Process flexibility and inventory flexibility via product substitution

This paper studies the optimal control of and interaction between two types of flexibility under Markov models of demand and production: process flexibility and inventory flexibility. In our model, process flexibility is generated by a multi-functional production facility that can produce two types of products, and inventory flexibility is manifested in firm-driven one-way product substitution. Both process flexibility and inventory flexibility are important drivers of supply chain performance and are strategic design considerations. To analyze the interaction between these two types of flexibility, we model a dynamically controlled two-product, make-to-stock system with stochastic processing times and stochastic demand. We characterize the complex joint optimal production and post-production policy for a special case and numerically show that a simply structured multi-threshold policy is a near-optimal heuristic policy for the general case. We gain further insight into the impact of system parameters on the value of process flexibility and inventory flexibility via a comprehensive numerical study. We find that for a wide range of capacity and cost parameters, process flexibility and inventory flexibility complement each other, so pursuing both forms of flexibility is effective.     

Sustainability guidelines and forest market response: an assessment of European Union pellet demand in the southeastern United States

Woody biomass from the southeast United States is expected to play an important role in meeting European Union renewable energy targets. In crafting policies to guide bioenergy development and in guiding investment decisions to meet established policy goals, a firm understanding of the interaction between policy targets and forest biomass markets is necessary, as is the effect that this interaction will have on environmental and economic objectives. This analysis increases our understanding of these interactions by modeling the response of southern US forest markets to new pellet demand in the presence of sustainability sourcing or harvest criteria. We first assess the influence of EU recommended sustainability guidelines on the forest inventory available to supply EU markets, and then model changes in forest composition and extent in response to expected increases in pellet demand. Next, we assess how sustainability guidelines can influence the evolution of forest markets in the region, paying particular attention to changes in land use and forest carbon. Regardless of whether sustainability guidelines are applied, we find increased removals, an increase in forest area, and little change in forest inventory. We also find annual gains in forest carbon in most years of the analysis. The incremental effect of sustainability guideline application on forest carbon and pellet greenhouse gas (GHG) balance is difficult to discern, but results suggest that guidelines could be steering production away from sensitive forest types inherently less responsive to changing market conditions. Pellet GHG balance shows significant annual change and is attributable to the complexity of the underlying forest landscape. The manner by which GHG balance is tracked is thus a critical policy decision, reinforcing the importance and relevance of current efforts to develop approaches to accurately account for the GHG implications of biomass use both in the United States and European Union.     

Revenue management for outpatient appointments: joint capacity control and overbooking with class-dependent no-shows

Outpatient appointment scheduling balances efficiency with access to healthcare services, yet appointment no-shows, cancellations, and delay are significant barriers to effective healthcare delivery. Patients with longer appointment delay often waste appointments more frequently, prompting a need for greater flexibility in appointment allocation. We present a joint capacity control and overbooking model where a clinic maximizes profits by controlling bookings from two sequential patient classes with different no-show rates. When booking advance requests, the clinic must balance high no-show probability with the probability of subsequent requests at lower waste rates. We show the optimal policy is computationally intensive to derive; therefore, we develop bounds and approximations which we compare via numerical study with the optimal policy as well as policies from practice and previous literature. We find the optimal policy increases profits 17.8% over first-come-first-serve allocation. We develop a simple policy which performs 0.3% below optimal on average. While pure open access can achieve optimality, it performs 23.0% below optimal on average.     

Aligning demand and supply flexibility in custom product co-design

Flexibility of supply and demand is essential for successful implementation of a mass customization strategy that delivers sustained competitive advantage. Supply flexibility, i.e., a choice of alternative products designed to perform the same basic function, is made possible by the range of capabilities available in flexible and agile manufacturing systems and in supply chains. Demand flexibility is derived from the degree to which a customer is willing to compromise on product features or performance levels in order to meet budgetary (reflected in price) or schedule (reflected in delivery) constraints. Flexibility of both supply and demand can have significant strategic and financial value if they are properly aligned. However, customers are mostly unaware of mapping of demand flexibility on to supply flexibility and its impact on production cost and time. Recent advances in information technology have made it possible to co-design a product that involves customer on one end and the manufacturer on the other. This creates an aura and an opportunity where a middle ground between the supply and demand flexibility can be explored and a “deal” can be struck where both parties settle for a product that is beneficial to both through a negotiated settlement. In this paper, we develop a framework for such negotiations. The customer requirements are treated as a range of negotiable options instead of a set of fixed inputs. Demand and supply for customization is then matched by aligning the flexibility of manufacturing systems with customers’ requirement options. Based on this framework, a negotiation scheme is developed to assist customers and manufacturers in exploring and utilizing demand and supply flexibility information in co-design. The negotiation scheme is formulated using goal programming. Finally, an interactive problem-solving procedure is developed and implemented with an illustrative example.     

