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Joint Inventory Replenishment and Component Allocation Optimization in an Assemble-to-Order System

This paper considers a multicomponent, multiproduct periodic-review assemble-to-order (ATO) system that uses an independent base-stock policy for inventory replenishment. Product demands in each period are integer-valued correlated random variables, with each product being assembled from multiple units of a subset of components. The system quotes a prespecified response time window for each product and receives a reward if the demand for that product is filled within its response time window. We formulate a two-stage stochastic integer program with recourse to determine the optimal base-stock policy and the optimal component allocation policy for the ATO system. We show that the component allocation problem is a general multidimensional knapsack problem (MDKP) and is NP-hard. We propose a simple, order-based component allocation rule and show that it can be solved in either polynomial or pseudopolynomial time. We also use the sample average approximation method to determine the optimal base-stock levels and compare it with two variations of the equal fractile heuristic. Intensive testing indicates that our solution method for each stage of the stochastic program is robust, effective, and that it significantly outperforms existing methods. Finally, we discuss several managerial implications of our findings.

GREEDY ALGORITHM FOR THE GENERAL MULTIDIMENSIONAL KNAPSACK PROBLEM

In this paper, we propose a new greedy-like heuristic method, which is primarily intended for the general MDKP, but proves itself effective also for the 0-1 MDKP. Our heuristic differs from the existing greedy-like heuristics in two aspects. First, existing heuristics rely on each item’s aggregate consumption of resources to make item selection decisions, whereas our heuristic uses the effective capacity, defined as the maximum number of copies of an item that can be accepted if the entire knapsack were to be used for that item alone, as the criterion to make item selection decisions. Second, other methods increment the value of each decision variable only by one unit, whereas our heuristic adds decision variables to the solution in batches and consequently improves computational efficiency significantly for large-scale problems. We demonstrate that the new heuristic significantly improves computational efficiency of the existing methods and generates robust and near-optimal solutions. The new heuristic proves especially efficient for high dimensional knapsack problems with small-to-moderate numbers of decision variables, usually considered as “hard” MDKP and no computationally efficient heuristic is available to treat such problems.

On the structural properties of a discrete-time single product revenue management problem

We consider a multi-period revenue management problem in which multiple classes of demand arrive over time for the common inventory. The demand classes are differentiated by their revenues and their arrival distributions. We investigate monotonicity properties of varying problem parameters on the optimal reward and the policy.

Revenue Management for Intermodal Transportation: The Role of Dynamic Forecasting

We study a joint capacity leasing and demand acceptance problem in intermodal transportation. The model features multiple sources of evolving supply and demand, and endogenizes the interplay of three levers --- forecasting, leasing, and demand acceptance. We characterize the optimal policy, and show how dynamic forecasting coordinates leasing and acceptance. We find (1) the value of dynamic forecasting depends critically on scarcity, stochasticity, and volatility; (2) traditional mean-value equivalence approach performs poorly in volatile intermodal context; (3) mean-value based forecast may outperform stationary-distribution based forecast. Our work enriches revenue management models and applications. It advances our understanding on when and how to use dynamic forecasting in intermodal revenue management.

Cross-Selling Investment Products with a Win-Win Perspective in Portfolio Optimization

We propose a novel approach to cross-selling investment products that considers both the customers’ and the bank’s interests. Our goal is to improve the risk–return profile of the customer’s portfolio and the bank’s profitability concurrently, essentially creating a win-win situation, while deepening the relationship with an acceptable product. Our cross-selling approach takes the customer’s status quo bias into account by starting from the existing customer portfolio, rather than forming an efficient portfolio from scratch. We estimate a customer’s probability of accepting a product offer with a predictive model using readily available data. Then, we model the investment product cross-selling problem as a nonlinear mixed-integer program that maximizes a customer’s expected return from the proposed portfolio, while ensuring that the bank’s profitability improves by a certain factor. We implemented our methodology at the private banking division of Yapi Kredi, the fourth-largest private bank in Turkey. Emp...

Pricing When Customers Have Limited Attention

We study the optimal pricing problem of a firm facing customers with limited attention and capability to process information about the value (quality) of the offered products. We model customer choice based on the theory of rational inattention in the economics literature, which enables us to capture not only the impact of true qualities and prices, but also the intricate effects of customer’s prior beliefs and cost of information acquisition and processing. We formulate the firm’s price optimization problem and characterize the pricing and revenue implications of customer’s limited attention. We test the robustness of our results under various modelling generalizations such as prices signaling quality and customer heterogeneity, and study extensions such as multiple products, competition, and joint inventory and pricing decisions. We also show that using alternative pricing policies that ignore the limited attention of customers or their ability to allocate this attention judiciously can potentially lead to significant profit losses for the firm. We discuss the managerial implications of our key findings and prescribe insights regarding information provision and product positioning.

Consumer Choice Under Limited Attention When Options Have Different Information Costs

Consumers often do not have complete information about the choices they face and therefore have to spend time and effort in acquiring information. Since information acquisition is costly, consumers trade-off the value of better information against its cost, and make their final product choices based on imperfect information. We model this decision using the rational inattention approach and describe the rationally inattentive consumer’s choice behavior when she faces alternatives with different information costs. To this end, we introduce an information cost function that distinguishes between direct and implied information. We then analytically characterize the optimal choice probabilities. We find that non-uniform information costs can have a strong impact on product choice, which gets particularly conspicuous when the product alternatives are otherwise very similar. There are significant implications on how a seller should provide information about its products and how changes to the product set impacts consumer choice. For example, non-uniform information costs can lead to situations where it is disadvantageous for the seller to provide easier access to information for a particular product, and to situations where the addition of an inferior (never chosen) product increases the market share of another existing product (i.e., failure of regularity). We also provide an algorithm to compute the optimal choice probabilities and discuss how our framework can be empirically estimated from suitable choice data.

A Near-Optimal Order-Based Inventory Allocation Rule in an Assemble-To-Order System and its Applications to Resource Allocation Problems

Assemble-to-order (ATO) manufacturing strategy has taken over the more traditional make-to-stock (MTS) strategy in many high-tech firms. ATO strategy has enabled these firms to deliver customized demand timely and to benefit from risk pooling due to component commonality. However, multi-component, multi-product ATO systems pose challenging inventory management problems. In this chapter, we study the component allocation problem given a specific replenishment policy and realized customer demands. We model the problem as a general multi-dimensional knapsack problem (MDKP) and propose the primal effective capacity heuristic (PECH) as an effective and simple approximate solution procedure for this NP-hard problem. Although the heuristic is primarily designed for the component allocation problem in an ATO system, we suggest that it is a general solution method for a wide range of resource allocation problems. We demonstrate the effectiveness of the heuristic through an extensive computational study which covers problems from the literature as well as randomly generated instances of the general and 0–1 MDKP. In our study, we compare the performance of the heuristic with other approximate solution procedures from the ATO system and integer programming literature.