Optimization of continuous-time production planning using hybrid genetic algorithms-simulated annealing

Abstract

Evolutionary algorithms are stochastic search methods that mimic the principles of natural biological evolution to produce better and better approximations to a solution and have been used widely for optimization problems. A general problem of continuous-time aggregate production planning for a given total number of changes in production rate over the total planning horizon is considered. It is very important to identify and solve the problem of continuous-time production planning horizon with varying production rates over the interval of the planning period horizon. Some of the researchers have proposed global search methods for the continuous-time aggregate productionplanning problem. So far, less work is reported to solve the problem of continuous-time production planning using local search methods like genetic algorithms (GA) and simulated annealing (SA). So in this work, we propose a modified single objective evolutionary program approach, namely GA, SA, and hybrid genetic algorithms-simulated annealing (GA-SA) for continuoustime production plan problems. The results are compared with each other and it was found that the hybrid algorithm performs better.

Hybridization of Evolutionary Algorithms

“…• hybridization between an evolutionary algorithm and other heuristics, like local search (Moscato, 1999), tabu search (Galinier & Hao, 1999), simulated annealing (Ganesh & Punniyamoorthy, 2004), hill climbing (Koza et al, 2003), dynamic programming (Doerr et al, 2009), etc.…”

Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews

“…Change constraints in the GA using current simulation completion time and go to step 1 6. Determine the production scheduling which is considered to be the realistic optimal solution Ganesh and Punniyamoorthy [16] proposed a hybrid GA -simulated annealing (SA) algorithm for continuous-time aggregate production-planning problems. The motivation behind the GA-SA combination is the power of GA to work on the solution in a global sense while allowing SA to locally optimize each individual solution [16].…”

“…Once the SA is finished for all solutions in one generation of GA, the best solutions of population size obtained from SA are the solutions of GA for the next generation. The GA and SA exchange continues until the required number of generations are completed [16].…”

Optimization of Operation Sequencing in CAPP for Cylindrical Part Using Super Hybrid Genetic Algorithm-Expert System (S-Genex) Approach

“…The assignment of task sequencing in CAPP is sequenced by various decisions in the activity or machine determination, as cited previously. In this manner, a mix of different decisions and imperatives makes process arranging a combinatorial issue [18].…”

“…Ganesh and Punniyamoorthy [18] built up a hybrid GA SA algorithm and found that the algorithm performs better. Among these heuristic techniques, Genetic…”