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Statistical inference and optimum life-testing plans with joint progressively type-II censoring scheme

QUALITY TECHNOLOGY AND QUANTITATIVE MANAGEMENT 2023年第3期20卷 279-306页

作者： Lin, Chien-Tai Chen, Yen-Chou Yeh, Tzu-Chi Ng, Hon Keung Tony Tamkang Univ Dept Math New Taipei Taiwan Bentley Univ Dept Math Sci Waltham MA USA

This article discusses the estimation of model parameters and the determination of optimal life-testing plans for Weibull-distributed lifetimes under a joint progressive Type-II censoring scheme. We obtain the maximum likelihood estimates numerically and propose a modified variable-neighborhood-search-based approach for determining the optimum joint progressive Type-II censoring scheme. Different optimal criteria, including a cost minimization criterion, the A-optimality criterion, and the D-optimality criterion, are considered. A sensitivity analysis is used to study the effect of misspecification of parameter values on the optimal schemes.

关键词： Maximum likelihood estimation monte carlo simulation optimal criteria variable neighborhood search algorithm

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Hybrid multi-objective metaheuristic algorithms for solving airline crew rostering problem with qualification and language

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第1期20卷 1460-1487页

作者： Deng, Bin Ding, Ran Li, Jingfeng Huang, Junfeng Tang, Kaiyi Li, Weidong Yunnan Univ Sch Math & Stat Kunming 650000 Peoples R China China Eastern Yunnan Airlines Kunming 650200 Peoples R China

In order to cope with the rapid growth of flights and limited crew members, the rational allocation of crew members is a strategy to greatly alleviate scarcity. However, if there is no appropriate allocation plan, some flights may be canceled because there is no pilot in the scheduling period. In this paper, we solved an airline crew rostering problem (CRP). We model the CRP as an integer programming model with multiple constraints and objectives. In this model, the schedule of pilots takes into account qualification restrictions and language restrictions, while maximizing the fairness and satisfaction of pilots. We propose the design of two hybrid metaheuristic algorithms based on a genetic algorithm, variable neighborhood search algorithm and the Aquila optimizer to face the trade-off between fairness and crew satisfaction. The simulation results show that our approach preserves the fairness of the system and maximizes the fairness at the cost of crew satisfaction.

关键词： airline crew rostering problem multi-objective optimization genetic algorithm variable neighborhood search algorithm Aquila optimizer

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Automated Course Timetabling Optimization Using Tabu-variable neighborhood search Based Hyper-Heuristic algorithm

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Procedia Computer Science 2019年 161卷 656-664页

作者： Ahmad Muklason Redian Galih Irianti Ahsanul Marom Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya

Course timetabling problems are challenging, laborious and repetitive work in the universities. However, in many universities this repetitive works was still carried out manually. It is because there are so many constraints that must be considered either from the students and lecturers’ requirement or the infrastructure such as room availability. Therefore, to automate the process of timetabling is hard problem. Scientifically, in the literature, course timetabling optimization is one of non-deterministic polynomial problems, usually abbreviated as NP-hard problem. For NP-hard problem, there is not any exact algorithm known could solve the problem within polynomial-time. The state-of-the-art methods for solving the problem are approximation algorithms that are mainly meta-heuristics. This paper presents a new approach, namely, hyper-heuristics as opposed to meta-heuristics, to cope with the need of intensive problem specific parameter tuning in meta-heuristics approach. The algorithms employed within hyper-heuristic approach presented in this paper are tabu search hybridize with variable neighborhood search. Tested over two real-world course timetabling problem datasets, the computational results from the experiments showed that the proposed algorithm could automate the process of timetabling. Furthermore, compared to the timetable produced manually, in term of soft constraint violation penalties, the proposed algorithm could improve by 1855 and 1110 respectively. In addition to new approach, the main contribution of this paper is two real-world course timetabling problems available for public to encourage further research as future works.

关键词： Course Timetabling variable neighborhood search algorithm Tabu search algorithm Hyper-Heuristic algorithm

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Hybridizing Basic variable neighborhood search With Particle Swarm Optimization for Solving Sustainable Ship Routing and Bunker Management Problem

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2020年第3期21卷 986-997页

作者： De, Arijit Wang, Junwei Tiwari, Manoj Kumar Newcastle Univ Business Sch Newcastle Upon Tyne NE1 7RU Tyne & Wear England Univ Hong Kong Shenzhen Inst Res & Innovat Hong Kong Peoples R China Univ Hong Kong Shenzhen Inst Res & Innovat Dept Ind & Mfg Syst Engn Hong Kong Peoples R China IIT Kharagpur Dept Ind & Syst Engn Kharagpur 721302 W Bengal India

This paper studies a novel sustainable ship routing problem considering a time window concept and bunker fuel management. Ship routing involves the decisions corresponding to the deployment of vessels to multiple ports and time window concept helps to maintain the service level of the port. Reducing carbon emissions within the maritime transportation domain remains one of the most significant challenges as it addresses the sustainability aspect. Bunker fuel management deals with the fuel bunkering issues faced by different ships, such as selection of bunkering ports and total bunkered amount at a port. A novel mathematical model is developed capturing the intricacies of the problem. A hybrid particle swarm optimization with a basic variable neighborhood search algorithm is proposed to solve the model and compared with the exact solutions obtained using Cplex and other popular algorithms for several problem instances. The proposed algorithm outperforms other popular algorithms in all the instances in terms of the solution quality and provides good quality solutions with an average cost deviation of 5.99% from the optimal solution.

