Using system simulation to search for the optimal multi-ordering policy for perishable goods

[EN] This paper explores the possibility that perishable goods can be ordered several times in a single period after considering the cost of Marginal contribution, Marginal loss, Shortage, and Purchasing under stochastic demand. In order to determine the optimal ordering quantity to improve the traditional newsvendor and maximize the total expected profits, and then sensitivity analysis is taken to realize the influence of the parameters on total expected profits and decision variables respectively. In addition, this paper designed a multi-order computerized system with Monte Carlo method to solve the optimal solution under stochastic demand. Based on numerical examples, this paper verified the feasibility and efficiency of the proposed model. Finally, several specific conclusions are drawn for practical applications and future studies.Huang, Y.; Chang, X.; Ding, Y. (2019). Using system simulation to search for the optimal multi-ordering policy for perishable goods. International Journal of Production Management and Engineering. 7(1):49-62. https://doi.org/10.4995/ijpme.2019.10745SWORD496271Azoury, K.S., Miller, B.L. (1984). A comparison of the optimal ordering levels of bayesian and non-bayesian inventory models, Management Science, 30(8), 993-1003. https://doi.org/10.1287/mnsc.30.8.993Alfares, H.K., Elmorra, H.H. (2005). The distribution-free newsboy problem: extensions to the shortage penalty case, International Journal of Production Economics, 93-94(8), 465-477. https://doi.org/10.1016/j.ijpe.2004.06.043Chung, C.S., Flynn, J. (2001). A newsboy problem with reactive production, Computers & Operations Research, 28(8), 751-765. https://doi.org/10.1016/S0305-0548(00)00006-XChun, Y.H. (2003). Optimal pricing and ordering policies for perishable commodities, European Journal of Operational Research, 144(1), 68-82. https://doi.org/10.1016/S0377-2217(01)00351-4Chen, L.H., Chen, Y.C. (2009). A newsboy problem with a simple reservation arrangement. Computers & Industrial Engineering, 56(1), 157-160. https://doi.org/10.1016/j.cie.2008.04.010Dian, J.R. (1990). A high-low search algorithm for a newsboy problem with delayed information feedback. Operations Research, 38(5), 838-846. https://doi.org/10.1287/opre.38.5.838Dye, C.Y., Ouyang, L.Y. (2005). An EOQ model for perishable items under stock-dependent selling rate and time-dependent partial backlogging. European Journal of Operational Research, 163(3), 776-783. https://doi.org/10.1016/j.ejor.2003.09.027Fujiwara, O., Soewandi, H., Sedarage, D. (1997). An optimal ordering and issuing policy for a two-stage inventory system for perishable products. European Journal of Operational Research, 99(2), 412-424. https://doi.org/10.1016/S0377-2217(95)00365-7Gallego, G., Moon, I. (1993). The distribution free newsboy problem: review and extensions. Journal of the Operational Research Society, 44(8), 825-834. https://doi.org/10.1057/jors.1993.141Khouja, M. (1999). The single-period (news-vendor) problem: literature review and suggestions for future research. Omega, 27(5), 537-553. https://doi.org/10.1016/S0305-0483(99)00017-1Khouja, M.J. (2000). Optimal ordering, discounting, and pricing in the single-period problem. International Journal of Production Economics, 65(2), 201-216. https://doi.org/10.1016/S0925-5273(99)00027-4Lau, H., Lau, H. (1998). Decision models for single-period products with two ordering opportunities. International Journal of Production Economics, 55(1), 57-70. https://doi.org/10.1016/S0925-5273(98)00040-1Padmanabhan, G., Vrat, P. (1995). EOQ models for perishable items under stock dependent selling rate. European Journal of Operational Research, 86(2), 281-292. https://doi.org/10.1016/0377-2217(94)00103-JPando, V., Luis, A.S.J., Juan, G.L., Sicilia, J. (2013). A newsboy problem with an emergency order under a general backorder rate function. Omega, 41(6), 1020-1028. https://doi.org/10.1016/j.omega.2013.01.003Zheng, M., Wu, K., Shu, Y. (2016). Newsvendor problems with demand forecast updating and supply constraints. Computers & Operations Research, 67, 193-206. https://doi.org/10.1016/j.cor.2015.10.00

