Designing for Economies of Scale vs. Economies of Focus in Hospital Departments

Subject/Research problem: Hospitals traditionally segregate resources into centralized functional departments such as diagnostic departments, ambulatory care centres, and nursing wards. In recent years this organizational model has been challenged by the idea that higher quality of care and efficiency in service delivery can be achieved when services are organized around patient groups. Examples are specialized clinics for breast cancer patients and clinical pathways for diabetes patients. Hospitals are struggling with the question whether to become more centralized to achieve economies of scale or more decentralized to achieve economies of focus. In this paper service and patient group characteristics are examined to determine conditions where a centralized model is more efficient and conversely where a decentralized model is more efficient. - Research Question: When organizing hospital capacity what service and patient group characteristics indicate efficiency can be gained through economies of scale vs. economies of focus? - Approach: Using quantitative Queueing Theory and Simulation models the performance of centralized and decentralized hospital clinics is compared. This is done for a variety of services and patient groups. - Result: The study results in a model measuring the tradeoffs between economies of scale and economies of focus. From this model management guidelines are derived. - Application: The general results support strategic planning for a new facility at the Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital. A model developed during this research is also applied in the Chemotherapy Department of the same hospital

Estimating process batch flow times in a two-stage stochastic flowshop with overlapping operations.

Processes; Time;

Differential evolution to solve the lot size problem.

An Advanced Resource Planning model is presented to support optimal lot size decisions for performance improvement of a production system in terms of either delivery time or setup related costs. Based on a queueing network, a model is developed for a mix of multiple products following their own specific sequence of operations on one or more resources, while taking into account various sources of uncertainty, both in demand as well as in production characteristics. In addition, the model includes the impact of parallel servers and different time schedules in a multi-period planning setting. The corrupting influence of variabilities from rework and breakdown is explicitly modeled. As a major result, the differential evolution algorithm is able to find the optimal lead time as a function of the lot size. In this way, we add a conclusion on the debate on the convexity between lot size and lead time in a complex production environment. We show that differential evolution outperforms a steepest descent method in the search for the global optimal lot size. For problems of realistic size, we propose appropriate control parameters for the differential evolution in order to make its search process more efficient.Production planning; Lot sizing; Queueing networks; Differential evolution;

Development and Simulation Assessment of Semiconductor Production System Enhancements for Fast Cycle Times

Long cycle times in semiconductor manufacturing represent an increasing challenge for the industry and lead to a growing need of break-through approaches to reduce it. Small lot sizes and the conversion of batch processes to mini-batch or single-wafer processes are widely regarded as a promising means for a step-wise cycle time reduction. Our analysis with discrete-event simulation and queueing theory shows that small lot size and the replacement of batch tools with mini-batch or single wafer tools are beneficial but lot size reduction lacks persuasive effectiveness if reduced by more than half. Because the results are not completely convincing, we develop a new semiconductor tool type that further reduces cycle time by lot streaming leveraging the lot size reduction efforts. We show that this combined approach can lead to a cycle time reduction of more than 80%

Healthcare queueing models.

Healthcare systems differ intrinsically from manufacturing systems. As such, they require a distinct modeling approach. In this article, we show how to construct a queueing model of a general class of healthcare systems. We develop new expressions to assess the impact of service outages and use the resulting model to approximate patient flow times and to evaluate a number of practical applications. We illustrate the devastating impact of service interruptions on patient flow times and show the potential gains obtained by pooling hospital resources. In addition, we present an optimization model to determine the optimal number of patients to be treated during a service session.Operations research; Health care evaluation mechanisms; Organizational efficiency; Management decision support systems; Time management; Queueing theory;

Queueing theory and operations management.

Management; Theory;

Optimizing campaign sizing policies: an application to a real-life setting.

This paper presents an integrated production inventory model that enables to capture the tradeoffs between average inventory, production capacity and customer service level in a semiprocess industry setting. The model includes different features that are specific for such a setting, such as differences in reactor yield and quality requirements across products, the need for cleaning reactors when switching between product types, and the requirement to produce products in campaign sizes that are an integer multiple of the reactor’s batch size. The model can be used to support midterm planning procedures. In this paper, we illustrate the application of the model to real-life data of two product families at a large specialty chemicals company, which for reasons of confidentiality is further referred to as Company C.Queueing; Campaign sizing; (Semi)process industries;

Capacity Planning and Leadtime management

In this paper we discuss a framework for capacity planning and lead time management in manufacturing companies, with an emphasis on the machine shop. First we show how queueing models can be used to find approximations of the mean and the variance of manufacturing shop lead times. These quantities often serve as a basis to set a fixed planned lead time in an MRP-controlled environment. A major drawback of a fixed planned lead time is the ignorance of the correlation between actual work loads and the lead times that can be realized under a limited capacity flexibility. To overcome this problem, we develop a method that determines the earliest possible completion time of any arriving job, without sacrificing the delivery performance of any other job in the shop. This earliest completion time is then taken to be the delivery date and thereby determines a workload-dependent planned lead time. We compare this capacity planning procedure with a fixed planned lead time approach (as in MRP), with a procedure in which lead times are estimated based on the amount of work in the shop, and with a workload-oriented release procedure. Numerical experiments so far show an excellent performance of the capacity planning procedure

Controlling the order pool in make-to-order production systems

Voor ‘Make-To-Order’ (MTO, oftewel klantordergestuurde) productiesystemen is de tijd die orders moeten wachten op beschikbare productiecapaciteit cruciaal. Het beheersen van die wachttijd is van groot belang om zowel korte als betrouwbare doorlooptijden te realiseren. Daarom analyseerde en ontwierp Remco Germs regels voor orderacceptatie en ordervrijgave, om daarmee de wachttijden te beheersen. Orderacceptatie en -vrijgave zijn de twee belangrijkste mechanismen om de lengte van wachttijden te beïnvloeden en zodoende de productie te sturen. De logistieke prestatie hangt in grote mate af van specifieke kenmerken van MTO-systemen, zoals routing variabiliteit, beperkte productiecapaciteit, omsteltijden, strikte leveringsvoorwaarden en onzekerheid in het aankomstpatroon van orders. Om een beter begrip te krijgen van de afwegingen die MTO-bedrijven in dit opzicht moeten maken richt het proefschrift zich op de modellering van de belangrijkste kenmerken van MTO-systemen. De inzichten die dat oplevert worden vervolgens gebruikt om orderacceptatie- en ordervrijgaveregels te ontwikkelen die eenvoudig te begrijpen en daarom makkelijk in praktijksituaties te implementeren zijn. Deze relatief eenvoudige beslissingsregels kunnen al leiden tot significante verbeteringen in de logistieke prestaties van MTO-bedrijven. The thesis of Remco Germs analyses and develops order acceptance and order release policies to control queues in make-to-order (MTO) production systems. Controlling the time orders spend waiting in queues is crucial for realizing short and reliable delivery times, two performance measures which are of strategic importance for many MTO com-panies. Order acceptance and order release are the two most important production con-trol mechanisms to influence the length of these queues. Their performance depends on typical characteristics of MTO systems, such as random (batch) order arrival, routing variability, fixed capacities, setup times and (strict) due-dates. To better understand the underlying mechanisms of good order acceptance and order release policies the models in this thesis focus on the main characteristics of MTO systems. The insights obtained from these models are then used to develop order acceptance and order release policies that are easy to understand and thereby easy to implement in practice. The results show that these relatively simple policies may already lead to significant performance improvements for MTO companies.