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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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Multi-objective reinforcement learning framework for dynamic flexible job shop scheduling problem with uncertain events

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APPLIED SOFT COMPUTING 2022年 131卷

作者： Wang, Hao Cheng, Junfu Liu, Chang Zhang, Yuanyuan Hu, Shunfang Chen, Liangyin Sichuan Univ Sch Comp Sci Chengdu 610065 Peoples R China Civil Aviat Adm Res Inst 2 Chengdu 610041 Peoples R China Sichuan Univ Inst Ind Internet Res Chengdu 610065 Peoples R China

The economic benefits for manufacturing companies will be influenced by how it handles potential dynamic events and performs multi-objective real-time scheduling for existing dynamic events. Based on these, we propose a new dynamic multi-objective flexible job shop scheduling problem (DMFJSP) to simulate realistic production environment. Six dynamic events are involved in the problem including job insertion, job cancellation, job operation modification, machine addition, machine tool replacement and machine breakdown. As well as three objectives of longest job processing time (makespan), average machine utilization and average job processing delay rate with a set of constraints are also raised in the study. Then this research designs a novel dynamic multi-objective scheduling algorithm based on deep reinforcement learning. The algorithm uses two deep Q-learning networks and a real-time processing framework to process each dynamic event and generate complete scheduling scheme. In addition, an improved local search algorithm is adopted to further optimize the scheduling results and the idea of combination is used to make the scheduling rules more comprehensive. Experiments on 27 instances show the superiority and stability of our approach compared to each proposed combined rule, well-known scheduling rules and standard deep Q-learning based algorithms. Compared to the current optimal deep Q-learning method, the maximum performance improvement for our three objectives are approximately 57%, 164% and 28%.(c) 2022 Published by Elsevier B.V.

关键词： scheduling problem Real-time processing framework Deep reinforcement learning local search algorithm Dynamic multi-objective flexible job shop

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Development of Heuristic Approaches for Last-Mile Delivery TSP with a Truck and Multiple Drones

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DRONES 2023年第7期7卷

作者： Rinaldi, Marco Primatesta, Stefano Bugaj, Martin Rostas, Jan Guglieri, Giorgio Politecn Torino Dept Mech & Aerosp Engn Corso Duca Abruzzi 24 I-10129 Turin Italy Univ Zilina Air Transport Dept Univ 8215-1 Zilina 01026 Slovakia

Unmanned Aerial Vehicles (UAVs) are gaining momentum in many civil and military sectors. An example is represented by the logistics sector, where UAVs have been proven to be able to improve the efficiency of the process itself, as their cooperation with trucks can decrease the delivery time and reduce fuel consumption. In this paper, we first state a mathematical formulation of the Travelling Salesman Problem (TSP) applied to logistic routing, where a truck cooperates synchronously with multiple UAVs for parcel delivery. Then, we propose, implement, and compare different sub-optimal routing approaches to the formulated mFSTSP (multiple Flying Sidekick Travelling Salesman Problem) since the inherent combinatorial computational complexity of the problem makes it unattractable for commercial Mixed-Integer Linear Programming (MILP) solvers. A local search algorithm, two hybrid genetic algorithms that permutate feasible and infeasible solutions, and an alternative ad-hoc greedy method are evaluated in terms of the total delivery time of the output schedule. For the sake of the evaluation, the savings in terms of delivery time over the well-documented truck-only TSP solution are investigated for each proposed routing solution, and this is repeated for two different scenarios. Monte Carlo simulations corroborate the results.

关键词： TSP genetic algorithm last-mile delivery task scheduling local search algorithm truck and drones UAV heuristics routing urban air mobility logistics mFSTSP

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In silico evaluation of the influence of the translocon on partitioning of membrane segments

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BMC BIOINFORMATICS 2014年第1期15卷 1-7页

作者： Tessier, Dominique Laroum, Sami Duval, Beatrice Rath, Emma M. Church, W. Bret Hao, Jin-Kao INRA Biopolymeres Interact & Assemblages UR1268 F-44316 Nantes France LERIA F-49045 Angers France Univ Sydney Fac Pharm Grp Biomol Struct & Informat Sydney NSW 2006 Australia

Background: The locations of the TM segments inside the membrane proteins are the consequence of a cascade of several events: the localizing of the nascent chain to the membrane, its insertion through the translocon, and the conformation adopted to reach its stable state inside the lipid bilayer. Even though the hydrophobic h-region of signal peptides and a typical TM segment are both composed of mostly hydrophobic side chains, the translocon has the ability to determine whether a given segment is to be inserted into the membrane. Our goal is to acquire robust biological insights into the influence of the translocon on membrane insertion of helices, obtained from the in silico discrimination between signal peptides and transmembrane segments of bitopic proteins. Therefore, by exploiting this subtle difference, we produce an optimized scale that evaluates the tendency of each amino acid to form sequences destined for membrane insertion by the translocon. Results: The learning phase of our approach is conducted on carefully chosen data and easily converges on an optimal solution called the PMIscale (Potential Membrane Insertion scale). Our study leads to two striking results. Firstly, with a very simple sliding-window prediction method, PMIscale enables an efficient discrimination between signal peptides and signal anchors. Secondly, PMIscale is also able to identify TM segments and to localize them within protein sequences. Conclusions: Despite its simplicity, the localization method based on PMIscale nearly attains the highest level of TM topography prediction accuracy as the current state-of-the-art prediction methods. These observations confirm the prominent role of the translocon in the localization of TM segments and suggest several biological hypotheses about the physical properties of the translocon.

