Umit Akinc

A New Approach to Aggregate Production Planning

Abstract This paper proposes a new framework for modelling aggregate production planning problems in which emphasis is placed upon offering the user the flexibility to specify (1) the production options to be employed, (2) the relationships among those options (some of which may be highly situation-specific), and (3) the relevant cost structure. The procedure offered for solving the problem embeds Bowmanns “transportation” approach to aggregate production planning into a large mixed integer programming framework.

Approximate and exact algorithms for the fixed-charge knapsack problem

Abstract The subject of this paper is the formulation and solution of a variation of the classical binary knapsack problem. The variation that is addressed is termed the “fixed-charge knapsack problem”, in which sub-sets of variables (activities) are associated with fixed costs. These costs may represent certain set-ups and/or preparations required for the associated sub-set of activities to be scheduled. Several potential real-world applications as well as problem extensions/generalizations are discussed. The efficient solution of the problem is facilitated by a standard branch-and-bound algorithm based on (1) a non-iterative, polynomial algorithm to solve the LP relaxation, (2) various heuristic procedures to obtain good candidate solutions by adjusting the LP solution, and (3) powerful rules to peg the variables. Computational experience shows that the suggested branch-and-bound algorithm shows excellent potential in the solution of a wide variety of large fixed-charge knapsack problems.

An Algorithm for the Knapsack Problem

Abstract This paper presents an efficient algorithm for the general (as opposed to the binary) knapsack problem. The algorithm generates tight bounds on the variables from the LP relaxation of the problem which give the optimal values of most of the variables very quickly. A branch-and-bound procedure is then employed to solve the remaining reduced problem. Extensive computatonal tests have confirmed the efficiency of this approach for a wide variety of problems with as many as 5000 variables.

Make-to-forecast: customization with fast delivery

Purpose – The purpose of this paper is to attempt to achieve customization while maintaining quick delivery to customers by use of a hybrid of make-to-order (MTO) and make-to-stock (MTS) strategies. Design/methodology/approach – The paper reports on various studies based on simulation, optimization, and Markov analysis to respond to some of the unique challenges of the hybrid strategy. Findings – Implementation of the make-to-forecast (MTF) hybrid strategy provides considerable competitive advantage in certain manufacturing situations by achieving as much as 50 percent shorter delivery times for highly customized products than would be possible with a pure MTO strategy. It also has potential to offer competitive advantages to pure MTS and MTO operations. Research limitations/implications – The manufacturing situations, appropriate for MTF paradigm, are fairly complex. The paper reviews only several of the many managerial challenges. For instance, it assumes a given product variety but does not dwell on wh...

Dynamic Pricing and Demand Estimation for a Single Unique Asset

We address the situation of a firm that needs to dispose of a large, expensive asset (e.g., car, machine tool, earth mover, turbine, house, airplane), with or without a given deadline (and either known or unknown to the buyer). If a deadline exists, the asset is salvaged at a known value which may be zero, or even negative if there is a disposal cost. The asset has a known holding cost and may also have an initial nominal (undiscounted) price. The question is how, if at all, the price should be discounted as time progresses to maximize the expected proceeds. We use a dynamic recursion where each decision stage can be optimized based on classic economic monopoly pricing theory with a demand intensity function estimated from sales data, and show that the model is well-behaved in the sense that the optimal price and optimal expected revenue monotonically decline as the deadline approaches. We test the model by comparing its optimal price pattern to the official pricing policy practiced at a used-car dealer. We then extend the model to situations where the buyer knows the sellers deadline and thus may alter his behavior as the deadline approaches.