Research on Distribution and Inventory Cooperation of Agricultural Means Supply Chain

This paper constructed a biobjective model based on the total cost and time satisfaction to provide a desirable solution to the distribution and inventory cooperation of agricultural means supply chain. The model simulated how the distribution center and retailers collaborate to meet the needs of the order customer in the random lead time and out-of-stock loss costs. By the features of the model, the biobjective genetic algorithm was improved based on elitism selection, aiming to improve the quality of noninferior solution in biobjective model. Finally, the influence degree of the lead time of delivery, unit inventory cost, and unit transport cost on the total cost of the system was quantified through the analysis of examples and sensitivity of model parameters. This research has provided valuable new insights into the distribution and inventory coordination of supply chain.

Multi-level two-way cross-chain inventory coordination model that combines the same level with different levels in multi-supply chains

A cross-chain inventory emergency replenishment model that comprises the enterprises of manufacturers, wholesalers, and retailers at the same level and at different levels based on a co-opetition relationship in cluster supply chains is constructed in this work. The model is built by studying two three-level supply chains that each comprises manufacturers, wholesalers, and retailers using the system dynamics method. Subsequently, the proposed cross-chain inventory collaboration model is compared with currently available models and then simulated using the system dynamics simulation software Vensim. Simulation results show that the proposed model further improves existing theoretical research achievements in the field both locally and abroad, thereby advancing theoretical research on cross-chain inventory collaboration. This study also provides decision-making references for the practice of inventory collaboration among enterprises in industrial clusters.

Supply Chain Inventory Coordination under Uncertain Demand via Combining Monte Carlo Simulation and Fitness Inheritance PSO

Supply chain coordination consists of multiple aspects, among which inventory coordination is the most widely used in practice. Inventory coordination is challenging due to the uncertainty of customers' demand. Existing researches typically assume that the demand is either a deterministic constant or a stochastic variable following a known distribution function. However, the former cannot reflect the practical costumers' demand, and the later make the model inaccurate when the demand distribution is ambiguous or highly variable. In this paper, the authors propose a Monte Carlo simulation model of such problem, which can mimic the inventory changing procedure of a supply chain with uncertain demand following an arbitrary distribution function. Then, a PSO is combined with the simulation model to achieve a coordination decision scheme to minimize the total inventory cost. Experiments show that their approach is able to produce a high quality solution within a short computational time and outperforms comparative approaches.