fatemeh Amini
Summary: Reliable prediction of spare parts demand is a major challenge for the manufacturing industry and equipment owners. This study focuses on predicting slow-moving spare parts by developing a reliable and ensemble data mining approach. The results show that this approach significantly increases the reliability of spare parts inventory.
COMPUTERS IN INDUSTRY
(2023)
Article
Engineering, Industrial
Jian-Xun Zhang, Dang-Bo Du, Xiao-Sheng Si, Chang-Hua Hu, Han-Wen Zhang
Summary: Standby redundancy technique is widely used to enhance system reliability. The study proposes an iterative method for lifetime estimation of standby systems with mixed spare-part degradation process, and establishes a joint optimization model to minimize costs.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Mathematics
Jae-Dong Kim, Tae-Hyeong Kim, Sung Won Han
Summary: An advanced model was developed to accurately forecast the demand for spare parts in military logistics. The results showed that selecting suitable methods could enhance the performance of forecasting models in this domain.
Article
Operations Research & Management Science
Bakhtiar Ostadi, Ramtin Hamedankhah
Summary: The study introduces a two-stage reliability optimization method for redundancy allocation considering sale of worn-out parts, aiming to address the series-parallel redundancy allocation problem. Part of the budget is allocated to maximize system reliability upon launch, while the rest is used for replacement of parts.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Computer Science, Hardware & Architecture
Wang Yu, Guo Linhan, Wen Meilin, Kang Rui
Summary: This article introduces a new definition of availability based on uncertainty theory, called belief availability, to address the epistemic uncertainty in system availability evaluation. By studying the belief availability model, formulas for several belief availability metrics are derived, and a case study on the oxygen generation system is conducted to analyze the impact of belief availability on system availability. The results demonstrate the potential application of belief availability in tradeoff between availability-related parameters and its effectiveness in reducing evaluation deviation with insufficient data.
IEEE TRANSACTIONS ON RELIABILITY
(2021)
Article
Engineering, Industrial
Mohammad Farhadi, Mahmoud Shahrokhi, Seyed Habib A. Rahmati
Summary: Spare parts management is crucial in industrial systems to reduce downtime and minimize costs, and this study proposes a model for determining the optimal number, supplier, and quality of spare parts.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Engineering, Industrial
Alessandra Cantini, Mirco Peron, Filippo De Carlo, Fabio Sgarbossa
Summary: This paper aims to assist managers and practitioners in determining how to design their spare parts supply chains and adopt the appropriate manufacturing technology through the development of a decision support system. The authors propose a user-friendly decision tree that allows comparison of total costs between different levels of centralization and between additive manufacturing and conventional manufacturing for spare parts.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Engineering, Aerospace
C. Malyemez, O. F. Baykoc
Summary: This study examines the commonly used problems of Spare Parts Allocation (SPA) and Level of Repair Analysis (LORA) within Performance Based Logistics (PBL). A comprehensive multi-objective simulation-optimization model is developed for a military aircraft operations case study, resulting in significantly better results in terms of cost and flight hours compared to the SPA optimization approach alone. The proposed model offers a profit-centric approach and serves as an efficient decision support tool for both customers and suppliers in complex logistics support activities.
AERONAUTICAL JOURNAL
(2022)
Article
Computer Science, Interdisciplinary Applications
Leonie M. Johannsmann, Emily M. Craparo, Thor L. Dieken, Armin R. Fugenschuh, Bjoern O. Seitner
Summary: The study focuses on optimizing the use of spare parts in a limited warehouse space for military operations to address parts failures. It utilizes a two-stage stochastic programming model and scenario-based approach.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Oceanography
Ke Yang, Taiwei Yang, Yunan Yao, Shi-dong Fan
Summary: The study classified spare parts using deep convolutional neural networks and transfer learning theory, achieving an average accuracy of 96.36% through a three-phase model. The proposed model demonstrated superb performance with an overall accuracy of 95.87% compared to other neural network learning methods.
