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).

This study was conducted during summer and winter of 2018- 2019 in the agricultural research field of Shahid Chamran University. Experimental design was split- plot based on RCBD with three replications. The main plot was the type of agricultural system in three levels including conventional (Conv), organic (Org) and sustainable (Sust) (integrated between Conv and Org) and sup- plot was the type of pre- cultivated crop in sequence with wheat including cultivation of mung bean (M- W), corn (C- W), sesame (S- W) and fallow (F- W). Yield quantity (yield and its component) and quality (grain protein), an estimate of photosynthesis matter transfer index of wheat and soil organic carbon (SOC) after one double-cropping were measured. The result showed that the highest (545.04 g/m2) and the lowest (409.28 g/m2) seed yields were obtained in Conv and Org respectively. In contract, with the changing type of system from Conv to Org, grain protein was increased significantly (from 8.3 to 9.6 %). In addition, the highest (535.47 g/m2) yield of wheat was obtained from M- W double cropping. On the other hands the highest remobilization and current photosynthesis matter were obtained in the organic agricultural system with M- W and conventional with M- W double cropping. The situation of SOC showed that the highest (33.18 mg/g) SOC was obtained in the organic agricultural system with C- W double cropping. The reason for improving SOC in the organic and sustainable agricultural system was application of organic matter (compost and vermicompost) and crop residue management. Totally, from the crop ecology point of view, sustainable agricultural method with a sequence of M- W was the most desirable system.

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.

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.

Different INVENTORY control SYSTEMS try to determine how much and when to order at the least relevant cost while maintaining a desirable service level for customers. In this article, a continuous review stochastic INVENTORY system, with three objectives, is optimized. In this model, contrary to the traditional INVENTORY models, customer service is not considered a shortage cost in the objective function. But the frequency of stock out occasions and the number of items stocked out annually are to be minimized. For determining the Pareto optimal set, multi-objective evolutionary algorithms are used. First, NSGA-II, MOGA, VEGA, RWGA are developed. Then some improvements in NSGA-II mechanisms are made and R-NSGA-II is developed.Subsequently, these algorithms are examined for some criteria such as set coverage and spacing, and the best algorithms for each criteria are presented. The Result shows that R-NSGA-II has good scores for most criteria. Afterwards, Pareto optimal set is ranked using the method of global criteria.

Considering the competition in today’s business environment, tactical planning of a supply chain becomes more complex than before. In many multi-product INVENTORY SYSTEMS, substitution flexibility can improve profits. This paper aims to prepare a comprehensive substitution INVENTORY model, where an INVENTORY system with two substitute products with ignorable lead time has been considered, and effects of simultaneous ordering have been examined. In this paper, demands of customers for both of the products have been regarded as stochastic parameters, and queuing theory has been used to construct a mathematical model. The model has been coded by C++, and it has been analyzed due to a real example, where the results indicate efficiency of proposed model.