Metaheuristic algorithms for the flexible job-shop scheduling problem (original) (raw)
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Artificial Bee Colony Algorithm Applied to Dynamic Flexible Job Shop Problems
Information Processing and Management of Uncertainty in Knowledge-Based Systems, 2020
This work introduces a scheduling technique using the Artificial Bee Colony (ABC) algorithm for static and dynamic environments. The ABC algorithm combines different initial populations and generation of new food source methods, including a moving operations technique and a local search method increasing the variable neighbourhood search that, as a result, improves the solution quality. The algorithm is validated and its performance is tested in a static environment in 9 instances of Flexible Job Shop Problem (FJSP) from Brandimarte dataset obtaining in 5 instances the best known for the instance under study and a new best known in instance mk05. The work also focus in developing tools to process the information on the factory through the development of solutions when facing disruptions and dynamic events. Three real-time events are considered on the dynamic environment: jobs cancellation, operations cancellation and new jobs arrival. Two scenarios are studied for each real-time eve...
Research study of state-of-the-art algorithms for flexible job-shop scheduling problem
Czasopismo Techniczne, 2014
The paper discusses various approaches used to solve flexible job-shop scheduling problem concentrating on formulations proposed in the last ten years. It mainly refers to the applied metaheuristic techniques which have been exploited in this research area. A comparison of presented approaches is attempted, some concluding insights are highlighted. Finally future research directions are suggested.
Performance Evaluation of Various Heuristic Algorithms to Solve Job Shop Scheduling Problem (JSSP)
International Journal of Intelligent Engineering and Systems, 2021
Scheduling is a famous optimization problem that seeks the best strategy of allocating resources over time to perform jobs/tasks satisfying specific criteria. It exists everywhere in everyday life, particularly in manufacturing or industrial applications. An essential class of scheduling problems is a job shop scheduling problem (JSSP), an NPhard optimization problem. Several researchers have reported the use of heuristic methods to solve JSSP. This paper aims to investigate the performance of various heuristic algorithms to solve JSSP. Firstly, we developed a Genetic Algorithm (GA and compared the performance of some heuristic algorithms, including Particle Swarm Optimization (PSO), Upper-level algorithm (UPLA), Differential-based Harmony Search (DHS), Grey Wolf Optimization (GWO), Ant Colony Optimization (ACO), Bacterial Foraging Optimization (BFO), Parallel Bat Optimization (PBA), and Tabu Search (TS). The experimental results of the 28 benchmark test problems validated that the algorithms, except ACO, can provide the optimal solution of JSSP. PBA delivers the most impressive performance that solves 26 cases optimally, with the average error equal to 0.05%. Among those 28 test problems, TS, DHS, and PBA can solve 26 instances optimally, followed by GA that solves 21 cases.
Journal of Intelligent Manufacturing, 2015
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A heuristic algorithm for solving flexible job shop scheduling problem
The International Journal of Advanced Manufacturing Technology, 2014
This paper deals with the flexible job shop scheduling problem with the objective of minimizing the makespan. An efficient heuristic based on a constructive procedure is developed to obtain high-quality schedules very quickly. The algorithm is tested on benchmark instances from the literature in order to evaluate its performance. Computational results show that, despite its simplicity, the proposed heuristic can obtain effective solutions in very short and nearly zero time and is comparable with even metaheuristic algorithms and promising for practical problems.
