Mitigating supply and production uncertainties with dynamic scheduling using real-time transport information

Supply and production uncertainties can affect the scheduling and inventory performance of final production systems. Facing such uncertainties, production managers normally choose to maintain the original production schedule, or follow the first-in-first-out policy. This paper develops a new, dynamic algorithm policy that considers scheduling and inventory problems, by taking advantage of real-time shipping information enabled by today’s advanced technology. Simulation models based on the industrial example of a chemical company and the Taguchi’s method are used to test these three policies under 81 experiments with varying supply and production lead times and uncertainties. Simulation results show that the proposed dynamic algorithm outperforms the other two policies for supply chain cost. Results from Taguchi’s method show that companies should focus their long-term effort on the reduction of supply lead times, which positively affects the mitigation of supply uncertainty

Uncertainty propagation and sensitivity analysis techniques in building performance simulation to support conceptual building and system design

Due to advances in computing and modeling, the Architecture Engineering and Construction (AEC)industry has arrived at an era of digital empiricism. Computational simulation tools are widely used across many engineering disciplines for design, evaluation and analysis. Experts in the field agree that design decisions taken during the early design stages have a significant impact on the real performance of the building. Nevertheless, building performance simulation is still hardly used during conceptual design. The European Commission has targeted a 20% reduction of CO2 emissions, a 20% increase of energy efficiency and a 20% increase in the use of renewable energy by 2020. These ambitious aims have resulted in the recasting of the Energy for Buildings Directive, demanding nearly-zero-net energy-buildings for new buildings and major refurbishments by 2020. The formulated aim requires for the first time an integrated design of the building’s demand and supply systems. The current research was triggered by the above observation. It uses semi-structured interviews and critical reviews of literature and software to establish the reasons that prevent Heating, Ventilation and Air Conditioning (HVAC) consultants from adopting Building Performance Simulation (BPS) tools and to identify the needs of practitioners during the conceptual design stage. In response to the identified needs, a rapid iterative development process is deployed to produce a prototypical software tool. Finally, the tool is heuristically tested on expert users to evaluate its capability to support the conceptual design process. The results obtained from interviews and reviews highlight that HVAC consultants work with an increasing number of design alternatives to prevent dysfunctional buildings. The complexity of design problems is increasing on the one hand due to the need for an early integration of engineering discipline’s and on the other hand due to the challenges in meeting the even more stringent requirements of new buildings. Furthermore, design teams run the risk of only identifying suboptimal solutions for the design problem when they limit themselves too early to a small number of design alternatives. The use of simulation tools helps facilitate a quick turnaround of performance evaluations for a great number of design alternatives early in the design process. By doing so, performance simulation tools have the potential to supplement design experience and support decision making. However, simulation tools are perceived by many as too detailed to be readily used for conceptual design support. Research findings suggest that tools for the early design stages are required to enable parametric studies and to provide facilities to explore the relationships between potential design decisions and performance aspects. Tools need to be able to dynamically scale the resolution of their interfaces to fit the different levels of information density characteristic of the different design stages. In addition, they need to be flexible enough to facilitate expansion of the system representations with innovative design concepts as the design progresses. Due to the need for parametric studies and the exploration of the relationships between potential design decision and performance aspects, this research explores the extension and application of BPS tools with techniques for uncertainty propagation and sensitivity analysis for