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In the present study, a mathematical model is provided for inventory management in a three-level supply chain including a supplier, a manufacturer, and a distributor, taking into account the potential for product deficiency. Significant parameters such as the optimal order size for raw materials, optimal production rate, optimal allowed deficiencies at each supply chain level, the of the vehicles and the number of times the products are delivered between the supply chain components have been provided aiming at minimizing inventory management costs in the supply chain. The problem was solved using a heuristic algorithm. The proposed model is implemented for 20 different numerical examples. According to the results, the difference between the solutions obtained from the algorithm and the solutions obtained from the optimal values with the real variables is an average of 1.52%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The optimal inventory control is closely related to an enterprise's operational efficiency, survival, and development. Market price uncertainty is introduced into the newsvendor model and the uncertainty's impact on the firm's optimal stocking quantity is discussed. The results show that the impact of stochastic market price on the optimal stocking quantity under a given condition mainly depends on the magnitude of inventory cost. When the inventory cost is low, the market price's uncertainty leads the firm to increase the stocking quantity. In contrast, when the inventory cost is high, market price uncertainty leads the firm to decrease inventory. Besides, the risk-averse behaviour leads the firm to reduce its stocking quantity. [ABSTRACT FROM AUTHOR]

Water retailer managed inventory is a classical and inevitable inventory management mode in present economic society. Stochastic models can more clearly explain demand uncertainty and are closely related to water supply chains. Risk preferences are widely valued in behavioral operation management. Related to the risk preferences in inventory management, the research on risk aversion is dominant, while risk-seeking is insufficient. Based on the model assumptions, the risk-seeking retailer's optimal decision-making inventory model with stochastic demand in a water supply chain is studied. The risk-seeking retailer's optimal inventory quantity, optimal inventory cost, supplier profit, retailer profit, and the profit of the entire water supply chain are derived. The validity of the equations is proved. The sensitivity analysis of the risk-seeking retailer's optimal inventory decision-making is carried out. The risk level effects on the five dimensions, the retail price, wholesale price, unit shortage cost, unit inventory cost, and unit residual value, are displayed through numerical simulation. The optimal inventory quantity and optimal inventory cost of the risk-seeking retailer are obtained. [ABSTRACT FROM AUTHOR]

To reduce the inventory cost and ensure product quality while meeting the diverse demands of customers, manufacturers yield products in batches. However, the raw materials required for manufacturing need to be obtained from suppliers in advance, making it necessary to understand beforehand how to best structure the pickup routes so as to reduce the cost of picking up and stocking while also ensuring the supply of raw materials required for each batch of production. To reduce the transportation and inventory costs, therefore, this paper establishes a mixed integer programming model for the joint optimization of multibatch production and vehicle routing problems involving a pickup. Following this, a two-stage hybrid heuristic algorithm is proposed to solve this model. In the first stage, an integrated algorithm, combining the Clarke-Wright (CW) algorithm and the Record to Record (RTR) travel algorithm, was used to solve vehicle routing problem. In the second stage, the Particle Swarm Optimization (PSO) algorithm was used to allocate vehicles to each production batch. Multiple sets of numerical experiments were then performed to validate the effectiveness of the proposed model and the performance efficiency of the two-stage hybrid heuristic algorithm. [ABSTRACT FROM AUTHOR]