A demand classification scheme for spare part inventory model subject to stochastic demand and lead time

In this study, we aim to develop a demand classification methodology for classifying and controlling inventory spare parts subject to stochastic demand and lead time. Using real data, the developed models were tested and their performances were evaluated and compared. The results show that the Laplace model provided superior performance in terms of service level, fill rate (FR) and inventory cost. Compared with the current system based on normal distribution, the proposed Laplace model yielded significant savings and good results in terms of the service level and the FR. The Laplace and Gamma optimisation models resulted in savings of 82 and 81%, respectively

A multi-echelon inventory model with fixed reorder intervals

Includes bibliographical references (p. 30-31).by Stephen C. Graves

Finished goods management for JIT production: new models for analysis

A firm is considered that manages its internal manufacturing operations according to a just-in-time system, but maintains an inventory of finished goods as a buffer against random demands from external customers. The finished goods inventory may be analysed by the methods of classical inventory theory in order to characterize the trade-off between inventory costs and schedule stability. A model is formulated in which the supply of finished goods is replenished by a small fixed quantity each time period. The size of the replenishment quantity may be revised only at pre-specified intervals. The single-interval problem is analysed, the cost-minimizing value of the replenishment quantity for a given revision interval length is computed, and the optimal cost is characterized as a function of the revision interval length. The dynamic problem is shown to be convex, with relatively easily computed optima. Finally, alternative formulations of the problem are described and suggestions made for further research

A model of JIT make-to-stock inventory with stochastic demand

We consider a firm that manages its internal manufacturing operations according to a just-in-time (JIT) system but maintains an inventory of finished goods as a buffer against random demands from external customers. We formulate a model in which finished goods are replenished by a small fixed quantity each time period. In the interest of schedule stability, the size of the replenishment quantity must remain fixed for a predetermined interval of time periods. We analyse the single-interval problem in depth, showing how to compute a cost-minimising value of the replenishment quantity for a given interval length, and characterising the optimal cost, inventory levels and service as functions of the interval length and initial inventory. The model displays significant cost and service penalties for schedule stability. A dynamic version of the problem is also formulated, and shown to be convex in nature with relatively easily computed optima

Stochastic scheduling and set-ups in flexible manufacturing systems

Bibliography: p. 12.by Stanley B. Gershwin

Supply chain dynamics

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2003.Includes bibliographical references (p. 121-123).The strong bargaining power of major retailers and the higher requirements for speed, service excellence and customization have significantly contributed to transform the Supply Chain Management. These increasing challenges call for an integrated and dynamic Supply Chain Management and for a better integration and alignment with key customers, in order to reduce the firm's time-to-market and build competitive advantage. The thesis aims at providing the partner company, a major player in the consumer goods industry, with a more robust and efficient vendor managed inventory practice, so that the partner can determine the optimum inventory level to satisfy turnover, service level and lead time requirements, whereas minimizing lost sales and total costs in the system. The team developed a Supply Chain Dynamics framework to help the partner to establish new service level strategies, strongly oriented to the strategic importance of its products and customers, and to map the key system-wide drivers that impact the overall number of inventory turns, service level and total costs. Additionally, in order to run simulations and estimate the outcomes of the proposed recommendations, the team developed a "Multi-Echelon" simulator and used a commercial "Supply Chain Dynamics" simulator.by Ricardo Wagner Lopes Barbosa [and] Edward Fan.M.Eng.in Logistic

Deep Reinforcement Learning for a Two-Echelon Supply Chain with Seasonal Demand

This paper leverages recent developments in reinforcement learning and deep learning to solve the supply chain inventory management problem, a complex sequential decision-making problem consisting of determining the optimal quantity of products to produce and ship to different warehouses over a given time horizon. A mathematical formulation of the stochastic two-echelon supply chain environment is given, which allows an arbitrary number of warehouses and product types to be managed. Additionally, an open-source library that interfaces with deep reinforcement learning algorithms is developed and made publicly available for solving the inventory management problem. Performances achieved by state-of-the-art deep reinforcement learning algorithms are compared through a rich set of numerical experiments on synthetically generated data. The experimental plan is designed and performed, including different structures, topologies, demands, capacities, and costs of the supply chain. Results show that the PPO algorithm adapts very well to different characteristics of the environment. The VPG algorithm almost always converges to a local maximum, even if it typically achieves an acceptable performance level. Finally, A3C is the fastest algorithm, but just like the VPG, it never achieves the best performance when compared to PPO. In conclusion, numerical experiments show that deep reinforcement learning performs consistently better than standard inventory management strategies, such as the static (s, Q)-policy. Thus, it can be considered a practical and effective option for solving real-world instances of the stochastic two-echelon supply chain problem.Comment: 30 pages, 6 figures, 5 tables, submitted to European Journal of Operational Research, for source code see https://github.com/frenkowski/SCIMAI-Gy

Efficient regularized isotonic regression with application to gene--gene interaction search

Isotonic regression is a nonparametric approach for fitting monotonic models to data that has been widely studied from both theoretical and practical perspectives. However, this approach encounters computational and statistical overfitting issues in higher dimensions. To address both concerns, we present an algorithm, which we term Isotonic Recursive Partitioning (IRP), for isotonic regression based on recursively partitioning the covariate space through solution of progressively smaller "best cut" subproblems. This creates a regularized sequence of isotonic models of increasing model complexity that converges to the global isotonic regression solution. The models along the sequence are often more accurate than the unregularized isotonic regression model because of the complexity control they offer. We quantify this complexity control through estimation of degrees of freedom along the path. Success of the regularized models in prediction and IRPs favorable computational properties are demonstrated through a series of simulated and real data experiments. We discuss application of IRP to the problem of searching for gene--gene interactions and epistasis, and demonstrate it on data from genome-wide association studies of three common diseases.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS504 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed