Background: International transportation has grown substantially, causing total logistics costs (TLCs) to rise … importance of a country-based logistics strategy. The paper successfully establishes the trends and relations between logistics … parameters, which assists the logistics decision making. Research identifies the gaps in the existing literature and bridges them …

: Distribution managers may find here guidance for defining a proper design of logistics centers and evaluating the operators' actual …

cycle time, reduces the major ordering costs. An efficient algorithm to determine the optimal policy of this type is … discussed in this paper. It is shown that this algorithm can be used for deterministic multi-item inventory problems, with … linear cost rate functions. Numerical results for this case show that the algorithm significantly outperforms other solution …

learning algorithm. The algorithm separates the planning horizon into a disjoint exploration phase and an exploitation phase … grid to obtain a pair of recommended price and target inventory level. During the exploitation phase, the algorithm …

We propose the first learning algorithm for single-product, periodic-review, backlogging inventory systems with random … cyclic stochastic gradient descent type of algorithm whose running average cost asymptotically converges to the clairvoyant … optimal cost. We prove that the rate of convergence guarantee of our algorithm is $O(1/\sqrt{T})$, which is provably tight for …

We develop the first nonparametric learning algorithm for periodic-review perishable inventory systems. In contrast to …, lost-sales and outdating cost is convex in the base-stock level. Then, we develop a nonparametric learning algorithm that … establish a square-root convergence rate of the proposed algorithm, which is the best possible. Our algorithm and analyses …

propose a non-parametric algorithm that generates a sequence of adaptive ordering decisions based on the stochastic gradient … descent method. We compare the T-period cost of our algorithm to the clairvoyant, who knows the underlying demand distribution …