OCEAN & COASTAL MANAGEMENT
(2021)
Review
Management
Vinicius N. Motta, Miguel F. Anjos, Michel Gendreau
Summary: This survey presents a review of optimization approaches for the integration of demand response in power systems planning and highlights important future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Philipp Schulze, Armin Scholl, Rico Walter
Summary: This paper proposes an improved branch-and-bound algorithm, R-SALSA, for solving the simple assembly line balancing problem, which performs well in balancing workloads and providing initial solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Roshan Mahes, Michel Mandjes, Marko Boon, Peter Taylor
Summary: This paper discusses appointment scheduling and presents a phase-type-based approach to handle variations in service times. Numerical experiments with dynamic scheduling demonstrate the benefits of rescheduling.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Oleg S. Pianykh, Sebastian Perez, Chengzhao Richard Zhang
Summary: Efficient scheduling is crucial for optimizing resource allocation and system performance. This study focuses on critical utilization and efficient scheduling in discrete scheduling systems, and compares the results with classical queueing theory.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Review
Management
Hamed Jahani, Babak Abbasi, Jiuh-Biing Sheu, Walid Klibi
Summary: Supply chain network design is a large and growing area of research. This study comprehensively surveys and analyzes articles published from 2008 to 2021 to detect and report financial perspectives in SCND models. The study also identifies research gaps and offers future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Patrick Healy, Nicolas Jozefowiez, Pierre Laroche, Franc Marchetti, Sebastien Martin, Zsuzsanna Roka
Summary: The Connected Max-k-Cut Problem is an extension of the well-known Max-Cut Problem, where the objective is to partition a graph into k connected subgraphs by maximizing the cost of inter-partition edges. The researchers propose a new integer linear program and a branch-and-cut algorithm for this problem, and also use graph isomorphism to structure the instances and facilitate their resolution. Extensive computational experiments show that, if k > 2, their approach outperforms existing algorithms in terms of quality.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Victor J. Espana, Juan Aparicio, Xavier Barber, Miriam Esteve
Summary: This paper introduces a new methodology based on the machine learning technique MARS for estimating production functions that satisfy classical production theory axioms. The new approach overcomes the overfitting problem of DEA through generalized cross-validation and demonstrates better performance in reducing mean squared error and bias compared to DEA and C2NLS methods.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Stefano Nasini, Rabia Nessah
Summary: In this paper, the authors investigate the impact of time flexibility in job scheduling, showing that it can significantly affect operators' ability to solve the problem efficiently. They propose a new methodology based on convex quadratic programming approaches that allows for optimal solutions in large-scale instances.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Zhiqiang Liao, Sheng Dai, Timo Kuosmanen
Summary: Nonparametric regression subject to convexity or concavity constraints is gaining popularity in various fields. The conventional convex regression method often suffers from overfitting and outliers. This paper proposes the convex support vector regression method to address these issues and demonstrates its advantages in prediction accuracy and robustness through numerical experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Kuo-Hao Chang, Ying-Zheng Wu, Wen-Ray Su, Lee-Yaw Lin
Summary: The damage and destruction caused by earthquakes necessitates the evacuation of affected populations. Simulation models, such as the Stochastic Pedestrian Cell Transmission Model (SPCTM), can be utilized to enhance disaster and evacuation management. The analysis of SPCTM provides insights for government officials to formulate effective evacuation strategies.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Qinghua Wu, Mu He, Jin-Kao Hao, Yongliang Lu
Summary: This paper studies a variant of the orienteering problem known as the clustered orienteering problem. In this problem, customers are grouped into clusters and a profit is associated with each cluster, collected only when all customers in the cluster are served. The proposed evolutionary algorithm, incorporating a backbone-based crossover operator and a destroy-and-repair mutation operator, outperforms existing algorithms on benchmark instances and sets new records on some instances. It also demonstrates scalability on large instances and has shown superiority over three state-of-the-art COP algorithms. The algorithm is also successfully applied to a dynamic version of the COP considering stochastic travel time.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Bjorn Bokelmann, Stefan Lessmann
Summary: Estimating treatment effects is an important task for data analysts, and uplift models provide support for efficient allocation of treatments. However, evaluating uplift models is challenging due to variance issues. This paper theoretically analyzes the variance of uplift evaluation metrics, proposes variance reduction methods based on statistical adjustment, and demonstrates their benefits on simulated and real-world data.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Congzheng Liu, Wenqi Zhu
Summary: This paper proposes a feature-based non-parametric approach to minimizing the conditional value-at-risk in the newsvendor problem. The method is able to handle both linear and nonlinear profits without prior knowledge of the demand distribution. Results from numerical and real-life experiments demonstrate the robustness and effectiveness of the approach.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Laszlo Csato
Summary: This paper compares the performance of the eigenvalue method and the row geometric mean as two weighting procedures. Through numerical experiments, it is found that the priorities derived from the two eigenvectors in the eigenvalue method do not always agree, while the row geometric mean serves as a compromise between them.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Guowei Dou, Tsan-Ming Choi
Summary: This study investigates the impact of channel relationships between manufacturers on government policies and explores the effectiveness of positive incentives versus taxes in increasing social welfare. The findings suggest that competition may be more effective in improving sustainability and social welfare. Additionally, government incentives for green technology may not necessarily enhance sustainability.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)