Adriano O. Solis

An evaluation of lot-sizing heuristics for deteriorating inventory in material requirements planning systems

In this paper, we consider the effects of deteriorating inventory on lot-sizing in material requirements planning systems. Five existing heuristics were extended to address the single-level lot-sizing problem with deteriorating inventory and evaluated via a large-scale simulation study. Three factors were taken into consideration for the simulation study, namely, rate of inventory deterioration, percentage of periods with zero demand, and setup cost. Our computational results, undertaken within a wide range of experimental conditions, indicate that one of the modified heuristics exhibits the best overall cost performance. We also provide insights in regard to the cost performance behavior of the five modified heuristics. Scope and Purpose Material requirements planning (MRP) is a computerized information system for managing dependent demand inventory and scheduling stock replenishment orders. The purpose of this study is to investigate the effect of deteriorating inventory on the lot-sizing decision in a single-level MRP system. Deteriorating inventory is defined in terms of decay, shrinkage, damage, obsolescence, or pilferage of an item, resulting in a decrease in quantity or volume over time. The comparative performance of various lot-sizing heuristics, aimed at minimizing the sum of setup, holding, and deterioration costs, is reported.

Two modifications of the least cost per period heuristic for dynamic lot-sizing

This paper proposes two constructive heuristics for the well-known single-level uncapacitated dynamic lot-sizing problem. The proposed heuristics, called net least period cost (nLPC) and nLPC(i), are developed by modifying the average period cost concept from Silver and Meals heuristic, commonly known as least period cost (LPC). An improved tie-breaking stopping rule and a locally optimal decision rule are proposed in the second heuristic to enhance performance. We test the effectiveness of the proposed heuristics by using 20 benchmarking test problems frequently used in the literature. Furthermore, we perform a large-scale simulation study involving three factors, 50 experimental conditions, and 100 000 randomly generated problems to evaluate the proposed heuristics against LPC and six other well-known constructive heuristics in the literature. The simulation results show that both nLPC and nLPC(i) produce average holding and setup costs lower than or equal to those of LPC in every one of the 50 experimental conditions. The proposed heuristics also outperform each of the six other heuristics evaluated in all experimental conditions, without an increase in computational requirements. Lastly, considering that both nLPC and nLPC(i) are fairly simple for practitioners to understand and that lot-sizing heuristics have been commonly used in practice, there should be a very good chance for practical applications of the proposed heuristics.

An Empirical Study on Telecommunication Product Adoption in Latin America and the Caribbean

This study discusses some empirical findings on telephone and cell phone adoptions in Latin American and Caribbean nations. Exhaustive fixed‐effect pooled regressions are undertaken for this set of nations, using data from the World Bank for the period 1989–1999. The results show that telephone and cell phone adoption processes are different in nature. Moreover, diffusion is established to be a major factor in both telephone and cell phone adoptions in the region. Economic conditions as well as privatization also positively impact the adoptions.

From remote hosting to self-hosting of an ERP system: Lessons learned from the city of El Paso

Abstract This article describes how the city of El Paso, Texas, initially used an application service provider (ASP) model, allowing the city to fast track its enterprise resource planning (ERP) system implementation. After a change in the ASP arrangements led to less responsiveness to its needs, the city government moved to a self-hosted ERP system. Lessons learned in the evolution from ASP hosting to self-hosting of the citys ERP system are compared to previously published literature.

A modelling and simulation approach to assessment of a negative binomial approximation in a multi-echelon inventory system

Some studies in the multi-echelon inventory systems literature have used a negative binomial distribution to approximate a critical random variable arising in the model. Graves (1996) developed a multi-echelon inventory model with fixed replenishment intervals, where each site follows a base stock policy. He proposed in the one-warehouse, N-retailer case a negative binomial distribution to approximate a random variable he referred to as ‘uncovered demand’. Computational evidence was provided to demonstrate the effectiveness of the approximation. Graves then suggested search procedures for approximately optimal base stock levels at the warehouse and N identical retailers under two customer service criteria: 1) probability of no stockout; 2) fill rate. A separate analytical evaluation of the negative binomial approximation has been preliminarily reported elsewhere. In the current study, we apply a modelling and simulation approach to assess whether the approximation-based search procedures, in fact, lead to optimal or near-optimal stock levels.

