Algorithm For Flexible Job Shop Scheduling Research Articles

An Efficient Two-Stage Genetic Algorithm for Flexible Job-Shop Scheduling

Flexible job shop Scheduling Problem (FJSP) is considered as an expansion of classical Job-shop Scheduling Problem (JSP) where operations have a set of eligible machines, unlike only a single machine at JSP. FJSP is classified as non-polynomial-hard (NP-hard) problem. Researchers developed different techniques including Genetic Algorithm (GA) that is widely used for solving FJSP. Regular GAs for FJSP determine both operation sequencing and machine assignment through genetic search. In this paper, we developed a highly efficient Two-Stage Genetic Algorithm (2SGA) that in the first stage, GA coding only determines the order of operations for assignment. But machines are assigned through an evaluation process that starts from the first operation in the chromosome and chooses machines with the shortest completion time considering current machine load and process time. At the end of the first stage, we have a high-quality solution population that will be fed to the second stage. The second stage follows the regular GA approach for FJSP and searches the entire solution space to explorer solutions that might have been excluded at the first stage because of its greedy approach. The efficiency of proposed 2SGA has been successfully tested using published benchmark problems and also generated examples of different sizes. The quality of the 2SGA solutions greatly exceeds regular GA, especially for larger size problems.

Linear programming assisted (not embedded) genetic algorithm for flexible jobshop scheduling with lot streaming

Developed hybrid sequential GA which is superior to pure parallel GA.Detailed the common and distinct features of the hybrid GA and the pure GA.Distinguished two levels of hybridization of metaheuristics with Linear programming.Evaluated two alternative implementation strategies in hybridizing GA with LP. The hybridization of metaheuristics with other techniques for optimization has been one of the most interesting trends. The focus of research on metaheuristics is also becoming problem oriented rather than algorithm oriented. This has led researchers to try combining different algorithmic components in order to design more powerful algorithms. In this paper, we developed a linear programming assisted genetic algorithm for solving a flexible jobshop lot streaming problem. The genetic algorithm searches over both discrete and continuous variables in the problem solution space. A linear programming is used to assist the genetic algorithm by further refining promising solutions in a population periodically through determining the optimal values of the continuous variables corresponding to those promising solutions. This is different from one common way of hybridization referred to as linear programming embedded metaheuristics where the algorithm searches only over the integer variables and a linear programming subproblem is solved corresponding to every solution visited, which can be computationally prohibitive. Numerical examples showed that the proposed linear programming assisted (not embedded) genetic algorithm is superior to the embedded approach and as well as to a resource intensive multi-population pure parallel genetic algorithm.

An effective genetic algorithm for flexible job-shop scheduling with overlapping in operations

Flexible job-shop scheduling problem (FJSP) is extension of job-shop scheduling problem which allows an operation to be performed by any machine among a set of available machines. In many FJSP, it is assumed that a lot which contains a batch of identical items is transferred from one machine to the next only when all items in the lot have completed their processing. In this paper, FJSP with overlapping in operations is handled. According to this approach, sublots are transferred from one machine to the next for processing without waiting for the entire lot to be processed at the predecessor machine. The study is carried out in two steps. In the first step, a new mathematical model is developed for the considered problem and compared to other model in the literature in terms of computational efficiency. However, it is quite difficult to achieve an optimal solution for real size problems with mathematical modelling approach because of its NP-hard structure. Thus, in the second step, a genetic algorithm is proposed to solve this problem. An effective chromosome representation is used and in generation of initial population, a new search methodology is developed. At the same time, efficient decoding methodology is adopted considering only active schedule in order to reduce the search space. The proposed algorithm was tested on benchmark problems taken from literature of different scales. Obtained results were compared with the results obtained by other algorithms. Computational studies show that our algorithm surpasses other known algorithms for the same problem, and gives results comparable with the best algorithm known so far.

A dispatching algorithm for flexible job-shop scheduling with transfer batches: an industrial application

In this study, we present a dispatching algorithm to solve a real-world case of the flexible job-shop scheduling problem with transfer batches and the objective of minimising the average tardiness of production orders. The proposed algorithm considers two variants: (i) an ordered variant, where the priority dispatching rules are applied in a predefined order, and (ii) a randomised variant, where the user can assign probabilities (weights) to the priority rules. Using the information of the number of units and due dates requested by the only customer, the algorithm provides the sequence of operations that must be performed on each machine, as well as the start and completion times of operations. In order to reduce the impact of unexpected events on a generated schedule, several robustness rules are considered. The obtained results substantially improve the former method used in the company in terms of minimising average tardiness. Additionally, other important benefits are obtained, including significant saving in the time spent on scheduling, simplicity of use of the proposed procedure, robustness against unexpected events, reduction of idle times, improvement of decision-making information, and improvement of on-time delivery performance.

A Two-Stage Hybrid Algorithm for Flexible Job-Shop Scheduling

A mathematical model was constructed with two objectives. A two-stage hybrid algorithm was developed for solving this problem. At first, the man-hour optimization based on genetic algorithm and dynamic programming method, the model decomposes the flow shop into two layers: sub-layer and patrilineal layer. On the basis of the man-hour optimization，A simulated annealing genetic algorithm was proposed to optimize the sequence of operations. A new selection procedure was proposed and hybrid crossover operators and mutation operators were adopted. A benchmark problem solving result indicates that the proposed algorithm is effective.

A New Artificial Immune Algorithm for Flexible Job-Shop Scheduling

Flexible job-sop scheduling problem (FJSP) is based on the classical job-shop scheduling problem (JSP). however, it is even harder than JSP because of the addition of machine selection process in FJSP. An improved artificial immune algorithm, which combines the stretching technique and clonal selection algorithm is proposed to solve the FJSP. The algorithm can keep workload balance among the machines, improve the quality of the initial population and accelerate the speed of the algorithm’s convergence. The details of implementation for the multi-objective FJSP and the corresponding computational experiments are reported. The results indicate that the proposed algorithm is an efficient approach for the multi-objective FJSP.

A GRASP algorithm for flexible job-shop scheduling with maintenance constraints

In most realistic situations, machines may be unavailable due to maintenance, pre-schedules and so on. The availability constraints are non-fixed in that the completion time of the maintenance task is not fixed and has to be determined during the scheduling procedure. In this paper a greedy randomised adaptive search procedure (GRASP) algorithm is presented to solve the flexible job-shop scheduling problem with non-fixed availability constraints (FJSSP-nfa). The GRASP algorithm is a metaheuristic algorithm which is characterised by multiple initialisations. Basically, it operates in the following manner: first a feasible solution is obtained, which is then further improved by a local search technique. The main objective is to repeat these two phases in an iterative manner and to preserve the best found solution. Representative FJSSP-nfa benchmark problems are solved in order to test the effectiveness and efficiency of the proposed algorithm.