Dispatching,routing, and scheduling of two automated guided vehicles in a flexible manufacturing system

This article presents a new approach for planning the dispatching, conflict-free routing, and scheduling of automated guided vehicles in a flexible manufacturing system. The problem is solved optimally in an integrated manner, contrary to the traditional approach in which the problem is decomposed in three steps that are solved sequentially. The algorithm is based on dynamic programming and is solved on a rolling time horizon. Three dominance criteria are used to limit the size of the state space. The method finds the transportation plan minimizing the makespan (the completion time for all the tasks). Various results are discussed. A heuristic version of the algorithm is also proposed for an extension of the method to many vehicles.     

Rabies in raccoons: optimal control for a discrete time model on a spatial grid

An epidemic model for rabies in raccoons is formulated with discrete time and spatial features. The goal is to analyze the strategies for optimal distribution of vaccine baits to minimize the spread of the disease and the cost of implementing the control. Discrete optimal control techniques are used to derive the optimality system, which is then solved numerically to illustrate various scenarios.     

An Efficient Heuristic Approach to Recognize the Infeasibility of a Loading Problem

The success of hierarchical production planning approaches for flexible manufacturing systems lies in the consistency of decision outcomes at various decision levels. For instance, the loading problem, which is solved at a lower level, may not yield a feasible loading solution to a set of part types selected at a higher level. This paper attemps to address the issue of recognizing the infeasibility of a loading solution. We present a modified loading model that includes a penalty for each operation not assigned to any machine. We develop a Lagrangian-based heuristic procedure and provide a sufficient condition on the quality of heuristic solutions that, if satisfied, will enable us to use the heuristic solutions to recognize the infeasibility of a loading problem. The proposed model and the dual-based heuristic can be effectively incorporated in an FMS hierarchical production planning approach that finds a good loading solution by iteratively comparing different part grouping scenarios.     

Minimization of intermediate concentrations as a suggested optimality principle for biochemical networks

The multiobjective problem of minimizing all intermediate concentrations is solved for a model of glycolysis, the pentose monophosphate shunt and the glutathione system in human erythrocytes. It turns out that one solution out of four obtained corresponds qualitatively to the real system. Furthermore, it is shown that for any reaction system, the mentioned optimality principle implies distinct time hierarchy in that some reactions are infinitely fast and subsist in quasi-equilibrium. Finally, the relationships to the standard method of deriving enzymatic rate laws are discussed.     

A model for the minimum cost configuration problem in flexible manufacturing systems

This paper presents a mathematical programming model to help select equipment for a flexible manufacturing system, i.e., the selection of the types and numbers of CNC machines, washing stations, load/unload stations, transportation vehicles, and pallets. The objective is to minimize equipment costs and work-in-process inventory cost, while fulfilling production requirements for an average period. Queueing aspects and part flow interactions are considered with the help of a Jacksonian-type closed queueing network model in order to evaluate the system's performance. Since the related decision problem of our model can be shown to be NP-complete, the proposed solution procedure is based on implicit enumeration. Four bounds are provided, two lower and two upper bounds. A tight lower bound is obtained by linearizing the model through the application of asymptotic bound analysis. Furthermore, asymptotic bound analysis allows the calculation of a lower bound for the number of pallets in the system. The first upper bound is given by the best feasible solution and the second is based on the anti-starshaped form of the throughput function.     

A tractable revenue management model for capacity allocation and overbooking over an airline network

In this paper, we develop a revenue management model to jointly make the capacity allocation and overbooking decisions over an airline network. The crucial observation behind our model is that if the penalty cost of denying boarding to the reservations were given by a separable function, then the optimality equation for the joint capacity allocation and overbooking problem would decompose by the itineraries. We exploit this observation by building an approximation to the penalty cost that is separable by the numbers of reservations for different itineraries. In this case, we can obtain an approximate solution to the optimality equation by plugging the separable approximation into the boundary condition of the optimality equation. Our computational experiments compare our approach with a standard deterministic linear programming formulation, as well as a recent joint capacity allocation and overbooking model. When compared with the standard deterministic linear programming formulation, our approach can provide significant profit improvements. On the other hand, when compared with the recent joint capacity allocation and overbooking model, our approach can provide similar profit performance with substantially shorter runtimes.     

