Mohan Gopalakrishnan

A Tabu-Search Heuristic for the Capacitated Lot-Sizing Problem with Set-up Carryover

This paper presents a tabu-search heuristic for the capacitated lot-sizing problem (CLSP) with set-up carryover. This production-planning problems allows multiple items to be produced within a time period, and setups for items to be carried over from one period to the next. Two interrelated decisions, sequencing and lot sizing, are present in this problem. Our tabu-search heuristic consists of five basic move types--three for the sequencing decisions and two for the lot-sizing decisions. We allow infeasible solutions to be generated at a penalty during the course of the search. We use several search strategies, such as dynamic tabu list, adaptive memory, and self-adjusting penalties, to strengthen our heuristic. We also propose a lower-bounding procedure to estimate the quality of our heuristic solution. We have also modified our heuristic to produce good solutions for the CLSP without set-up carryover. The computational study, conducted on a set of 540 test problems, indicates that on average our heuristic solutions are within 12% of a bound on optimality. In addition, for the set of test problems our results indicate an 8% reduction in total cost through set-up carryover.

Current pulse: Can a production system reduce medical errors in health care?

One of the reasons for rising health care costs is medical errors, a majority of which result from faulty systems and processes. Health care in the past has used process-based initiatives such as Total Quality Management, Continuous Quality Improvement, and Six Sigma to reduce errors. These initiatives to redesign health care, reduce errors, and improve overall efficiency and customer satisfaction have had moderate success. Current trend is to apply the successful Toyota Production System (TPS) to health care since its organizing principles have led to tremendous improvement in productivity and quality for Toyota and other businesses that have adapted them. This article presents insights on the effectiveness of TPS principles in health care and the challenges that lie ahead in successfully integrating this approach with other quality initiatives.

A modified framework for modelling set-up carryover in the capacitated lotsizing problem

In this note, a modified framework to model set-up carryovers in the capacitated lotsizing problem is presented. The proposed framework allows product dependent set-up times and costs to be incorporated. This is an extension of an earlier published work on modelling set-up carryovers for the constant set-up time scenario. An example to illustrate the modified framework is also provided.

A note on modelling the capacitated lot-sizing problem with set-up carryover and set-up splitting

The capacitated lot-sizing problem with set-up carryover and set-up splitting (CLSP-SCSS) is formulated as a mixed integer linear program. We define set-up carryover as the production of a product that is continued over from one period to another without incurring an extra set-up. Set-up splitting occurs when the set-up for a product is started at the end of a period and completed at the beginning of the next period. We allow product dependent set-ups. Initial experimentation highlights the importance of including set-up splitting in the CLSP model. In 12 out of the 18 problem instances tested, our model yielded better solutions or removed infeasibility when compared with a CLSP model without set-up splitting.

Capacity Planning and Allocation for Web‐Based Applications

Motivated by the technology division of a financial services firm, we study the problem of capacity planning and allocation for Web-based applications. The steady growth in Web traffic has affected the quality of service (QoS) as measured by response time (RT), for numerous e-businesses. In addition, the lack of understanding of system interactions and availability of proper planning tools has impeded effective capacity management. Managers typically make decisions to add server capacity on an ad hoc basis when systems reach critical response levels. Very often this turns out to be too late and results in extremely long response times and the system crashes. We present an analytical model to understand system interactions with the goal of making better server capacity decisions based on the results. The model studies the relationships and important interactions between the various components of a Web-based application using a continuous time Markov chain embedded in a queuing network as the basic framework. We use several structured aggregation schemes to appropriately represent a complex system, and demonstrate how the model can be used to quickly predict system performance, which facilitates effective capacity allocation decision making. Using simulation as a benchmark, we show that our model produces results within 5% accuracy at a fraction of the time of simulation, even at high traffic intensities. This knowledge helps managers quickly analyze the performance of the system and better plan server capacity to maintain desirable levels of QoS. We also demonstrate how to utilize a combination of dedicated and shared resources to achieve QoS using fewer servers.

A discrete event simulation tool for performance management of web-based application systems

Web-based computer applications connect the end-user with information via the internet. These applications typically operate in distributed computing environments, collecting and collating information from multiple computers that host databases or traditional computer applications. The increase in internet traffic is a clear reflection of the increase in demand for various web-based applications. Managers often find it essential to have the capability to predict performance of these systems in order to maintain service levels, as measured by response time. We present a generic discrete event simulation tool for modelling web-based application systems. Modelling information flow for decision making in such systems requires constructs not typically available in simulation software. We have developed several constructs that can be used in constructing models for various kinds of web-based applications. An example system is modelled using the tool, the impact of different variables (eg transaction volume) on the system performance is analysed and some preliminary insights are presented.

Procurement in public & private hospitals in Australia and Costa Rica – a comparative case study

This article summarizes exploratory research conducted on supply chain management practices in public hospital systems in the Australian State of Victoria and in Costa Rica. Victoria is the site of a clearly articulated (centralized) supply chain strategy as opposed to Costa Rica, where there is a strong presence of government procurement rules but no such articulated strategy. Importantly, both systems have mixed governance structures (public vs private) and had a willingness to share information pertaining to their purchasing practices. Relatively open-ended interviews, analyzed utilizing MAXQDA 11, allowed us to scrutinize the influence of public policy, supply chain integration, supply chain/clinician collaboration, value analysis teams and group purchasing. We found that centralization of procurement was prevalent in public hospitals in both countries, with more regional centralization in Victoria, Australia due to its size. Also, Private hospital systems are encouraged to take advantage of the centralized procurement policy of the government in both the countries. While standardization was achieved in both countries by better integrating procurement and information technology functions, collaboration between clinicians led to more standardization.