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An effective iterative greedy algorithm for distributed blocking flowshop scheduling problem with balanced energy costs criterion

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APPLIED SOFT COMPUTING 2022年 129卷

作者： Han, Xue Han, Yuyan Zhang, Biao Qin, Haoxiang Li, Junqing Liu, Yiping Gong, Dunwei Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Shandong Normal Univ Sch Comp Sci Jinan 252000 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Hunan 410082 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China

With the increase in production levels, a pattern of industrial production has shifted from a single factory to multiple factories, resulting in a distributed production model. The distributed flowshop scheduling problem (DPFSP) is of great research significance as a frequent pattern in real production activities. In this paper, according to real-world scenarios, we have added blocking constraints and sequence-dependent setup times (SDST) to the DFSP and proposed a distributed blocking flowshop scheduling problem with sequence-dependent setup times (DBFSP_SDST). In a distributed environment, the allocation of resources and utilization have become an urgent problem to be solved. In addition, scheduling problems related to resource conservation have also attracted increasing attention. Therefore, we study DBFSP_SDST and consider minimizing the energy consumption cost of the critical factory (critical factory is the factory with maximum energy consumption cost) under resource balance. To tackle this problem, an effective iterated greedy algorithm based on a learning-based variable neighborhood search algorithm (VNIG) is proposed. In VNIG, an efficient construction heuristic is well designed. Two different local searches based on the characteristics of the proposed problem are developed to enhance the local exploitation by neighborhood searching. A learning-based selection variable neighborhood search strategy is designed to avoid the solution trapping in local optima. By conducting extensive simulation experiments, the proposed VNIG shows superior performance compared with artificial chemical reaction optimization (CRO, 2017), the discrete artificial bee colony algorithm (DABC, 2018), the iterative greedy algorithm with a variable neighborhood search scheme (IGR, 2021), and the evolution strategy approach (ES, 2022).(c) 2022 Elsevier B.V. All rights reserved.

关键词： Distributed flowshop scheduling problem Blocking Energy consumption cost local search algorithm Variable neighborhood search

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A mix integer programming model and solution approach to determine the optimum machine number in the unrelated parallel machine scheduling problem

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2022年第1期37卷 329-345页

作者： Sarac, Tugba Tutumlu, Busra Eskisehir Osmangazi Univ Ind Engn Dept TR-26480 Eskisehir Turkey Kutahya Dumlupinar Univ Ind Engn Dept TR-43100 Kutahya Turkey

It is assumed that all machines will be used in studies dealing with parallel machine scheduling problems. However, for some businesses having special processes, where large furnaces with very intense energy consumption are used during commissioning, it can be very critical to complete jobs using the least number of furnaces. In addition, for many businesses, doing their jobs with fewer machines creates opportunities for unused machines to be rented to another company or to accept additional jobs as much as the capacity of idle machines. For this reason, in this study, the assumption that all machines will be used has been removed and a mathematical model has been proposed that will decide both which machines will be used and which jobs will be produced in which order on these machines, for the unrelated parallel machine scheduling problem with sequence and machine dependent setup times and machine eligibility restriction. The objectives of the considered problem are minimizing the number of machines to be used and the completion time of the last job. The objective functions of the proposed multi-objective mathematical model are scalarized using the weighted sum method. In order to show the solution performance of the mathematical model, randomly generated test problems were solved with GAMS / CPLEX. To solve the large problems, a local search algorithm and a genetic algorithm have been proposed due to the lack of feasible solutions with GAMS / CPLEX. In the large-scale problem, when all weight pairs are taken into account, genetic algorithm is more successful than local search algorithm an average of 25.64% in terms of solution quality and 50.31% in terms of time.

