Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan (original) (raw)
Related papers
Cuckoo Search Algorithm for Hybrid Flow Shop Scheduling Problem with Multi-layer Assembly Operations
Most of studies assume that jobs are simple string type and independent, while in the real-world situations almost all of the final products have several components and they are produced by assembling components together in different layers of subassemblies. In spite this fact, the studies addressing assembly operations are relatively less in the literature. This study considered the hybrid flowshop scheduling problem followed by assembly stage with identical parallel machines. Frist, components of products are processed in the hybrid flowshop, and then they go under assembly operations based on the predefined products assembly structure. Each product may have several assembly operations and final product is obtained by completion of its last assembly operation. The goal is to find a schedule, sequence of products and parts, which minimizes completion time of the last product, i.e., makespan. For this end, a new mathematical model is proposed; on the other hand, since the studied problem is NP-hard, a nature inspired meta-heuristic method, Cuckoo Search (CS) algorithm, is then employed such that it can handle the precedence constraints among parts and assembly operations. To evaluate the performance of the proposed algorithm, the computational results are demonstrated.
Metaheuristic methods in hybrid flow shop scheduling problem
Memetic algorithms are hybrid evolutionary algorithms that combine global and local search by using an evolutionary algorithm to perform exploration while the local search method performs exploitation. This paper presents two hybrid heuristic algorithms that combine particle swarm optimization (PSO) with simulated annealing (SA) and tabu search (TS), respectively. The hybrid algorithms were applied on the hybrid flow shop scheduling problem. Experimental results reveal that these memetic techniques can effectively produce improved solutions over conventional methods with faster convergence.
World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 2010
This study compares three meta heuristics to minimize makespan (Cmax) for Hybrid Flow Shop (HFS) Scheduling Problem with Parallel Machines. This problem is known to be NP-Hard. This study proposes three algorithms among improvement heuristic searches which are: Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). SA and TS are known as deterministic improvement heuristic search. GA is known as stochastic improvement heuristic search. A comprehensive comparison from these three improvement heuristic searches is presented. The results for the experiments conducted show that TS is effective and efficient to solve HFS scheduling problems. Keywords—Flow Shop, Genetic Algorithm, Simulated Annealing, Tabu Search.
Hybrid Flow-Shop Scheduling (HFS) Problem Solving with Migrating Birds Optimization (MBO) Algorithm
Mathematics and Statistics , 2020
The development of an increasingly rapid industrial development resulted in increasingly intense competition between industries. Companies are required to maximize performance in various fields, especially by meeting customer demand with agreed timeliness. Scheduling is the allocation of resources to the time to produce a collection of jobs. PT. Bella Agung Citra Mandiri is a manufacturing company engaged in making spring beds. The work stations in the company consist of 5 stages consisting of ram per with three machines, clamps per 1 machine, firing mattresses with two machines, sewing mattresses three machines and packing with one machine. The model problem that was solved in this study was Hybrid Flowshop Scheduling. The optimization method for solving problems is to use the metaheuristic method Migrating Birds Optimization. To avoid problems faced by the company, scheduling is needed to minimize makespan by paying attention to the number of parallel machines. The results of this study are scheduling for 16 jobs and 46 jobs. Decreasing makespan value for 16 jobs minimizes the time for 26 minutes 39 seconds, while for 46 jobs can minimize the time for 3 hours 31 minutes 39 seconds.
A novel metaheuristic approach for the flow shop scheduling problem
Engineering Applications of Artificial Intelligence, 2004
Advances in modern manufacturing systems such as CAD/CAM, FMS, CIM, have increased the use of intelligent techniques for solving various combinatorial and NP-hard sequencing and scheduling problems. Production process in these systems consists of workshop problems such as grouping similar parts into manufacturing cells and proceeds by passing these parts on machines in the same order. This paper presents a new hybrid simulated annealing algorithm (hybrid SAA) for solving the flow-shop scheduling problem (FSSP); an NP-hard scheduling problem with a strong engineering background. The hybrid SAA integrates the basic structure of a SAA together with features borrowed from the fields of genetic algorithms (GAs) and local search techniques. Particularly, the algorithm works from a population of candidate schedules and generates new populations of neighbor schedules by applying suitable small perturbation schemes. Further, during the annealing process, an iterated hill climbing procedure is stochastically applied on the population of schedules with the hope to improve its performance.
