An ACO Algorithm for Scheduling a Flow Shop with Setup Times

Rojas-Santiago, Miguel; Muthuswamy, Shanthi; Hulett, Maria

doi:10.1504/ijise.2020.10023650

Cited by 2 publications

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“…The ACO algorithms are based on an analogy with natural phenomena and rely on the collective behavior of ants to organize the search for food [35]. Ants explore their environment by leaving behind volatile traces, called pheromone traces.…”

Section: The Proposed Acomentioning

confidence: 99%

Multi-Objective Production Scheduling of Perishable Products in Agri-Food Industry

2021

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This paper deals with dynamic industry scheduling problem in agri-food production. The decision-making study in this paper is articulated around the management of perishable products under constrained resources. The scheduling of logistics operations is considered at the operational level. Two metaheuristics are proposed to solve dynamic scheduling under perturbations. The uncertainty sources considered in this study are the expiration date of product components and production delays. The proposed Genetic Algorithm (GA) and the Ant Colony Optimization Algorithm (ACO) take into consideration two objective functions: minimizing the makespan and reducing the number of perishable products. The algorithms are tested on a flow-shop agri-food system.

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Section: The Proposed Acomentioning

confidence: 99%

Multi-Objective Production Scheduling of Perishable Products in Agri-Food Industry

2021

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“…In recent years, the methods for solving path planning problems are mostly heuristic intelligent algorithms [1]. This type of algorithm refers to solving path planning problems by simulating some natural phenomena or biological behavior processes, such as particle swarm optimization [2], ant colony algorithm [3], genetic algorithm [4], clone selection algorithm [5], etc. Li et al [6] proposed an improved particle swarm optimization algorithm to optimize the path planning of mobile robots.…”

Section: Introductionmentioning

confidence: 99%

A Path Planning Method Based on Improved Single Player-Monte Carlo Tree Search

2020

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With the thorough application and development of Intelligent Logistics System, the Path Planning Problem, which is one of the key problems of logistics scheduling, is accelerating to the direction of dynamics, diversification and complexity. In order to better adapt to the new changes, this paper proposes a path planning method based on improved SP-MCTS (Single-Player Monte Carlo Tree Search) algorithm. Unlike the previous heuristic algorithms, SP-MCTS does not completely depend on parameters, and can more effectively solve variable combinatorial optimization problems. Aiming at the problem of path planning, this paper enhances the simulation process of SP-MCTS by clonal selection algorithm, in order to improve the algorithm's accuracy of locating high-potential branches, and further improve the accuracy of the decisions made by SP-MCTS. Experiments show that the method proposed in this paper is less affected by the parameters and can better solve the changing path planning problem. Compared with the existing path planning methods and original SP-MCTS algorithm, it can solve the changeable path planning problem more stably, faster and more accurately. INDEX TERMS single player-monte carlo tree search; clonal selection algorithm; path planning

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An ACO Algorithm for Scheduling a Flow Shop with Setup Times

Cited by 2 publications

References 0 publications

Multi-Objective Production Scheduling of Perishable Products in Agri-Food Industry

Multi-Objective Production Scheduling of Perishable Products in Agri-Food Industry

A Path Planning Method Based on Improved Single Player-Monte Carlo Tree Search

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