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Hybridizing a multi-objective simulated annealing algorithm with a multi-objective evolutionary algorithm to solve a multi-objective project scheduling problem

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第7期40卷 2421-2434页

作者： Yannibelli, Virginia Amandi, Analia UNCPBA Fac Cs Exactas ISISTAN Res Inst RA-7000 Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn RA-1033 Buenos Aires DF Argentina

In this paper, a multi-objective project scheduling problem is addressed. This problem considers two conflicting, priority optimization objectives for project managers. One of these objectives is to minimize the project makespan. The other objective is to assign the most effective set of human resources to each project activity. To solve the problem, a multi-objective hybrid search and optimization algorithm is proposed. This algorithm is composed by a multi-objective simulated annealing algorithm and a multi-objective evolutionary algorithm. The multi-objective simulated annealing algorithm is integrated into the multi-objective evolutionary algorithm to improve the performance of the evolutionary-based search. To achieve this, the behavior of the multi-objective simulated annealing algorithm is self-adaptive to either an exploitation process or an exploration process depending on the state of the evolutionary-based search. The multi-objective hybrid algorithm generates a number of near non-dominated solutions so as to provide solutions with different trade-offs between the optimization objectives to project managers. The performance of the multi-objective hybrid algorithm is evaluated on nine different instance sets, and is compared with that of the only multi-objective algorithm previously proposed in the literature for solving the addressed problem. The performance comparison shows that the multi-objective hybrid algorithm significantly outperforms the previous multi-objective algorithm. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： multi-objective project scheduling multi-objective hybrid algorithm multi-objective simulated annealing algorithm multi-objective evolutionary algorithm Non-dominated solutions

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A multi-Agent System based on the multi-objective simulated annealing algorithm for the Static Dial a Ride Problem

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IFAC Proceedings Volumes 2011年第1期44卷 2172-2177页

作者： Issam ZIDI Kamel ZIDI Khaled MESGHOUNI Khaled GHEDIRA LI3 : Laboratoire d'Ingénierie Informatique Intelligente / Institut Supérieur de Gestion de Tunis 41 Rue de la Liberté Cité Bouchoucha 2000 Le Bardo Tunis –TUNISIE LAGIS : Laboratoire d'Automatique Génie Informatique et Signal / Ecole Centrale de Lillle Cité scientifique – B.P. 48-59651 Villeneuve d'Ascq

Abstract The Dial a Ride Problem (DRP) is to take passengers from a place of departures to places of arrivals. Different versions of the Dial a Ride Problem are found in every day practice; transportation of people in low-density areas, transportation of the handicapped and elderly persons and parcel pick-up and delivery service in urban areas. The ultimate aim is to offer an alternative to displacement optimized individually and collectively. Indeed the DRP is a multi-criteria problem, the proposed solution of which aims to reduce both route duration in response to a certain quality of service provided. In this work, we offer our contribution to the study and solving the DRP in the application using a multi agent system based on the multi-objective simulated annealing algorithm. Tests show competitive results on (Cordeau and Laporte, 2003) benchmark datasets while improving processing times to obtain a pareto solution for the problem in concern.

关键词： Heuristics Dial a Ride Problem Passenger Transportation multi-Criteria Optimization multi-objective simulated annealing algorithm multi-Agent System

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A multi-objective simulated annealing for the multi-criteria dial a ride problem

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2012年第6期25卷 1121-1131页

作者： Zidi, Issam Mesghouni, Khaled Zidi, Kamel Ghedira, Khaled Inst Super Gest Tunis 41 SOIE Lab Strategies Optimisat & Informat Intellig Tunis Tunisia Ecole Cent Lille Cite Sci LAGIS F-59651 Villeneuve Dascq France

The paper describes a multi-objective mathematical model for Dial a Ride Problem (DRP) and an application of multi-objective simulated annealing (MOSA) to solve it. DRP is to take over the passenger from a place of departure to a place of arrival. In the DRP, customers send transportation requests to an operator. A request consists of a specified pickup location and destination location along with a desired departure or arrival time. The ultimate aim is to offer an alternative to displacement optimized individually and collectively. The DRP is classified as NP-hard problem that's why most research has been concentrated on the use of approximate methods to solve it. Indeed the DRP is a multi-criteria problem, the proposed solution of which aims to reduce both route duration in response to a certain quality of service provided. In this work, we offer our contribution to the study and solving the DRP in the application using the MOSA algorithm. Tests show competitive results on (Cordeau and Laporte, 2003a) benchmark datasets while improving processing times. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Heuristics Dial a ride problem Passenger transportation multi-criteria optimization multi-objective simulated annealing algorithm

