Metaheuristics for generalized order acceptance and scheduling problem with batch delivery

Abstract

In order to address a more complex form of the generalized order acceptance and scheduling problem, the study looks into incorporating logistics into the production scheduling decisions. The order acceptance and scheduling element of the problem involves shared decision-making regarding which orders to accept and how to schedule them due to the restricted production environment capacity and the customers' needs for order delivery periods. The decision of how to batch the accepted orders for delivery must be made in conjunction with the production scheduling due to the logistical nature of the issue. The objective is to optimize net revenue, according to the literature on order acceptance and scheduling problems. We initially offer a mixed integer linear programming approach for this Problem. We offer an Equilibrium Optimizer metaheuristics algorithm along with an updated Time-varying transfer function to address cases of large size problems where these models fall short. A variation of this algorithm is created and compared with the most effective method employed in the prior study, iterated local search implementing on variable neighborhood search methodology, in order to assess the efficacy of the suggested search scheme. It is shown that the proposed models can attain very small optimality gaps for the small size challenges, but as the problem size increases, their performances rapidly degrade. When compared to the one employing the tabu search method, the iterated local search algorithm using the suggested local search scheme produces less optimality gaps for the enormous size issue circumstances.