A fuzzy ant colony optimization to solve an open shop scheduling problem with multi-skills resource constraints

Campos, Guillermo; Dugardin, Frédéric; Yalaoui, Farouk; Kelly, Russell

doi:10.1016/j.ifacol.2015.06.167

Cited by 10 publications

(7 citation statements)

References 14 publications

(15 reference statements)

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“…The authors develop experiments for different worker flexibilities (the higher the flexibility, the higher the number of tests a worker is suitable to process) and worker/machine balances. Ciro et al (2015) propose a MILP formulation for the DRC Open Shop Problem (OPP), where workers are required for the full duration of a job processing in a machine. The authors propose an Ant Colony Optimization algorithm to obtain good solutions in acceptable computational times, which, when compared to an exact method, shows promise.…”

Section: Dual Resource Constrained Flexible Job Shop Schedulingmentioning

confidence: 99%

Minimizing total completion time in large-sized pharmaceutical quality control scheduling

et al. 2023

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This paper proposes an algorithm for the effective scheduling of analytical chemistry tests in the context of quality control for pharmaceutical manufacturing. The problem is formulated as an extension of a dual resource constrained flexible job shop scheduling problem for the allocation of both machines and analysts resources for analytical laboratory work of real dimensions. The formulation is novel and custom made to fit real quality control laboratory. The novelty comes from allowing multiple analyst interventions for each machine allocation while minimising the total completion time, formulated as a mixed integer linear programming model. A three-level dynamic heuristic is proposed to solve the problem efficiently for instances representative of real world schedules. The CPLEX solver and a Tabu Search algorithm are used for comparison. Results show that the heuristic is competitive with the other strategies for medium-sized instances while outperforming them for large-sized instances. The dynamic heuristic runs in a very short amount of time, making it suitable for real world environments. This work is valuable for the development of laboratory management solutions for quality control as it presents a way to provide automatic scheduling of resources.

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Section: Dual Resource Constrained Flexible Job Shop Schedulingmentioning

confidence: 99%

Minimizing total completion time in large-sized pharmaceutical quality control scheduling

et al. 2023

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“…Table II summarizes the research results of applying a SIOA to solve OSSP. ACO Minimize makespan [32] ABC Minimize makespan [33] ABC Minimize makespan [34] BA Minimize makespan [37] CSA Minimize makespan [28] Two-stage PSO Minimize makespan [40] Firefly-cuckoo hybrid algorithm Minimize makespan [42] ACO Minimize total flow time [26] ACO Minimize total flow time [29] PSO Minimize makespan, total flow time, machine idle time [30] Multi-objective PSO Minimize makespan, maximize completion satisfaction [39] WOA Minimize total lead time/delay time [41] Multi-objective simulated annealingant colony hybrid algorithm Minimize total completion time and total delay time…”

Section: Swarm Intelligence Algorithm For Solving Osspmentioning

confidence: 99%

“…Experiments show that the performance of ACO is better than that of GA in real industrial cases. Aiming at the problem that it is challenging to optimize the parameters of ACO, Ciro et al [26] proposed a fuzzy ACO to improve the quality of the solution, and the results demonstrate the program's feasibility…”

Section: Ant Colony Algorithmmentioning

confidence: 99%

Research Survey on Open Shop Scheduling Strategies Based on Intelligent Algorithms

2022

Proceedings of WCSE 2022 Spring Event: 2022 9th International Conference on Industrial Engineering and Applications

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Open shop scheduling problem (OSSP) is one of the fundamental scheduling problems. Many scenarios such as vehicle maintenance, the medical system, and food processing can be modeled as OSSP in production and life. Therefore, this problem has essential application value and research significance. With the rapid development of artificial intelligence, the research of intelligent algorithms has increased explosively. Genetic algorithms, swarm intelligence algorithms, and artificial neural networks are several important intelligent algorithms that can solve OSSP. This paper summarizes the OSSP based on intelligent algorithms. Firstly, it briefly introduces the intelligent algorithm and OSSP. Then, the research on evolutionary algorithms and swarm intelligence algorithms to solve the OSSP is classified and summarized. Finally, the current algorithms for solving the problem are analyzed and discussed, the problems not solved by the existing algorithms are proposed. Furthermore, the development direction of intelligent algorithms for OSSP has been prospected.

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“…In addition, formulation of the OSSP is less attractive to researchers. Since an increase in the number of machines (m > 3) turns the problem into an NP-hard one [7,17,18], heuristic and meta-heuristic algorithms have been used in most studies on the OSSP. Tautenhahn and Woeginger [19] studied the OSSP with regard to unit-time operations so that the availability of resources varies over time in their problem.…”

Section: Introductionmentioning

confidence: 99%

An improved bat optimization algorithm to solve the tasks scheduling problem in open shop

Shareh

Bargh

Hosseinabadi

et al. 2020

Neural Comput & Applic

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The open shop scheduling problem involves a set of activities that should be run on a limited set of machines. The purpose of scheduling open shops problem is to provide a timetable for implementation of the entire operation so that the total execution time is reduced. The tasks scheduling problem in open shops is important in many applications due to the arbitrariness of the processing sequence of each job and lack of a prioritization of its operations. This is an NP-hard problem and obtaining an optimal solution to this problem requires a high time complexity. Therefore, heuristic techniques are used to solve these problems. In this paper, we investigate the tasks scheduling problem in open shops using the Bat Algorithm (BA) based on ColReuse and substitution meta-heuristic functions. The heuristic functions are designed to increase the rate of convergence to the optimal solution. To evaluate the performance of the proposed algorithm, standard open shop benchmarks were used. The results obtained in each benchmark are compared with those of the previous methods. Finally, after analyzing the results, it was found that the proposed BA had a better performance and was able to generate the best solution in all cases.

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A fuzzy ant colony optimization to solve an open shop scheduling problem with multi-skills resource constraints

Cited by 10 publications

References 14 publications

Minimizing total completion time in large-sized pharmaceutical quality control scheduling

Minimizing total completion time in large-sized pharmaceutical quality control scheduling

Research Survey on Open Shop Scheduling Strategies Based on Intelligent Algorithms

An improved bat optimization algorithm to solve the tasks scheduling problem in open shop

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