A note on “A mathematical model and extension algorithm for assembly flexible flow shop scheduling problem”

The flexible flow-shop scheduling problem (FFSSP) is an important branch of production scheduling, the flexible flow shop is a combination of two well-known machine environments, which are flow shops and parallel machines, and it is well known that this problem is NP-hard. The no-wait requirement is a phenomenon that may occur in flow shops, and it's prevent the jobs to wait between two successive machines (or stages), jobs must be processed from the start to finish, without any interruption on machines (or stages) or between them. Many different approaches have been applied to FFSSP with no-wait. In this research, two-stage no-wait flexible flow shop scheduling problem (NWFFSSP) has been solved using two meta-heuristics, which are Tabu Search (TS) and Particle Swarm Optimization (PSO). We solved the NWFFSSP with minimum makespan as a performance measure. The performance of the proposed algorithms are studied and compared with previous research results using the same problem data. The results of the study proposes the effective algorithm.

The cutting and sewing process is a traditional flow shop scheduling problem in the real world. This two-stage flexible flow shop is often commonly associated with manufacturing in the fashion and textiles industry. Many investigations have demonstrated that the ant colony optimization (ACO) algorithm is effective and efficient for solving scheduling problems. This work applies a novel effective ant colony optimization (EACO) algorithm to solve two-stage flexible flow shop scheduling problems and thereby minimize earliness, tardiness, and makespan. Computational results reveal that for both small and large problems, EACO is more effective and robust than both the particle swarm optimization (PSO) algorithm and the ACO algorithm. Importantly, this work demonstrates that EACO can solve complex scheduling problems in an acceptable period of time.

In simple flow shop problems, each machine operation center includes just one machine. If at least one machine center includes more than one machine, the scheduling problem becomes a flexible flow shop problem (FFSP). Flexible flow shops are thus generalization of simple flow shops. Flexible flow shop scheduling problems have a special structure combining some elements of both the flow shop and the parallel machine scheduling problems. FFSP can be stated as finding a schedule for a general task graph to execute on a multiprocessor system so that the schedule length can be minimized. FFSP is known to be NP-hard. In this study, we present a particle swarm optimization (PSO) algorithm to solve FFSP. PSO is an effective algorithm which gives quality solutions in a reasonable computational time and consists of less numbers parameters as compared to the other evolutionary metaheuristics. Mutation, a commonly used operator in genetic algorithm, has been introduced in PSO so that trapping of solutions at local minima or premature convergence can be avoided. Logistic mapping is used to generate chaotic numbers in this paper. Use of chaotic numbers makes the algorithm converge fast towards near-optimal solution and hence reduce computational efforts further. The performance of schedules is evaluated in terms of total completion time or makespan (Cmax). The results are presented in terms of percentage deviation (PD) of the solution from the lower bound. The results are compared with different versions of genetic algorithm (GA) used for the purpose from open literature. The results indicate that the proposed PSO algorithm is quite effective in reducing makespan because average PD is observed as 2.961, whereas GA results in average percentage deviation of 3.559. Finally, influence of various PSO parameters on solution quality has been investigated.

Effective upper and lower bounds for a two-stage reentrant flexible flow shop scheduling problem

Scheduling rules for two-stage flexible flow shop scheduling problem subject to tail group constraint

Flexible flow shop scheduling problem (FFSP) is recognized as an important class of problems in manufacturing systems. To consider more actual production factors, such as transportation time, setup times, blocking constraint, etc., we solved the FFSP using the improved genetic algorithm (GA) and discrete event simulation (DES). Firstly, a mathematical model for FFSP with the objective of minimizing total completion time was established. Besides, a GA mixed with Palmer heuristic algorithm was proposed to solve the mathematical model. Moreover, a DES software-plant simulation-was used to establish a more realistic production model, and an actual production workshop was modeled and optimized. After optimization, the total completion time and equipment utilization were greatly improved compared with the original scheduling. It shows the effectiveness and rationality of the proposed method to solve the actual FFSP.

Flexible flow shop scheduling with uniform parallel machines

The paper suggests a new rule; called no-wait process. The rule has two stages, and is a flexible flow shop scheduling. The process is the subject to maximize tardiness while minimizing the makespan. This hybrid flow shop problem is known to be NP-hard. Therefore, we come to first, Non-dominated Sorting Genetic Algorithm (NSGA-II), then, Multi-Objective Imperialist Competitive Algorithm (MOICA) and finally, Pareto Archive Evolutionary Strategy (PAES) as three multi-objective Pareto based metaheuristic optimization methods. They are developed to solve the problem to approximately figure out optimal Pareto front. The method is investigated in several problems that differed in size and terms of relative percentage deviation of performance metrics. The conclusion, developed by this method is the most efficient and practicable algorithm at the end.

Auxiliary resource planning in a flexible flow shop scheduling problem considering stage skipping

A constraint programming approach for resource-constrained flexible assembly flow shop scheduling problem with batch direct delivery

Bi-objective optimization of flexible flow shop scheduling problem with multi-skilled human resources

Scheduling of multi-component products in a two-stage flexible flow shop

GPU based parallel genetic algorithm for solving an energy efficient dynamic flexible flow shop scheduling problem

Effective constructive heuristics for distributed no-wait flexible flow shop scheduling problem

This paper investigates the multistage hybrid flow shop (HFS) scheduling problems using the new bat algorithm. A HFS is the generalisation of flowshop with multiple machines. HFS is one of the important scheduling problems that represent many industries like iron and steel, chemical, textile and ceramic industries. The HFS scheduling problems have been proved to be NP-hard. A recently developed bat meta-heuristic algorithm is proposed to solve the HFS problems. The proposed algorithm is validated with a well-chosen set of benchmark problems in the literature. Computational results indicate that the proposed bat algorithm is more efficient than the genetic algorithm and particle swarm optimisation.