Evaluation of Task Assignment Policies for Supercomputing Servers: The Case for Load Unbalancing and Fairness

While the MPP is still the most common architecture in supercomputer centers today, a simpler and cheaper machine configuration is appearing at many supercomputing sites. This alternative setup may be described simply as a collection of multiprocessors or a distributed server system. This collection of multiprocessors is fed by a single common stream of jobs, where each job is dispatched to exactly one of the multiprocessor machines for processing.The biggest question which arises in such distributed server systems is what is a good rule for assigning jobs to host machines: i.e. what is a good task assignment policy. Many task assignment policies have been proposed, but not systematically evaluated under supercomputing workloads.In this paper we start by comparing existing task assignment policies using a trace-driven simulation under supercomputing workloads. We validate our experiments by providing analytical proofs of the performance of each of these policies. These proofs also help provide much intuition. We find that while the performance of supercomputing servers varies widely with the task assignment policy, none of the above task assignment policies perform as well as we would like.We observe that all policies proposed thus far aim to balance load among the hosts. We propose a policy which purposely unbalances load among the hosts, yet, counter-to-intuition, is also fair in that it achieves the same expected slowdown for all jobs – thus no jobs are biased against. We evaluate this policy again using both trace-driven simulation and analysis. We find that the performance of the load unbalancing policy is significantly better than the best of those policies which balance load.     

Control of an Assembly System with Processing Time and Subassembly-Type Uncertainty

We address the problem of controlling an assembly system in which the processing times as well as the types of subassemblies are stochastic. The quality (or performance) of the final part depends on the characteristics of the subassemblies to be assembled, which are not constant. Furthermore, the processing time of a subassembly is random. We analyze the trade-off between the increase in the potential value of parts gained by delaying the assembly operation and the inventory costs caused by this delay. We also consider the effects of processing time uncertainty. Our problem is motivated by the assembly of passive and active plates in flat panel display manufacturing. We formulate the optimal control problem as a Markov decision process. However, the optimal policy is very complex, and we therefore develop simple heuristic policies. We report the results of a simulation study that tests the performance of our heuristics. The computational results indicate that the heuristics are effective for a wide variety of cases.     

Optimal production control of a service-oriented manufacturing system with customer balking behavior

The great challenge for service-oriented manufacturing (SOM) is how to cope with customer behavior while making decision on production planning and scheduling. In this paper, we consider a single-stage manufacturing system for SOM with impatient customers. In order to represent customer balking behavior caused by backlog, we employ a balking function, which is an arbitrary non-decreasing function of the backlog for characterizing the customer’s response to the backlog. The objective is to find the optimal production policy that minimizes the system cost. The problem is formulated as a Markov decision process. The optimal production policy is proved to be a base-stock policy. The effects of system parameters on the optimal base-stock level are analytically investigated, and the impact of customer balking behavior on the system is illustrated by numerical example in which linear balking function is employed. Numerical example shows that customer balking has a significant impact on the optimal control and the performance measures of the system under the optimal policy.     

Ginseng

Although there is no known medicinal value in ginseng, there is a continuing demand for the root. Two species ofPanax are acceptable sources, one Asiatic, the other North American. As a crop, ginseng is no gold mine, but may give a reasonable return to growers willing to wait the five to seven years required for maturation.     

Assessing capacity scalability policies in RMS using system dynamics

This paper presents a model for assessing different capacity scalability policies in Reconfigurable Manufacturing System (RMS) for different changing demand scenarios. The novelty of this approach is two fold: (1) it is the first attempt to explore different capacity scalability policies in RMS based on multiple performance measures, mainly scaling rate, Work In Process level, inventory level and backlog level; and (2) the dynamic scalability process in RMS is modeled for the first time using System Dynamics. Different policies for capacity scalability for various demand scenarios were assessed. Numerical simulation results obtained using the developed capacity scalability model showed that the best capacity scalability policy to be adopted for RMS is dependent on the anticipated demand pattern as well as the various manufacturing objectives. The presented assessment results will help the capacity scalability planners better decide the different tradeoffs between the competing strategic and operational objectives of the manufacturing enterprise, before setting the suitable capacity scalability plan parameters.     

Bayesian time-varying autoregressive models of COVID-19 epidemics

The COVID-19 pandemic has highlighted the importance of reliable statistical models which, based on the available data, can provide accurate forecasts and impact analysis of alternative policy measures. Here we propose Bayesian time-dependent Poisson autoregressive models that include time-varying coefficients to estimate the effect of policy covariates on disease counts. The model is applied to the observed series of new positive cases in Italy and in the United States. The results suggest that our proposed models are capable of capturing nonlinear growth of disease counts. We also find that policy measures and, in particular, closure policies and the distribution of vaccines, lead to a significant reduction in disease counts in both countries.     

