This paper investigates an integrated production and distribution scheduling problem in a hybrid flow shop with a batch delivery system. The goal is to schedule jobs and assign them to batches to minimize the total weighted number of tardy jobs and delivery costs, thereby improving both operational efficiency and customer satisfaction. To achieve this, a novel mixed-integer linear programming model is developed. Given its computational complexity for large instances, two metaheuristic algorithms, simulated annealing (SA) and a genetic algorithm (GA), are proposed and calibrated using the Taguchi method to enhance performance. Experimental results demonstrate that simulated annealing consistently outperforms the genetic algorithm in both solution quality and computational time. The results also demonstrate that the proposed approach reduces the overall cost of production and distribution by 10.45%.

This paper presents a two-stage hybrid flow shop scheduling problem with setup and assembly operations. The proposed study of this paper considers one kind of product with a quantity of demand where each product is made by assembling a set of different parts. At first, the parts are manufactured in a two-stage hybrid flow-shop and then the parts are assembled into products on assembly stage. Setup operations are needed when a machine starts processing the parts or it changes items. The considered objective is minimizing the completion time of all products. Since the problem is classified as NP-hard class, a combinatorial algorithm is proposed. The proposed algorithm is a three-step procedure where we use heuristic, genetic algorithm (GA), simulated annealing (SA), NEH and Johnson’s algorithm. Three lower bounds are presented and improved to evaluate the proposed algorithms. An extensive computational experiment is conducted to compare the performances of the proposed algorithms.