An agent-based genetic algorithm for hybrid flowshops with sequence dependent setup times to minimise makespan

This paper deals with a variant of flowshop scheduling, namely, the hybrid or flexible flowshop with sequence dependent setup times. This type of flowshop is frequently used in the batch production industry and helps reduce the gap between research and operational use. This scheduling problem is NP-hard and solutions for large problems are based on non-exact methods. An improved genetic algorithm (GA) based on software agent design to minimise the makespan is presented. The paper proposes using an inherent characteristic of software agents to create a new perspective in GA design. To verify the developed metaheuristic, computational experiments are conducted on a well-known benchmark problem dataset. The experimental results show that the proposed metaheuristic outperforms some of the well-known methods and the state-of-art algorithms on the same benchmark problem dataset.The translation of this paper was funded by Universidad Politecnica de Valencia, Spain.Gómez Gasquet, P.; Andrés Romano, C.; Lario Esteban, FC. (2012). An agent-based genetic algorithm for hybrid flowshops with sequence dependent setup times to minimise makespan. Expert Systems with Applications. 39(9):8095-8107. https://doi.org/10.1016/j.eswa.2012.01.158S8095810739

An integrated ACO approach for the joint production and preventive maintenance scheduling problem in the flowshop sequencing problem.

International audienceIn this paper, an integrated ACO approach to solve joint production and preventive maintenance scheduling problem in permutation flowshops is considered. A newly developed antcolony algorithm is proposed and analyzed for solving this problem, based on a common representation of production and maintenance data, to obtain a joint schedule that is, subsequently, improved by a new local search procedure. The goal is to optimize a common objective function which takes into account both maintenance and production criteria. We compare the results obtained with our algorithm to those of an integrated genetic algorithm developed in previous works. The results and experiments carried out indicate that the proposed ant-colony algorithm provide very effective solutions for this problem

Akış tipi çizelgeleme problemlerinin yapay bağışıklık sistemleri ile çözümünde yeni bir yaklaşım

The n-job, m-machine flow shop scheduling problem is one of the most general job scheduling problems. This study deals with the criteria of makespan minimization for the flow shop scheduling problem. Artificial Immune Systems (AIS) are new intelligent problem solving techniques that are being used in scheduling problems. AIS can be defined as computational systems inspired by theoretical immunology, observed immune functions, principles and mechanisms in order to solve problems. In this research, a computational method based on clonal selection principle and affinity maturation mechanisms of the immune response is used. The operation parameters of meta-heuristics have an important role on the quality of the solution. Thus, a generic systematic procedure which bases on a multi-step experimental design approach for determining the efficient system parameters for AIS is presented. Experimental results show that, the artificial immune system algorithm is more efficient than both the classical heuristic flow shop scheduling algorithms and simulated annealing.n iş m makina akış tipi iş çizelgeleme problemi en genel iş çizelgeleme problemlerinden biridir. Bu çalışma akış tipi çizelgeleme problemi için toplam tamamlanma zamanı minimizasyonu ile ilgilenmektedir. Yapay Bağışıklık Sistemleri (YBS), çizelgeleme problemlerinde son dönemlerde kullanılan yeni bir problem çözme tekniğidir. YBS, doğal bağışıklık sisteminin prensiplerini ve mekanizmalarını kullanarak problemlere çözüm üreten bir hesaplama sistemidir. Bu çalışmada, bağışıklık tepkisinin iki ayrı mekanizması olan klonel seçim prensibi ve benzerlik mekanizması üzerine kurulmuş bir metod kullanılmıştır. Meta sezgisel yöntemlerde seçilen operatörler, çözüm kalitesi üzerinde önemli bir role sahiptir. Bu nedenle, yapay bağışıklık sisteminin etkin parametrelerinin belirlenmesinde çok aşamalı bir deney tasarımı prosedürü uygulanmıştır. Deney sonuçları, yapay bağışıklık sistemlerinin klasik çizelgeleme ve tavlama benzetimi algoritmalarından daha iyi sonuçlar verdiğini göstermiştir

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 New Approach to Solve Flowshop Scheduling Problems By Artificial Immune Systems

