Solving Process Planning, ATC Scheduling and Due-date Assignment Problems Concurrently Using Genetic Algorithm for Weighted Customers

Abstract

Traditionally process planning, scheduling, and due-date assignment functions are applied sequentially and separately. Since these three functions affect each other and if we don't integrate, then they will become poor input for downstream and overall performance will be poor. In this competitive era, we must be competitive also. Integrating these functions will improve overall performance. In this study, we investigated the benefit of integration. We tested different integration level. First, we looked at unintegrated results and later step by step three functions are integrated and finally, we integrated these two functions with WATC (Weighted Apparent Tardiness Cost) Dispatching. In this study, we observed that as integration level increases the solution becomes better. Integrating due-date assignment or scheduling with process planning improves overall performance and if we integrate WATC dispatching with these two functions then we get the best performance. In addition to integration levels, we also compared the benefit of search techniques, especially genetic (directed) and hybrid (semi-directed) searches. At this study every customer has weight and they are scheduled by considering weights to improve performance value.

Solving Integrated Process Planning, Dynamic Scheduling, and Due Date Assignment Using Metaheuristic Algorithms

“…For better reading of simulation results, Gantt charts for the first two shop floors are given in Figures 7 and 8. Figure 7 shows the Gantt chart for the first shop floor in which the best solution from TA algorithm is calculated with the chromosome of [9,6,4 , 1, 3, 0, 4, 3, 2, 4, 2, 2, 0, 3, 3, 0, 2, 1, 4, 1, 2, 2, 1, 1, 3, 0, 1] with 163.28 fitness value. Figure 8 shows the Gantt chart for the second shop floor in which the best solution from GA/TA is calculated with the chromosome of [3, 17, 3, 2, 2, 2, 2, 2, 4, 3, 3, 1, 2, 2, 4, 4, 1, 1, 0, 0, 1, 3, 3, 3, 0, 3, 0, 0, 3, 2, 2, 2, 2, 2, 3, 2, 0, 1, 4, 2, 3, 2, 2, 4, 1, 2, 1, 3, 2, 0, 1, 3] with…”

“…SA evaluates the neighborhoods with the fitness value, but it might choose the worst move 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 in each iteration to escape from a local minimum. The probability of selecting a solution is given by an exponential control function in which the parameter of the function will decrease during the execution which is given in (9). SA differs from TA as it diversifies the solutions by randomizing them, while TA diversifies them by forcing new solutions.…”

“…The first study on IPPSDDA was conducted by Demir and Taskin [7]. Demir et al [8][9][10][11][12] also presented genetic algorithm (GA), simulated annealing (SA), tabu algorithm (TA), evolutionary strategies (ES), and hybrid algorithms to improve the global performance of the IPPSDDA problem. In this study, in addition to the other studies on IPPSDDA, we considered a dynamic scheduling model in which each job enters the system at a random time and we introduced the integration of process planning, dynamic scheduling, and due date assignment (DIPPSDDA) problem.…”