A Multi-Objective and Multi-Dimensional Optimization Scheduling Method Using a Hybrid Evolutionary Algorithms with a Sectional Encoding Mode

Xu, Wenxiang; Guo, Sen

doi:10.3390/su11051329

Cited by 14 publications

(3 citation statements)

References 50 publications

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“…Cui et al [25] used a negotiation method based on game theory to study the MRTA problem, and designed a negotiation robot selection and negotiation set construction method based on a utility function, a negotiation mechanism is suitable for a distributed task allocation, and a negotiation strategy is based on game theory. Miyamoto and Inoue [26] established a model aiming at Automated Guided Vehicle (AGV) driving distance and delivery delay time, and proposed a local/random method. Xu and Guo [27] set up a model with the maximum completion time, machine energy consumption, and the number of AGVs as targets, and proposed a hybrid evolutionary algorithm based on a segmented coding mode.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Teaching-Learning-Based Optimizer for a Multi-Weeding Robot Task Assignment Problem

Kang,

Miao,

Pan

et al. 2024

Tsinghua Sci. Technol.

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With the emergence of the artificial intelligence era, all kinds of robots are traditionally used in agricultural production. However, studies concerning the robot task assignment problem in the agriculture field, which is closely related to the cost and efficiency of a smart farm, are limited. Therefore, a Multi-Weeding Robot Task Assignment (MWRTA) problem is addressed in this paper to minimize the maximum completion time and residual herbicide. A mathematical model is set up, and a Multi-Objective Teaching-Learning-Based Optimization (MOTLBO) algorithm is presented to solve the problem. In the MOTLBO algorithm, a heuristicbased initialization comprising an improved Nawaz Enscore, and Ham (NEH) heuristic and maximum loadbased heuristic is used to generate an initial population with a high level of quality and diversity. An effective teaching-learning-based optimization process is designed with a dynamic grouping mechanism and a redefined individual updating rule. A multi-neighborhood-based local search strategy is provided to balance the exploitation and exploration of the algorithm. Finally, a comprehensive experiment is conducted to compare the proposed algorithm with several state-of-the-art algorithms in the literature. Experimental results demonstrate the significant superiority of the proposed algorithm for solving the problem under consideration.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Teaching-Learning-Based Optimizer for a Multi-Weeding Robot Task Assignment Problem

Kang,

Miao,

Pan

et al. 2024

Tsinghua Sci. Technol.

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“…The work addresses an optimization problem that aims to reduce cost and increase satisfaction among the tenants in the building connected to the air conditioning system. Farhadiana [19] presents a solution for planning how to allocate virtual machines on physical equipment considering the operation and the resources used by each one. The objective is to reduce energy consumption-allocation planning is undertaken through a genetic algorithm solution.…”

Section: Literature Reviewmentioning

confidence: 99%

Reducing the Cost of Electricity by Optimizing Real-Time Consumer Planning Using a New Genetic Algorithm-Based Strategy

et al. 2020

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To ensure the use of energy produced from renewable energy sources, this paper presents a method for consumer planning in the consumer–producer–distributor structure. The proposed planning method is based on the genetic algorithm approach, which solves a cost minimization problem by considering several input parameters. These input parameters are: the consumption for each unit, the time interval in which the unit operates, the maximum value of the electricity produced from renewable sources, and the distribution of energy production per unit of time. A consumer can use the equipment without any planning, in which case he will consume energy supplied by a distributor or energy produced from renewable sources, if it is available at the time he operates the equipment. A consumer who plans his operating interval can use more energy from renewable sources, because the planning is done in the time interval in which the energy produced from renewable sources is available. The effect is that the total cost of energy to the consumer without any planning will be higher than the cost of energy to the consumer with planning, because the energy produced from renewable sources is cheaper than that provided from conventional sources. To be validated, the proposed approach was run on a simulator, and then tested in two real-world case studies targeting domestic and industrial consumers. In both situations, the solution proposed led to a reduction in the total cost of electricity of up to 25%.

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“…A distributed method of discrete-event simulation for highly distributed manufacturing systems was discussed in [5]. The required time to complete work orders, the amount of energy used to transport the loads, and the utilization of work stations are the highest performance standards for AGV material handling systems [6,7], all of which are widely used to relocate material in modern, flexible manufacturing systems [8] and which affect the effectiveness of manufacturing processes by enhancing their efficiency. Though the benefits of allowing for AGV job preemptions can be significant, few studies have considered pre-emptive scheduling in the context of flow-shop predictive modeling.…”

Section: Introductionmentioning

confidence: 99%

Flow-Shop Predictive Modeling for Multi-Automated Guided Vehicles Scheduling in Smart Spinning Cyber–Physical Production Systems

Farooq

Bao

2020

Electronics

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Pointed at a problem that leads to the high complexity of the production management tasks in the multi-stage spinning industry, mixed flow batch production is often the case in response to a customer’s personalized demands. Manual handling cans have a large number of tasks, and there is a long turnover period in their semi-finished products. A novel heuristic research was conducted that considered mixed-flow shop scheduling problems with automated guided vehicle (AGV) distribution and path planning to prevent conflict and deadlock by optimizing distribution efficiency and improving the automation degree of can distribution in a draw-out workshop. In this paper, a cross-region shared resource pool and an inter-regional independent resource pool, two AGV predictive scheduling strategies are established for the ring-spinning combing process. Besides completion time, AGV utilization rate and unit AGV time also analyzed with the bottleneck process of the production line. The results of the optimal computational experiment prove that a draw frame equipped with multi-AGV and coordinated scheduling optimization will significantly improve the efficiency of can distribution. Flow-shop predictive modeling for multi-AGV resources is scarce in the literature, even though this modeling also produces, for each AGV, a control mode and, if essential, a preventive maintenance plan.

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A Multi-Objective and Multi-Dimensional Optimization Scheduling Method Using a Hybrid Evolutionary Algorithms with a Sectional Encoding Mode

Cited by 14 publications

References 50 publications

Multi-Objective Teaching-Learning-Based Optimizer for a Multi-Weeding Robot Task Assignment Problem

Multi-Objective Teaching-Learning-Based Optimizer for a Multi-Weeding Robot Task Assignment Problem

Reducing the Cost of Electricity by Optimizing Real-Time Consumer Planning Using a New Genetic Algorithm-Based Strategy

Flow-Shop Predictive Modeling for Multi-Automated Guided Vehicles Scheduling in Smart Spinning Cyber–Physical Production Systems

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