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Stochastic resource-constrained project scheduling problem with time varying weather conditions and an improved estimation of distribution algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 157卷 107322-107322页

作者： Zhou, Yifan Miao, Jindan Yan, Bin Zhang, Zhisheng Southeast Univ Sch Mech Engn Nanjing 211189 Peoples R China Beijing Goldwind Sci & Creat Windpower Equipment Beijing 100176 Peoples R China

Construction projects with outdoor operations are affected by time-varying weather conditions. However, most existing research on stochastic resource-constrained project scheduling problems (SRCPSPs) considers activity duration as a random variable from a time-independent distribution. To address this issue, this study investigates SRCPSP under time-varying weather conditions;an improved estimation of distribution algorithm (EDA) including a ranking and selection method using common random numbers is proposed for enhancing the performance of project scheduling. The benchmark J120 dataset from PSPLIB and a practical case of windfarm construction are used to validate the improved EDA. For three randomly selected cases from the J120 dataset, the improved EDA can reduce the expected makespan by 17.0, 29.4, and 12.5 days when compared with deterministic scheduling. The corresponding makespan reductions obtained by the original EDA are 10.8, 22.7, and 7.1 days. Similarly, the improved EDA obtains 23% higher expected makespan reduction for the practical case.

关键词： SRCPSP estimation of distribution algorithm Ranking and selection Common random numbers

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An estimation of distribution algorithm with branch-and-bound based knowledge for robotic assembly line balancing

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COMPLEX & INTELLIGENT SYSTEMS 2021年第3期7卷 1125-1138页

作者： Sun, Bin-qi Wang, Ling Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Robotic assembly lines are widely used in manufacturing industries. The robotic assembly line balancing (RALB) problem aims to balance the workloads among different workstations and optimize the assembly line efficiency. This paper addresses a particular type of RALB problem, which minimizes the assembly line cycle time by determining the task and robot assignment in each workstation under precedence constraints. To solve the problem, we present an effective hybrid algorithm fusing the estimation of distribution algorithm and branch-and-bound (B&B) based knowledge. A problem-specific probability model is designed to describe the probabilities of each task being assigned to different workstations. Based on the probability model, an incremental learning method is developed and a sampling mechanism with B&B based knowledge is proposed to generate new feasible solutions. The fuse of B&B based knowledge is able to reduce the search space of EDA while focusing the search on the promising area. To enhance the exploitation ability, a problem-specific local search is developed based on the critical workstation to further improve the quality of elite solutions. The computational complexity of the proposed algorithm is analyzed, and the effectiveness of the B&B based knowledge and the problem-specific local search is demonstrated through numerical experiments. Moreover, the performance of the proposed algorithm is compared with existing algorithms on a set of widely-used benchmark instances. Comparative results demonstrate the effectiveness and efficiency of the proposed algorithm.

关键词： estimation of distribution algorithm Sampling mechanism Branch-and-bound based knowledge Problem-specific local search Robotic assembly line balancing

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A matrix-cube-based estimation of distribution algorithm for blocking flow-shop scheduling problem with sequence-dependent setup times

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 205卷

作者： Zhang, Zi-Qi Qian, Bin Hu, Rong Jin, Huai-Ping Wang, Ling Yang, Jian-Bo Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 Peoples R China Kunming Univ Sci & Technol Sch Mech & Elect Engn Kunming 650500 Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming 650500 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Univ Manchester Alliance Manchester Business Sch Manchester M15 6PB Lancs England

The blocking flow-shop scheduling problem with sequence-dependent setup times (BFSP_SDST) is a strong NP- hard problem that exists widely in practice. However, research on this issue is still quite limited. Hence, this paper presents a novel matrix-cube-based estimation of distribution algorithm (MCEDA) to minimize the makespan criterion of the BFSP_SDST. In MCEDA's global search, a matrix cube is devised to reasonably learn the promising patterns in excellent solutions or individuals, and then a matrix-cube-based probabilistic model is developed to quickly guide global search toward the potential promising regions in solution space. A diversity controlling mechanism is also added to avoid the stagnation of global search. In MCEDA's local search, an iterated multi-neighborhood local search controlled by the probabilistic model in global search is designed to execute deeper exploitation from those promising regions. Additionally, two constructive heuristics for gener-ating high-quality initial individuals and one fast Insert-based neighbor evaluation method for accelerating the efficiency of local search are presented based on an analysis of the problem's features. MCEDA's efficacy and superiority in solving the BFSP SDST are demonstrated through comprehensive comparisons with 22 state-of-the- art algorithms.

