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Research on Multiple Products Aggregate Production Planning under Random Environment

IAENG international Journal of Applied Mathematics 2024年第3期54卷 562-575页

作者： Liu, Lanfen Yang, Xinfeng Yang, Ke School of Traffic and Transportation Lanzhou Jiaotong University Lanzhou730070 China School of Traffic and Transportation Lanzhou Jiaotong University Lanzhou730070 China Engineer of Beijing Rail Transit Design & Research Institute Beijing100037 China

Uncertain factors can significantly reduce the applicability of production plans and the production stability of enterprises. Therefore, considering the uncertain factors caused by external demands and internal capabilities, the impact of production in advance or backorder is analyzed, and the cost of backorder and loss of sales is determined. Moreover, a multi-objective stochastic programming model for aggregate production planning (APP) problem that minimizes the expected chance cost and employee instability is constructed considering external demand and internal capabilities as random variables. At a certain level of confidence, the opportunity constraints are transformed. In addition, combined with a local search strategy, two stochastic hybrid genetic algorithm-particle swarm optimization algorithms based on stage and multiple population strategies are designed to solve this model. Sensitivity analysis is performed on different confidence levels by using numerical examples, and numerical experiments show that the two hybrid algorithms proposed by this paper can effectively improve the solving efficiency. © (2024), (international Association of Engineers). All Rights Reserved.

关键词： stochastic programming

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Offline Palletizing Research Based on Greedy Tabu search Algorithm 14th

Offline Palletizing Research Based on Greedy Tabu Search Alg...

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14th international workshop of Advanced Manufacturing and Automation, IWAMA 2024

作者： Zhang, Linjun Zhou, Chuanhong Chen, Yuan School of Automation and Mechanical Engineering Shanghai University Shanghai200072 China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai200072 China

ISBN: (纸本)9789819626243

This paper implements a greedy tabu search algorithm to deal with the offline palletizing problem, and proposes a two-phase greedy tabu search algorithm. In the first phase, a greedy stochastic adaptive search algorithm is utilized to obtain the local optimal solution of the palletizing problem in the construction process and the local search process, which includes the residual space strategy and the combination of the cargo block strategy, as well as the optimization of the space utilization rate by using the tiling wall strategy. In the second stage, the local optimal solution obtained in the first stage is used as the initial solution, and the tabu search algorithm is used to record the history information of the solution through the tabu table to prevent the algorithm from falling into the local optimal solution, so as to dig deeper into the global optimal solution. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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Twin delayed deep deterministic policy gradient based stochastic optimal dispatch of hydro and wind power considering the uncertainty of wind power in power-time dimensions

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international JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 163卷

作者： Wang, Yuhong Zhou, Xu Shi, Yunxiang Zhou, Chenyu Jiang, Qiliang Zheng, Zongsheng Sichuan Univ Coll Elect Engn Chengdu 610065 Sichuan Peoples R China

The randomness, fluctuation and uncertainty of wind power brings great challenges to the dispatch and control of the power system. In order to raise the economy of hydro and wind power optimal dispatch, a stochastic optimal dispatching model considering the uncertainty of wind power in both time and power dimensions is proposed. The versatile distribution is used to describe the uncertainty of wind power in different power- time intervals, providing foundation for positive and negative reserves of hydro power. Moreover, to solve the multi-objective stochastic optimal dispatching model, the twin delayed deep deterministic policy gradient (TD3) algorithm is utilized, which can avoid falling into local optimum and has stronger search ability when dealing with high-dimensional multi-objective optimization problem. Simulations in a wind farm and hydro power station of western China show that the proposed model can accurately describe the uncertainty of wind power, and TD3 algorithm can find the global optimal solution more effectively than the traditional intelligent algorithms and other deep reinforcement learning algorithms.

关键词： stochastic optimal dispatching Improved versatile distribution Wind power uncertainty Power-time dimension Twin delayed deep deterministic policy gradient (TD3)

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Fast and efficient heuristics for the (s, S, T) inventory policy

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international JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS 2024年第1期11卷

作者： Christou, Ioannis T. Lagodimos, Athanasios G. Skouri, Konstantina Amer Coll Greece Gravias 6 Athens 15342 Greece Netcompany Intrasoft Res & Innovat Dev Dept Luxembourg Luxembourg Univ Piraeus Dept Business Adm Piraeus Greece Univ Ioannina Dept Math Ioannina Greece

We consider a single-echelon inventory system facing stochastic demand controlled by the standard (s,S,T) periodic review policy. While an exact algorithm for optimising all three policy variables exists, high computational requirements prohibit real industrial applications. To deal with this problem, here we propose three search heuristic algorithms that can approximate the optimal policy solution very fast and accurately. All heuristics make use of the empirical observation that the optimisers of the (s,S,T) policy are closely related to those of the classical (r,nQ,T) policy. For the latter, however, a fast near-optimal heuristic exists. Therefore, starting with the (r,nQ,T) optimal solution, all algorithms employ local neighbourhood search to determine the required (s,S,T)) policy solution approximation. Experiments with well-known meta-heuristics such as Simulated Annealing, Tabu search, and others show that the heuristics compare favourably in both solution time and quality. Moreover, compared with the existing exact algorithm heuristic solutions are very satisfactory in quality and are obtained in a fraction of CPU-time.

