A speed-up procedure for the hybrid flow shop scheduling problem

Article number 115903During the last decades, hundreds of approximate algorithms have been proposed in the literature addressing flow-shop-based scheduling problems. In the race for finding the best proposals to solve these problems, speedup procedures to compute objective functions represent a key factor in the efficiency of the algorithms. This is the case of the well-known Taillard’s accelerations proposed for the traditional flow shop with makespan minimisation or several other accelerations proposed for related scheduling problems. Despite the interest in proposing such methods to improve the efficiency of approximate algorithms, to the best of our knowledge, no speed-up procedure has been proposed so far in the hybrid flow shop literature. To tackle this challenge, we propose in this paper a speed-up procedure for makespan minimisation, which can be incorporate in insertion-based neighbourhoods using a complete representation of the solutions. This procedure is embedded in the traditional iterated greedy algorithm. The computational experience shows that even incorporating the proposed speed-up procedure in this simple metaheuristic results in outperforming the best metaheuristic for the problem under consideration.Junta de Andalucía(España) US-1264511Ministerio de Ciencia e Innovación (España) PID2019-108756RB-I0

Internet of Things in urban waste collection

Nowadays, the waste collection management has an important role in urban areas. This paper faces this issue and proposes the application of a metaheuristic for the optimization of a weekly schedule and routing of the waste collection activities in an urban area. Differently to several contributions in literature, fixed periodic routes are not imposed. The results significantly improve the performance of the company involved, both in terms of resources used and costs saving

COMBINATION OF ACO AND PSO TO MINIMIZE MAKESPAN IN ORDERED FLOWSHOP SCHEDULING PROBLEMS

The problem of scheduling flowshop production is one of the most versatile problems and is often encountered in many industries. Effective scheduling is important because it has a significant impact on reducing costs and increasing productivity. However, solving the ordered flowshop scheduling problem with the aim of minimizing makespan requires a difficult computation known as NP-hard. This research will contribute to the application of combination ACO and PSO to minimize makespan in the ordered flowshop scheduling problem. The performance of the proposed scheduling algorithm is evaluated by testing the data set of 600 ordered flowshop scheduling problems with various combinations of job and machine size combinations. The test results show that the ACO-PSO algorithm is able to provide a better scheduling solution for the scheduling group with small dimensions, namely 76 instances from a total of 600 inctances and is not good at obtaining makespan in the scheduling group with large dimensions. The ACO-PSO algorithm uses execution time which increases as the dimension size (multiple jobs and many machines) increases in a scheduled instanc

A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

The mode of production in the modern manufacturing enterprise mainly prefers to MTO (Make-to-Order); how to reasonably arrange the production plan has become a very common and urgent problem for enterprises’ managers to improve inner production reformation in the competitive market environment. In this paper, a mathematical model of production planning is proposed to maximize the profit with capacity constraint. Four kinds of cost factors (material cost, process cost, delay cost, and facility occupy cost) are considered in the proposed model. Different factors not only result in different profit but also result in different satisfaction degrees of customers. Particularly, the delay cost and facility occupy cost cannot reach the minimum at the same time; the two objectives are interactional. This paper presents a mathematical model based on the actual production process of a foundry flow shop. An improved genetic algorithm (IGA) is proposed to solve the biobjective problem of the model. Also, the gene encoding and decoding, the definition of fitness function, and genetic operators have been illustrated. In addition, the proposed algorithm is used to solve the production planning problem of a foundry flow shop in a casting enterprise. And comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm

Migrating Birds Optimization-Based Feature Selection for Text Classification

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Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities

A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron’s benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm

A robust flexible flow shop problem under processing and release times uncertainty

The aim of this paper is to present a simheuristic approach that obtains robust solutions for a multi-objective hybrid flow shop problem under uncertain processing and release times. This approach minimizes the expected tardiness and standard deviation of tardiness, as a robustness measure for the stated problem. The simheuristic algorithm hybridizes the NSGA-II with a Monte Carlo Simulation process. Initially, the deterministic scenario was tested on 32 different created small size instances and 32 medium and large benchmarked instances. As a result, the proposed algorithm improved quality of solutions by 1.21% against the MILP model and it also performed better than ERD, NEHedd, and ENS2, while consuming a reasonable computational time. Afterwards, one experimental design was carried out using 10 random instances from the same benchmark as a blocking factor, where four factors of interest were considered. The factors and their respective values are number of generations (50, 100), crossover probability (0.8, 0.9), mutation probability (0.1, 0.2), and population size (60, 100). Results show that the factors instance, mutation probability and number of generations, as well as other interactions between them, have a significant effect in the total tardiness for the deterministic scenario, proving the importance of an appropriate selection of parameters when using genetic algorithms to obtain quality solutions. Then, the performance of the proposed NSGA-II was compared against ERD, NEHedd, and ENS2 methods. Results show that our algorithm improves the quality of the solutions for both objective functions, proving the robustness of our solutions for the HFS problem. Finally, two additional generalized experiments were carried out to analyze the effect of number of jobs (10, 20), number of stages (2, 3), shop condition (0.2, 0.6), probability distribution (uniform, lognormal), and CV (0.05, 0.25, 0.4) on both objective functions. The shop condition, probability distribution and CV were proven to be highly influential on the variability of the results, with the only exception being the coefficient of variation having no statistically significant effect on the total tardiness.The aim of this paper is to present a simheuristic approach that obtains robust solutions for a multi-objective hybrid flow shop problem under uncertain processing and release times. This approach minimizes the expected tardiness and standard deviation of tardiness, as a robustness measure for the stated problem. The simheuristic algorithm hybridizes the NSGA-II with a Monte Carlo Simulation process. Initially, the deterministic scenario was tested on 32 different created small size instances and 32 medium and large benchmarked instances. As a result, the proposed algorithm improved quality of solutions by 1.21% against the MILP model and it also performed better than ERD, NEHedd, and ENS2, while consuming a reasonable computational time. Afterwards, one experimental design was carried out using 10 random instances from the same benchmark as a blocking factor, where four factors of interest were considered. The factors and their respective values are number of generations (50, 100), crossover probability (0.8, 0.9), mutation probability (0.1, 0.2), and population size (60, 100). Results show that the factors instance, mutation probability and number of generations, as well as other interactions between them, have a significant effect in the total tardiness for the deterministic scenario, proving the importance of an appropriate selection of parameters when using genetic algorithms to obtain quality solutions. Then, the performance of the proposed NSGA-II was compared against ERD, NEHedd, and ENS2 methods. Results show that our algorithm improves the quality of the solutions for both objective functions, proving the robustness of our solutions for the HFS problem. Finally, two additional generalized experiments were carried out to analyze the effect of number of jobs (10, 20), number of stages (2, 3), shop condition (0.2, 0.6), probability distribution (uniform, lognormal), and CV (0.05, 0.25, 0.4) on both objective functions. The shop condition, probability distribution and CV were proven to be highly influential on the variability of the results, with the only exception being the coefficient of variation having no statistically significant effect on the total tardiness.Ingeniero (a) IndustrialPregrad