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APPROXIMATION algorithm FOR SPHERICAL k-MEANS PROBLEM WITH PENALTY

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2022年第4期18卷 2277-2287页

作者： Wu, Chenchen Lv, Wei Wang, Yujie Xu, Dachuan Nankai Univ Sch Business Tianjin 300071 Peoples R China Tianjin Univ Technol Coll Sci Tianjin 300384 Peoples R China Tianjin Renai Coll Comp Sci & Technol Dept Tianjin 306136 Peoples R China Beijing Univ Technol Dept Operat Res & Informat Engn Beijing 100124 Peoples R China

The k-means problem is a classical combinatorial optimization problem which has lots of applications in many fields such as machine learn-ing, data mining, etc. We consider a variant of k-means problem in the spherical space, that is, spherical k-means problem with penalties. In the problem, it is allowable that some nodes in the spherical space can not be clustered by paying some penalty costs. Based on local search scheme, we propose a (4(11 + 4 root 7) + epsilon)-approximation algorithm using singe-swap oper-ation, where E is a positive constant.

关键词： k-means local search algorithm approximation algorithm

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Towards a distributed local-search approach for partitioning large-scale social networks

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INFORMATION SCIENCES 2020年 508卷 200-213页

作者： Zheng, Bin Liu, Ouyang Li, Jing Lin, Yong Chang, Chong Li, Bo Chen, Tefeng Peng, Hao State Grid Zhejiang Elect Power Co Beijing Zhejiang Peoples R China State Grid Jiaxing Elect Power Supply Co Jiaxing Peoples R China State Grid Taizhou Elect Power Supply Co Taizhou Peoples R China State Grid Shaoxing Elect Power Supply Co Shaoxing Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

Large-scale social graph data poses significant challenges for social analytic tools to monitor and analyze social networks. A feasible solution is to parallelize the computation and leverage distributed graph computing frameworks to process such big data. However, it is nontrivial to partition social graphs into multiple parts so that they can be computed on distributed platforms. In this paper, we propose a distributed local search algorithm, named dLS, which enables quality and efficient partition of large-scale social graphs. With the vertex-centric computing model, dLS can achieve massive parallelism. We employ a distributed graph coloring strategy to differentiate neighbor nodes and avoid interference during the parallel execution of each vertex. We convert the original graph into a small graph, Quotient Network, and obtain local search solution from processing the Quotient Network, thus further improving the partition quality and efficiency of dLS. We have evaluated the performance of dLS experimentally using real-life and synthetic social graphs, and the results show that dLS outperforms two state-of-the-art algorithms in terms of partition quality and efficiency. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Graph partitioning Social network local search algorithm

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What Makes The Dynamic Capacitated Arc Routing Problem Hard To Solve: Insights From Fitness Landscape Analysis

What Makes The Dynamic Capacitated Arc Routing Problem Hard ...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Tong, Hao Minku, Leandro L. Menzel, Stefan Sendhoff, Bernhard Yao, Xin Univ Birmingham Sch Comp Sci Birmingham W Midlands England Honda Res Inst Europe Offenbach Germany SUSTech Dept Comp Sci & Engn Shenzhen Peoples R China SUSTech Res Inst Trustworthy Autonomous Syst RITAS Shenzhen Peoples R China SUSTech Guangdong Key Lab Brain Inspired Intelligent Comp Shenzhen Peoples R China

ISBN: (纸本)9781450392372

The Capacitated Arc Routing Problem (CARP) aims at assigning vehicles to serve tasks which are located at different arcs in a graph. However, the originally planned routes are easily affected by different dynamic events like newly added tasks. This gives rise to Dynamic CARP (DCARP) instances, which need to be efficiently optimized for new high-quality service plans in a short time. However, it is unknown which dynamic events make DCARP instances especially hard to solve. Therefore, in this paper, we provide an investigation of the influence of different dynamic events on DCARP instances from the perspective of fitness landscape analysis based on a recently proposed hybrid local search (HyLS) algorithm. We generate a large set of DCARP instances based on a variety of dynamic events and analyze the fitness landscape of these instances using several different measures such as fitness correlation length. From the empirical results we conclude that cost-related events have no significant impact on the difficulty of DCARP instances, but instances which require more new vehicles to serve the remaining tasks are harder to solve. These insights improve our understanding of the DCARP instances and pave the way for future work on improving the performance of DCARP algorithms.

