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An estimation distribution algorithm for wave-picking warehouse management

JOURNAL OF INTELLIGENT MANUFACTURING 2022年第4期33卷 929-942页

作者： Liang, Jingran Wu, Zhengning Zhu, Chenye Zhang, Zhi-Hai Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Univ Sydney Fac Engn & Informat Technol Sydney NSW 2008 Australia

Recently, market has witnessed a tremendous growth in E-commerce sales, which bring tons of opportunities as well as challenges. Warehouses have to handle unique characteristics of customer orders in the era of E-commerce which consists of small order scales, large items count, unexpected irregular order arrival patterns, seasonality demand peeks, and high service level expectations. Warehouses are adopting wave-picking as an effective policy composed of item-batching, load-assignment and picker-routing problems. In this research, principle combination of load-assignment and picker-routing problems is studied. A mixed integer mathematical model is established based on features of a wave-picking warehouse. In order to conquer the complexity caused by routing decision of the proposed problem, a set of effective modified estimation distribution algorithms is developed. The set of proposed algorithms is proved to have stable gaps (1% on average and maximum less than 2%) compared with Cplex 12.8, while can be solved in much larger scale within quite short time (100 pickers and 350 items in each wave within less than 2 min).

关键词： Wave picking Warehouse management Load assignment problem Picker routing problem estimation distribution algorithm

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An improved differential evolution by hybridizing with estimation-of-distribution algorithm

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INFORMATION SCIENCES 2023年 619卷 439-456页

作者： Li, Yintong Han, Tong Tang, Shangqin Huang, Changqiang Zhou, Huan Wang, Yuan AF Engn Univ Aviat Engn Sch Xian 710038 Peoples R China

To fully exploit the strong exploitation of differential evolution (DE) and the strong exploration of the estimation-of-distribution algorithm (EDA), an improved differential evolution by hybridizing the estimation-of-distribution algorithm named IDE-EDA is proposed in the study. Firstly, a novel cooperative evolutionary framework is proposed to hybridize LSHADE-RSP, a state-of-the-art DE variant incorporating DE-based effective improvement strategies, with EDA. Secondly, the dominant individuals generated by LSHADE-RSP are used to establish the probability distribution model for EDA to enhance its exploitation in each generation, and a new control parameter is introduced to balance exploitation and exploration. Then, the use of greed strategy works via EDA to fully retain highquality solutions to the next generation to improve the convergence speed. Finally, the greedy strategy is used to shrink the external archive when its size decreases due to the reduction of the population size. A comparison of IDE-EDA with cutting-edge DE-based and EDA-based variants, including AAVS-EDA, EB-LSHADE, ELSHADE-SPACMA, jSO, LSHADE-RSP, RWGEDA, HSES, and APGSK-IMODE, was implemented to verify its efficiency. The statistical test results on the IEEE CEC 2018 and IEEE CEC 2021 test suites demonstrate that IDE-EDA is an excellent hybrid algorithm. The MATLAB source code of IDE-EDA can be downloaded from https://***/Yintong-Li/IDE-EDA. (c) 2022 Elsevier Inc. All rights reserved.

关键词： Artificial intelligence Differential evolution Hybridization estimation distribution algorithm

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A knowledge-based constructive estimation of distribution algorithm for bi-objective portfolio optimization with cardinality constraints

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APPLIED SOFT COMPUTING 2023年 146卷

作者： Zhang, Zhi-Xuan Chen, Wei-Neng Hu, Xiao-Min South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China South China Univ Technol State Key Lab Subtrop Bldg Sci Guangzhou 510006 Peoples R China Guangdong Univ Technol Sch Comp Guangzhou 510006 Peoples R China

Portfolio optimization is an essential and practical model for financial decision making. With the consideration of some real-world constraints, especially the cardinality constraints, the problem becomes much more challenging as it converts to a mixed-integer quadratic multi-objective optimization problem. To solve this problem, we propose a knowledge-based constructive estimation of distribution algorithm (KC-EDA) with the following three features. First, a hybrid design of Ant colony optimization (ACO) and estimation distribution algorithm (EDA) is used to solve this mixed-variable optimization problem based on knowledge information. Second, a knowledge accumulation mechanism is designed to discover the internal relationship among the assets. The mechanism can not only guide the selection of assets effectively but also enable the use of historical information during evolution to direct the allocation of investment proportion. Third, a constructive approach is applied to construct portfolios under the constraints. This hybrid and constructive approach is incorporated into the multiobjective evolutionary framework and the experiment has been performed on the SZ50, SZ180, and SZ380 datasets (from January 2014 to December 2018). The experimental results demonstrate the effectiveness of KC-EDA in solving the portfolio optimization problem with cardinality constraints.& COPY;2023 Published by Elsevier B.V.