关键词： Fuels Marine vehicles Routing Carbon dioxide Transportation Mathematical model Microsoft Windows Ship routing bunker fuel management mixed integer linear programming model variable neighborhood search algorithm

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A bi-level optimization of storage allocation and shelf sequencing in a goods-to-person picking system 5

A bi-level optimization of storage allocation and shelf sequ...

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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Feng, Jinhan Qin, Kun Chen, Zhongwei Kou, Hongrui Sun, Shulin Wang, Ting Civil Aviat Univ China Sch Elect Informat & Automat Tianjin Peoples R China

ISBN: (纸本)9798350386783;9798350386776

With the rapid development of e-commerce economy, Robotic Mobile Fulfillment Systems based on "goods-to-person" warehouse order picking mode are widely used in large e-commerce platforms such as Amazon and Alibaba. A Bi-level optimization algorithm is proposed to jointly optimize storage allocation and shelf sorting for the shelf scheduling problem with multiple storage locations in the "goods-to-person" picking system. The upper-layer optimization uses an improved genetic algorithm to allocate storage locations for the shelves to be picked, adopts a problem-specific knowledge strategy to generate an elite initial population, and introduces crossover and neighborhood search operators with adaptive transformation probabilities to enhance the local search and global optimization capabilities. According to the given storage location, shelf sorting optimization is carried out at the lower level, which is abstracted into the variant traveling salesman problem for modeling and analysis, based on which two neighborhood structures are designed and solved with the variable neighborhood search algorithm. The experimental results show that the performance of the improved genetic algorithm is improved by 2.31% compared with the ordinary genetic algorithm, the combined algorithm has certain advantages over the optimization solver in terms of speed and solution quality, which is of great significance to improve the operational efficiency of the "goods-to-person" picking system.

关键词： "goods-to-person" picking system Bi-level optimization genetic algorithm variable neighborhood search algorithm

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Churn Prediction Using Mathematical Programming via a Linear Classification algorithm

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Mathematical Models and Computer Simulations 2024年第3期16卷 504-519页

作者： Barhdadi, Mohamed Benyacoub, Badreddine Ouzineb, Mohamed National Institute for Statistics and Applied Economics Rabat Morocco

Abstract: The initial idea in developing customer churn prediction was to use statistical analysis of a sample of previous customers to help operators predict which existing customers were likely migrate to other operators. In this paper, a nonstatistical approach to this problem can also be envisaged by formulating the problem as a linear program. The main concept of our proposed algorithm is to hybridize the Jackknife resampling technique with an efficient heuristic based on a variable neighborhood search algorithm to construct a new approach that can present an alternative to black-box machine learning methods. The goal is to find possible solutions (or weights) to minimize error distances and the number of misclassified points. We tested the suggested algorithm with an imbalanced instance and the comparisons show that the proposed model outperforms the most existing machine learning classifiers, both in terms of the solution quality and the execution time. The numerical results indicate that the best solution is achieved in 85% over G-mean of all instances tested. The average gap between our solution and the best solution is still quite small (1.15%). © Pleiades Publishing, Ltd. 2024. ISSN 2070-0482, Mathematical Models and Computer Simulations, 2024, Vol. 16, No. 3, pp. 504–519. Pleiades Publishing, Ltd., 2024.

关键词： customer churn prediction linear classification mathematical modeling resampling technique variable neighborhood search algorithm

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Robust assembly line balancing problem considering preventive maintenance scenarios with interval time

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期116卷

作者： Meng, Kai Tang, Qiuhua Zhang, Zikai Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Mfg Inst Wuhan 430081 Peoples R China

Considering preventive maintenance (PM) scenarios in the assembly line balancing problem (ALBP) have been proved effective and can be used to increase production efficiency. All these studies assumed that the processing time of tasks was fixed. However, many factors can affect the processing time in the actual production process and make the original task allocation plans infeasible. The robust method is introduced to solve the uncertain processing time, and the robust ALBP considering PM scenarios (RALBP_PMs) is studied in this paper. In this problem, three objectives are optimized: the cycle time under the regular scenario, the maximum cycle time, and the maximum task alteration under all PM scenarios. A robust mixed-integer optimization model is proposed to hedge against uncertainty, and then it is linearized by duality to make it solvable. A Q-learning -based variable neighborhood search (QVNS) algorithm with several improvements is designed to solve this problem. Several heuristic rules are designed to improve the performance of the initial population. Roulette wheel selection based on crowding distance is designed to select a solution with more potential. Eleven problem-specific neighborhood structures are designed to improve search efficiency. These structures are sorted by the Q-learning-based sorting method to find a good solution quickly. Experiment results demonstrate that considering PM scenarios in RALBP is necessary. The proposed mathematical model can obtain the Pareto solutions of small-scale instances, and the proposed QVNS algorithm is more suitable for solving large-scale instances.

