Deep reinforcement learning has been coined as a promising research avenue to solve
sequential decision-making problems, especially if few is known about the optimal policy …

The aerospace industry faces challenges in managing inventory effectively due to long
product life cycles and unpredictable demand. Additive Manufacturing (AM) is a promising …

In the increasingly competitive market, supply chain decision-makers are making efforts to
improve operational efficiency and reduce costs by joint replenishment approach …

The increase in cost of supplies and services is outpacing the increase in revenues at many
hospitals. To address this cost increase hospitals are seeking more efficient ways to store …

An inventory system with stochastic demands is considered, where multiple items are jointly
replenished. The joint replenishment is controlled by a P (s, S) policy, where s defines the …

Inventory management practices (IMP) are prevalent in managing and controlling inventory
in an organisation. The study aims to measure the performance of steel manufacturing firms …

With the intensification of time-based competition, the importance of reducing lead-time by
rapidly delivering multi-orders has been underscored in the process of replenishment …

The purpose of this paper is to introduce a heuristic approach that uses a capacitated
inventory model as means for identifying a collaborative agreement between different …

In this paper, we study the periodic-review Joint-Replenishment Problem (JRP) with
stochastic demands and backorders-lost sales mixtures. We assume that lead times aare …

In this study, we consider the integrated inventory replenishment and transportation
operations in a supply chain where the orders placed by the downstream retailer are …