A comprehensive and integrated study of any supply chain (SC) environment is a vital requirement that can create various advantages for the SC owners. This consideration causes productive managing of the SC through its whole wide components from upstream suppliers to downstream retailers and customers. On this issue, despite many valuable studies reported in the current literature, considerable gaps still prevail. These gaps include integration and insertion of basic concepts, such as queuing theory, facility location, inventory management, or even fuzzy theory, as well as other new concepts such as strategic planning, data mining, business intelligence, and information technology.This study seeks to address some of these gaps. To do so, it proposes an integrated four-echelon multi-period multi-objective SC model. To make the model closer to the real world problems, it is also composed of inventory and facility location planning, simultaneously. The proposed model has a mixed integer linear programming (MILP) structure. The objectives of the model are reducing cost and minimizing the non-fill rate of customer zones demand. The cost reduction part includes cost values of raw material shipping from suppliers to plants, plant location, inventory holding costs in plants, distribution cost from plants to warehouses or distribution centers (DCs), and shipping costs from DCs to customer zones. Finally, since the literature of SC lacks efficient Pareto-based multi-objective evolutionary algorithms (MOEAs), a new multi-objective version of the biogeography-based optimization algorithm (MOBBO) is introduced to the literature of the SC. The efficiency of the algorithm is proved through its comparison with an existing algorithm called multi-objective harmony search (MOHS).

Consider a two - echelon repairable inventory system consisting of a central depot and multiple stocking centers. There are two kinds of defectives, both of them must be repaired at central depot. The difference between these kinds of defectives is related to their depot replenishment lead times. The centers provide parts replacement service to customers and the depot fills each kind of center replenishment orders on a FCFS (First Come First for Service) basis defective parts that are received by centers are passed to the depot for repair and depot inventory replenishment. For this system, existing models (for example, METRIC model) usually assume that there is only one kind of defective, and they also assume that the depot replenishment lead times (DRLTs) are i.i.d, which however, does not fit well into the service part logistics system, that motivated this research. Because the DRLTs consist of the sum of repair times, defective return times and transportation times, they are different across stocking centers, which are located globally. We study the impact of such center - dependent DRLTs on system performance. We show that for such systems, using the i.i.d DRLT assumption introduces errors in estimating system performance.

Consider a two-echelon repairable inventory system consisting of a centeral depot and Multiple stocking centers. There are two kinds of defective, type A defects must be repaired at centeral depot, and type B defects at stocking centers. The centers provide parts replacement service to customers and the depot fills center replenishment orders on a FCFS basis. Type A defects that are received at the centers are passed to the depot for repair and depot inventory replenishment. For this system, existing models (for exemple, METRIC model) usually assume that the depot replenishment lead times (DRLTs) (for type A defects) are i.i.d., which however, does not fit well into the service part logistics system, that motivated this research. Because the DRLTs consist of ti,e sum of repair times, defective return times and transportation times, they are different across stocking centers which are located globally. We study the impact of such center dependent DRLTs on system performance. We show that for such system. using the i.i.d. DRLT assumption introduces errors in estimating system performance.

In this study, a planning problem for multi-time, multi-product, multiechelon Supply Chains (SCs) is studied and the implications of formulating a tri-level model to integrate Procurement, Production, and Distribution (PPD) while maintaining the existing hierarchy in the decision process are discussed. In our model, there were three di erent decision makers controlling the PPD processes in the absence of cooperation because of di erent optimization strategies. First, a hierarchical Tri-Level Programming (TLP) model was developed to deal with decentralized SC problems. Then, an algorithm was formulated to solve the proposed model. A numerical sample was investigated to scrutinize applicability of the optimization model and the proposed algorithm. In order to evaluate the application of the model and the proposed algorithm, 10 sets of small and large problems were randomly generated and tested. The experimental results showed that our proposed fuzzy-stochastic Simulation-based Hierarchical Interactive Particle Swarm Optimization (Sim-HIPSO) performed well in  nding good approximate solutions within reasonable computational times.