关键词： Periodic review stochastic demand heuristics supply chain

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Inverse Hessian by stochastic projection and application to system identification in nonlinear mechanics of solids

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international JOURNAL OF NON-LINEAR MECHANICS 2024年 166卷

作者： Saxena, Mukul Sarkar, Saikat Roy, Debasish Indian Inst Sci Dept Civil Engn Ctr Excellence Adv Mech Mat Bangalore 560012 Karnataka India Indian Inst Sci Bangalore Ctr Excellence Adv Mech Mat Bangalore 560012 Karnataka India Indian Inst Technol Delhi Dept Civil Engn New Delhi 110016 India

Parameter identification in nonlinear mechanics of solids, a class of non-convex optimization problems of significant interest, often has to balance fast convergence with enhanced exploration. local optimization schemes based on Newton or quasi-Newton updates cannot tread this fine balance. A similar limitation also applies to many derivative-free global evolutionary searches that typically end up with slower convergence. Notwithstanding a relatively rapid convergence around a local optimum, schemes using classical gradients require that the objective function be smooth. Thus, applied to real-world scenarios, these algorithms face challenges such as non-smoothness including discontinuities in the objective function and costly evaluations of gradients or Hessians, whenever possible. Based on a novel stochastic projection to estimate inverse Hessians, this work aims at generalizing a fast Newton-type local search whilst retaining capabilities of noise- assisted exploration in non-convex optimization. In principle, such a meeting of the best of both worlds in optimization should provide for a powerful paradigm shift, offering a versatile and robust tool for solving complex identification and variational problems arising in nonlinear mechanics of solids. The approach is well-suited even for cases where the objective function is implicitly defined and not amenable to explicit evaluation. In a first implementation of our proposal, we assess its performance against a few benchmark non-convex problems. Finally, we solve a parameter estimation problem for an isotropic and incompressible hyperelastic material.

关键词： Optimization Hessian Derivative-free directionality Filtering stochastic differential equations

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Evaluating the impact of grammar complexity in automatic algorithm design

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international TRANSACTIONS IN OPERATIONAL REsearch 2022年第5期29卷 2789-2814页

作者： Pagnozzi, Federico Stutzle, Thomas Univ Libre Bruxelles ULB IRIDIA Brussels Belgium

Grammar-based automatic algorithm design has been shown to generate stochastic local search algorithms that compete with or outperform state-of-the-art methods. In such systems, algorithms are divided in components and a grammar is used to describe how to properly combine the components to create a working algorithm. In our approach, the grammar is converted into parameters and an automatic parameter configuration tool is used to find the best configuration. This approach allows us to consider and hybridize different metaheuristic templates producing combinations never tested before, but this flexibility leads to a very large configuration space to explore. Is such complexity really needed to achieve state-of-the-art performance? In this paper, we investigate this question by creating grammars that allow the hybridization of stochastic local search algorithms at most two, one, or zero times. We use these grammars to generate algorithms for the three most studied objectives of the permutation flowshop problem: makespan, total completion time, and total tardiness. The generated algorithms are compared using benchmark sets from the literature as well as a quantitative measure of algorithm complexity using a metric based on concept directed acyclic graphs. The experiments show that our system tends to generate hybridized algorithms only when they can provide a substantial performance improvement. On the contrary, when such algorithms do not improve performance, the system generates simpler algorithms regardless of the grammar used.

关键词： grammar‐ based automatic algorithm design stochastic local search permutation flowshop

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Improving QUIC Slow Start Behavior in Wireless Networks with search 30

Improving QUIC Slow Start Behavior in Wireless Networks with...

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30th IEEE international Symposium on local and Metropolitan Area Networks (LANMAN) - Emerging Technologies for Smart Future Networks

作者： Cronin, Amber Kachooei, Maryam Ataei Chung, Jae Li, Feng Peters, Benjamin Claypool, Mark Worcester Polytech Inst Worcester MA 01609 USA Viasat Marlborough MA USA

ISBN: (纸本)9798350352108;9798350352092

QUIC is increasingly being deployed on the Internet as an alternative to TCP. However, QUIC over satellite links faces particular challenges as high and variable round-trip times (RTTs) make it difficult to determine and then reach link capacity. Standard slow start algorithms to detect link capacity can perform poorly over satellite links, often exiting slow start too early and limiting throughput or exiting too late and causing unnecessary packet loss. The Slow start Exit At Right CHokepoint (search) algorithm aims to exit slow start after reaching link capacity but before incurring packet loss by tracking delivery rates and exiting when rates have not increased by the expected amount. search has shown benefits over traditional slow start for TCP connections but has yet to be implemented and evaluated in QUIC. This paper presents the design and implementation of search in an open-source QUIC library, with the code publicly available as a contribution. Evaluation of search over a geostationary satellite link show search successfully exits slow start before loss in the majority of cases, improving goodput compared to the baseline.

