Retail category management: State-of-the-art review of quantitative research and software applications in assortment and shelf space management

Retail requires efficient decision support to manage increasing product proliferation and various consumer choice effects with limited shelf space. Our goal is to identify, describe and compare decision support systems for category planning. This research analyzes quantitative models and software applications in assortment and shelf space management and contributes to a more integrated modeling approach. There are difficulties commonly involved in the use of commercial software and the implementation and transfer of scientific models. Scientific decision models either focus on space-dependent demand or substitution effects, whereas software applications use simplistic rules of thumb. We show that retail assortment planning models neglect space-elastic demand and largely also ignore constraints of limited shelf space. Shelf space management streams on the other hand, mostly omit substitution effects between products when products are delisted orout-of-stock, which is the focus of consumer choice models in assortment planning. Also, the problem sizes of the models are often not relevant for realistic category sizes. Addressing these issues, this paper provides a state-of-the-art overview and research framework for integrated assortment and shelf space planning.     

Exact and heuristic methods for a class of selective newsvendor problems with normally distributed demands

In this paper we study a class of selective newsvendor problems, where a decision maker has a set of raw materials each of which can be customized shortly before satisfying demand. The goal is then to select which subset of customizations maximizes expected profit. We show that certain multi-period and multi-product selective newsvendor problems fall within our problem class. Under the assumption that the demands are independent and normally, but not necessarily identically, distributed we show that some problem instances from our class can be solved efficiently using an attractive sorting property that was also established in the literature for some related problems. For our general model we use the KKT conditions to develop an exact algorithm that is efficient in the number of raw materials. In addition, we develop a class of heuristic algorithms. In a numerical study, we compare the performance of the algorithms, and the heuristics are shown to have excellent performance and running times as compared to available commercial solvers.     

Incorporating preference information in interactive reference point methods for multiobjective optimization

In this paper, we introduce new ways of utilizing preference information specified by the decision maker in interactive reference point based methods. A reference point consists of desirable values for each objective function. The idea is to take the desires of the decision maker into account more closely when projecting the reference point onto the set of nondominated solutions. In this way we can support the decision maker in finding the most satisfactory solutions faster. In practice, we adjust the weights in the achievement scalarizing function that projects the reference point. We identify different cases depending on the amount of additional information available and demonstrate the cases with examples. Finally, we summarize results of extensive computational tests that give evidence of the efficiency of the ideas proposed.     

The single-period inventory problem: Extension to random yield from the perspective of the supply chain

A special form of the single-period inventory problem (newsvendor problem) with a known demand and stochastic supply (yield) is studied. A general analytic solution for two types of yield risks, additive and multiplicative, is described. Numerical examples demonstrate the solutions for special cases of uniform distribution yield risks. An analysis of a two-tier supply chain of customer and producer reveals that the customer may find it optimal to order more than is needed, since a larger order increases the producer's optimal production quantity.     

A comparison of simplified value function approaches for treating uncertainty in multi-criteria decision analysis

Uncertainty is present in many decisions where an action's consequences are unknown because they depend on future events. Multi-attribute utility theory (MAUT) offers an axiomatic basis for choice, but practitioners may prefer to use simpler decision models for transparency, ease of use, or other practical reasons. We identify some ‘simplified’ models currently in use and use a simulation experiment to evaluate their ability to approximate results obtained using MAUT. Our basic message is that avoiding assessment errors in the application of a simplified model is more important than the choice of a particular type of model, but that the best performance over a range of decision problems is from a model using a small number of quantiles.     

Policies for knowledge refreshing in databases

Knowledge discovery in databases (KDD) provides organizations necessary tools to sift through vast data stores to extract knowledge. This process supports and improves decision making in organizations. In this paper, we introduce and define the concept of knowledge refreshing, a critical step to ensure the quality and timeliness of knowledge discovered in a KDD process. This has been unfortunately overlooked by prior researchers. Specifically, we study knowledge refreshing from the perspective of when to refresh knowledge so that the total system cost over a time horizon is minimized. We propose a model for knowledge refreshing, and a dynamic programming methodology for developing optimal strategies. We demonstrate the effectiveness of the proposed methodology using data from a real world application. The proposed methodology provides decision makers guidance in running KDD effectively and efficiently.     