Stochastic leadtimes in a one-warehouse, N-retailer inventory system with the warehouse actually carrying stock

We previously investigated, analytically, effects of introducing stochastic leadtimes between the warehouse and retail sites in a multi-echelon model with deterministic leadtimes when the warehouse does not carry stock. In the current study, the warehouse does carry stock, making the model mathematically intractable. A heuristic involving simulation experiments is devised, taking off from deterministic leadtime model solutions. The resulting average system inventory is equal or very close to the optimal level. Our study suggests that the optimal solution to the deterministic leadtime case provides an appropriate starting point in searching, using simulation, for a solution in the stochastic leadtime case.

Stochastic leadtimes in a one-warehouse, N-retailer inventory system with the warehouse not carrying stock

We previously investigated, analytically, effects of introducing stochastic leadtimes between the warehouse and retail sites in a multi-echelon model with deterministic leadtimes when the warehouse does not carry stock. In the current study, the warehouse does carry stock, making the model mathematically intractable. A heuristic involving simulation experiments is devised, taking off from deterministic leadtime model solutions. The resulting average system inventory is equal or very close to the optimal level. Our study suggests that the optimal solution to the deterministic leadtime case provides an appropriate starting point in searching, using simulation, for a solution in the stochastic leadtime case.

The global diffusion of internet attack incidents: analysis of a bad innovation

Diffusion literature to date has not dealt with bad innovations. This article discusses the nature of global temporal diffusion of internet attack incidents – a bad form of innovation. A number of models are investigated to see which one can best fit the Computer Emergency Response Team Coordination Centre (CERT/CC) incident data (1988–2001) and explain the rationale behind such attacks. A push-pull model fits the data reasonably well and has better explanatory power than the traditional logistic and exponential models. The parameters of the model can provide measures of both attack imitation and deterrence. The model was subsequently validated with another set of incident data from Federal Computer Incident Response Centre (FedCIRC) and its predictive power was also verified to be reasonable. The implication of this study is that security measures are inadequate as compared to the volume of attack incidents and that serious attention needs to be paid to increase security measures of US firms.

Using simulation to investigate the performance of a batch order manufacturing system

This paper describes the development and application of a simulation model characterising an existing manufacturing system devoted to producing furniture for schools, universities and offices. The simulation model is equipped with dedicated animation and input/output sections, which allow changing various system parameters and monitoring multiple performance measures. The simulation model is also integrated with optimisation algorithms, specifically genetic algorithms. After verification and validation, the simulation model is used to pursue two different objectives: 1) evaluate the economic viability of acquiring new automated machines for the painting department; 2) investigate shop orders scheduling by using genetic algorithms. As far as the first objective is concerned, performance of the current production system is compared with that of the potential production scenario involving automated painting. An economic analysis based on the discounted payback period is also carried out. With respect to shop order scheduling, genetic algorithms are implemented as an additional module able to perform optimisation in terms of two fitness functions (flow time and fill rate).

On The Effectiveness Of The Negative Binomial Approximation In A Multi‐Echelon Inventory Model: A Mathematical Analysis

Graves (1996) developed a multi‐echelon inventory model and used a negative binomial distribution to approximate the distribution of a random variable in the model. Two earlier multi‐echelon inventory studies (Graves, 1985; Lee and Moinzadeh, 1987) have similarly used negative binomial approximations. Only computational evidence has been offered in support of the approximations. We provide, for the latest model (Graves, 1996), a mathematical analysis of the effectiveness of such an approximation.