Optimal Planning Quantities for Product Transition

The replacement of an existing product with a new one presents many challenges. In particular, uncertainties in a new product introduction often lead to extreme cases of demand and supply mismatches. This paper addresses inventory planning decisions for product upgrades when there is no replenishment opportunity during the transition period. We allow product substitution: when a company runs out of the old product, a customer may be offered the new product as a substitute. We show that the optimal substitution decision is a time-varying threshold policy and establish the optimal planning policy. Further, we determine the optimal delay in a new product introduction, given the initial inventory of the old product

Optimal policy for multi-item systems with stochastic demands, backlogged shortages and limited storage capacity

Producción CientíficaIn this paper, an inventory model for multiple products with stochastic demands is developed. The scheduling period or inventory cycle is known and prescribed. Demands are independent random variables and they follow power patterns throughout the inventory cycle. For each product, an aggregate cycle demand is realized first and then the demand is released to the inventory system gradually according to power patterns within a cycle. These demand patterns express different ways of drawing units from inventory and can be a good approach to modelling customer demands in inventory systems. Shortages are allowed and they are fully backlogged. It is assumed that the warehouse where the items are stored has a limited capacity. For this inventory system, we determine the inventory policy that maximizes the expected profit per unit time. An efficient algorithmic approach is proposed to calculate the optimal inventory levels at the beginning of the inventory cycle and to obtain the maximum expected profit per unit time. This inventory model is applicable to on-line sales of a wide variety of products. In this type of sales, customers do not receive the products at the time of purchase, but sellers deliver goods a few days later. Also, this model can be used to represent inventories of products for in-shop sales when the withdrawal of items from the inventory is not at the purchasing time, but occurs in a period after the sale of the products. This inventory model extends various inventory systems studied by other authors. Numerical examples are introduced to illustrate the theoretical results presented in this work.Ministerio de Ciencia, Innovación y Universidades - Fondo Europeo de Desarrollo Regional (project MTM2017-84150-P

Determining optimal inventory levels for items nearing the end of a production run

Optimal inventory policies determined for items in steady state production are no longer optimal when reaching the end of production. This is due to the obvious fact that during this time, production is no longer in steady state. At the end of a production run, steady state inventory policies can lead to excess costs, as on hand and due-in inventory is no longer need as there is no following period's demand. In this thesis, a newsvendor inventory optimization model which considers salvage value and initial inventory level along with two alternative (s, S) model formulations are tailored to fit items nearing the end of a production run. One of the (s, S) inventory models is modified to include a salvage value and reduce computation time. The three models are demonstrated on a twenty-item example problem and the newsvendor model is selected as the best application for items one lead-time period from the end of production. The cost related benefits of the alternative inventory policies generated by the newsvendor model are analyzed using a simulation-based approach. Although the ideas and analysis presented here were developed and tailored to fit the aerospace industry, the mathematical models can be extended to fit a variety of different applications.Includes bibliographical references (pages 69-71

Minimum cost decision for the tradeoff between finished goods inventory and product capacity

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1999.Includes bibliographical references (p. 59).by William E. Dutcher, Jr.S.M.M.B.A

Application of Optimization in Production, Logistics, Inventory, Supply Chain Management and Block Chain

The evolution of industrial development since the 18th century is now experiencing the fourth industrial revolution. The effect of the development has propagated into almost every sector of the industry. From inventory to the circular economy, the effectiveness of technology has been fruitful for industry. The recent trends in research, with new ideas and methodologies, are included in this book. Several new ideas and business strategies are developed in the area of the supply chain management, logistics, optimization, and forecasting for the improvement of the economy of the society and the environment. The proposed technologies and ideas are either novel or help modify several other new ideas. Different real life problems with different dimensions are discussed in the book so that readers may connect with the recent issues in society and industry. The collection of the articles provides a glimpse into the new research trends in technology, business, and the environment

