The depletion of natural resources and the degradation of the ecosystem have led many countries to adopt closed-loop supply activities in both their industrial and service sectors. With the widespread use of Internet technology, these aspects motivate the incorporation of e-commerce with the classical closed-loop supply chain. This study suggests a novel mixed-integer linear programming (MILP) model that addresses the integration of e-commerce with a multi-echelon closed-loop supply chain with a multi-period planning time horizon by considering dual channels in manufacturing, and recovery facilities. To validate the model, we obtain optimal decision variables and examine the robustness and applicability of the model, and comprehensive computational experiments are performed. Moreover, sensitivity analysis is carried out to illustrate the efficacy of e-commerce integration by considering the two channels in the closed-loop supply chain. Accordingly, the total cost of the dual-channel CLSC decreases with an increase in customer demand via online retailers, the returned end of life (EOL) products, recycling ratio, and recovery ratio. Some useful managerial implications are provided based on the conducted analysis.

In this study, we propose a robust bi-objective optimization model of the green closed-loop supply chain network considering presorting, a heterogeneous transportation system, and carbon emissions. The proposed model is an uncertain bi-objective mixed-integer linear optimization model that maximizes profit and minimizes carbon emissions by considering uncertain costs, selling price, and carbon emissions. The robust optimization approach is implemented using the combined interval and polyhedral, “Interval+ Polyhedral,” uncertainty set to develop the robust counterpart of the proposed model. Robust Pareto optimal solutions are obtained using a lexicographic weighted Tchebycheff optimization approach of the bi-objective model. Intensive computational experiments are conducted and a robust Pareto optimal front is obtained with a probability guarantee that the constraints containing uncertain parameters are not violated (constraint satisfaction).

Automated guided vehicles have been widely used in flexible manufacturing systems especially in a job shop and flow shop. Recently, they are included in other fields such as container terminals. This paper demonstrates a literature review of the last three decades about the scheduling problems of the automated guided vehicles in three main classes, namely job shops, flow shops, and container terminals. We classify the literature according to the scheduling problems and the solution approaches. The results of the survey are that the solution methods mainly focus on using simulation as a key performance analysis tool, whereas, the big computation times of the developed mathematical models make heuristics approaches are dominant for solving such models. After that, we discuss research gaps to the purpose of finding new research directions.

Most real-life optimization problems are subjected to uncertainty, and the robust optimization approach is one of the efficient techniques to deal with uncertain optimization problems. Supply chain optimization problems are highly sensitive to data perturbations mostly due to inappropriate estimation of the problems’ parameters and the highly dynamic environment. In this study, we propose an adaptable robust optimization model for the dual-channel closed-loop supply chain (CLSC) and present two counterpart models; the first model is a mixed integer linear programming (MILP) model based on the adjustable box uncertainty set, while the second robust model is a mixed integer nonlinear programming (MINLP) model based on the adjustable ellipsoidal uncertainty set. We provide a novel approach for considering multiple uncertainty sets in the objective function, that provide flexibility and control risk based on the preferences of the decision-makers. This study aims at minimizing the total cost of the dual-channel CLSC network considering uncertain purchasing, transportation, fixed, and processes costs, in addition to uncertain customer demand. Intensive computational experiments are conducted on the two robust models using GAMS software. Robust solutions are obtained and sensitivity analysis is conducted on both models considering 10% perturbation of the uncertain parameters around their nominal values as well as probability guarantee for not violating the constraints.

Flexible manufacturing systems (FMSs) have widely expanded in industry sectors all over the world. Scheduling is one of the problems that face the life cycle of FMSs. This paper introduces the simultaneous scheduling problem of automated guided vehicles (AGVs) and machines in FMSs. The study is based on discrete event simulation (DES) to solve that problem using benchmark data used by previous studies. The objective of this study is to determine the starting and completion times of each job to minimise the makespan. The results validate the robustness of the simulation model, especially for large-scale problems. Furthermore, they are compared against the other approaches and show consistency.