Moutaz Khouja

The single-period (news-vendor) problem: literature review and suggestions for future research

The single-period problem (SPP), also known as the newsboy or news-vendor problem, is to find the order quantity which maximizes the expected profit in a single period probabilistic demand framework. Interest in the SPP remains unabated and many extensions to it have been proposed in the last decade. These extensions include dealing with different objectives and utility functions, different supplier pricing policies, different news-vendor pricing policies and discounting structures, different states of information about demand, constrained multi-products, multiple-products with substitution, random yields, and multi-location models. This paper builds a taxonomy of the SPP literature and delineates the contribution of the different SPP extensions. This paper also suggests some future directions for research.

Use of genetic algorithms to solve production and operations management problems: A review

Operations managers and scholars in their search for fast and good solutions to real-world problems have applied genetic algorithms to many problems. While genetic algorithms are promising tools for problem solving, future research will benefit from a review of the problems that have been solved and the designs of the genetic algorithms used to solve them. This paper provides a review of the use of genetic algorithms to solve operations problems. Reviewed papers are classified according to the problems they solve. The basic design of each genetic algorithm is described, the shortcomings of the current research are discussed and directions for future research are suggested.

The use of data envelopment analysis for technology selection

Abstract The range of manufacturing technologies available to firms has significantly increased in the past 20 years. A potential buyer of a technology such as machine tools, industrial robots, or flexible manufacturing systems is faced with many options in both performance and cost. This paper proposes a decision model for technology selection problems using a two-phase procedure. In phase 1, data envelopment analysis is used to identify technologies that provide the best combinations of vendor specifications on the performance parameters of the technology. In phase 2, a multi-attribute decision making model is used to select a technology from those identified in phase 1. Unlike most other models for technology selection, this model takes into consideration the fact that the performance of a technology, as specified by its vendor, is often unobtainable in reality. The proposed model is illustrated using robot selection and is tested on an actual robot data set.

A review of the joint replenishment problem literature: 1989-2005

The purpose of this paper is to review and summarize the literature on the joint replenishment problem (JRP) since 1989. Our review indicates that while research on the basic form of the JRP under the original classic assumptions may have slowed, there is much interest in new versions of the problem with relaxed assumptions, including dynamic or stochastic demand. Furthermore, recent research on the problem has focused on finding faster algorithms to the classic JRP rather than on improving the solution quality.

OPTIMIZING INVENTORY DECISIONS IN A MULTI-STAGE MULTI-CUSTOMER SUPPLY CHAIN

Most supply chain models focus on two-stage chains in which vendors supply material to one customer. In this paper, we formulate a three-stage supply chain model where a firm can supply many customers. We deal with three inventory coordination mechanisms between chain members and solve a cost minimization model for each. We show that some of the coordination mechanisms can result in a significantly lower total cost than matching production and delivery along the chain. We provide some insights into when the added complexity of the second and third coordination mechanisms lead to significant cost reductions.

The use of genetic algorithms to solve the economic lot size scheduling problem

The purpose of this paper is to investigate the use genetic algorithms (GAs) for solving the Economic Lot Size Scheduling Problem (ELSP). The ELSP is formulated using the Basic Period (BP) approach which results in a problem having one continuous decision variable and a number of integer decision variables equal to the number of products being produced. This formulation is ideally suited for using GAs. The GA is tested on Bombergers classical problem. The resulting solutions were better than those obtained using an iterative dynamic programming (DP) approach. The total cost of GA solutions to the problem with utilization up to 65% were within 3.4% of the lower bound. The GA also performed well for higher utilization yielding solutions within 13.87% of the lower bound for utilization up to 86%. The GA was tested on a 30-item problem and good solutions were obtained. The results of the GA under different binary representations, crossover methods, and initialization methods are compared to identify the best settings. The results indicate that for this particular problem, binary representation works better than Gray coding, 2-point crossover is best, and an infeasible starting population is better than feasible.

The newsboy problem under progressive multiple discounts

Abstract Previous research on the newsboy problem is based on the assumption that excess inventory is either discounted once and sold or disposed of. Real world settings, especially in the apparel industry, involve many cases where multiple discounts are used. Under multiple discounts, a retailer has a series of discounts that are progressively used as the product remains on the shelf. In this paper we formulate and solve a newsboy problem with multiple e discounts. The new problem is solved with the objectives of 1) maximizing the expected profit and 2) maximizing the probability of achieving a target profit. We show that multiple discounts, when possible, provide higher expected profit than using a single discount. We verify the analytical results using numerical integration and illustrate the results with numerical examples.

Optimal inventory policy under different supplier credit policies

The purpose of this paper is to investigate the effect of supplier credit policies on the optimal order quantity within the economic order quantity framework. The supplier credit policies addressed in this paper have been neglected in previous work and fall into two categories: (1) supplier credit policies where credit terms are independent of the order quantity and (2) supplier credit policies where credit terms are linked to the order quantity. In the latter case, suppliers use favorable credit terms to encourage customers to order large quantities. In other words, the favorable credit terms apply only at large order quantities and are used in place of quantity discounts. As shown, supplier credit policies in some cases can lead to substantially different order quantities from classical economic order quantities. Numerical examples are used to illustrate the effects of different credit policies.

Optimal ordering, discounting, and pricing in the single-period problem

Abstract The single-period problem (SPP), also known as the newsboy or newsvendor problem, is to find the order quantity which maximizes the expected profit in a single-period probabilistic demand framework. Previous extensions to the SPP include, in separate models, the simultaneous determination of the optimal price and quantity when demand is price-dependent, and the determination of the optimal order quantity when progressive discounts with preset prices are used to sell excess inventory. In this paper, we extend the SPP to the case in which demand is price-dependent and multiple discounts with prices under the control of the newsvendor are used to sell excess inventory. First, we develop two algorithms for determining the optimal number of discounts under fixed discounting cost for a given order quantity and realization of demand. Then, we identify the optimal order quantity before any demand is realized. We also analyze the joint determination of the order quantity and initial price. We illustrate the models and provide some insights using numerical examples.

Time Series Forecasting for Dynamic Environments: The DyFor Genetic Program Model

Several studies have applied genetic programming (GP) to the task of forecasting with favorable results. However, these studies, like those applying other techniques, have assumed a static environment, making them unsuitable for many real-world time series which are generated by varying processes. This study investigates the development of a new ldquodynamicrdquo GP model that is specifically tailored for forecasting in nonstatic environments. This dynamic forecasting genetic program (DyFor GP) model incorporates features that allow it to adapt to changing environments automatically as well as retain knowledge learned from previously encountered environments. The DyFor GP model is tested for forecasting efficacy on both simulated and actual time series including the U.S. Gross Domestic Product and Consumer Price Index Inflation. Results show that the performance of the DyFor GP model improves upon that of benchmark models for all experiments. These findings highlight the DyFor GPs potential as an adaptive, nonlinear model for real-world forecasting applications and suggest further investigations.