Supply Chain

Traditionally supply chain management has meant factories, assembly lines, warehouses, transportation vehicles, and time sheets. Modern supply chain management is a highly complex, multidimensional problem set with virtually endless number of variables for optimization. An Internet enabled supply chain may have just-in-time delivery, precise inventory visibility, and up-to-the-minute distribution-tracking capabilities. Technology advances have enabled supply chains to become strategic weapons that can help avoid disasters, lower costs, and make money. From internal enterprise processes to external business transactions with suppliers, transporters, channels and end-users marks the wide range of challenges researchers have to handle. The aim of this book is at revealing and illustrating this diversity in terms of scientific and theoretical fundamentals, prevailing concepts as well as current practical applications

A competitive approach to airline revenue management

Thesis (S.M. in Transportation)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis. Page 140 blank.Includes bibliographical references (p. 137-139).Since the 1980s, the airline industry has seen two major changes: Deregulation, which led to an increase in competition, and the development of revenue management systems. Paradoxically the revenue management models used have not incorporated many competitive considerations. In this thesis we study the interactions between existing airline revenue management systems in competitive markets. We use the Passenger Origin Destination Simulator (PODS) for simulations. After a review of the past research on such interactions, we develop our own model and use simulations to identify and measure the extent to which revenue management systems of competing airlines affect each other. The model introduced highlights the importance of spill-of-demand between airlines. We show that with current revenue management practice, a legacy carrier should be less sensitive than a low-cost carrier to revenue management competitive interactions. As compared to an equivalent monopoly, an airline oligopoly tends to allocate more seats to high-fare passengers and fewer seats to low-fare passengers. With steady demand distributions, an airline's expected revenues are a decreasing function of the seat capacity allocated by its competitors to high-fare passengers. Existing revenue management systems react to competitor moves automatically only if a change in the seat allocation rule by an airline occurs over a large enough number of successive departures to be detected by forecasters. We then suggest how to improve revenue management based on the interactions identified. With steady demands, if a competitor increases (respectively decreases) its seat allocation for high-fare passengers, the best response to optimize revenues on the short-term is to decrease (respectively increase) seat allocation for high-fare passengers. We also show that the use of EMSRb-optimization by competitors results in a near-optimum competitive equilibrium, and a near-optimum cooperative equilibrium if airlines do not share revenues. Rarely can competitive interactions justify an airline to override the EMSRb seat-allocation rule to optimize revenues. Last, we introduce LOCO-based Forecast Multiplication, a heuristic forecast adjustment made in response to the current seat availability of the competitors that can increase an airline's revenues substantially.by Olivier d'Huart.S.M.in Transportatio

Data-Driven Robust Optimization in Healthcare Applications

abstract: Healthcare operations have enjoyed reduced costs, improved patient safety, and innovation in healthcare policy over a huge variety of applications by tackling prob- lems via the creation and optimization of descriptive mathematical models to guide decision-making. Despite these accomplishments, models are stylized representations of real-world applications, reliant on accurate estimations from historical data to jus- tify their underlying assumptions. To protect against unreliable estimations which can adversely affect the decisions generated from applications dependent on fully- realized models, techniques that are robust against misspecications are utilized while still making use of incoming data for learning. Hence, new robust techniques are ap- plied that (1) allow for the decision-maker to express a spectrum of pessimism against model uncertainties while (2) still utilizing incoming data for learning. Two main ap- plications are investigated with respect to these goals, the first being a percentile optimization technique with respect to a multi-class queueing system for application in hospital Emergency Departments. The second studies the use of robust forecasting techniques in improving developing countries’ vaccine supply chains via (1) an inno- vative outside of cold chain policy and (2) a district-managed approach to inventory control. Both of these research application areas utilize data-driven approaches that feature learning and pessimism-controlled robustness.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201