关键词： Portfolio optimization Mean-variance model Constraint optimization Ant colony optimization estimation distribution algorithm Multi-objective optimization

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Multi-objective green flowshop scheduling problem under uncertainty: estimation of distribution algorithm

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JOURNAL OF CLEANER PRODUCTION 2020年 251卷 119734-000页

作者： Amiri, M. Faraji Behnamian, J. Bu Ali Sina Univ Fac Engn Dept Ind Engn Hamadan Hamadan Iran

Flowshop scheduling is a well-known NP-hard problem. Sustainable scheduling problem has recently attracted the attention of researchers due to the importance of energy and environmental issues. Moreover, considering uncertainty in the real-world manufacturing environment makes the problem more realistic. Insufficient researches on energy issue under uncertainty were encouraging to conduct this study. In this paper, a mathematical formulation and a scenario-based estimation of distribution algorithm (EDA) are proposed to address the flowshop scheduling problem to optimize makespan and energy consumption under uncertainty. To the best of knowledge, scenario-based EDA has not been used to solve this problem. In this study, it is assumed that the processing times are stochastic and follow the normal distribution with known average and variance. In this problem, the machines have different processing speeds and reducing machine speeds increase makespan and decrease energy consumption and conversely. So, machine speeds affect the objective values which are conflicting. The proposed formulation assigns speeds to machines as well as decides about job sequencing. Different scenarios are used to consider stochastic processing times;so, the E-model approach is used for evaluation of objective functions. At the end, the computational experiment is presented and its results show promising performance of EDA in comparison to another algorithm. The proposed algorithm as practical method gives through insight about the problem and because of the suitable number of solutions in the Pareto set, the decision maker has more choice compared to the competing algorithm. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Sustainable manufacturing Green scheduling Flowshop problem Multi-objective optimization Stochastic programming estimation distribution algorithm

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Evolutionary estimation of distribution algorithm for Agricultural Routing Planning in Field Logistics 5

Evolutionary Estimation of Distribution Algorithm for Agricu...

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5th Information Systems International Conference (ISICO) on Role of Information Systems in Industry 4.0

作者： Utamima, Amalia Reiners, Torsten Ansaripoor, Amir H. Curtin Univ Informat Syst Perth WA 6102 Australia Inst Teknol Sepuluh Nopember Surabaya 60111 Indonesia

Agricultural Routing Planning (ARP), a problem in field logistics, has the objective to minimize the headland distance used by machines when performing agricultural tasks. This study gathers for its datasets the data for several fields obtained from previous research. The estimation of distribution algorithm (EDA) is an algorithm that employs a probabilistic model to produce candidate solutions. This paper extends the EDA to become the Evolutionary EDA that combines a general EDA, a neighborhood search, and an elitism technique. Evolutionary EDA is tested on the optimization of ARP. The experimental results show that Evolutionary EDA can get the same or outperform the solutions generated by previously applied algorithms on ARP problems. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Evolutionary algorithm estimation distribution algorithm agricultural routing planning field logistics

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Evolutionary estimation of distribution algorithm for Agricultural Routing Planning in Field Logistics

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Procedia Computer Science 2019年 161卷 560-567页

作者： Amalia Utamima Torsten Reiners Amir H. Ansaripoor Information Systems Curtin University Perth 6102 Australia Institut Teknologi Sepuluh Nopember Surabaya 60111 Indonesia

关键词： Evolutionary algorithm estimation distribution algorithm agricultural routing planning field logistics

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A modified estimation distribution algorithm based on extreme elitism

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BIOSYSTEMS 2016年 150卷 149-166页

作者： Gao, Shujun de Silva, Clarence W. Univ British Columbia Dept Mech Engn 2054-6250 Appl Sci Lane Vancouver BC V6T 1Z4 Canada

An existing estimation distribution algorithm (EDA) with univariate marginal Gaussian model was improved by designing and incorporating an extreme elitism selection method. This selection method highlighted the effect of a few top best solutions in the evolution and advanced EDA to form a primary evolution direction and obtain a fast convergence rate. Simultaneously, this selection can also keep the population diversity to make EDA avoid premature convergence. Then the modified EDA was tested by means of benchmark low-dimensional and high-dimensional optimization problems to illustrate the gains in using this extreme elitism selection. Besides, no-free-lunch theorem was implemented in the analysis of the effect of this new selection on EDAs. (C) 2016 Elsevier Ireland Ltd. All rights reserved.