Model for the Part Manufacturing and Vehicle Assembly Component of the Vehicle Life Cycle Inventory

A model is presented for calculating the environmental burdens of the part manufacturing and vehicle assembly (VMA) stage of the vehicle life cycle. The model is based on a process‐level approach, accounting for all significant materials by their transformation processes (aluminum castings, polyethylene blow molding; etc.) and plant operation activities (painting; heating, ventilation, and air conditioning [HVAC], etc.) germane to VMA. Using quantitative results for these material/transformation process pairings, a percent‐by‐weight material/transformation distribution (MTD) function was developed that permits the model to be applied to a range of vehicles, both conventional and advanced (e.g., hybrid electric, light weight, aluminum intensive). Upon consolidation of all inputs, the model reduces to two terms: one proportional to vehicle mass and a plant overhead per vehicle term. When the model is applied to a materially well‐characterized conventional vehicle, reliable estimates of cumulative energy consumption (34 gigajoules/vehicle) and carbon dioxide (CO₂) emissions (2 tonnes/vehicle) with coefficients of variation are computed for the VMA life cycle stage. Due to the more comprehensive coverage of manufacturing operations, our energy estimates are on the higher end of previously published values. Nonetheless, they are still somewhat underestimated due to a lack of data on overhead operations in part manufacturing facilities and transportation of parts and materials between suppliers and vehicle manufacturing operations. For advanced vehicles, the material/transformation process distribution developed above needs some adjusting for different materials and components. Overall, energy use and CO₂ emissions from the VMA stage are about 3.5% to 4.5% of total life cycle values for vehicles.     

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.     

Integration of system dynamics approach toward deepening and broadening the life cycle sustainability assessment framework: a case for electric vehicles

Purpose

Quantitative life cycle sustainable assessment requires a complex and multidimensional understanding, which cannot be fully covered by the current portfolio of reductionist-oriented tools. Therefore, there is a dire need on a new generation of modeling tools and approaches that can quantitatively assess the economic, social, and environmental dimensions of sustainability in an integrated way. To this end, this research aims to present a practical and novel approach for (1) broadening the existing life cycle sustainability assessment (LCSA) framework by considering macrolevel environmental, economic, and social impacts (termed as the triple bottom line), simultaneously, (2) deepening the existing LCSA framework by capturing the complex dynamic relationships between social, environmental, and economic indicators through causal loop modeling, (3) understanding the dynamic complexity of transportation sustainability for the triple bottom line impacts of alternative vehicles, and finally (4) investigating the impacts of various vehicle-specific scenarios as a novel approach for selection of a macrolevel functional unit considering all of the complex interactions in the environmental, social, and economic aspects.

Methods

To alleviate these research objectives, we presented a novel methodology to quantify macrolevel social, economic, and environmental impacts of passenger vehicles from an integrated system analysis perspective. An integrated dynamic LCSA model is utilized to analyze the environmental, economic, and social life cycle impact as well as life cycle cost of alternative vehicles in the USA. System dynamics modeling is developed to simulate the US passenger transportation system and its interactions with economy, the environment, and society. Analysis covers manufacturing and operation phase impacts of internal combustion vehicles (ICVs), hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs). In total, seven macrolevel indicators are selected; global warming potential, particulate matter formation, photochemical oxidant formation, vehicle ownership cost, contribution to gross domestic product, employment generation, and human health impacts. Additionally, contribution of vehicle choices to global atmospheric temperature rise and public welfare is investigated.