A hybridisation of metaheuristics for flow shop scheduling
2004
The present paper deals with the formation of an optimal sequence of flow shop scheduling (FSS) for efficient operation. The primary concern of FSS is to obtain the optimal sequence, which minimises the idle time, tardiness, makespan, etc. Among these, the criteria of minimising the makespan plays a vital part. Thus, in this paper, the sequencing of the FSS for minimising the makespan is addressed. An effective hybrid has been formed with the metaheuristics, namely an ant system and a genetic algorithm (GA). A number of illustrative examples with different combinations of machines and jobs have been solved using the proposed hybrid method.
Discrete penguins search optimization algorithm to solve flow shop scheduling problem
International Journal of Electrical and Computer Engineering (IJECE), 2020
Flow shop scheduling problem is one of the most classical NP-hard optimization problem. Which aims to find the best planning that minimizes the makespan (total completion time) of a set of tasks in a set of machines with certain constraints. In this paper, we propose a new nature inspired metaheuristic to solve the flow shop scheduling problem (FSSP), called penguins search optimization algorithm (PeSOA) based on collaborative hunting strategy of penguins.The operators and parameter values of PeSOA redefined to solve this problem. The performance of the penguins search optimization algorithm is tested on a set of benchmarks instances of FSSP from OR-Library, The results of the tests show that PeSOA is superior to some other metaheuristics algorithms, in terms of the quality of the solutions found and the execution time. 1. INTRODUCTION The Scheduling is a branch of this operational research in production management that aims to improve the efficiency of a company in terms of production costs and delivery times. Scheduling problems appear in all areas of the economy: computers, construction (project management), industry (workshops problems, production management), administration (schedule) .The flow shop problem scheduling [1] is one of the most difficult combinatorial optimization problem belonging to NP-hard problem [2] family, it is widely known in the industry. The solution to the problem in finding an order for execution of tasks on machines subject to many constraints in an optimal time. Over the last few decades, nature has been a source of inspiration for many metaheuristics, which has been introduced to solve optimization problems. A set of these metaheuristics has been tested to solve discrete problems. The results of these tests are not unique, the quality of solutions varies according to the characteristics and category of method. Generally, methods based on particle population swarm intelligence algorithms providing solutions of good quality, for example: bat algorithm (BA) [3, 4], particle swarm optimization (PSO) [5], artificial bee colony (ABC) [6], cat swarm optimization (CSO) [7, 8], hunting search algorithm (HuS) [9], elephant herding optimization (EHO [10], swallow swarm optimization (SSO) [11], golden ball algorithm (GBA) [12, 13], cuckoo search (CS) [14], chicken swarm algorithm (CSA) [15-16] and flower pollination algorithm (FPA) [17]. In this context we proposed a new metaheuristic of swarm intelligence inspired by nature nominate PeSOA, to solve the flow shop scheduling problem one of the NP-hard problem in combinatorial optimization. PeSOA is an optimization technique inspired by the hunting strategy of penguins, which was developed to deal with optimization problems in the continuous
International Journal of Technology
In this study, we discuss the problem of permutation flowshop scheduling problem (PFSP) to reduce total energy consumption (TEC). We offer a new hybrid meta-heuristic algorithm for solving the problem. The paper aims to combine the cross entropy and genetic algorithm (CEGA) with the simulated annealing (SA) algorithm. The CEGA is applied to find the best initial solution inside the SA algorithm and the proposed algorithm is compared to previous tests of the famous NSGA-II and GA-SA algorithm. During study of the numerical test, the proposed algorithm genuinely useful is compared certain efficient algorithms of the from previous research.
Computers & Operations Research, 2009
This paper proposes a hybrid metaheuristic for the minimization of makespan in permutation flow shop scheduling problems. The solution approach is robust, fast, and simply structured, and comprises three components: an initial population generation method based on a greedy randomized constructive heuristic, a genetic algorithm (GA) for solution evolution, and a variable neighbourhood search (VNS) to improve the population. The hybridization of a GA with VNS, combining the advantages of these two individual components, is the key innovative aspect of the approach. Computational experiments on benchmark data sets demonstrate that the proposed hybrid metaheuristic reaches high-quality solutions in short computational times. Furthermore, it requires very few user-defined parameters, rendering it applicable to real-life flow shop scheduling problems.