Jamie Rogers

Managing dynamic facility layout with multiple objectives

The Dynamic Facility Layout Problem (DFLP) has garnered increasing attention in this day and age. Traditionally, the facility layout is usually viewed as static — that is, once the facility layout is planned and executed, the layout remains unchanged for years. This is not the case in the real world, in which the facility layout may undergo change as frequently as every few months, especially in high-tech industries. Furthermore, current research efforts are primarily into the quantitative aspect of DFLP alone, ignoring the qualitative aspect of facility layout. In light of this issue, a dynamic multi-objective facility layout model is proposed to explore several aspects of facility layout planning including time, distance-based objective as well as the adjacency-based objective. The steps for implementing the dynamic multi-objective facility layout application are also discussed.

Reason‐code based model to forecast product returns

Purpose – This paper aims to propose a method for forecasting product returns based on reason codes. The methodology uses two approaches, namely central tendency approach and extreme point approach, and is developed for the consumer electronics industry.Design/methodology/approach – The methodology presented here is based on the return reason codes (RC). The incoming returns are split into different categories using reason codes. These reason codes are further analyzed to forecast returns. The computation part of this model uses a combination of two approaches, namely extreme point approach and central tendency approach. Both the approaches are used separately for separate types of reason codes and then results are added together. The extreme point approach is based on data envelopment analysis (DEA) as a first step combined with a linear regression while central tendency approach uses a moving average. For certain type of returns, DEA evaluates relative ranks of products using single input and multiple o...

A Methodology for Design Ontology Modeling

Recent research has focused on the use of ontologies to promote the sharing of knowledge. Ontologies are becoming increasingly important because they provide the critical semantic foundation for the rapidly expanding field of knowledge. They are very useful for knowledge reuse, knowledge sharing, and enterprise modeling. A design ontology is a hierarchically structured set of terms for describing a design domain that can be used as a skeletal foundation for a knowledge base. It can help the collaborative design team by providing accurate design information and guidelines. This research develops a methodology called domain knowledge acquisition process (DKAP) for creating an ontology of product and process design using IDEF5 and generates a consistency matrix for checking the accuracy of captured information. DKAP is a step-by-step methodology, which captures the product & process design knowledge, stores this knowledge in a reusable format, and shares this knowledge across enterprises. DKAP addresses three critical aspects of a design ontology. It explores the availability of similar domain ontologies for reuse, checks the accuracy & consistency of captured knowledge, and allows the sharing of the captured knowledge.

The impact of a shared pharmaceutical supply chain model on counterfeit drugs, diverted drugs, and drug shortages

The pharmaceutical supply chain in the United States of America (USA) is getting complicated and is often not controllable due to a globally open market, increasing online market, and many illegal activities. Some consumers who cannot afford to buy high priced genuine products are tempted by easily accessible counterfeit drugs on illegal web site pharmacies in or out of the USA. Corrupt participants including wholesalers or pharmacies in the supply chain take advantage of weak enforcement and a flawed drug supply chain for financial gain. The public health system and numerous patients are in, or could be in, painful situations caused from pharmaceutical supply chain problems including counterfeit drugs, diverted drugs, and drug shortage. In order to secure the drug supply chain, several solutions have been discussed, including a unit level serialized trace and tracking system, ePedigiree, and more. In this research, current problems and their causes will be discussed, and current solutions with their limitations will be presented. The proposed model, third party centralized integrated system (TPCIS) is presented, which overcomes some of barriers of existing solutions and several simulation models including ePedgiree, drug shortage, and recall models which have been developed for comparison. The simulation models show how the proposed model may help improve the current problems with public health systems.

Wireless LAN Access Point Location Planning

With the fast-growing demand for mobile services, where to place the access points (APs) for providing uniformly and appropriately distributed signals in a wireless local area network (WLAN) becomes an important issue in the wireless network planning. Basically, AP placement will affect the coverage and strength of signals in a WLAN. The number and locations of APs in a WLAN are often decided on the basis of the trial-and-error method. Based on this method, the network planner first selects suitable locations to place APs through observation, and then keeps changing the locations to improve the signal strength based on the received signal. Such process is complicated, laborious and time-consuming. To overcome this problem, we investigate the back-propagation neural network (BPNN) algorithm to improve over the traditional trial-and-error method. Without increasing the number of APs, our approach only needs to adjust AP locations to overcome weak signal problems and thus increase the signal coverage for the Internet connection anywhere within the area. In our experiment, we established a WLAN on a C campus. Our experiments also indicate that placing APs according to the BPNN provided better signal coverage and met the students’ demands for connecting to the Internet from anywhere in the classroom.

Build to Order supply chain efficiency using Stochastic Frontier Analysis (SFA)

The Build-to-Order supply chain (BTO-SC) is one agile supply chain that has received great attention in research and industry. Flexibility and responsiveness in mass customization has become a major objective of many companies and this has led to the further development of BTO-SC. The main purpose of this study has been to measure, compare and analyse the impact of the BTO-SC and traditional supply chain on the companys performance and highlight the key performance measures that can influence the decision making. A methodology for analysing the efficiency of the BTO-SC is developed using Stochastic Frontier Analysis (SFA) which is compared to the traditional supply chain in which both are then compared to the deterministic Data Envelopment Analysis (DEA) model. The targeted population for study has been companies in the computer industry functioning in BTO-SC and/or a traditional supply chain network. Such a comparative analysis provides a more informative tool for use as part of an investment guideline for companies who wish to adopt BTO-SC principles.