Results and discussion

BEVs are found to be a better alternative for most of sustainability impact categories. While some of the benefits such as contribution to employment and GDP, CO₂ emission reduction potential of BEVs become greater toward 2050, other sustainability indicators including vehicle ownership cost and human health impacts of BEVs are higher than the other vehicle types on 2010s and 2020s. While the impact shares of manufacturing and operation phases are similar in the early years of 2010s, the contribution of manufacturing phase becomes higher as the vehicle performances increase toward 2050. Analysis results revealed that the US transportation sector, alone, cannot reduce the rapidly increasing atmospheric temperature and the negative impacts of the global climate change, even though the entire fleet is replaced with BEVs. Reducing the atmospheric climate change requires much more ambitious targets and international collaborative efforts. The use of different vehicle types has a small impact on public welfare, which is a function of income, education, and life expectancy indexes.

Conclusions

The authors strongly recommend that the dynamic complex and mutual interactions between sustainability indicators should be considered for the future LCSA framework. This approach will be critical to deepen the existing LCSA framework and to go beyond the current LCSA understanding, which provide a snapshot analysis with an isolated view of all pillars of sustainability. Overall, this research is a first empirical study and an important attempt toward developing integrated and dynamic LCSA framework for sustainable transportation research.

     

Ethanol distribution, dispensing, and use: analysis of a portion of the biomass-to-biofuels supply chain using system dynamics

The Energy Independence and Security Act of 2007 targets use of 36 billion gallons of biofuels per year by 2022. Achieving this may require substantial changes to current transportation fuel systems for distribution, dispensing, and use in vehicles. The U.S. Department of Energy and the National Renewable Energy Laboratory designed a system dynamics approach to help focus government action by determining what supply chain changes would have the greatest potential to accelerate biofuels deployment. The National Renewable Energy Laboratory developed the Biomass Scenario Model, a system dynamics model which represents the primary system effects and dependencies in the biomass-to-biofuels supply chain. The model provides a framework for developing scenarios and conducting biofuels policy analysis. This paper focuses on the downstream portion of the supply chain-represented in the distribution logistics, dispensing station, and fuel utilization, and vehicle modules of the Biomass Scenario Model. This model initially focused on ethanol, but has since been expanded to include other biofuels. Some portions of this system are represented dynamically with major interactions and feedbacks, especially those related to a dispensing station owner's decision whether to offer ethanol fuel and a consumer's choice whether to purchase that fuel. Other portions of the system are modeled with little or no dynamics; the vehicle choices of consumers are represented as discrete scenarios. This paper explores conditions needed to sustain an ethanol fuel market and identifies implications of these findings for program and policy goals. A large, economically sustainable ethanol fuel market (or other biofuel market) requires low end-user fuel price relative to gasoline and sufficient producer payment, which are difficult to achieve simultaneously. Other requirements (different for ethanol vs. other biofuel markets) include the need for infrastructure for distribution and dispensing and widespread use of high ethanol blends in flexible-fuel vehicles.     

An in silico model of enterocytic glutamine to citrulline conversion pathway

Enterocyte is one of the main sites of amino acids metabolism and particularly of the citrulline biosynthesis. Working at the cellular scale and applying ordinary differential equations (ODEs) formalism, we have built a mathematical model of the enterocytic glutamine to citrulline conversion in the fasting state. This model enables us to test different physiopathological scenarios of enzyme activity loss. Results from two different approaches were compared: a standard approach (KA) based on the Michaelis–Menten assumptions and an association–dissociation approach (VH) based on the kinetic mass action law. For both approaches, ODEs system was numerically solved using Mathematica?. In both cases, the model correctly predicts the physiological plasma citrulline steady-state, but the two approaches present clear differences for metabolites of enzymes having a complex mechanism, challenging the validity of the KA approach in such cases. When physiopathological scenarios of enzyme activity loss are simulated, both approaches predict a very sharp transition from the physiological citrulline plasma level to the lack of its production: the concentration profiles of these simulations show a clear threshold of which characteristics vary with the involved enzyme. Moreover, amongst all enzymes included in the model, the ornithine aminotransferase (OAT) shows the highest sensitivity in the system whatever the approach used. This model points out the limits of the Michaelis–Menten approach to model complex enzyme mechanisms. It highlights the key role of OAT in the studied citrulline synthesis pathway and also suggests an order of magnitude about the optimal ratio of enzyme concentrations in this pathway.     