关键词： estimation distribution algorithm Gaussian model Top best solutions No-free-lunch theorem

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Hybridising heuristics within an estimation distribution algorithm for examination timetabling

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APPLIED INTELLIGENCE 2015年第4期42卷 679-693页

作者： Qu, Rong Nam Pham Bai, Ruibin Kendall, Graham Univ Nottingham Sch Comp Sci Automated Scheduling Optimisat & Planning Nottingham NG8 1BB England Univ Nottingham Ningbo Sch Comp Sci Ningbo 315100 Zhejiang Peoples R China Univ Nottingham Nottingham Malaysia

This paper presents a hybrid hyper-heuristic approach based on estimation distribution algorithms. The main motivation is to raise the level of generality for search methodologies. The objective of the hyper-heuristic is to produce solutions of acceptable quality for a number of optimisation problems. In this work, we demonstrate the generality through experimental results for different variants of exam timetabling problems. The hyper-heuristic represents an automated constructive method that searches for heuristic choices from a given set of low-level heuristics based only on non-domain-specific knowledge. The high-evel search methodology is based on a simple estimation distribution algorithm. It is capable of guiding the search to select appropriate heuristics in different problem solving situations. The probability distribution of low-level heuristics at different stages of solution construction can be used to measure their effectiveness and possibly help to facilitate more intelligent hyper-heuristic search methods.

关键词： estimation distribution algorithm Hyper-heuristic Exam timetabling Graph colouring

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A comparative study of hybrid estimation distribution algorithms in solving the facility layout problem

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EGYPTIAN INFORMATICS JOURNAL 2021年第4期22卷 505-513页

作者： Utamima, Amalia Inst Teknol Sepuluh Nopember Fac Intelligent Elect & Informat Technol Surabaya Indonesia

The estimation distribution algorithm (EDA) is an evolutionary algorithm that uses probabilistic models to create candidate solutions. Previous researchers have suggested various hybrid methods to avoid the premature convergence of EDA. This research conducts a comparative study between several variations of hybridization in EDA with regards to the descriptive statistics in the objective values. This study also proposes a new hybrid approach, named Adapted EDA (AEDA), by adapting the structure of EDA by adding a lottery procedure, an elitism strategy, and a neighborhood search. The proposed AEDA, several hybridizations of EDA, and Genetic algorithm (GA) plus Tabu Search (TS) are applied to the facility layout design in manufacture - Enhanced Facility Layout Problem (EFLP) - to analyze their solutions. The hybrid EDAs that are being compared are EDA plus GA (EDAGA), EDA plus Particle Swarm Optimization (EDAPSO), the combination of EDAPSO plus TS (EDAhybrid), and AEDA. The experimental results show that the AEDA can significantly improves the solution quality in solving all the EFLP instances compared to other algorithms. (C) 2021 THE AUTHOR. Published by Elsevier BV. on behalf of Faculty of Computers and Artificial Intelligence, Cairo University.

关键词： estimation distribution algorithm Facility layout problem Hybrid algorithms Safe work

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An evolutionary algorithm based on fully connected weight networks for mixed-variable multi-objective optimization

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INFORMATION SCIENCES 2024年 659卷

作者： Dong, Nanjiang Zhang, Tao Wang, Rui Liao, Xiangke Wang, Ling Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Xiangjiang Lab Changsha 410205 Peoples R China Coll Comp Sci Natl Univ Def Technol Coll Comp Sci Changsha 410073 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Multi -objective optimization research has mostly focused on continuous -variable problems. However, real -world optimization problems often involve multiple types of variables (continuous, integer, and discrete) and multiple conflicting optimization objectives, called mixed -variable multi -objective optimization problems (MVMOPs). Discrete variables make the decision space of the problem discrete. In contrast, while different types of variables need different treatments by the evolutionary algorithm, which poses a challenge to the efficient search of the evolutionary algorithm. Therefore, we propose an evolutionary algorithm based on a fully connected weight network (FCWNEA). The fully connected network structure characterizes the entire decision space, the node access count records the frequency of visits to the node, and the weights of connections and the activity of variables estimate the distribution of the decision space. This information assists in generating offspring solutions. To evaluate the performance of the proposed algorithm, we conduct empirical experiments on different types of problems. The results show that the proposed algorithm has a significant advantage in mixed -variable multi -objective problems. Moreover, the proposed algorithm is also quite competitive in continuous problems and can better handle the correlation between variables in optimization problems.

关键词： Mixed-variable Multi-objective optimization Evolutionary algorithms estimation distribution algorithm

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