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A multi-objective simulated annealing for the multi-Criteria Dial a Ride Problem

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IFAC Proceedings Volumes 2010年第13期43卷 263-268页

作者： Issam ZIDI Khaled MESGHOUNI Kamel ZIDI Khaled GHEDIRA SOIE: Strétégies d'Optimisation de l'Ingénierie des Informations et de la connaissancE / Institut Supérieur de Gestion de Tunis 41Rue de la Liberté Cité Bouchoucha 2000 Le Bardo Tunis –TUNISIE LAGIS: Laboratoire d'Automatique Génie Informatique et Signal / Ecole Centrale de Lillle Cité scientifique - B.P. 48-59651 Villeneuve d'Ascq

The paper describes a multi-objective mathematical model for Dial a Ride Problem (DRP) and an application of multi-objective simulated annealing (MOSA) to solve it. The ultimate aim is to offer an alternative to displacement optimized individually and collectively. Indeed, the DRP is a multi-criteria problem, the proposed solution of which aims to reduce both route duration in response to a certain quality of service provided. In this work, we offer our contribution to the study and solving the DRP in the application using the MOSA algorithm. Tests show competitive results on (Cordeau and Laporte, 2003) benchmark datasets while improving processing times.

关键词： Heuristics Dial a Ride Problem Passenger Transportation multi-Criteria Optimization multi-objective simulated annealing algorithm

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Sequencing Mixed-model Production Systems by Modified multi-objective Genetic algorithms

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Chinese Journal of Mechanical Engineering 2010年第5期23卷 537-546页

作者： WANG Binggang Department of Industry and Business Administration Henan University of Urban Construction Pingdingshan 467044 China

As two independent problems,scheduling for parts fabrication line and sequencing for mixed-model assembly line have been addressed respectively by many ***,these two problems should be considered simultaneously to improve the efficiency of the whole fabrication/assembly *** far,little research effort is devoted to sequencing problems for mixed-model fabrication/assembly *** paper is concerned about the sequencing problems in pull production systems which are composed of one mixed-model assembly line with limited intermediate buffers and two flexible parts fabrication flow lines with identical parallel machines and limited intermediate *** objectives are considered simultaneously：minimizing the total variation in parts consumption in the assembly line and minimizing the total makespan cost in the fabrication/assembly *** integrated optimization framework,mathematical models and the method to construct the complete schedules for the fabrication lines according to the production sequences for the first stage in fabrication lines are *** the above problems are non-deterministic polynomial-hard（NP-hard）,a modified multi-objective genetic algorithm is proposed for solving the models,in which a method to generate the production sequences for the fabrication lines from the production sequences for the assembly line and a method to generate the initial population are put forward,new selection,crossover and mutation operators are designed,and Pareto ranking method and sharing function method are employed to evaluate the individuals＇ *** feasibility and efficiency of the multi-objective genetic algorithm is shown by computational comparison with a multi-objective simulated annealing *** sequencing problems for mixed-model production systems can be solved effectively by the proposed modified multi-objective genetic algorithm.

关键词： mixed-model production system sequencing parallel machine buffers multi-objective genetic algorithm multi-objective simulated annealing algorithm

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A multi-objective Optimization Approach for Emergency Medical Service Facilities Location-Allocation in Rural Areas

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RISK MANAGEMENT AND HEALTHCARE POLICY 2022年 15卷 473-490页

作者： Chen, Yulong Lai, Zhizhu Henan Univ Key Res Inst Yellow River Civilizat & Sustainable Kaifeng Henan Peoples R China Henan Univ Collaborat Innovat Ctr Yellow River Civilizat Hen Kaifeng Henan Peoples R China Henan Univ Coll Environm & Planning Kaifeng Henan Peoples R China Gannan Normal Univ Sch Geog & Environm Engn 1 South Shida Rd Ganzhou 341000 Jiangxi Peoples R China