关键词： Robust assembly line balancing problem Preventive maintenance scenarios Q-learning variable neighborhood search algorithm

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Integrated optimization of truck appointment quotas and container relocations for multiple blocks considering transshipment containers handling

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ADVANCED ENGINEERING INFORMATICS 2024年 62卷

作者： Duan, Shuang Zheng, Hong-Xing Wang, Zhao-Yang Dalian Maritime Univ Coll Transportat Engn Dalian 116026 Peoples R China

The container terminal is a crucial transshipment node in the sea and land cargo transport supply chain, and the efficiency of container collection and distribution has always been a research hotspot. In this paper, we consider the impact of transshipment containers resulting from various uncertainties and integrate the operations of external truck pickups with the handling of transshipment containers in the import yard of the container terminal. In light of the varying number of transshipment containers and transit times for different blocks, coupled with the unpredictable arrival order of external trucks leading to the unknown number of relocations, it is proposed to dynamically adjust the upper limit of the appointed quota for each block. We establish a two stage mixed integer programming model for the truck appointment quota and the block relocation problem. The objective functions aim to minimize the number of relocations and minimize the number of yard crane moves under the maximal appointment quota, respectively. This model is utilized for dynamically regulating the upper limit of the appointed quota for each block and determining the time intervals for picking up the appointed and transshipment containers, along with the yard crane scheduling scheme. The problem is solved using CPLEX and the improved variable neighborhood search algorithm (IVNS). The IVNS incorporates the correction strategy, decoding strategy, and scheduling rules designed to align closely with the characteristics of the problem. Through scheme comparison and sensitivity analysis, five management insights are proposed for terminals. Experimental results demonstrate that the approach presented in this paper effectively enhances the utilization efficiency of yard cranes, reduces yard congestion, and yields benefits for both terminals and trucking companies.

关键词： Truck appointment quota Transshipment container Yard crane Scheduling Block relocation variable neighborhood search algorithm

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Solution approaches for the integrated production and outbound distribution scheduling problem with multiple plants and perishable items

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartC期237卷

作者： Atasagun, Gozde Can Karaoglan, Ismail Konya Tech Univ Fac Engn & Nat Sci Dept Ind Engn TR-42250 Konya Turkiye

In this manuscript, we consider the integrated production and outbound distribution system in which there are several geographically dispersed facilities. These facilities are responsible for producing and delivering timesensitive customer demands. We propose a mixed integer programming (MIP) formulation, MIP with an initial solution using Clarke and Wright (MIPCW) and a metaheuristic algorithm (variable neighborhood search-VNS) for the problem whose objective is to minimize the maximum delivery time whereby satisfying all customer demands. We conduct a two-stage experimental study to determine the effectiveness of the proposed metaheuristic algorithm. We compare the VNS algorithm with the best-known solutions for the single plant version of the problem, in the first stage. These results indicate that the VNS algorithm yields promising results. The second stage includes the results for the MIP formulation and the VNS algorithm on a set of newly generated test instances. The average percentage gap between the VNS algorithm and the MIPCW formulation is about 5% which means that the VNS algorithm gives better results in average about 70 s computation time. These results show how effective the suggested VNS algorithm is at solving the test instances.

关键词： Time -sensitive products Machine scheduling Vehicle routing problem Mixed integer programming formulation variable neighborhood search algorithm

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Effective metaheuristic and rescheduling strategies for the multi-AGV scheduling problem with sudden failure

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 250卷

作者： Wang, Xue Zou, Wenqiang Meng, Leilei Zhang, Biao Li, Junqing Sang, Hongyan Liaocheng Univ Sch Comp Sci 1 Hunan Rd Liaocheng 252000 Peoples R China

This paper investigates a novel scheduling problem, the multi-AGV scheduling problem with sudden failure (MASP-SF), which is of great importance in modern intelligent manufacturing systems. The problem considers how to assign tasks to multiple AGVs with the objective of minimizing the total cost under two events of normal production and sudden failure in the workshop. In the event of normal production, a population-based variable neighborhood search (PVNS) algorithm with effective strategies such as rule-based stochastic method in the initialization stage, pancake flipping strategy in the shaking stage, improved neighborhood structures in the variable neighborhood descent stage is presented to solve the multi-AGV scheduling problem. In the event of sudden failure, a mixed-integer linear programming model and two rescheduling strategies are proposed to solve MASP-SF. The two rescheduling strategies are the rapid repair rescheduling strategy and a rescheduling strategy based on an improved NNH. To validate the above ideas, we conduct comprehensive and in-depth experiments on a practical plant and statistical analysis of the resulting data. The results show that the scheduling solutions generated by the rescheduling strategies are feasible, and the proposed PVNS algorithm has a superior performance compared to existing algorithms in multi-AGV scheduling problem.

关键词： Automated guided vehicle Rescheduling Matrix manufacturing workshop Sudden failure variable neighborhood search algorithm

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