A two-stage stochastic supply chain scheduling problem with production in cellular manufacturing environment: A case study

Abstract

An integrated decision in supply chain is a significant principle in order to compete in today's market. This paper proposes a novel mathematical model in a two-stage supply chain scheduling to cooperate procurement and manufacturing activities. The supply chain scheduling along with the production approach of cellular manufacturing under demand, processing time, and transportation time uncertainties makes business environment sustainably responsive to the changing needs of customers. Uncertainties are formulated by queuing theory. In this paper, a new mixed-integer nonlinear programming formulation is used to determine types of vehicles to carry raw materials, suppliers to procure, priority of each part in order to process, and cell formation to configure work centers. The goal is to minimize total tardiness. A linearization method is used to ease tractability of the model. A genetic algorithm is developed due to the NP-hard nature of the problem. The parameters of the genetic algorithm are set and estimated by Taguchi's experimental design. Numerous test problems are employed to validate the effectiveness of the modeling and the efficiency of solution approaches. Finally, a real case study and a sensitivity analysis are discussed to provide significant managerial insights and assess the applicability of the proposed model.

Optimization-based Scheduling of Construction Projects with Generalized Precedence Relationships: A Real-Life Case Study

“…Genetic Algorithm (GA) has been the subject of the well-established research domain of optimization ever since it was originally proposed by Holland [34] and has often been specifically tweaked for addressing various problems. For instance, Namazian et al [35] combined goal programming and GA for project selection and scheduling; Akhbari [36] embedded GA to four other meta-heuristics to solve resource-constrained project scheduling problem; Erden et al [37] presented a PSO with GA operators for solving integrated process planning dynamic scheduling and due date assignment problem; and Esmailnezhad and Saidi-Mehrabad [38] used GA for stochastic supply chain scheduling.…”