Data quality

A methodology is presented that enables incorporating expert judgment regarding the variability of input data for environmental life cycle assessment (LCA) modeling. The quality of input data in the life-cycle inventory (LCI) phase is evaluated by LCA practitioners using data quality indicators developed for this application. These indicators are incorporated into the traditional LCA inventory models that produce non-varying point estimate results (i.e., deterministic models) to develop LCA inventory models that produce results in the form of random variables that can be characterized by probability distributions (i.e., stochastic models). The outputs of these probabilistic LCA models are analyzed using classical statistical methods for better decision and policy making information. This methodology is applied to real-world beverage delivery system LCA inventory models. The inventory study results for five beverage delivery system alternatives are compared using statistical methods that account for the variance in the model output values for each alternative. Sensitivity analyses are also performed that indicate model output value variance increases as input data uncertainty increases (i.e., input data quality degrades). Concluding remarks point out the strengths of this approach as an alternative to providing the traditional qualitative assessment of LCA inventory study input data with no efficient means of examining the combined effects on the model results. Data quality assessments can now be captured quantitatively within the LCA inventory model structure. The approach produces inventory study results that are variables reflecting the uncertainty associated with the input data. These results can be analyzed using statistical methods that make efficient quantitative comparisons of inventory study alternatives possible. Recommendations for future research are also provided that include the screening of LCA inventory model inputs for significance and the application of selection and ranking techniques to the model outputs.     

Efficient nonviral transfection of dendritic cells and their use for in vivo immunization

Immunization with dendritic cells (DCs) transfected with genes encoding tumor-associated antigens (TAAs) is a highly promising approach to cancer immunotherapy. We have developed a system, using complexes of plasmid DNA expression constructs with the cationic peptide CL22, that transfects human monocyte-derived DCs much more efficiently than alternative nonviral agents. After CL22 transfection, DCs expressing antigens stimulated autologous T cells in vitro and elicited primary immune responses in syngeneic mice, in an antigen-specific manner. Injection of CL22-transfected DCs expressing a TAA, but not DCs pulsed with a TAA-derived peptide, protected mice from lethal challenge with tumor cells in an aggressive model of melanoma. The CL22 system is a fast and efficient alternative to viral vectors for engineering DCs for use in immunotherapy and research.     

Optimal control of epidemics with limited resources

We extend the existing work on the time-optimal control of the basic SIR epidemic model with mass action contact rate. Previous results have focused on minimizing an objective function that is a linear combination of the cost associated with using control and either the outbreak size or the infectious burden. We instead, provide analytic solutions for the control that minimizes the outbreak size (or infectious burden) under the assumption that there are limited control resources. We provide optimal control policies for an isolation only model, a vaccination only model and a combined isolation–vaccination model (or mixed model). The optimal policies described here contain many interesting features especially when compared to previous analyses. For example, under certain circumstances the optimal isolation only policy is not unique. Furthermore the optimal mixed policy is not simply a combination of the optimal isolation only policy and the optimal vaccination only policy. The results presented here also highlight a number of areas that warrant further study and emphasize that time-optimal control of the basic SIR model is still not fully understood.     

Toward Reproducible Computational Research: An Empirical Analysis of Data and Code Policy Adoption by Journals

Journal policy on research data and code availability is an important part of the ongoing shift toward publishing reproducible computational science. This article extends the literature by studying journal data sharing policies by year (for both 2011 and 2012) for a referent set of 170 journals. We make a further contribution by evaluating code sharing policies, supplemental materials policies, and open access status for these 170 journals for each of 2011 and 2012. We build a predictive model of open data and code policy adoption as a function of impact factor and publisher and find higher impact journals more likely to have open data and code policies and scientific societies more likely to have open data and code policies than commercial publishers. We also find open data policies tend to lead open code policies, and we find no relationship between open data and code policies and either supplemental material policies or open access journal status. Of the journals in this study, 38% had a data policy, 22% had a code policy, and 66% had a supplemental materials policy as of June 2012. This reflects a striking one year increase of 16% in the number of data policies, a 30% increase in code policies, and a 7% increase in the number of supplemental materials policies. We introduce a new dataset to the community that categorizes data and code sharing, supplemental materials, and open access policies in 2011 and 2012 for these 170 journals.     