conceptual design support. This endeavor requires (1) the evaluation and selection of an extension strategy, (2) the determination of the format and availability of input to techniques for uncertainty propagation and sensitivity analysis, as well as (3) developing knowledge regarding the extent and content of the design option space. To avoid the need to modify the source code of BPS tools, an external strategy is applied that embeds an existing simulation engine into a shell with extra features for statistical pre and post-processing by Latin Hypercube sampling and regression based sensitivity analysis. With regards to the model resolution, results suggest that it is more beneficial to use detailed models with adaptive interfaces rather than simpler tools. The advantages are twofold. Firstly, the BPS tool can use an existing validated simulation model - rather than a specifically developed abstract model with limited applicability. Secondly, the model is able to provide consistent feedback throughout the lifetime of the building. Within the iterative process, the conceptual design stage has some distinctive tasks, such as to explore the option space and to generate and evaluate design concepts. The option space is multidimensional, due to its multi-disciplinary set-up and wide-ranging interests of the participating practitioners. An empirical study as part of the research demonstrates the presence of at least two attributes, four subsystem categories and four relationships. Depending on the experience of the practicing designer, components, attributes and relationships are used to a very different extent. While experienced HVAC consultants seem to work mainly with relationships when compiling a design concept, novice designers prefer to work with components. The sampling based analysis strategy requires knowledge about the uncertainty of the parametric model input in the form of probability distribution functions. On the basis of a survey on internal gains for offices, this thesis concludes that current design guidelines provide useful data in a suitable format. Measurements conducted in an office building in Amsterdam confirm the trend towards decreasing equipment gains and the proportional increase of lighting gains. However, in the absence of data to derive a probability density function, this research suggests the definition of "explanatory" scenarios. It is common practice to use "normative" scenarios as input in building performance studies aiming to prove compliance with building regulations. The use of "exploratory" scenarios is less common. Scenario based load profiles have to meet three characteristics. They have to be: (1) locally representative; (2) up-to date and (3) need to match workplace culture. As part of this thesis explanatory data sets were developed representing climate change scenarios for The Netherlands. The exploratory scenarios facilitate the robustness assessment of the future performance of design alternatives. Tests with the Dutch data sets confirm that neither the current reference data nor the projected reference data provide valid results to predict uncertainty ranges for the peak cooling load as a potential robustness indicator. A simulation based comparative robustness assessment of three HVAC concepts over 15 and 30 years is reported. The results indicate a robust future performance for the floor-cooling based design alternative with respect to thermal comfort and cooling energy demand. The software prototype shows that detailed simulation tools with features for uncertainty propagation and sensitivity analysis provide the facilities to explore consequences of potential design decisions on performance aspects. In addition, they enable parametric studies and create the possibility to quantify parameter interactions and their collective impact on the performance aspect. Heuristic usability evaluation of the software prototype confirms the value to design practice. 85% of approached HVAC consultants state that the uncertainty of performance aspects is an important parameter to support conceptual design. More importantly, 80% of the practitioners consider the prototype to have great potential to reduce the number of necessary design iterations. This thesis concludes that simulation tools that quantitatively address uncertainties and sensitivities related to conceptual building design generate value by (1) providing an indication of the accuracy of the performance predictions; (2) allowing the identification of parameters and systems to which performance metrics react sensitively and in-sensitively, respectively; and (3) enabling a robustness assessment of design alternatives

