A numerical filtering method for linear state-space models with Markov switching

A class of discrete-time random processes arising in engineering and econometrics applications consists of a linear state-space model whose parameters are modulated by the state of a finite-state Markov chain. Typical filtering approaches are collapsing methods, which approximate filtered distributions by mixtures of Gaussians, each Gaussian corresponding to one possibility of the recent history of the Markov chain, and particle methods. This article presents an alternative approach to filtering these processes based on keeping track of the values of the underlying filtered density and its characteristic function on grids. We prove that it has favorable convergence properties under certain assumptions. On the other hand, as a grid method, it suffers from the curse of dimensionality, and so is only suitable for low-dimensional systems. We compare our method to collapsing filters and a particle filter with examples, and find that it can outperform them on 1- and 2-dimensional problems, but loses its speed advantage on 3-dimensional systems. Meanwhile, our method has a proven theoretical convergence rate that is probably not achieved by collapsing and particle methods.     

亚磷酸酯功能化聚硅氧烷扩链聚乳酸的阻燃性能研究

将聚氨基环氧基硅氧烷（PSQ）以及亚磷酸酯功能化的聚氨基环氧基硅氧烷（PPSQ）与聚乳酸（PLA）通过熔融共混制备了扩链PLA。研究了亚磷酸酯基团的引入对PSQ在PLA中的扩链和阻燃性能的影响，探究PPSQ在PLA中的阻燃机理。结果表明，相比于PSQ，PPSQ可以明显提高PLA的分子量和复合黏度，具有更好的扩链作用；PPSQ的引入可以明显改善PLA的阻燃性能，使得PLA?PPSQ的极限氧指数提高至25.4 %，比纯PLA提升了31.6 %，且使得PLA的热释放速率峰值和总热释放量分别下降了18.1 %和16.6 %。分析可知，由于亚磷酸酯基团的引入，促使PLA?PPSQ中的硅元素在燃烧过程中向炭层表面迁移形成更多富含碳化硅和氧化硅的致密炭层，起到隔热隔氧的作用，进而发挥优异的阻燃性能。     

Irradiated Ch/GG/PVP-based stimuli-responsive hydrogels for controlled drug release

Hydrogels based on gamma (γ) irradiated chitosan (pre-irradiated), guar gum, and polyvinyl pyrrolidone were crosslinked with various concentrations of (3-mercapto propyl)trimethoxysilane and fabricated by solution casting technique for the drug delivery applications. High molecular weight chitosan (Ch) possesses lower solubility and higher viscosity, these problems overcame by γ irradiation, which also generated hydrophilicity and effect of irradiated Ch on controlled drug release was assessed. FTIR analysis showed the development of chemical and physical interactions and confirmed the incorporation of characteristic peaks. SEM micrographs revealed porous structure of the prepared hydrogels. Swelling analysis of the hydrogels was performed in distilled water, buffer, and electrolyte mediums. All the hydrogel samples showed higher swelling at acidic pH and lower swelling at neutral and basic pH. These pH-responsive characteristics made these RCGP hydrogels an important contender for injectable controlled drug release. The ampicillin sodium drug was loaded and in vitro controlled release mechanism was evaluated in the PBS, SIF, and SGF which shown out of all prepared hydrogels (RCGP-1, RCGP-2, and RCGP-3), RCGP-1 has exhibited 87.4% release in PBS and 81.3% in SIF in 180 min.     

Selecting supplier combination based on fuzzy multicriteria analysis

Existing multicriteria analysis (MCA) methods are probably ineffective in selecting a supplier combination. Thus, an MCA-based fuzzy 0-1 programming method is introduced. The programming relates to a simple MCA matrix that is used to select a single supplier. By solving the programming, the most feasible combination of suppliers is selected. Importantly, this result differs from selecting suppliers one by one according to a single-selection order, which is used to rank sole suppliers in existing MCA methods. An example highlights such difference and illustrates the proposed method.     