Making robust policy decisions using global biodiversity indicators

In order to influence global policy effectively, conservation scientists need to be able to provide robust predictions of the impact of alternative policies on biodiversity and measure progress towards goals using reliable indicators. We present a framework for using biodiversity indicators predictively to inform policy choices at a global level. The approach is illustrated with two case studies in which we project forwards the impacts of feasible policies on trends in biodiversity and in relevant indicators. The policies are based on targets agreed at the Convention on Biological Diversity (CBD) meeting in Nagoya in October 2010. The first case study compares protected area policies for African mammals, assessed using the Red List Index; the second example uses the Living Planet Index to assess the impact of a complete halt, versus a reduction, in bottom trawling. In the protected areas example, we find that the indicator can aid in decision-making because it is able to differentiate between the impacts of the different policies. In the bottom trawling example, the indicator exhibits some counter-intuitive behaviour, due to over-representation of some taxonomic and functional groups in the indicator, and contrasting impacts of the policies on different groups caused by trophic interactions. Our results support the need for further research on how to use predictive models and indicators to credibly track trends and inform policy. To be useful and relevant, scientists must make testable predictions about the impact of global policy on biodiversity to ensure that targets such as those set at Nagoya catalyse effective and measurable change.     

A Dynamic Tool Requirement Planning Model for Flexible Manufacturing Systems

Despite their strategic potential, tool management issues in flexible manufacturing systems (FMSs) have received little attention in the literature. Nonavailability of tools in FMSs cuts at the very root of the strategic goals for which such systems are designed. Specifically, the capability of FMSs to economically produce customized products (flexibility of scope) in varying batch sizes (flexibility of volume) and delivering them on an accelerated schedule (market response time) is seriously hampered when required tools are not available at the time needed. On the other hand, excess inventory of tools in such systems represents a significant cost due to the expensive nature of FMS tool inventory. This article constructs a dynamic tool requirement planning (DTRP) model for an FMS tool planning operation that allows dynamic determination of the optimal tool replenishments at the beginning of each arbitrary, managerially convenient, discrete time period. The analysis presented in the article consists of two distinct phases: In the first phase, tool demand distributions are obtained using information from manufacturing production plans (such as master production schedule (MPS) and material requirement plans (MRP)) and general tool life distributions fitted on actual time-to-failure data. Significant computational reductions are obtained if the tool failure data follow a Weibull or Gamma distribution. In the second phase, results from classical dynamic inventory models are modified to obtain optimal tool replenishment policies that permit compliance with such FMS-specific constraints as limited tool storage capacity and part/tool service levels. An implementation plan is included.     

Optimal intervention for an epidemic model under parameter uncertainty

We will be concerned with optimal intervention policies for a continuous-time stochastic SIR (susceptible-->infective-->removed) model for the spread of infection through a closed population. In previous work on such optimal policies, it is common to assume that model parameter values are known; in reality, uncertainty over parameter values exists. We shall consider the effect upon the optimal policy of changes in parameter estimates, and of explicitly taking into account parameter uncertainty via a Bayesian decision-theoretic framework. We consider policies allowing for (i) the isolation of any number of infectives, or (ii) the immunisation of all susceptibles (total immunisation). Numerical examples are given to illustrate our results.     

Interaction between Fiscal and Monetary Policy in a Dynamic Nonlinear Model

The objective of this study is to verify the dynamics between fiscal policy, measured by public debt, and monetary policy, measured by a reaction function of a central bank. Changes in monetary policies due to deviations from their targets always generate fiscal impacts. We examine two policy reaction functions: the first related to inflation targets and the second related to economic growth targets. We find that the condition for stable equilibrium is more restrictive in the first case than in the second. We then apply our simulation model to Brazil and United Kingdom and find that the equilibrium is unstable in the Brazilian case but stable in the UK case.     

Optimal control strategy for prevention of avian influenza pandemic

The spread of H5N1 virus to Europe and continued human infection in Southeast Asia have heightened pandemic concern. Although, fortunately, sustained human-to-human transmissions have not been reported yet, it is said that a pandemic virus which can be easily transmitted among humans certainly emerges in the future. In this study, we extended the previous studies for the prevention of the pandemic influenza to evaluate the time-dependent optimal prevention policies, which are associated with elimination policy and quarantine policy, considering its execution cost. Actually, the execution cost affects the optimal strategy of prevention policies and the prevention of the disease spread. We found that the quarantine policy is very important rather than the elimination policy during the disease spread, even if the unit execution cost of the quarantine policy is more expensive than that of the elimination policy. And also, the change of the unit execution cost does affect the total cumulative cost of the optimal prevention policies but does not affect the relative frequency of each cumulative execution cost. Furthermore, interestingly, we revealed that an optimal strategy to reduce the number of total infected humans might increase a chance of invadability of the mutant influenza.     

A new and efficient synthetic route for the anxiolytic agent CL285032

CL285032 is an anxiolytic compound currently under investigation as a possible treatment for canine noise phobia associated anxiety. A robust scale-up and manufacturing process is essential for the development and marketability of the drug. The current synthetic route, although reliable, requires seven steps and has a low overall yield (18%), leaving opportunity for improvement. We are presenting an efficient alternative approach toward the synthesis of CL285032 and the results thereof.