TRADE-OFF BALANCING FOR STABLE AND SUSTAINABLE OPERATING ROOM SCHEDULING

The implementation of the mandatory alternative payment model (APM) guarantees savings for Medicare regardless of participant hospitals ability for reducing spending that shifts the cost minimization burden from insurers onto the hospital administrators. Surgical interventions account for more than 30% and 40% of hospitals total cost and total revenue, respectively, with a cost structure consisting of nearly 56% direct cost, thus, large cost reduction is possible through efficient operation management. However, optimizing operating rooms (ORs) schedules is extraordinarily challenging due to the complexities involved in the process. We present new algorithms and managerial guidelines to address the problem of OR planning and scheduling with disturbances in demand and case times, and inconsistencies among the performance measures. We also present an extension of these algorithms that addresses production scheduling for sustainability. We demonstrate the effectiveness and efficiency of these algorithms via simulation and statistical analyses

Giga-Investments: Modelling the Valuation of Very Large Industrial Real Investments

In this doctoral dissertation characteristics of very large industrial real investments (VLIRI) are investigated and a special group of VLIRI is defined as giga-investments. The investment decision-making regarding to giga-investments is discussed from the points of view of discounted cash-flow based methods and real option valuation. Based on the bacground of establishing giga-investments, state-of-the-art in capital budgeting (including real options) and by applying fuzzy numbers a novel method for the evaluation and profitability analysis of giga-investments is presented. Application of the method is illustrated and issues regarding investment decision-making of large industrial real investments are discussed.Real Options; Fuzzy Real Option Valuation; Giga-Investments; Very Large Industrial Real Investments; Dissertation

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Application of lean scheduling and production control in non-repetitive manufacturing systems using intelligent agent decision support

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Lean Manufacturing (LM) is widely accepted as a world-class manufacturing paradigm, its currency and superiority are manifested in numerous recent success stories. Most lean tools including Just-in-Time (JIT) were designed for repetitive serial production systems. This resulted in a substantial stream of research which dismissed a priori the suitability of LM for non-repetitive non-serial job-shops. The extension of LM into non-repetitive production systems is opposed on the basis of the sheer complexity of applying JIT pull production control in non-repetitive systems fabricating a high variety of products. However, the application of LM in job-shops is not unexplored. Studies proposing the extension of leanness into non-repetitive production systems have promoted the modification of pull control mechanisms or reconfiguration of job-shops into cellular manufacturing systems. This thesis sought to address the shortcomings of the aforementioned approaches. The contribution of this thesis to knowledge in the field of production and operations management is threefold: Firstly, a Multi-Agent System (MAS) is designed to directly apply pull production control to a good approximation of a real-life job-shop. The scale and complexity of the developed MAS prove that the application of pull production control in non-repetitive manufacturing systems is challenging, perplex and laborious. Secondly, the thesis examines three pull production control mechanisms namely, Kanban, Base Stock and Constant Work-in-Process (CONWIP) which it enhances so as to prevent system deadlocks, an issue largely unaddressed in the relevant literature. Having successfully tested the transferability of pull production control to non-repetitive manufacturing, the third contribution of this thesis is that it uses experimental and empirical data to examine the impact of pull production control on job-shop performance. The thesis identifies issues resulting from the application of pull control in job-shops which have implications for industry practice and concludes by outlining further research that can be undertaken in this direction

Improving healthcare supply chains and decision making in the management of pharmaceuticals

The rising cost of quality healthcare is becoming an increasing concern. A significant part of healthcare cost is the pharmaceutical supply component. Improving healthcare supply chains is critical not only because of the financial magnitude but also because it impacts so many people. Efforts such as this project are essential in understanding the current operations of healthcare pharmacy systems and in offering decision support tools to managers struggling to make the best use of organizational resources. The purpose of this study is to address the objectives of a local hospital that exhibits typical problems in pharmacy supply chain management. We analyze the pharmacy supply network structure and the different, often conflicting goals in the decisions of the various stakeholders. We develop quantitative models useful in optimizing supply chain management and inventory management practices. We provide decision support tools that improve operational, tactical, and strategic decision making in the pharmacy supply chain and inventory management of pharmaceuticals. On one hand, advanced computerized technology that manages pharmaceutical dispensation and automates the ordering process offers considerable progress to support pharmacy product distribution. On the other hand, the available information is not utilized to help the managers in making the appropriate decisions and control the supply chain management. Quantitative methods are presented that provide simplified, practical solutions to pharmacy objectives and serve as decision support tools. For operational inventory decisions we provide the min and max par levels (reorder point and order up to level) that control the automated ordering system for pharmaceuticals. These parameters are based on two near-optimal allocation policies of cycle stock and safety stock under storage space constraint. For the tactical decision we demonstrate the influence of varying inventory holding cost rates on setting the optimal reorder point and order quantity for items. We present a strategic decision support tool to analyze the tradeoffs among the refill workload, the emergency workload, and the variety of drugs offered. We reveal the relationship of these tradeoffs to the three key performance indicators at a local care unit: the expected number of daily refills, the service level, and the storage space utilization