Stability analysis and optimal control of an SIR epidemic model with vaccination

This paper focuses on the study of a nonlinear mathematical SIR epidemic model with a vaccination program. We have discussed the existence and the stability of both the disease free and endemic equilibrium. Vaccine induced reproduction number is determined and the impact of vaccination in reducing the vaccine induced reproduction number is discussed. Then to achieve control of the disease, a control problem is formulated and it is shown that an optimal control exists for our model. The optimality system is derived and solved numerically using the Runge-Kutta fourth order procedure.     

Economic Assessment of Greenhouse Gas Emissions Reduction by Vehicle Lightweighting Using Aluminum and High‐Strength Steel

The life cycle greenhouse gas (GHG) reduction benefits of vehicle lightweighting (LW) were evaluated in a companion article. This article provides an economic assessment of vehicle LW with aluminum and high‐strength steel. Relevant cost information taken from the literature is synthesized, compiled, and formed into estimates of GHG reduction costs through LW. GHG emissions associated with vehicle LW scenarios between 6% and 23% are analyzed alongside vehicle life cycle costs to achieve these LW levels. We use this information to estimate the cost to remove GHG emissions per metric ton by LW, and we further calculate the difference between added manufacturing cost and fuel cost savings from LW. The results show greater GHG savings derived from greater LW and added manufacturing costs as expected. The associated production costs are, however, disproportionately higher than the fuel cost savings associated with higher LW options. A sensitivity analysis of different vehicle classes confirms that vehicle LW is more cost‐effective for larger vehicles. Also, the cost of GHG emissions reductions through lightweighting is compared with alternative GHG emissions reduction technologies for passenger vehicles, such as diesel, hybrid, and plug‐in hybrid electric powertrains. The results find intensive LW to be a competitive and complementary approach relative to the technological alternatives within the automotive industry but more costly than GHG mitigation strategies available to other industries.     

Diffusion-based learning theory for organizing visuo-motor coordination

A diffusion-based learning theory is presented and applied to organize the visuomotor coordination of an eye-hand system which has redundant motion degree of freedom (dof). This theory considers the spatial optimality of the coordination: to minimize the end-effector position error of the eye-hand system as well as the differentiation of the joint angles with respect to the end-effector positions over all the bounded work space. By introducing variational methods with respect to the space, we derive a partial differential equation (PDE) of the joint angles with respect to the work space. The equation includes a diffusion term. For the given boundary conditions and the initial conditions, it can be solved uniquely, and the solution is a well organized map. From the motor learning point of view, our approach contains both the aspects of supervised learning as well as self-organization. Firstly, we assume that the forward relation from the hand system's joint angles to its end-effector positions can be obtained using supervised learning, and at the boundary of the work space, the supervisor can provide correct joint information. Then, by evolving the diffusion equation, we organize the visuomotor coordination. We show the effectiveness of this approach using a 3-dof scale manipulator. The problems of how to realize the visuomotor map; how to utilize the resultant map in several motions; and what are the influences of the initial conditions on the map formation and the relation to the boundary conditions are also discussed using computer simulations. Our approach has three advantages: (1) it does not require too many trial motions for the eye-hand system; (2) during the map formation process, it requires only the local interactions between each node; and (3) it guarantees the final map's spatial optimality over all the bounded work space. Received: 8 May 1997 / Accepted in revised form: 12 June 1998     

A variational approach to behavioral and neuroelectrical laws

Variational methods play a fundamental and unifying role in several fields of physics, chemistry, engineering, economics, and biology, as they allow one to derive the behavior of a system as a consequence of an optimality principle. A possible application of these methods to a model of perception is given by considering a psychophysical law as the solution of an Euler-Lagrange equation. A general class of Lagrangians is identified by requiring the measurability of prothetic continua on interval scales. The associated Hamiltonian (the energy of the process) is tentatively connected with neurophysiological aspects. As an example of the suggested approach a particular choice of the Lagrangian, that is a sufficient condition to obtain classical psychophysical laws, while accounting for psychophysical adaptation and the stationarity of neuronal activity, is used to explore a possible relation between a behavioral law and a neuroelectrical ,response based on the Naka-Rushton model.     

