An Imperfect Production Model for Breakable Multi-Item with Dynamic Demand and Learning Effect on Rework over Random Planning Horizon

In recent times, in the literature of inventory management there exists a notorious interest in production-inventory models focused on imperfect production processes with a deterministic time horizon. Nevertheless, it is well-known that there is a high influence and impact caused by the learning effect on the production-inventory models in the random planning horizon. This research work formulates a mathematical model for a re-workable multi-item production-inventory system, in which the demand of the items depends on the accessible stock and selling revenue. The production-inventory model allows shortages and these are partial backlogged over a random planning horizon. Also, the learning effect on the rework policy, inflation, and the time value of money are considered. The main aim is to determine the optimum production rates that minimize the expected total cost of the multi-item production-inventory system. A numerical example is solved and a detailed sensitivity analysis is conducted in order to study the production-inventory model.

Behavioral operations effect of fairness and unfairness concern in the decision of the supply chain production-inventory system

Purpose The purpose of this paper is to investigate the behavioral operations effect in production inventory decision of supply chain consisting of one manufacturer and one buyer, and analyze how the unfairness concerns impact the decision of production inventory in a supply chain system. Design/methodology/approach First, a model without the buyer’s unfairness concern is established; then, advantage unfairness concern and disadvantage unfairness concern behavior of buyer are taken into account in the production inventory system. The authors analyze how advantage unfairness concern and disadvantage unfairness concern impact the optimal decisions and channel coordination. Findings The result shows several important conclusions. First, the buyer’s optimal ordering quantity and expected utility show opposing trend when the buyer has advantage unfairness concern. Second, the stronger bargaining power of the manufacturer results in an increasing buyer’s optimal ordering quantity under the advantage unfairness concern case, but decreasing under the disadvantage unfairness concern case. Third, the supply chain production-inventory can be coordinated under advantage unfairness concern case, but cannot be coordinated under disadvantage unfairness concern. Practical implications The study can provide to practitioners with important implications that when the vendor or the buyer in supply chain wants to make the decision of inventory replenishment, taking unfairness concerns into account will lead to different results. Therefore, to effectively improve the operations performance of supply chain, partners of the supply chain should not only care about their own interest, but also need to consider the fairness concern of the other partner, reflecting the cooperation consciousness of supply chain management. Originality/value This paper contributes to the new field of creative management–behavioral operations, offering managerial implications for the decision and optimization of supply chain production-inventory problem.

Optimization and Simulation of Dynamic Performance of Production–Inventory Systems with Multivariable Controls

The production–inventory system is a problem of multivariable input and multivariant output in mathematics. Selecting the best system control parameters is a crucial managerial decision to achieve and dynamically maintain an optimal performance in terms of balancing the order rate and stock level under dynamic influence of many factors affecting the system operations. The dynamic performance of the popular APIOBPCS model and the newly modified 2APIOBPCS model for optimal control of production–inventory systems is examined in the study. This examination is based on the leveled ground with a new simulation scheme that incorporates a designated multi-objective particle swarm optimization (MOPSO) algorithm into the simulation, which enables the optimal set of system control parameters to be selected for achieving the situational best possible performance of the production–inventory system under study. The dynamic performance is measured by the variance ratio between the order rate and the sales rate related to the bullwhip effect, and the integral of absolute error related to the inventory responsiveness in response to a random customer demand. Our simulation indicates that the 2APIOBPCS model performed better than or at least no worse than, and more robust than the APIOBPCS model under different conditions.