关键词： Translocon Transmembrane helix prediction local search algorithm

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Indicator Weighted Based Multi-Objective Approach using Self-Adaptive Neighborhood Operator

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Procedia Computer Science 2021年 192卷 338-347页

作者： Imen BenMansour Ines Alaya Moncef Tagina Esprit School Of Engineering Tunis Tunisia ENSI-LARIA University of Manouba Tunisia

Balancing diversity and convergence seems to be a difficult task when solving multi-objective optimization problems (MOPs). For addressing this issue, researchers’ interest has been drawn to hybrid approaches since this cooperation allows benefiting from the advantages of both approaches and gains better trade-offs overall. Considering such fact, this paper aims at introducing a hybrid approach as a synergy of a multi-directional Ant Colony Optimization algorithm with a local search method based on a weighted version of epsilon Indicator using a self-adaptive neighborhood operator coined as Indicator Weighted Based local search with Ant Colony Optimization (IWBLS/ACO) to handle the knapsack problem within the multi-objective framework. In IWBLS/ACO, initial solutions are created by the ant colony. Then, the enhancement phase is ensured by the local search procedure. The algorithm is evolving based on different configurations of the epsilon quality indicator through different weight vectors. Moreover, we propose in this work, a novel self-adaptive neighborhood operator which changes automatically and dynamically as the IWBLS algorithm runs. The proposed IWBLS/ACO was tested on widely used Multi-objective Multidimensional Knapsack Problem (MOMKP) instances and compared with powerful state-of-the-art algorithms. Experimental results highlight that the proposed approach can lead to finding a good compromise between exploration and exploitation.

关键词： Multi-objective optimization Ant Colony Optimization local search algorithm Weighted epsilon quality indicator Self-adaptive neighborhood operator

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A task scheduling algorithm with deadline constraints for distributed clouds in smart cities

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PEERJ COMPUTER SCIENCE 2023年 9卷 e1346页

作者： Zhou, Jincheng Liu, Bo Gao, Jian Qiannan Normal Univ Natl Sch Comp & Informat Duyun Guizhou Peoples R China Key Lab Complex Syst & Intelligent Optimizat Guiz Duyun Guizhou Peoples R China Guizhou Univ Coll Comp Sci & Technol State Key Lab Publ Big Data Guiyang Guizhou Peoples R China Northeast Normal Univ Coll Informat Sci & Technol Changchun Jilin Peoples R China

Computing technologies and 5G are helpful for the development of smart cities. Cloud computing has become an essential smart city technology. With artificial intelligence technologies, it can be used to integrate data from various devices, such as sensors and cameras, over the network in a smart city for management of the infrastructure and processing of Internet of Things (IoT) data. Cloud computing platforms provide services to users. Task scheduling in the cloud environment is an important technology to shorten computing time and reduce user cost, and thus has many important applications. Recently, a hierarchical distributed cloud service network model for the smart city has been proposed where distributed (micro) clouds, and core clouds are considered to achieve a better network architecture. Task scheduling in the model has attracted many researchers. In this article, we study a task scheduling problem with deadline constraints in the distributed cloud model and aim to reduce the communication network's data load and provide low-latency services from the cloud server in the local area, hence promoting the efficiency of cloud computing services for local users. To solve the task scheduling problem efficiently, we present an efficient local search algorithm to solve the problem. In the algorithm, a greedy search strategy is proposed to improve the current solutions iteratively. Moreover, randomized methods are used in selecting tasks and virtual machines for reassigning tasks. We carried out extensive computational experiments to evaluate the performance of our algorithm and compared experimental results with Swarm-based approaches, such as GA and PSO. The comparative results show that the proposed local search algorithm performs better than the comparative algorithms on the task scheduling problem.