Efficient Optimization of Stimuli for Model-Based Design of Experiments to Resolve Dynamical Uncertainty

This model-based design of experiments (MBDOE) method determines the input magnitudes of an experimental stimuli to apply and the associated measurements that should be taken to optimally constrain the uncertain dynamics of a biological system under study. The ideal global solution for this experiment design problem is generally computationally intractable because of parametric uncertainties in the mathematical model of the biological system. Others have addressed this issue by limiting the solution to a local estimate of the model parameters. Here we present an approach that is independent of the local parameter constraint. This approach is made computationally efficient and tractable by the use of: (1) sparse grid interpolation that approximates the biological system dynamics, (2) representative parameters that uniformly represent the data-consistent dynamical space, and (3) probability weights of the represented experimentally distinguishable dynamics. Our approach identifies data-consistent representative parameters using sparse grid interpolants, constructs the optimal input sequence from a greedy search, and defines the associated optimal measurements using a scenario tree. We explore the optimality of this MBDOE algorithm using a 3-dimensional Hes1 model and a 19-dimensional T-cell receptor model. The 19-dimensional T-cell model also demonstrates the MBDOE algorithm’s scalability to higher dimensions. In both cases, the dynamical uncertainty region that bounds the trajectories of the target system states were reduced by as much as 86% and 99% respectively after completing the designed experiments in silico. Our results suggest that for resolving dynamical uncertainty, the ability to design an input sequence paired with its associated measurements is particularly important when limited by the number of measurements.     

Bayesian Analysis Using a Simple Likelihood Model Outperforms Parsimony for Estimation of Phylogeny from Discrete Morphological Data

Despite the introduction of likelihood-based methods for estimating phylogenetic trees from phenotypic data, parsimony remains the most widely-used optimality criterion for building trees from discrete morphological data. However, it has been known for decades that there are regions of solution space in which parsimony is a poor estimator of tree topology. Numerous software implementations of likelihood-based models for the estimation of phylogeny from discrete morphological data exist, especially for the Mk model of discrete character evolution. Here we explore the efficacy of Bayesian estimation of phylogeny, using the Mk model, under conditions that are commonly encountered in paleontological studies. Using simulated data, we describe the relative performances of parsimony and the Mk model under a range of realistic conditions that include common scenarios of missing data and rate heterogeneity.     

Estimation of Part Waiting Time and Fleet Sizing in AGV Systems

As manufacturing systems have grown in size and complexity, material flow control has become one of the key issues for system efficiency, and determination of the number of vehicles required is an important issue in the design of the AGV (automatic guided vehicle) systems for automated material flow control. In an AGV system, a part issues a delivery request for its transportation, and then an empty vehicle is assigned based on a pre-determined vehicle selection rule and provides delivery service.This research presents a fleet sizing procedure for an AGV system with multiple pickup and delivery stations. A queueing model is used to estimate part waiting times. The fleet sizing procedure estimates the minimum number of vehicles needed to ensure a predefined part waiting time limit. While most stochastic models assume first-come-first-served or random vehicle selection rules for the selection of an empty vehicle, this model considers such additional rules as the nearest vehicle selection rule, which is the most popular among all vehicle selection rules. The performance of the proposed model is examined through computational experiments.     

Optimal HIV treatment by maximising immune response

We present an optimal control model of drug treatment of the human immunodeficiency virus (HIV). Our model is based upon ordinary differential equations that describe the interaction between HIV and the specific immune response as measured by levels of natural killer cells. We establish stability results for the model. We approach the treatment problem by posing it as an optimal control problem in which we maximise the benefit based on levels of healthy CD4+ T cells and immune response cells, less the systemic cost of chemotherapy. We completely characterise the optimal control and compute a numerical solution of the optimality system via analytic continuation.Research supported by the Natural Science and Engineering Research Council (NSERC) and the Mathematics of Information Technology and Complex Systems (MITACS) of Canada