Ray R. Venkataraman

An integrated analysis of nurse staffing and scheduling policies

In this paper we present an integrated nurse staffing and scheduling system for analyzing nurse workforce management policies. A mixed-integer linear programming nurse staffing model is used to determine aggregate labor requirements for a 6-month planning horizon. Subsequently, a mixed-integer linear programming model is used to disaggregate the nurse staffing plan into 2-week labor schedules. The integrative system permits recursion between the staffing and scheduling models and thus enables nurse workforce management to rapidly evaluate the impact of both staffing and scheduling policies. We use the new system to study the effects of staffing and scheduling policies on labor costs. The results suggest that there are indeed important interactions between staffing and scheduling policies.

A conceptual research framework for analyzing online auctions in a B2B environment

The rapid growth of online auctions underscores the need to analyze the mechanism of online auctions and to establish a theoretical research framework based on the business models adopted by successful organizations. While the theoretical and empirical research bases for traditional auctions are well established, current understanding of online auctions is still very limited. A broad conceptual model is developed that can form the basis for future research in online auctions. A review of prior research and use systems theory and empirical analysis is presented to identify the potential antecedents to online auction success. Then dimensions of the input, process, and output factors are discussed to develop the conceptual model. The conceptual model provides an impetus and direction for future research into online auctions, taking advantage of existing tradition but also forming the basis for the development and testing of research hypotheses that will expand the frontiers of knowledge in online auctions.

Disaggregation to a rolling horizon master production schedule with minimum batch-size production restrictions

Building on previous research in the area of hierarchical production planning and rolling schedules, this paper is concerned with the disaggregation of aggregate plans to a rolling horizon master production schedule when production lot-sizes require minimum batch-size production. Actual data from a process industry firm is used to test and validate the proposed rolling horizon master production scheduling model. The paper also examines the impact of forecast windows on the performance of a rolling schedule when production quantities of individual product items are based on minimum batch-size production. Results indicate that the models performance is superior to actual company performance in terms of total cost and increasing the length of the forecast window can increase costs.

Master Production Scheduling for a Process Industry Environment

Previous research has not addressed the problem of developing a master production schedule (MPS) for production systems with minimum batch‐size production restrictions. Proposes a weighted integer goal‐programming model for the development of a rolling horizon master production schedule, under conditions of demand certainty, for a process industry environment with multiple production lines and minimum batch‐size production restrictions. The presence of multiple and often conflicting goals prevalent in production planning and scheduling is explicitly incorporated in the model. The model can easily be implemented on a microcomputer and the master production schedule developed is in spreadsheet format and can easily be understood by a practitioner. Uses a case study conducted for a paint company to illustrate and validate the model. Results show that the MPS developed using the proposed model is superior in terms of total cost when compared with actual company performance.

Effect of forecast errors on rolling horizon master production schedule cost performance for various replanning intervals

Master production schedules are usually updated by the use of a rolling schedule. Previous studies on rolling schedules seem to form the consensus that frequent replanning of a master production schedule (MPS) can increase costs and schedule instability. Building on previous research on rolling schedules, this study addresses the impact of overestimation or underestimation of demand on the rolling horizon MPS cost performance for various replanning frequencies. The MPS model developed in this paper is based on actual data collected from a paint company. Results indicate that under both the forecast errors conditions investigated in this study, a two-replanning interval provided the best MPS cost performance for this company environment. However, results from the sensitivity analysis performed on the MPS model indicate that when the setup and inventory carrying costs are high, a 1-month replanning frequency (frequent replanning) seems more appropriate for both of the above forecast error scenarios.

Rolling horizon master production schedule performance: A policy analysis

In this paper, a simulation experiment has been developed to examine the combined influence of the design, inventory and environmental factors on the cost performance of a rolling horizon master production schedule. Specifically, a 2 5 factorial design was used to examine the effects associated with three rolling schedule design policies, one inventory policy and one environmental condition of forecast error on MPS cost performance. The study was based on actual data from a paint company. Results suggest that the choice of appropriate lot-size and inventory policies have a significant influence on MPS costs and that there are indeed important interactions between these policies and other design factors of a rolling schedule.

Determination of master production schedule replanning frequency for various forecast window intervals

This paper examines the impact of forecast window intervals on replanning frequencies for a rolling horizon master production schedule (MPS). The problem environment for this study is an actual MPS operation of a paint company and includes features such as multiple production lines, multiple products, capacity constraints, minimum inventory requirements. A mixed integer goal programming model formulated for the MPS problem is used to analyze the impact of forecast window interval length on replanning frequencies and MPS performance in a rolling horizon setting. Given demand certainty, results indicate that the length of the forecast window interval influences the choice of replanning frequency for this company environment. A three‐month forecast window interval with a two‐month replanning frequency provided the best MPS performance in terms of total cost.

Relationship between Weibull and AMSAA models in reliability analysis

Historically, reliability of systems has been tracked based on a common assumption that, at the system level, the failure rate follows the exponential distribution, and is therefore assumed to be constant over the useful life of the system. However, this method, while adequate for many purposes, does not necessarily provide the early warning system that many companies need to stay ahead of expensive quality or reliability fixes. This paper presents a new method that provides the needed early warning, at a reasonable analysis cost, by combining the use of two reliability distributions for the purpose of analyzing fielded systems. In particular, this paper describes a hypothesized relationship between a key parameter contained in the Weibull distribution and within the Army Material Systems Analysis Activity (AMSAA) reliability growth model. Actual data from General Electric Transportation Systems (GETS) were used to explore this relationship. The results suggest that there indeed exists a significant relationship between the two models and both can be used in tandem to track reliability of systems.

Project Decision Chain

To add value to project performance and help obtain project success, a new framework for decision making in projects is defined. It introduces the project decision chain inspired by the supply chain thinking in the manufacturing sector and uses three types of decisions: authorization, selection, and plan decision. A primitive decision element is defined where all the three decision types can be accommodated. Each task in the primitive element can in itself contain subtasks that in turn will comprise new primitive elements. The primitive elements are nested together in a project decision chain.

Linkage between the AMSAA models at higher levels of indenture in reliability analysis

Purpose – The purpose of this article is to determine if there is a linkage between the army material systems analysis activity (AMSAA) reliability growth models at the system and subsystem levels and at the subsystem and functional levels of indenture. If such a linkage exists, how this information can be used when tracking reliability of fielded systems to provide early warning signals to detect unwanted reliability problems at lower levels, where improvements are typically made.Design/methodology/approach – Actual performance data from large equipments were analyzed for several groupings of such equipment, where equipment age and generation of design determined the groupings. For each dataset, the system‐level trend was measured using the AMSAA model for three different cases. For each of the three cases, all subsystem trends were measured, in addition to the system‐level trend. This was done to see if any relationship exists, as hypothesized, between system and subsystem trends. Data were analyzed usi...