关键词： QUIC search Satellite Congestion Control

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Multimodal Optimization by Speciation-Based Differential Evolution using Graphs with stochastic Global and local searches and Crossover 13

Multimodal Optimization by Speciation-Based Differential Evo...

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Joint 13th international Conference on Soft Computing and Intelligent Systems and 25th international Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Takahama, Tetsuyuki Sakai, Setsuko Hiroshima City University Department of Intelligent Systems Asaminami-ku Hiroshima731-3194 Japan Hiroshima Shudo University Faculty of Commercial Sciences Asaminami-ku Hiroshima731-3195 Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373332

Multimodal optimization is a very difficult task to search for all optimal solutions at once in optimization problems with multiple optimal solutions. Speciation using proximity graphs has been proposed for multimodal optimization. In multimodal optimization, it is necessary to first discover many candidates of optimal solutions by global search, and then to converge to optimal solutions using local search. In this study, the speciation-based differential evolution using graphs (SDE- G) is improved by introducing βRNG, a stochastic change from global search to local search, and a crossover operation. By using βRN G, it is possible to first discover many candidates of optimal solutions using the coarse graph, and to gradually narrow down the candidates using the dense graph. Since it is difficult to decide when to switch from global to local search, we propose a stochastic switch as the search progresses. Also, the crossover operation with adaptive crossover rate is proposed to generate new solutions by combining elements of good candidates. The performance of the proposed method is shown by optimizing well-known benchmark problems for 'CEC'2013 special session and competition on niching methods for multimodal function optimization'. © 2024 IEEE.

关键词： Optimization algorithms

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VLSs: A local search Algorithm for Distributed Constraint Optimization Problems

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international JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2022年第3期36卷

作者： Li, Fukui He, Jingyuan Zhou, Mingliang Fang, Bin Chongqing Univ Sch Comp Sci Chongqing 400044 Peoples R China

local search algorithms are widely applied in solving large-scale distributed constraint optimization problem (DCOP). Distributed stochastic algorithm (DSA) is a typical local search algorithm to solve DCOP. However, DSA has some drawbacks including easily falling into local optima and the unfairness of assignment choice. This paper presents a novel local search algorithm named VLSs to solve the issues. In VLSs, sampling according to the probability corresponding to assignment is introduced to enable each agent to choose other promising values. Besides, each agent alternately performs a greedy choice among multiple parallel solutions to reduce the chance of falling into local optima and a variance adjustment mechanism to guide the search into a relatively good initial solution in a periodic manner. We give the proof of variance adjustment mechanism rationality and theoretical explanation of impact of greed among multiple parallel solutions. The experimental results show the superiority of VLSs over state-of-the-art DCOP algorithms.

关键词： Multi-agent system distributed constraint optimization problem local search algorithm incomplete algorithm

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Enhancing CNN Classification with Lamarckian Memetic algorithms and local search

Enhancing CNN Classification with Lamarckian Memetic Algorit...

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2024 IEEE international Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Ghosh, Akhilbaran Kalidindi, Rama Sai Adithya Tata Consultancy Services Limited Mumbai India Shiv Nadar University Uttar Pradesh Dadri India

ISBN: (纸本)9798350385199

Optimization is critical for achieving optimal performance in deep neural networks (DNNs). Traditional gradient-based methods, such as stochastic gradient descent (SGD) and ADAM, often face challenges like local minima entrapment and slow convergence rates. This paper explores population-based metaheuristic optimization algorithms for image classification networks. We propose a novel approach integrating a two-stage training technique with population-based optimization algorithms and local search capabilities to enhance solution quality and convergence speed. In the first stage, a global search is conducted using a population-based algorithm to explore the solution space extensively, allowing the network to escape local minima and find promising regions. In the second stage, a local search fine-tunes the solutions from the global search, ensuring improved accuracy and stability. Our experiments show that the proposed method outperforms state-of-the-art gradient-based techniques like ADAM in accuracy and computational efficiency, particularly in scenarios with high computational complexity and numerous trainable parameters. The results suggest that our approach offers a robust and efficient alternative for weight optimization in convolutional neural networks (CNNs). Future work will explore integrating adaptive mechanisms for parameter tuning and extending the method to other types of neural networks and real-time applications. © 2024 IEEE.

关键词： Genetic algorithms

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