Stochastic Optimization Models for Perishable Products

For many years, researchers have focused on developing optimization models to design and manage supply chains. These models have helped companies in different industries to minimize costs, maximize performance while balancing their social and environmental impacts. There is an increasing interest in developing models which optimize supply chain decisions of perishable products. This is mainly because many of the products we use today are perishable, managing their inventory is challenging due to their short shelf life, and out-dated products become waste. Therefore, these supply chain decisions impact profitability and sustainability of companies and the quality of the environment. Perishable products wastage is inevitable when demand is not known beforehand. A number of models in the literature use simulation and probabilistic models to capture supply chain uncertainties. However, when demand distribution cannot be described using standard distributions, probabilistic models are not effective. In this case, using stochastic optimization methods is preferred over obtaining approximate inventory management policies through simulation. This dissertation proposes models to help businesses and non-prot organizations make inventory replenishment, pricing and transportation decisions that improve the performance of their system. These models focus on perishable products which either deteriorate over time or have a fixed shelf life. The demand and/or supply for these products and/or, the remaining shelf life are stochastic. Stochastic optimization models, including a two-stage stochastic mixed integer linear program, a two-stage stochastic mixed integer non linear program, and a chance constraint program are proposed to capture uncertainties. The objective is to minimize the total replenishment costs which impact prots and service rate. These models are motivated by applications in the vaccine distribution supply chain, and other supply chains used to distribute perishable products. This dissertation also focuses on developing solution algorithms to solve the proposed optimization models. The computational complexity of these models motivated the development of extensions to standard models used to solve stochastic optimization problems. These algorithms use sample average approximation (SAA) to represent uncertainty. The algorithms proposed are extensions of the stochastic Benders decomposition algorithm, the L-shaped method (LS). These extensions use Gomory mixed integer cuts, mixed-integer rounding cuts, and piecewise linear relaxation of bilinear terms. These extensions lead to the development of linear approximations of the models developed. Computational results reveal that the solution approach presented here outperforms the standard LS method. Finally, this dissertation develops case studies using real-life data from the Demographic Health Surveys in Niger and Bangladesh to build predictive models to meet requirements for various childhood immunization vaccines. The results of this study provide support tools for policymakers to design vaccine distribution networks

An Efficient Inventory Model-Based GA For Food Deterioration Products In The Tourism Industry

Background: The inventory control practice of deteriorating food products that are subject to an expiration date is a challenging process. Inappropriate inventory control practice leads to substantial waste of products and significant holding and purchasing costs. Purpose: This paper aims to develop an inventory control model-based Genetic Algorithm (GA) to minimize the Total Annual Inventory Cost (TAIC) function developed explicitly for the proposed model. Methodology: GA is used and tailored to provide the best reorder level of deteriorating food products. A case study of one of the five-star hotels in Iraq is conducted, followed by a sensitivity analysis study to validate the proposed model for varying reorder levels. Results and Conclusion: A minimum inventory cost is obtained with an optimum reorder level achieved by running GA. It is concluded that the optimal reorder level provided by the proposed GA minimized the monthly inventory cost of products

Mathematics in the Supply Chain

[no abstract available

Stochastic regret minimization for revenue management problems with nonstationary demands

We study an admission control model in revenue management with nonstationary and correlated demands over a finite discrete time horizon. The arrival probabilities are updated by current available information, that is, past customer arrivals and some other exogenous information. We develop a regret‐based framework, which measures the difference in revenue between a clairvoyant optimal policy that has access to all realizations of randomness a priori and a given feasible policy which does not have access to this future information. This regret minimization framework better spells out the trade‐offs of each accept/reject decision. We proceed using the lens of approximation algorithms to devise a conceptually simple regret‐parity policy. We show the proposed policy achieves 2‐approximation of the optimal policy in terms of total regret for a two‐class problem, and then extend our results to a multiclass problem with a fairness constraint. Our goal in this article is to make progress toward understanding the marriage between stochastic regret minimization and approximation algorithms in the realm of revenue management and dynamic resource allocation. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 433–448, 2016Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135128/1/nav21704.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135128/2/nav21704_am.pd

Service Level Constrained Inventory Systems

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151878/1/poms13060_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151878/2/poms13060.pd

SOLVING THE DATA-DRIVEN NEWSVENDOR WITH ATTENTION TO TIME

Inventory management systems support firms in planning for an uncertain future by using demand forecasts and optimization models to make restocking decisions. Recent work on the data-driven newsvendor found that incorporating machine learning (ML) can improve the success of inventory management by accounting for demand-driving information. However, ML methods are infamously hard to interpret, which may hinder their acceptance. To ameliorate this, we show how to apply an interpretable attention-based architecture, the Temporal Fusion Transformer (TFT), to the data-driven newsvendor problem. Our approach replicates and extends the original TFT time series forecasting method to the inventory management domain. We evaluate our method on two real-world retail datasets, each covering 260 perishable food items, and provide domain-specific benchmarks. The computational study illustrates TFT’s interpretable predictions and their comparatively high accuracy. Our work aims to lay the groundwork for further design science research on transparency in human-AI collaboration in this domain

Integrated Production and Distribution planning of perishable goods

Tese de doutoramento. Programa Doutoral em Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201

Improving healthcare supply chains and decision making in the management of pharmaceuticals

The rising cost of quality healthcare is becoming an increasing concern. A significant part of healthcare cost is the pharmaceutical supply component. Improving healthcare supply chains is critical not only because of the financial magnitude but also because it impacts so many people. Efforts such as this project are essential in understanding the current operations of healthcare pharmacy systems and in offering decision support tools to managers struggling to make the best use of organizational resources. The purpose of this study is to address the objectives of a local hospital that exhibits typical problems in pharmacy supply chain management. We analyze the pharmacy supply network structure and the different, often conflicting goals in the decisions of the various stakeholders. We develop quantitative models useful in optimizing supply chain management and inventory management practices. We provide decision support tools that improve operational, tactical, and strategic decision making in the pharmacy supply chain and inventory management of pharmaceuticals. On one hand, advanced computerized technology that manages pharmaceutical dispensation and automates the ordering process offers considerable progress to support pharmacy product distribution. On the other hand, the available information is not utilized to help the managers in making the appropriate decisions and control the supply chain management. Quantitative methods are presented that provide simplified, practical solutions to pharmacy objectives and serve as decision support tools. For operational inventory decisions we provide the min and max par levels (reorder point and order up to level) that control the automated ordering system for pharmaceuticals. These parameters are based on two near-optimal allocation policies of cycle stock and safety stock under storage space constraint. For the tactical decision we demonstrate the influence of varying inventory holding cost rates on setting the optimal reorder point and order quantity for items. We present a strategic decision support tool to analyze the tradeoffs among the refill workload, the emergency workload, and the variety of drugs offered. We reveal the relationship of these tradeoffs to the three key performance indicators at a local care unit: the expected number of daily refills, the service level, and the storage space utilization