Using relatively simple and widely used models, we illustrate how carbon emission concerns could be integrated into operational decision-making with regard to procurement, production, and inventory management. We show how, by associating carbon emission parameters with various decision variables, traditional models can be modified to support decision-making that accounts for both cost and carbon footprint. We examine how the values of these parameters as well as the parameters of regulatory emission control policies affect cost and emissions. We use the models to study the extent to which carbon reduction requirements can be addressed by operational adjustments, as an alternative (or a supplement) to costly investments in carbon-reducing technologies. We also use the models to investigate the impact of collaboration among firms within the same supply chain on their costs and carbon emissions and study the incentives firms might have in seeking such cooperation. We provide a series of insights that highlight the impact of operational decisions on carbon emissions and the importance of operational models in evaluating the impact of different regulatory policies and in assessing the benefits of investments in more carbon efficient technologies. Note to Practitioners-Firms worldwide, responding to the threat of government legislation or to concerns raised by their own consumers or shareholders, are undertaking initiatives to reduce their carbon footprint. It is the conventional thinking that such initiatives will require either capital investments or a switch to more expensive sources of energy or input material. In this paper, we show that firms could effectively reduce their carbon emissions without significantly increasing their costs by making only operational adjustments and by collaborating with other members of their supply chain. We describe optimization models that can be used by firms to support operational decision making and supply chain collaboration, while taking into account carbon emissions. We analyze the effect of different emission regulations, including strict emission caps, taxes on emissions, cap-and-offset, and cap-and-trade, on supply chain management decisions. In particular, we show that the presence of emission regulation can significantly increase the value of supply chain collaboration.

https://www.sutd.edu.sg/cmsresource/idc/papers/2013_Carbon_Footprint_and_the_Management_of_Supply_Chains_Insights_From_Simple_Models.pdf

Carbon Footprint and the Management of Supply Chains: Insights From Simple Models

This paper addresses the deadlock problems in flexible manufacturing systems (FMS) by using a Petri net siphon control method and the theory of regions. The proposed policy consists of two stages. The first one, called siphons control, is to add, for every siphon that we identify, a monitor to the original net model such that it is optimally invariant controlled. In the second stage, the theory of regions is utilized to derive the net supervisors such that deadlocks can be prevented. The first-stage work significantly lowers the computational cost compared with the approach where the theory of regions is used alone. An FMS example is presented to illustrate the technique. By varying the markings of given net structures, this paper shows its computational advantages.

A Maximally Permissive Deadlock Prevention Policy for FMS Based on Petri Net Siphon Control and the Theory of Regions

Automatic Web service composition is gaining momentum as the potential silver bullet in service oriented architecture. The need for interservice compatibility analysis and indirect composition has gone beyond what the existing service composition/verification technologies can handle. Given two services whose interface invocation constraints are described by a Web services-business process execution language (WS-BPEL or BPEL), we analyze their compatibility and adopt mediation as a lightweight approach to make them compatible without changing their internal logic. We first transform a BPEL description into a service workflow net, which is a kind of colored Petri net (CPN). Based on this formalism, we analyze the compatibility of two services, and then devise an approach to check whether there exists any message mediator so that their composition does not violate the constraints imposed by either side. The method for mediator generation is finally proposed to assist the automatic composition of partially compatible services. Our approach is validated through a real-life case and further research directions are pointed out.

A Petri Net-Based Method for Compatibility Analysis and Composition of Web Services in Business Process Execution Language

Because of residency time constraints and activity time variation of cluster tools, it is very difficult to operate such integrated semiconductor manufacturing equipment. This paper addresses their real-time operational issues. To characterize their schedulability and achieve the minimum cycle time at their steady-state operation, Petri net (PN) models are developed to describe them, which are very compact, and independent of wafer flow pattern. It is due to the proposed models that scheduling cluster tools is converted into determining robot wait times. A two-level operational architecture is proposed to include an offline periodic schedule and real-time controller. The former determines when a wafer should be placed into a process module for processing, while the latter regulates robot wait times online in order to reduce the effect of activity time variation on wafer sojourn times in process modules. Therefore, the system can adapt to activity time variation. A cluster tool derived as a not-always-schedulable system by the existing methods is shown to be always-schedulable by using the proposed novel method.

Modeling, Analysis and Control of Dual-Arm Cluster Tools With Residency Time Constraint and Activity Time Variation Based on Petri Nets

The camera provides rich visual information regarding objects and becomes one of the most mainstream sensors in the automation community. However, it is often difficult to be applicable when the objects are not visually distinguished. On the other hand, tactile sensors can be used to capture multiple object properties, such as textures, roughness, spatial features, compliance, and friction, and therefore provide another important modality for the perception. Nevertheless, effective combination of the visual and tactile modalities is still a challenging problem. In this paper, we develop a visual–tactile fusion framework for object recognition tasks. This paper uses the multivariate-time-series model to represent the tactile sequence and the covariance descriptor to characterize the image. Further, we design a joint group kernel sparse coding (JGKSC) method to tackle the intrinsically weak pairing problem in visual–tactile data samples. Finally, we develop a visual–tactile data set, composed of 18 household objects for validation. The experimental results show that considering both visual and tactile inputs is beneficial and the proposed method indeed provides an effective strategy for fusion.

Martin Fabian

Papers

Ieee transactions on automation science and engineering