关键词： Smart cities Task scheduling Distributed clouds local search algorithm

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Modeling Newspaper Distribution as Capacitated Vehicle Routing Problem with Time Window: Case Study of Daily Graphic Newspaper, Ashanti Region, Ghana

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Archives of Current Research International 2015年第1期2卷

作者： Wallace Agyei Kwaku Darkwah William Obeng-Denteh Emmanuel Appoh Andam Tweneboah Kodua Senior High School Department of Mathematics Kwame Nkrumah University of Science and Technology

The distribution problem of Daily Graphic newspaper in Ashanti region, Ghana is discussed in this paper. The problem was modelled as Capacitated Vehicle Routing Problem with Time Window (CVRPTW) and the Clark and Wright"s Savings with local search algorithm was used to solve the problem. The algorithm takes the travel time matrix as input and proceeds to find the travel time savings between all the district capitals. The proposed algorithm was integrated into VRP heuristic program. Comparison of results in terms of the total traveling time obtained by the Clarke and Wright savings with local search algorithm and the current manual routes maintained by company indicated that the current total travelling time can be reduced by up to 21.9%.

关键词： Vehicle routing problem clarke and wright savings algorithm newspaper delivery problem local search algorithm

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Solving Travelling Salesman Problem (TSP) by Hybrid Genetic algorithm (HGA)

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第6期11卷 376-384页

作者： Al-Ibrahim, Ali Mohammad Hussein World Islamic Sci & Educ Univ WISE Fac Informat Technol Dept Comp Sci POB 1101 Amman 11947 Jordan

The Traveling Salesman Problem (TSP) is easy to qualify and describe but difficult and very hard to be solved. There is known algorithm that can solve it and find the ideal outcome in polynomial time, so it is NP-Complete problem. The Traveling Salesman Problem (TSP) is related to many others problems because the techniques used to solve it can be easily used to solve other hard Optimization problems, which allows of circulating it results on many other optimization problems. Many techniques were proposed and developed to solve such problems, including Genetic algorithms. The aim of the paper is to improve and enhance the performance of genetic algorithms to solve the Traveling Salesman Problem (TSP) by proposing and developing a new Crossover mechanism and a local search algorithm called the search for Neighboring Solution algorithm, with the goal of producing a better solution in a shorter period of time and fewer generations. The results of this study for a number of different size standard benchmarks of TSP show that the proposed algorithms that use Crossover proposed mechanism can find the optimum solution for many of these TSP benchmarks by (100%), and within the rate (96%-99%) of the optimal solution to some for others. The comparison between the proposed Crossover mechanism and other known Crossover mechanisms show that it improves the quality of the solutions. The proposed local search algorithm and Crossover mechanism produce superior results compared to previously propose local search algorithms and Crossover mechanisms. They produce near optimum solutions in less time and fewer generations.

关键词： Traveling Salesman Problem (TSP) NP-Complete problem genetic algorithms local search algorithm crossover mechanism neighboring solution algorithm

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Shrinking Hyper-Sphere based Artificial Bee Colony algorithm

Shrinking Hyper-Sphere based Artificial Bee Colony Algorithm

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International Conference on Computer, Communication and Control

作者： Ashik Hussain Shruti Gupta Rohit Singh Prakriti Trivedi Harish Sharma Gov. Eng. Coll. Ajmer India

ISBN: (纸本)9781479981656

Artificial Bee Colony (ABC) is a popular swarm intelligence based approach used to solve nonlinear and complex optimization problems. It is a simple to implement and swarm based probabilistic algorithm. As the case of other swarm based algorithms, ABC is also computationally expensive due to its slow nature of search process. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. Therefore, to balance the exploration and exploitation characteristics of the ABC, Shrinking Hyper-Sphere based local search approach is developed and hybridized with in the ABC solution search process. The proposed algorithm is named as Shrinking Hyper-Sphere based ABC (SHABC). The experiments over 14 well known benchmark functions of complex in nature, show that the SHABC algorithm perform better than the original version of ABC and its latest variant, namely Modified ABC (MABC) in most of the experiments.

关键词： Swarm intelligence Evolutionary computation local search algorithm Shrinking Hyper-Sphere

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Lattice-Reduction Aided Optimum and Sub-Optimum SIC Detection for MIMO Systems

Lattice-Reduction Aided Optimum and Sub-Optimum SIC Detectio...

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IEEE Vehicular Technology Conference

作者： Sung-Ho Lim Ji-Woong Choi Department of Information and Communication Engineering DGIST Daegu Korea

ISBN: (纸本)9781479980925

Lattice reduction (LR) has been researched a lot in conjunction with ordered successive interference cancellation (OSIC), contributing large enhancement of the performance with relatively small complexity. However, the performance gap between the LR-aided OSIC (LR-OSIC) and maximum likelihood (ML) still exists. This paper suggests LR-aided all-ordering successive interference cancellation (LR-AOSIC) as an optimum successive interference cancellation (SIC) detector showing near-ML performance almost unaffected by channel correlation and modulation order. In addition, LR-OSIC using modified local search algorithm (LR-MLS-OSIC) is also proposed as a sub-optimum SIC detector to reduce the complexity of LR-AOSIC affordably, nearly maintaining the performance.

关键词： spatial multiplexing MIMO (SM MIMO) successive interference cancellation lattice reduction local search algorithm Interference cancellation multiple input multiple output systems searchING algorithm Optimum detector

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