A Particle Swarm Optimization Algorithm on Job-Shop Scheduling Problems with Multi-Purpose Machines
Asia-pacific Journal of Operational Research, 2009
This paper is a contribution to the research which aims to provide an efficient optimization algorithm for job-shop scheduling problems with multi-purpose machines or MPMJSP. To meet its objective, this paper proposes a new variant of particle swarm optimization algorithm, called GLN-PSOc, which is an extension of the standard particle swarm optimization algorithm that uses multiple social learning topologies in its evolutionary process. GLN-PSOc is a metaheuristic that can be applied to many types of optimization problems, where MPMJSP is one of these types. To apply GLN-PSOc in MPMJSP, a procedure to map the position of particle into the solution of MPMJSP is proposed. Throughout this paper, GLN-PSOc combined with this procedure is named MPMJSP-PSO. The performance of MPMJSP-PSO is evaluated on well-known benchmark instances, and the numerical results show that MPMJSP-PSO performs well in terms of solution quality and that new best known solutions were found in some instances of the test problems. 161 162 P. Pongchairerks & V. Kachitvichyanukul scheduling problems are complex and cannot be solved to optimality in polynomial time. One of those is the job-shop scheduling problem with multi-purpose machines (MPMJSP). The problem normally comes with a given set of jobs where each job consists of a chain of operations. For this entire process, there is a set of multi-purpose machines which is equipped with different tools that enable it to function for more than one purpose. Associated with each operation, there is a set of machines which can process an operation where each operation must be processed during an uninterrupted time period of a given length. MPMJSP attempts to minimize the latest completion time of the given jobs. This latest completion time will be called makespan throughout this paper.
An Efficient Heuristic Algorithm for Solving Flexible Job Shop Scheduling Problem
An efficient heuristic algorithm for solving the Flexible Job Shop Scheduling Problem is presented and it is called artificial intelligence (A1) algorithm. AI is a new construction heuristic technique. It is mainly depending upon a new heuristic rule that has the capability for establishing feasible solutions. First Out First In, the new heuristic rule, is designed for minimizing the makespan of the Flexible Job Shop Scheduling Problem. The Flexible Job Shop Scheduling Problem is known as NP-hard combinatorial optimization problem that has long challenged researchers. Makespan is the maximum completion time of achieving all the jobs. Some problems from references are solved using the proposed algorithm and an implementation study is presented. The implementation study shows the efficiency of the proposed algorithm with respect to the tested problem.
Applied Mathematical Modelling, 2014
This paper presents a novel discrete artificial bee colony (DABC) algorithm for solving the multi-objective flexible job shop scheduling problem with maintenance activities. Performance criteria considered are the maximum completion time so called makespan, the total workload of machines and the workload of the critical machine. Unlike the original ABC algorithm, the proposed DABC algorithm presents a unique solution representation where a food source is represented by two discrete vectors and tabu search (TS) is applied to each food source to generate neighboring food sources for the employed bees, onlooker bees, and scout bees. An efficient initialization scheme is introduced to construct the initial population with a certain level of quality and diversity. A self-adaptive strategy is adopted to enable the DABC algorithm with learning ability for producing neighboring solutions in different promising regions whereas an external Pareto archive set is designed to record the non-dominated solutions found so far. Furthermore, a novel decoding method is also presented to tackle maintenance activities in schedules generated. The proposed DABC algorithm is tested on a set of the well-known benchmark instances from the existing literature. Through a detailed analysis of experimental results, the highly effective and efficient performance of the proposed DABC algorithm is shown against the best performing algorithms from the literature.
A genetic algorithm for the Flexible Job-shop Scheduling Problem
Computers & Operations Research, 2008
In this paper, we present a genetic algorithm for the Flexible Job-shop Scheduling Problem (FJSP). The algorithm integrates different strategies for generating the initial population, selecting the individuals for reproduction and reproducing new individuals. Computational result shows that the integration of more strategies in a genetic framework leads to better results, with respect to other genetic algorithms. Moreover, results are quite comparable to those obtained by the best-known algorithm, based on tabu search. These two results, together with the flexibility of genetic paradigm, prove that genetic algorithms are effective for solving FJSP. ᭧ Scheduling of operations is one of the most critical issues in the planning and managing of manufacturing processes. To find the best schedule can be very easy or very difficult, depending on the shop environment, the process constraints and the performance indicator [1]. One of the most difficult problems in this area is the Job-shop Scheduling Problem (JSP), where a set of jobs must be processed on a set of machines, each job is formed by a sequence of consecutive operations, each operation requires exactly one machine, machines are continuously available and can process one operation at a time without interruption. The decision concerns how to sequence the operations on the machines, such as a given performance indicator is optimized. A typical performance indicator for JSP is the makespan, i.e., the time needed to complete all the jobs. JSP is a well-known NP-hard problem .