This article considers an integration of two-echelon supply chain management (SCM) problem between a manufacturing site and customers. In the first echelon, jobs ordered by a number of customers are arranged and manufactured by one of a number of identical parallel machines. In the second, jobs are grouped by customer in batches and then delivered via trucks with a limited capacity. The problem is to determine batch delivery schedule of identical trucks. The batch delivery schedule is integrated with a parallel machine schedule of job orders from multi-customers. So, the objective of the problem is to simultaneously determine machine scheduling, batching and truck delivery scheduling to the corresponding customer to minimize the delivery completion time of whole the batched jobs. To solve the problem, two approaches are addressed in this article. The first approach uses a mathematical model (mixed integer programming model) to obtain the optimal solution. Since the problem is NP-hard, three kinds of genetic algorithm-based heuristics are proposed to increase solution efficiency for the second approach. The performances of the algorithms are compared using computational experiments with randomly generated examples. The computational experiments illustrate that the one of the proposed algorithms is capable of near-optimal solutions within a reasonable computing time.