关键词： Blocking flow-shop scheduling estimation of distribution algorithm Setup times Multi-dimensional probabilistic model Iterated local search

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Quantum-Inspired estimation of distribution algorithm To Solve The Travelling Salesman Problem

Quantum-Inspired Estimation Of Distribution Algorithm To Sol...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Soloviev, Vicente P. Bielza, Concha Larranaga, Pedro Univ Politecn Madrid Computat Intelligence Grp Madrid Spain

ISBN: (纸本)9781728183923

A novel Quantum-Inspired estimation of distribution algorithm (QIEDA) is proposed to solve the Travelling Salesman Problem (TSP). The QIEDA uses a modified version of the W state quantum circuits to sample new solutions during the algorithm runtime. The algorithm behaviour is compared with other state-of-the-art population-based algorithms. QIEDA convergence is faster than other algorithms, and the obtained solutions improve as the size of the problem increases. Moreover, we show that quantum noise enhances the search of an optimal solution. Because quantum computers differ from each other, partly due to the topology that distributes the qubits, the computational cost of executing the QIEDA in different topologies is analyzed and an ideal topology is proposed for the TSP solved with the QIEDA.

关键词： quantum computing quantum machine learning travelling salesman problem estimation of distribution algorithm

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Scheduling of energy-efficient distributed blocking flowshop using pareto-based estimation of distribution algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 200卷 116910-116910页

作者： Zhang, Xiaohui Liu, Xinhua Cichon, Andrzej Krolczyk, Grzegorz Li, Zhixiong Xuzhou Univ Technol Sch Elect & Control Engn Xuzhou 221018 Jiangsu Peoples R China China Univ Min & Technol Sch Mech & Elect Engn Xuzhou 211006 Jiangsu Peoples R China Technol & Innovat Res Ctr JiangYan EDZ Taizhou 225500 Peoples R China Opole Univ Technol Dept Elect Control & Comp Engn PL-45758 Opole Poland Opole Univ Technol Dept Mfg Engn & Automat Prod PL-45758 Opole Poland Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea

This study investigates the impact of production scheduling decisions aims at improving productive and energy- efficient performances simultaneously in distributed blocking flowshops (EDBFSP). To reach a compromise be-tween the conflicting objectives, a Pareto multi-objective optimization model based on the estimation of dis-tribution algorithm (MOEDA) is proposed. Firstly, an initialization method based on the problem-specific characteristics is designed to create a promising population with quality and diversity;secondly, a probabilistic model based on a Bayesian network is constructed to predict position relationships between jobs. Two neigh-borhood operators with modified insertion technique are proposed to realize the adjustments of both job sequence and processing speed;thirdly, two operators are developed to execute multi-objective local searches on the elite solutions. Aiming at efficient utilization of the resulted blocking and idle time, an energy-saving method is designed for EDBFSP. In the experimental parts, to gain the best performance, the key parameters of MOEDA have been calibrated. The validation is conducted to assess the performances of the designed initialization method, neighborhood search, local search, and energy-saving strategies. The proposed MOEDA is also compared with mainstream metaheuristics for solving green scheduling problems. The experiment results show that the optimization and search ability of MOEDA have gained prominent advantages over other metaheuristics in both precision and distributivity.

关键词： estimation of distribution algorithm Blocking constraint Multi-objective optimization Energy efficiency

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A matrix cube-based estimation of distribution algorithm for the energy-efficient distributed assembly permutation flow-shop scheduling problem

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期194卷 116484-116484页

作者： Zhang, Zi-Qi Hu, Rong Qian, Bin Jin, Huai-Ping Wang, Ling Yang, Jian-Bo Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Sch Mech & Elect Engn Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming 650500 Yunnan Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Univ Manchester Alliance Manchester Business Sch Manchester M15 6PB Lancs England

In this paper, a matrix-cube-based estimation of distribution algorithm (MCEDA) is proposed to solve the energy-efficient distributed assembly permutation flow-shop scheduling problem (EE_DAPFSP) that minimizes both the maximum completion time (C-max) and the total carbon emission (TCE) simultaneously. Firstly, a high-quality and diverse initial population is constructed via a hybrid initialization method. Secondly, a matrix-cube-based probabilistic model and its update mechanism are designed to appropriately accumulate the valuable pattern information from superior solutions. Thirdly, a suitable sampling strategy is developed to sample the probabilistic model to generate a new population per generation, so as to guide the search direction toward promising regions in solution space. Fourthly, a problem-dependent neighborhood search based on critical path is provided to perform an in-depth local search around the promising regions found by the global search. Fifthly, two types of speed adjustment strategies based on problem properties are also embedded to further improve the quality of the obtained solutions. Sixthly, the influence of the parameters is investigated based on the multi-factor analysis of variance of Design-of-Experiments. Finally, extensive experiments and comprehensive comparisons with several recent state-of-the-art multi-objective algorithms are carried out based on the well-known benchmark instances, and the statistical results demonstrate the efficiency and effectiveness of the proposed MCEDA in addressing the EE_DAPFSP.

