A Decision Support System Based on a Genetic Algorithm for the Utilization of Leftovers

Abstract

There are usually some leftovers (usable pieces of raw material) and scraps (unusable pieces of raw material).generated after the completion of a manufacturing process. These leftovers consist of many different types, materials, styles and sizes so the use of such materials is difficult to manage, resulting in a significant material management problem for manufacturers. This study examines ways to use these leftovers and proposes the application of a genetic algorithm to handle the problem of matching usable leftovers to incoming orders. In order to verify the feasibility of the solution, a decision support system was implemented, based on a genetic algorithm. With lots of experimental results, we conclude this method is thus of considerable value to the managers of manufacturing factories.

Decision making viva genetic algorithm for the utilization of leftovers

“…It has been shown earlier that the longer or shorter extreme values affect the proficiency of computations, and longer extreme values took more time for evaluation as compared with a shorter value. 51 In the proposed case, the shorter and longer extreme values do not affect the evaluation procedure. We can see this scenario in Table 2, where the longer or shorter extreme values have no effect on evaluation time, that is, a shorter extreme value may take more time in evaluation, whereas sometimes the longer extreme value may take less time in evaluation, and consequently every time results in the best solution, as shown in Figure 5.…”

“…We can see this scenario in Table 2, where the longer or shorter extreme values have no effect on evaluation time, that is, a shorter extreme value may take more time in evaluation, whereas sometimes the longer extreme value may take less time in evaluation, and consequently every time results in the best solution, as shown in Figure 5. There have been many other approaches where the optimization of materials loss and the computational time are discussed, 14,17,22,51,58,59 so the proposed algorithm is incomparable these approaches. Furthermore, the optimization of cost and profit is carried out, 59,60 so the comparison of them with the proposed algorithm is not possible.…”

“…The comparison of two-point crossover and partially matched crossover is presented which has not been taken into account the minimization of leftovers as scraps. 51 In the case study, we have minimized the leftovers up to a high extent that usually occurs after construction and manufacturing processes. Furthermore, the proposed algorithm has the capability to handle problems with many unknowns.…”

A decision support system to utilize leftovers by using meta-heuristic technique

“…Here, we present a metaheuristic procedure for the evaluation of the problem of utilization of leftovers when leftovers exist in the stock and, moreover, a procedure for the aforementioned problem. The metaheuristic procedure is attained, which modifies the concepts suggested by Ali et al [42] and Fu et al [43]. The existing metaheuristic procedure [42] is applicable only for those leftovers that have no overlapping sections when connecting items with each other.…”

“…The suggested approach expands the concept of Ali et al [42,43] to those leftover materials that have overlapping sections during connections, where the length of the overlapping sections is different for different materials. Moreover, this study contributes to expanding the usefulness of leftover reduction and the implementation of GA to tackle the difficult and real-life minimization issue of reusable leftovers.…”