In this paper, we use a Markov decision process (MDP) to model the joint inventory-promotion decision problem. The state variable of the MDP represents the demand state brought about by changing environmental factors as well as promotion decisions. The demand state in a period determines the...

Markov-modulated processes have been used in stochastic inventory models with setup costs for modeling demand under the influence of uncertain environmental factors, such as fluctuating economic and market conditions. The analyses of these models have been carried out in the literature only...

This paper is concerned with a generalization of classical inventory models (with fixed ordering costs) that exhibit (s, S) policies. In our model, the distribution of demands in successive periods is dependent on a Markov chain. The model includes the case of cyclic or seasonal demand. The...

"The Belt and Road Initiative promotes connectivity among countries and regions along the route and is a powerful tool for the Chinese government to respond to the rise of trade protectionism and the restructuring and shift of global industrial chains in the post-epidemic era. This study...

Operations Management (OM) has evolved to its current status due to the contributions of researchers in many fields including economics, finance, behavioral sciences, operations research/management science, mathematics, statistics, and computer science. Many of these contributors are Nobel...

Supply chains today routinely use third parties for many strategic activities, such as manufacturing, R&D, or software development. These activities often include relationship-specific investment on the part of the vendor, while final outcomes can be uncertain. Therefore, writing complete...

We study an economy in which the rate of change of population depends on population policy decisions. This requires population as well as capital as state variables. By showing the algebraic relationship between the shadow price of the population and the shadow price of the per capita capital...