A location analysis approach for military maintenance scheduling with geographically dispersed service areas

This research uses a two-stage maximal covering location problem (MCLP) model to develop Inter continental ballistic missile (ICBM) maintenance schedules for the US Air Force. Solutions are compared to actual missile maintenance activities accomplished at F. E. Warren Air Force Base (AFB), Wyoming in May 2005. Sensitivity analysis is performed to determine the impact of altering security response times and the number of security patrol areas on the quality of daily maintenance schedules and personnel usage. Results indicate marked improvement over traditional Air Force scheduling methods. In addition sensitivity analysis identifies the levels at which the quality and quantity of maintenance performance is impacted.     

Location optimization of strategic alert sites for homeland defense

This research uses a location analysis approach for selecting aircraft alert sites for the defense of important national areas of interest identified by the US Department of Defense. Solutions are generated in a two step approach where the minimum number of sites is first identified using the location set covering problem and then the result is improved by finding the minimum aggregate network distance or p-median solution from the alternate optimal solutions to the LSCP. This approach also identifies the p-center solution to the problem ensuring equitable response to all areas of interest. Sensitivity analysis is performed to determine the impact of altering aircraft launch and flying times on the number of required alert sites and the amount of coverage provided by selecting fewer locations. Results indicate a significant increase in the number of alert locations needed in comparison to original military estimates. The research points out significant implications about future military base closure decisions and the trade-offs between cost and required response times of aircraft in a defense emergency.     

A two-stage stochastic programming approach to employee scheduling in retail outlets with uncertain demand

This paper describes an employee scheduling system for retail outlets; it is a constraint-based system that exploits forecasts and stochastic techniques to generate schedules meeting the demand for sales personnel. Uncertain scenarios due to fluctuating demand are taken into account to develop a stochastic operational optimization of staffing levels. Mathematically, the problem is stated as a mixed-integer linear programming problem. Simulations with store data belonging to a major Swiss retailer show the effective performance of the proposed approach. The schedule quality is assessed through comparison with a deterministic scheduling package, which has been used at several outlets in Switzerland.     

Multi-attribute utility methods in group decision making: Past applications and potential for inclusion in GDSS

This paper reviews a variety of studies in which multi-attribute utility theory (MAU) has been used successfully to aid group decision processes and discusses the incorporation of MAU in Group Decision Support Systems (GDSS). GDSS of various levels of automation have been proposed to aid group decision making. A number of commercial GDSSs are available which are capable of aiding generation of alternatives, their evaluation, and selection. Many authors have promoted the incorporation of quantitative decision making models in GDSS. The most appropriate quantitative tool for GDSS is MAU, but MAU has rarely been utilized. The cases reviewed illustrate that the use of MAU techniques is not prohibitively difficult or complex.     

Pricing decisions in a multi-criteria setting for product recovery facilities

Unpredictability in the arrival time and quantity of discarded products at product recovery facilities (PRFs) and varying demand for recovered components contribute to the volatility in their inventory levels. Achieving profit under such capricious inventory levels and stringent environmental legislations remains a challenge to many PRFs. This paper presents a multi-criteria decision model to determine a pricing policy that can simultaneously address two issues: stabilize inventory fluctuations and boost profits. The model considers that PRFs passively accepts discarded products as well as acquires them proactively if necessary. Under a multi-criteria setting, the current work determines prices of reusable and recyclable components to maximize revenue and minimize product recovery costs. A genetic algorithm is employed to solve the multi-criteria decision making problem. Sensitivity analysis is performed to investigate the effect of sorting yield, disassembly yield, and reusable component yield on the profits, prices, inventory levels, and disposal quantities.     

On the use of multivariate regression methods for longest path calculations from earned value management observations

This paper explores the use of multivariate regression methods for project schedule control within a statistical project control framework. These multivariate regression methods monitor the activity level performance of an ongoing project from the earned value management/earned schedule (EVM/ES) observations that are made at a high level of the work breakdown structure (WBS). These estimates can be used to calculate the longest path in the project and to produce warning signals for project schedule control. The effort that is spent by the project manager is thereby reduced, since a drill-down of the WBS is no longer required for every review period. An extensive computational experiment was set up to test and compare four distinct multivariate regression methods on a database of project networks. The kernel principal component regression method, when used with a radial base function kernel, was found to outperform the other presented regression methods.