n iş m makina akış tipi iş çizelgeleme problemi en genel iş çizelgeleme problemlerinden biridir. Bu çalışma akış tipi çizelgeleme problemi için toplam tamamlanma zamanı minimizasyonu ile ilgilenmektedir. Yapay Bağışıklık Sistemleri (YBS), çizelgeleme problemlerinde son dönemlerde kullanılan yeni bir problem çözme tekniğidir. YBS, doğal bağışıklık sisteminin prensiplerini ve mekanizmalarını kullanarak problemlere çözüm üreten bir hesaplama sistemidir. Bu çalışmada, bağışıklık tepkisinin iki ayrı mekanizması olan klonel seçim prensibi ve benzerlik mekanizması üzerine kurulmuş bir metod kullanılmıştır. Meta sezgisel yöntemlerde seçilen operatörler, çözüm kalitesi üzerinde önemli bir role sahiptir. Bu nedenle, yapay bağışıklık sisteminin etkin parametrelerinin belirlenmesinde çok aşamalı bir deney tasarımı prosedürü uygulanmıştır. Deney sonuçları, yapay bağışıklık sistemlerinin klasik çizelgeleme ve tavlama benzetimi algoritmalarından daha iyi sonuçlar verdiğini göstermiştir.The n-job, m-machine flow shop scheduling problem is one of the most general job scheduling problems. This study deals with the criteria of makespan minimization for the flow shop scheduling problem. Artificial Immune Systems (AIS) are new intelligent problem solving techniques that are being used in scheduling problems. AIS can be defined as computational systems inspired by theoretical immunology, observed immune functions, principles and mechanisms in order to solve problems. In this research, a computational method based on clonal selection principle and affinity maturation mechanisms of the immune response is used. The operation parameters of meta-heuristics have an important role on the quality of the solution. Thus, a generic systematic procedure which bases on a multi-step experimental design approach for determining the efficient system parameters for AIS is presented. Experimental results show that, the artificial immune system algorithm is more efficient than both the classical heuristic flow shop scheduling algorithms and simulated annealing

A New Approach to Solve Flowshop Scheduling Problems By Artificial Immune Systems

n iş m makina akış tipi iş çizelgeleme problemi en genel iş çizelgeleme problemlerinden biridir. Bu çalışma akış tipi çizelgeleme problemi için toplam tamamlanma zamanı minimizasyonu ile ilgilenmektedir. Yapay Bağışıklık Sistemleri (YBS), çizelgeleme problemlerinde son dönemlerde kullanılan yeni bir problem çözme tekniğidir. YBS, doğal bağışıklık sisteminin prensiplerini ve mekanizmalarını kullanarak problemlere çözüm üreten bir hesaplama sistemidir. Bu çalışmada, bağışıklık tepkisinin iki ayrı mekanizması olan klonel seçim prensibi ve benzerlik mekanizması üzerine kurulmuş bir metod kullanılmıştır. Meta sezgisel yöntemlerde seçilen operatörler, çözüm kalitesi üzerinde önemli bir role sahiptir. Bu nedenle, yapay bağışıklık sisteminin etkin parametrelerinin belirlenmesinde çok aşamalı bir deney tasarımı prosedürü uygulanmıştır. Deney sonuçları, yapay bağışıklık sistemlerinin klasik çizelgeleme ve tavlama benzetimi algoritmalarından daha iyi sonuçlar verdiğini göstermiştir.The n-job, m-machine flow shop scheduling problem is one of the most general job scheduling problems. This study deals with the criteria of makespan minimization for the flow shop scheduling problem. Artificial Immune Systems (AIS) are new intelligent problem solving techniques that are being used in scheduling problems. AIS can be defined as computational systems inspired by theoretical immunology, observed immune functions, principles and mechanisms in order to solve problems. In this research, a computational method based on clonal selection principle and affinity maturation mechanisms of the immune response is used. The operation parameters of meta-heuristics have an important role on the quality of the solution. Thus, a generic systematic procedure which bases on a multi-step experimental design approach for determining the efficient system parameters for AIS is presented. Experimental results show that, the artificial immune system algorithm is more efficient than both the classical heuristic flow shop scheduling algorithms and simulated annealing

Deep Reinforcement Learning Techniques For Solving Hybrid Flow Shop Scheduling Problems: Proximal Policy Optimization (PPO) and Asynchronous Advantage Actor-Critic (A3C)

Well-studied scheduling practices are fundamental for the successful support of core business processes in any manufacturing environment. Particularly, the Hybrid Flow Shop (HFS) scheduling problems are present in many manufacturing environments. The current advances in the field of Deep Reinforcement Learning (DRL) attracted the attention of both practitioners and academics to investigate their adoption beyond synthetic game-like applications. Therefore, we present an approach that is based on DRL techniques in conjunction with a discrete event simulation model to solve a real-world four-stage HFS scheduling problem. The main narrative behind the presented concepts is to expose a DRL agent to a game-like environment using an indirect encoding. Two types of DRL techniques namely, Proximal Policy Optimization (PPO) and Asynchronous Advantage Actor-Critic (A3C), are evaluated for solving problems of different complexity. The computational results suggest that the DRL agents successfully learn appropriate policies for solving the investigated problem. In addition, the investigation shows that the agent can adjust their policies when we expose them to a different problem. We further evaluate the approach to solving problem instances published in the literature to establish a comparison

A survey of parallel hybrid applications to the permutation flow shop scheduling problem and similar problems

Parallel algorithms have focused an increased interest due to advantages in computation time and quality of solutions when applied to industrial engineering problems. This communication is a survey and classification of works in the field of hybrid algorithms implemented in parallel and applied to combinatorial optimization problems similar to the to the permutation flowshop problem with the objective of minimizing the makespan, Fm|prmu|Cmax according to the Graham notation, the travelling salesman problem (TSP), the quadratic assignment problem (QAP) and, in general, those whose solution can be expressed as a permutation