Flexible open-shop problem for minimizing weighted total completion time

2021 ◽  
pp. 1-14
Author(s):  
Iman Khosravi Mashizi ◽  
Vahid Momenaei Kermani ◽  
Naser Shahsavari-Pour
Keyword(s):  
Completion Time ◽  
Memetic Algorithm ◽  
Total Completion Time ◽  
Initial Population ◽  
Remarkable Improvement ◽  
Metaheuristic Methods ◽  
Open Shop Problem ◽  
Identical Machines ◽  
Overall Performance ◽  
Feasible Solutions

In this article, scheduling flexible open shops with identical machines in each station is studied. A new mathematical model is offered to describe the overall performance of the system. Since the problem enjoys an NP-hard complexity structure, we used two distinct metaheuristic methods to achieve acceptable solutions for minimizing weighted total completion time as the objective function. The first method is customary memetic algorithm (MA). The second one, MPA, is a modified version of memetic algorithm in which the new permutating operation is replaced with the mutation. Furthermore, some predefined feasible solutions were imposed in the initial population of both MA and MPA. According to the results, the latter action caused a remarkable improvement in the performance of algorithms.

scholarly journals Memetic algorithm used in a flow shop scheduling problem

2020 ◽  
pp. 8-14
Author(s):  
Jorge Armando Ramos-Frutos ◽  
Didia Carrillo-Hernández ◽  
Alan David Blanco-Miranda ◽  
Heraclio García-Cervantes
Keyword(s):  
Completion Time ◽  
Flow Shop ◽  
Memetic Algorithm ◽  
Idle Time ◽  
Flow Shop Scheduling ◽  
Initial Population ◽  
Time Cost ◽  
Flow Shops ◽  
Processing Times ◽  
Work In Process

Scheduling activities in flow shops involves generating a sequence in which the jobs must be processed. To generate the sequence, some criteria are taken into account, such as the completion time of all the jobs, delay time in delivery, idle time, cost of processing the jobs, work in process, among others. In this case, completion time of all jobs and idle time are taken as the objective function. To generate the sequence, a Memetic Algorithm (MA) is used that combines Simulated Annealing (SA) and Genetic Algorithms (GA) to solve the problem. A permutation type decoding was used for the vectors that make up the MA population. The SA was used for the generation of the initial population. Selection, recombination and mutation processes are generated in a similar way to GA. In this case there are 6 parameters to be set; temperature, z parameter, recombination probability, mutation probability, cycles and initial population. To set these parameters, the Response Surface Methodology is used for two objectives. Achieving improvements in the algorithm result of at least 2%. These results help to minimize processing times which impacts with the economics of the enterprise. Using the MA in an interface that helps the user to make a decisión about the Schedule of the Jobs.

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