关键词： Fitness Landscape Analysis Dynamic CARP local search algorithm Dynamic Events

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An Evolutionary Variable Neighbourhood search for the Unrelated Parallel Machine Scheduling Problem

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IEEE ACCESS 2021年 9卷 42857-42867页

作者： Abdullah, Salwani Turky, Ayad Nazri, Mohd Zakree Ahmad Sabar, Nasser R. Univ Kebangsaan Malaysia Ctr Artificial Intelligence Technol Data Min & Optimisat Res Grp Bangi 43600 Malaysia Victoria Univ Coll Engn & Sci Melbourne Vic 3011 Australia La Trobe Univ Dept Comp Sci & Informat Technol Melbourne Vic 3083 Australia

This article addresses a challenging industrial problem known as the unrelated parallel machine scheduling problem (UPMSP) with sequence-dependent setup times. In UPMSP, we have a set of machines and a group of jobs. The goal is to find the optimal way to schedule jobs for execution by one of the several available machines. UPMSP has been classified as an NP-hard optimisation problem and, thus, cannot be solved by exact methods. Meta-heuristic algorithms are commonly used to find sub-optimal solutions. However, large-scale UPMSP instances pose a significant challenge to meta-heuristic algorithms. To effectively solve a large-scale UPMSP, this article introduces a two-stage evolutionary variable neighbourhood search (EVNS) methodology. The proposed EVNS integrates a variable neighbourhood search algorithm and an evolutionary descent framework in an adaptive manner. The proposed evolutionary framework is employed in the first stage. It uses a mix of crossover and mutation operators to generate diverse solutions. In the second stage, we propose an adaptive variable neighbourhood search to exploit the area around the solutions generated in the first stage. A dynamic strategy is developed to determine the switching time between these two stages. To guide the search towards promising areas, a diversity-based fitness function is proposed to explore different locations in the search landscape. We demonstrate the competitiveness of the proposed EVNS by presenting the computational results and comparisons on the 1640 UPMSP benchmark instances, which have been commonly used in the literature. The experiment results show that our EVNS obtains better results than the compared algorithms on several UPMSP instances.

关键词： Switches Heuristic algorithms Space exploration Job shop scheduling Benchmark testing Optimization Licenses Machine scheduling problem genetic algorithm local search algorithm

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Train service design in an urban rail transit line incorporating multiple service routes and multiple train compositions

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TRANSPORTATION REsearch PART C-EMERGING TECHNOLOGIES 2021年 123卷

作者： Li, Zhengyang Zhao, Jun Peng, Qiyuan Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 611756 Sichuan Peoples R China Southwest Jiaotong Univ Natl United Engn Lab Integrated & Intelligent Tra Chengdu 611756 Sichuan Peoples R China Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China

This paper focuses on the train service design problem within a given period in an urban rail transit line, where multiple either full-length or short-turn service routes can be operated, and each service route can utilize one of several different train compositions. The problem lies on determining the turn-back stations, train composition and frequency of each service route operated on the line. Considering the interests of operators and passengers, we decompose the problem as two subproblems namely train service configuration and passenger assignment. The first subproblem is formulated as an integer linear programming model with the objective of minimizing operators' cost. Given a train service scheme, the second subproblem is modelled as a capacitated continuous multi-commodity flow model to minimize passengers' waiting time cost and transfer cost. The optimal strategy is extended to determine the behaviour of passengers and capture the extra waiting time of passengers under capacity constraint. The two sub-models are weighted and integrated into a mixed integer nonlinear programming model, which is further transformed into a mixed integer linear programming model using a novel linearization method. By exploiting the special characteristics of the model, a tailored and easy to implement local search algorithm is developed to solve large-scale instances. Starting from the operator-optimum solution which can be easily obtained, the algorithm solves the two sub-models iteratively to search better solutions within a precalculated search range which is smaller than the complete feasible domain. Finally, different sizes of instances constructed from two urban rail transit lines are utilized to demonstrate the performance and practicability of the proposed approaches.

