Joseph Geunes

Requirements Planning with Substitutions: Exploiting Bill-of-Materials Flexibility in Production Planning

Designing product lines with substitutable components and subassemblies permits companies to offer a broader variety of products while continuing to exploit economies of scale in production and inventory costs. Past research on models incorporating component substitutions focuses on the benefits from reduced safety-stock requirements. This paper addresses a dynamic requirements-planning problem for two-stage multi product manufacturing systems with bill-of-materials flexibility, i.e., with options to use substitute components or subassemblies produced by an upstream stage to meet demand in each period at the downstream stage. We model the problem as an integer program, and describe a dynamic-programming solution method to find the production and substitution quantities that satisfy given multi period downstream demands at minimum total setup, production, conversion, and holding cost. This methodology can serve as a module in requirements-planning systems to plan opportunistic component substitutions based on relative future demands and production costs. Computational results using real data from an aluminum-tube manufacturer show that substitution can save, on average, 8.7% of manufacturing cost. We also apply the model to random problems with a simple product structure to develop insights regarding substitution behavior and impacts.

NETWORK OPTIMIZATION IN SUPPLY CHAIN MANAGEMENT AND FINANCIAL ENGINEERING: AN ANNOTATED BIBLIOGRAPHY

During the past decade, two relatively new application areas have attracted the attention of a growing number of researchers who specialize in applying optimization techniques to large-scale real-world problems. The now well-known areas of supply chain management and financial engineering have provided extremely rich contexts for the definition of new large-scale optimization problems, the solutions of which can provide substantial value to organizations. This paper discusses the extent to which network optimization approaches have contributed to the advancement of supply chain management and financial engineering research through an examination of past literature involving network optimization applications within these still emerging fields.

Requirements Planning with Pricing and Order Selection Flexibility

Past requirements-planning research has typically assumed that the firms demands are determined prior to production planning. In contrast, we explore a single-stage planning model that implicitly decides, through pricing decisions, the demand levels the firm should satisfy in order to maximize contribution to profit. We briefly discuss solution methods and properties for these problems when production capacities are unlimited. The key result of this work is a polynomial-time solution approach to the problem under time-invariant finite production capacities and piecewise-linear and concave revenue functions in price.

An optimization framework for solving capacitated multi-level lot-sizing problems with backlogging

This paper proposes two new mixed integer programming models for capacitated multi-level lot-sizing problems with backlogging, whose linear programming relaxations provide good lower bounds on the optimal solution value. We show that both of these strong formulations yield the same lower bounds. In addition to these theoretical results, we propose a new, effective optimization framework that achieves high quality solutions in reasonable computational time. Computational results show that the proposed optimization framework is superior to other well-known approaches on several important performance dimensions.

Target market selection and marketing effort under uncertainty: The selective newsvendor

We consider a firm that markets, procures, and delivers a good with a single selling season in a number of different markets. The price for the good is market-dependent, and each market has an associated demand distribution, with parameters that depend on the amount of marketing effort applied. Given long procurement lead-times, the firm must decide which markets it will serve prior to procuring the good. We develop a profit maximizing model to address the firms integrated market selection, marketing effort, and procurement decisions. The model implicitly accounts for inventory pooling across markets, which reduces safety stock costs but increases model complexity. The resulting model is a nonlinear integer optimization problem, for which we develop specialized solution methods. For the case in which budget constraints exist, we provide a novel solution approach that uses a tailored branch-and-bound algorithm. Our approach solves a broad range of 3000 test instances in an average of less than 2 seconds, significantly outperforming a leading commercial global optimization solver.

Approximation algorithms for supply chain planning and logistics problems with market choice

We propose generalizations of a broad class of traditional supply chain planning and logistics models that we call supply chain planning and logistics problems with market choice. Instead of the traditional setting, we are given a set of markets, each specified by a sequence of demands and associated with a revenue. Decisions are made in two stages. In the first stage, one chooses a subset of markets and rejects the others. Once that market choice is made, one needs to construct a minimum-cost production plan (set of facilities) to satisfy all of the demands of all the selected markets. The goal is to minimize the overall lost revenues of rejected markets and the production (facility opening and connection) costs. These models capture important aspects of demand shaping within supply chain planning and logistics models. We introduce a general algorithmic framework that leverages existing approximation results for the traditional models to obtain corresponding results for their counterpart models with market choice. More specifically, any LP-based α-approximation for the traditional model can be leveraged to a

Market selection decisions for inventory models with price-sensitive demand

{\frac{1}{1-e^{-1/\alpha}}}

The static stochastic knapsack problem with normally distributed item sizes

-approximation algorithm for the counterpart model with market choice. Our techniques are also potentially applicable to other covering problems.