A Hybrid Meta-Heuristic Method to Optimize Bi-Objective Single Period Newsboy Problem with Fuzzy Cost and Incremental Discount

In this paper the real-world occurrence of the multiple-product multiple-constraint single period newsboy problem with two objectives, in which there is incremental discounts on the purchasing prices, is investigated. The constraints are the warehouse capacity and the batch forms of the order placements. The first objective of this problem is to find the order quantities such that the expected profit is maximized and the second objective is maximizing the service rate. It is assumed that holding and shortage costs, modeled by a quadratic function, occur at the end of the period, and that the decision variables are integer. A formulation to the problem is presented and shown to be an integer nonlinear programming model. Finally, an efficient hybrid algorithm of harmony search, goal programming, and fuzzy simulation is provided to solve the model. The results are illustrated by a numerical example

Essays on E-Commerce and Omnichannel Retail Operations

The advent of e-commerce has impacted the retail industry, as retail firms have innovated in response to customers increasingly preferring to purchase products online. This dissertation studies operational problems that accompany such retail innovations, and provides tractable heuristic solutions developed using stochastic and robust optimization methods. In particular, the first two chapters focus on the value of fulfillment flexibility - online orders can be fulfilled from any node in the firm's fulfillment network. The first chapter is devoted to omnichannel retailing, where e-commerce demand is integrated with the physical network of stores through ship-from-store fulfillment. For a retailer with a network of physical stores and fulfillment centers facing two demands (online and in-store), we consider the following interlinked decisions - how much inventory to keep at each location and where to fulfill each online order from. We show that the value of considering fulfillment flexibility in inventory planning is highest when there is a moderate mix of online and in-store demands, and develop computationally fast heuristics with promising asymptotic performance for large scale networks, which are shown to improve upon traditional strategies. The second chapter considers a pure play e-commerce fulfillment network, and studies the inventory placement decision. As e-commerce demands are volatile due to a variety of factors (price-matching, recommendation engines, etc.), we consider a distributionally robust setting, where the objective is to minimize the worst-case expected cost under given mean and covariance matrices of the underlying demand distribution. For this NP-hard problem, we develop computationally tractable heuristic in the form of a semi-definite program, with dimension quadratic in the size of the network. In the face of distribution uncertainty, we show that the robust heuristic outperforms inventory solutions that assume incorrect distributions. The final chapter offers a new take on a classic problem in retail - customer returns, which has grown to be an important issue in recent times with firms competing to provide lenient and convenient return policies to boost their e-commerce sales. However, several customers take advantage of such policies, which can lead to loss in revenue and increase in inventory costs. We study different return policies that a firm can employ depending on the information about customers' return behavior that is available to the firm. We derive the structure of the optimal return policies and show that personalizing return policies based on customers' historical data can significantly improve the firm's profits, but allows the firm to extract all customer surplus.PHDBusiness AdministrationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153348/1/arav_1.pd

THE CUTOFF TRANSACTION SIZE OF A QUADRATIC CONCAVE HOLDING AND PENALTY COST FUNCTIONS TO THE INFORMATION VALUE APPLYING TO THE NEWSBOY MODEL

This investigation adopts the perspective of the retailer and incorporates information flow between a retailer and customers. Two new models are considered that differ in terms of information completeness. The first model involves the retailer having incomplete information regarding the state of customers demand, namely extending the model of Dekker et al. (IIE Transactions, 32, 2000, 461) by considering the quadratic concave holding and penalty cost functions based on the law of diminishing marginal cost to fit in with the some practical situations. Meanwhile, the second model involves the retailer having full information on the state of customers demand. Precise expressions are derived for the expected total profit of these two newsboy models with a cutoff transaction size and compound Poisson demand distribution. It is worthwhile to measure the value of information and identify the effect of factors for enterprise decision making regarding whether or not to pay for information that can help increase profits. Moreover, we adopt and modify the golden section search technique (Haftka et al., 1990) to determine an optimal order-up-to level S and a cutoff transaction size q systematically. Finally, numerical examples are given to illustrate the result derived.Cutoff transaction size, newsboy model, concave function