关键词： Urban rail transit Train service design Multiple service routes Multiple train compositions local search algorithm

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Heuristic approaches for the family traveling salesman problem

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL REsearch 2021年第1期28卷 262-295页

作者： Bernardino, Raquel Paias, Ana Univ Lisbon Fac Ciencias DEIO C6Piso 4 P-1749016 Lisbon Portugal Univ Lisbon Fac Ciencias Ctr Matemat Aplicacoes Fundamentais & Invest Oper C6Piso 1 P-1749016 Lisbon Portugal

In this article, we address the family traveling salesman problem (FTSP), an NP-hard problem that may be seen as a generalization of the traveling salesman problem. In the FTSP, the set of cities is partitioned into several families and one wants to find the minimum cost route that visits a given number of cities in each family. We propose two metaheuristics, a population-based method and a local search method, and a hybrid algorithm, which combines a branch-and-cut algorithm with a local search procedure. All the proposed methods improve the best known upper bounds from the literature. The local search method and the hybrid algorithm improve the best known upper bounds for all the benchmark instances with unknown optimal value, while the population-based method improves the best known upper bounds for all instances, except two. Furthermore, we developed an instance generator to create additional test instances with different visit patterns. These newly generated instances were considered in the computational experiment and, thus, we provide optimal values or upper bounds for each instance. Additionally, we created a website dedicated to the FTSP, where this information is made available to the scientific community ().

关键词： family traveling salesman problem genetic algorithm local search algorithm hybrid algorithm

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A new hybrid genetic algorithm for protein structure prediction on the 2D triangular lattice

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2021年第2期29卷 499-513页

作者： Boumedine, Nabil Bouroube, Sadek Univ Sci & Technol Houari Boumediene Fac Math Dept Operat Res Algiers Algeria

The flawless functioning of the protein is essentially related to its three-dimensional structure. Therefore, predicting protein structure from its amino acid sequence is a fundamental problem that draws researchers' attention in many areas. The protein structure prediction problem (PSP) can be formulated as a combinatorial optimization problem based on simplified lattice models such as the hydrophobic-polar model. In this paper, we propose a new hybrid algorithm that combines three different known heuristic algorithms: the genetic algorithm, the tabu search strategy, and the local search algorithm to solve the PSP problem. Regarding the evaluation of the proposed approach, we present an experimental study, where we consider the quality of the product solution as the main assessment criterion. Furthermore, we compared the proposed algorithm with state-of-the-art algorithms using a selection of well-studied benchmark instances.

关键词： Protein structure prediction 2D triangular lattice HP model genetic algorithm local search algorithm tabu search strategy minimal energy conformation

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Stable Resolving - A Randomized local search Heuristic for MaxSAT 43rd

Stable Resolving - A Randomized Local Search Heuristic for M...

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43rd German Conference on Artificial Intelligence (KI)

作者： Reisch, Julian Grossmann, Peter Kliewer, Natalia Synopt GmbH Chemnitzer Str 48b D-01187 Dresden Germany Free Univ Berlin Garystr 21 D-14195 Berlin Germany

ISBN: (纸本)9783030582845;9783030582852

Many problems from industrial applications and AI can be encoded as Maximum Satisfiability (MaxSAT). Often, it is more desirable to produce practicable results in very short time compared to optimal solutions after an arbitrary long computation time. In this paper, we propose Stable Resolving (SR), a novel randomized local search heuristic for MaxSAT with that aim. SR works for both weighted and unweighted instances. Starting from a feasible initial solution, the algorithm repeatedly performs the three steps of perturbation, improvements and solution checking. In the perturbation, the search space is explored at the cost of possibly worsening the current solution. The local improvements work by repeatedly flipping signs of variables in over-satisfied clauses. Finally, the algorithm performs a solution checking in a simulated annealing fashion. We compare our approach to state-of-the-art MaxSAT solvers and show by numerical experiments on benchmark instances from the annual MaxSAT competition that SR performs comparable on average and is even the best solver for particular problem instances.

关键词： Maximum Satisfiability MaxSAT Incomplete solving Randomized algorithm local search algorithm Simulated annealing

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