Purpose: The location of emergency medical service (EMS) facilities is a basic facility location problem. Many scholars have examined this kind of problem, but research on the location of EMS facilities in rural areas is still lacking. Different from urban areas, the location optimization of EMS facilities in rural areas must consider the accessibility of roads. The objective of this study conducted the optimal locations of new EMS stations and construction/upgrading of transfer links aiming to improve the medical emergency efficiency of mountain rural areas. Methods: Three multi-objective models were constructed to examine the effects of varying assumptions (suppose existing roads cannot be upgraded, existing roads can be upgraded, and existing roads can be upgraded and new roads can be constructed) about minimizing the population considered uncovered (response time from the residential to the EMS station less than or equal to 0.5 h), time spent traveling from the residential area to the EMS station, construction costs for building new emergency facilities, and costs for improving or building new roads. Furthermore, we developed an improved multi-objective simulated annealing algorithm to examine the problem of optimizing the design of rural EMS facilities. Results: We tested the models and algorithm on the Miao Autonomous County of Songtao, Guizhou Province, China. According to the actual situation of the case area, the models and algorithm were tested with the assumption that only three new EMS stations would be constructed. The number of people not covered by EMS stations decreased from 30.7% in Model 1 to 22% in Model 2, and then to 18.9% in Model 3. Conclusion: Our study showed that the traffic network had a significant impact on the location optimization of EMS stations in mountainous rural areas. Improving the traffic network conditions could effectively improve the medical emergency efficiency of mountain rural areas.

关键词： emergency medical service station multi-objective simulated annealing algorithm the problem of facility location design traffic network

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Modelling Cross-Docking in a Three-Level Supply Chain with Stochastic Service and Queuing System: MOWFA algorithm

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algorithmS 2022年第8期15卷 265-265页

作者： Rostami, Parinaz Darestani, Soroush Avakh Movassaghi, Mitra Islamic Azad Univ Fac Ind & Mech Engn Dept Ind Engn Qazvin *** Iran London Metropolitan Univ Guidhall Sch Business & Law London N7 8DB England

In today's competitive world, it is essential to provide a new method through which maximum efficiency can be created in the production and supply cycle. In many production environments, sending goods directly from the producer to the consumer brings many problems. Therefore, an efficient transport system should be established between producers and consumers. Such a system is designed in the field of supply chain management knowledge. Supply chain management is the evolutionary result of warehousing management and is one of the important infrastructural foundations of business implementation, in many of which the main effort is to shorten the time between the customer's order and the actual delivery of the goods. In this research, the supply chain consists of three levels. Suppliers are placed on the first level, cross-docks on the second level, and factories on the third level. In this system, a number of suppliers send different raw materials to several different cross-docks. Each channel is assigned to a cross-dock for a specific product. The main goal of this article is to focus on optimizing the planning of incoming and outgoing trucks with the aim of minimizing the total operation time within the supply chain. The arrival rate of goods from suppliers to the cross-dock is stochastic with a general probability distribution. On the other hand, the time required to prepare and send the goods is random with a general probability distribution. The service time in each cross-dock depends on the number of its doors. Therefore, each cross-dock can be modeled as a G/G/m queueing system where m represents the number of doors. The mathematical model of the research has been developed based on these assumptions. Since the problem is NP-hard, the time to solve it increases drastically with the increase in the dimensions of the problem. Therefore, three metaheuristics, including multi-objective water flow, non-dominated sorting genetic, and a multi-objective simulated anneal

关键词： cross-docks multi-objective water flow algorithm non-dominated sorting genetic algorithm multi-objective simulated annealing algorithm

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Optimal deployment of the online monitoring equipment at the edges of substations considering spatial constraint

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ADVANCES IN MECHANICAL ENGINEERING 2024年第5期16卷

作者： Xu, Wenxiang Tong, Shaocong Xu, Shimin Du, Baigang Liu, Dezheng Qin, Tao Hubei Univ Arts & Sci Sch Mech Engn Xiangyang Peoples R China Hubei Univ Arts & Sci Hubei Longzhong Lab Xiangyang Peoples R China Wuhan Univ Technol Sch Mech & Elect Engn Wuhan Peoples R China Wuhan Univ Technol 122 Luoshi Rd Wuhan 430070 Hubei Peoples R China

To realize the optimal deployment of online monitoring equipment at the edges of substations under the cloud-edge collaboration framework, an optimal deployment model of edges considering spatial constraints is proposed. In the model, the constraints including edge deployment point, line of sight, as well as device pose, etc. are taken into account. To achieve the one-to-many collection of the deployed equipment, a mathematical model is constructed with the objectives of minimizing the shooting distance and the number of edge equipments. And an archive based multi-objective simulated annealing algorithm based on improved trending Markov chain (IAMOSA) is proposed to solve the problem. This algorithm utilizes greedy clustering to initialize deployment points, and the improved disturbance step length with tendency is used to search the neighborhood space. Besides, polynomial fitting Pareto front is also used to select and guide the Markov chain and archive population. Finally, the feasibility and effectiveness of the proposed model and algorithm are verified through an experiment of optimal deployment of the edge equipments in a certain substation.

关键词： Online monitoring equipment optimal deployment edges of substations multi-objective simulated annealing algorithm improved trending Markov chain

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