关键词： Energy-efficient scheduling estimation of distribution algorithm Distributed flowshop scheduling Assembly line Low-carbon manufacturing

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An estimation of distribution algorithm With Multi-Leader Search

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IEEE ACCESS 2020年 8卷 37383-37405页

作者： Wang, Xiaofei Han, Tong Zhao, Hui Air Force Engn Univ Aeronaut Engn Coll Xian 710038 Peoples R China

The estimation of distribution algorithm (EDA) is a well-known stochastic search method but is easily affected by the ill-shaped distribution of solutions and can thus become trapped in stagnation. In this paper, we propose a novel modified EDA with a multi-leader search (MLS) mechanism, namely, the MLS-EDA. To strengthen the exploration performance, an enhanced distribution model that considers the information of population and distribution is utilized to generate new candidates. Moreover, when the algorithm stagnates, the MLS mechanism will be activated to perform a local search and shrink the search scope. The performance of the MLS-EDA in addressing complex optimization problems is verified using the CEC 2014 and CEC 2017 testbeds with 30D, 50D and 100D tests. Several modern algorithms, including the top-performing methods in the CEC 2014 and CEC 2017 competitions, are considered as competitors. The competitive performance of our proposed MLS-EDA is discussed based on the comparison results.

关键词： estimation of distribution algorithm real-numerical optimization CEC 2014 CEC 2017 evolutionary computation

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Bound-guided hybrid estimation of distribution algorithm for energy-efficient robotic assembly line balancing

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 146卷 106604-106604页

作者： Sun, Bin-qi Wang, Ling Peng, Zhi-ping Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Guangdong Univ Petrochem Technol Sch Comp Maoming 525000 Peoples R China

Under the pressure of climate change, energy-efficient manufacturing has attracted much attention. Robotic assembly lines are widely-used in automotive and electronic manufacturing. It is necessary to consider the energy saving and economic criteria simultaneously when robots are utilized to operate assembly tasks replacing human labor. This paper addresses an energy-efficient robotic assembly line balancing (EERALB) problem with the criteria to minimize both the cycle time and total energy consumption. We present a multi-objective mathematical model and propose a bound-guided hybrid estimation of distribution algorithm to solve the problem. When designing the optimization algorithm, we adopt estimation of distribution algorithm (EDA) to tackle the task assignment, and design a non-dominated robot allocation (NGRA) heuristic which is embedded into the EDA to allocate suitable robot to each workstation. Moreover, we propose a bound-guided sampling (BGS) method, which is able to reduce the search space of EDA and focus the search on the promising area. The computational complexity of the proposed algorithm is analyzed and the effectiveness of the proposed NGRA and BGS is tested. In addition, we compare the performances of the proposed mathematical model and the proposed algorithm with those of the existing model and algorithms on a set of widely-used benchmark instances. Comparative results demonstrate the effectiveness of the proposed model and algorithm.

关键词： estimation of distribution algorithm Bound-guided sampling Energy-efficient robotic assembly line balancing Non-dominated robot allocation

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An enhanced two phase estimation of distribution algorithm for solving scheduling problem

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2023年第3期18卷 217-224页

作者： Hao, Xinchang Tian, Jing Ding, Hui Zhao, Keheng Gen, Mitsuo Changzhou Inst Technol Sch Art & Design Changzhou Jiangsu Peoples R China State Key Lab Air Traff Management Syst & Technol 1 Yongzhi Rd Nanjing Jiangsu Peoples R China Fuzzy Log Syst Inst Tokyo Japan Tokyo Univ Sci Tokyo Japan

Scheduling is one critical issue both in the field of industry engineering and combinatorial optimization research. In order to solve multi-objective scheduling problem with uncertainty, this paper presents a method of enhanced hybrid estimation of distribution algorithm (EDA) with Teaching and Learning-Based Optimization algorithm (TLBO). First, in order to concentrate their respective advantages, two algorithms of EDA and TLBO are integrated to enhance the capability of both global and local search. Second, scenario-based simulation is adopted to deal with uncertainty, and an adaptive sampling strategy is involved to dynamically adjust the number of scenarios during the evolving process. Third, a problem-specific local search is designed to further improve the optimality of candidate solutions. By comparing with existing algorithms on the benchmark problems of flexible job shop scheduling problem (FJSP), it is to demonstrate that our proposal can obtain better solutions in the aspects of optimality and computational efficiency.

关键词： Metaheuristic optimization estimation of distribution algorithm teaching and learning based optimization algorithm scheduling optimization

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A self-adaptive estimation of distribution algorithm with differential evolution strategy for supermarket location problem

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NEURAL COMPUTING & APPLICATIONS 2020年第10期32卷 5791-5804页

作者： Zhou, Bing-Hai Tan, Fen Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China

In modern production systems, an ever-rising product variety has imposed great challenges for in-plant part supply systems used to feed mixed-model assembly lines with required parts. In recent years, many automotive manufacturers have identified the supermarket concept as an efficient part feeding strategy to enable JIT (Just-in-time) deliveries at low costs. This paper studies a discrete supermarket location problem which considers the utilization rate and capacity constraint of the supermarkets simultaneously. Firstly, a mathematical model is developed with the objective of minimizing the total system cost consisting of operating cost and transportation cost. Then, a self-adaptive estimation of distribution algorithm with differential evolution strategy, named DE/AEDA, is proposed to solve the problem. Finally, computational experiments are carried out to analyze the performance of the proposed algorithm compared with the benchmark algorithm by using a non-parametric test method. The results indicate that the proposed algorithm is valid and efficient.

关键词： In-plant material delivery Supermarket Location estimation of distribution algorithm

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