Quantitative analysis of semiconductor supply chain contracts with order flexibility under demand uncertainty: A case study

The evidence base for the configuration of rolling horizon flexibility (RHF) contracts (a type of quantity flexibility contract) used in the semiconductor industry to coordinate production and demand remains meagre, more art than science. Informed by the characteristics of actual clauses and demand behaviors drawn from a company’s experience, a discrete-event simulation model is developed to represent the company’s supply chain. It comprises of three parties: a customer, a supplier (semiconductor manufacturer), and a capacity provider. Through analysis of customer forecasted demand the paper characterizes forecast demand as being under, over or unbiased. Models of these forecasted demands drives both long and short term planning. In long term planning, which is given twelve months before an order is delivered, capacity at the capacity provider is booked. Short term planning is also driven by this forecast which, within a binding period, is governed by an RHF contract. Results from the model report inventory levels, and delivery compliance, namely Delivery Performance (DP) and Delivery Reliability (DR), measures widely used in this sector. It is concluded from this work that on the balance of performance measures RHF contracts with asymmetrical flexibility bounds are substantially better than those with symmetrical boundaries, and that this conclusion is robust with regard to both over-planning and under-planning behaviors. This robustness is a critical attribute with respect to the endemic medium-term vacillation between both states experienced in practice in this sector.     

Theoretical framework for formulating MIP scheduling models with multiple and non-uniform discrete-time grids

We present a framework for the formulation of MIP scheduling models based on multiple and nonuniform discrete time grids. In a previous work we showed that it is possible to use different (possibly non-uniform) time grids for each task, unit, and material. Here, we generalize these ideas to account for general resources, and a range of processing characteristics such as limited intermediate storage and changeovers. Each resource has its own grid based on resource consumption and availability allowing resource constraints to be modeled more accurately without increasing the number of binary variables. We develop algorithms to define the unit-, task-, material-, and resource-specific grids directly from problem data. Importantly, we prove that the multi-grid formulation is able to find a schedule with the same optimal objective as the discrete-time single-grid model with an arbitrarily fine grid. The proposed framework leads to the formulation of models with reduced number of binary variables and constraints, which are able to find good solutions faster than existing models.     

Strategic planning optimization for natural gas to liquid transportation fuel (GTL) systems

A strategic planning optimization model is proposed for a network of natural gas to liquids (GTL) systems, and it is solved using a rolling horizon strategy. The model formulation determines the strategic and tactical decisions of the GTL supply chain over a long time horizon. The decisions to build new GTL refineries may be made over the span of 30 years and their operations cover the span of 60 years. Multiple capacities of GTL refineries (i.e., 1, 5, 10, 50, and 200 thousand barrels per day) that produce gasoline, diesel, and kerosene commensurate to the United States demand ratio may exist in the network. The parameter inputs include the locations, availabilities, and prices of natural gas in the United States discretized by county, the delivery locations of fuel products, and the transportation costs of every input and output of the refinery, defined for each time period. Formulated as a large-scale mixed-integer linear optimization (MILP) model, the problem is solved using a rolling horizon strategy for tractability. Case studies on the state of Pennsylvania are presented for different planning schemes and their impact on the economic performance of the GTL network is discussed.     

Supply chain planning and scheduling integration using Lagrangian decomposition in a knowledge management environment

The integration of planning and scheduling decisions in rigorous mathematical models usually results in large scale problems. In order to tackle the problem complexity, decomposition techniques based on duality and information flows between a master and a set of subproblems are widely applied. In this sense, ontologies improve information sharing and communication in enterprises and can even represent holistic mathematical models facilitating the use of analytic tools and providing higher flexibility for model building. In this work, we exploit this ontologies’ capability to address the optimal integration of planning and scheduling using a Lagrangian decomposition approach. Scheduling/planning sub-problems are created for each facility/supply chain entity and their dual solution information is shared by means of the ontological framework. Two case studies based on a STN representation of supply chain planning and scheduling models are presented to emphasize the advantages and limitations of the proposed approach.     

A multi-agent architecture for outsourcing SMEs manufacturing supply chain

In the present, global and competitive market customer's demands are very volatile. It is very difficult for small and medium size enterprises (SMEs) to satisfy their customer with quality product in reasonable price. Large firms are spending huge amount of their budget in generating technology development. To cope with this competitive market, usually, SMEs concentrate on their core expertise and outsource some non-core items or activities. The outsourcing decision (when, what, whom) is very crucial for small-scale industries. Realizing this scenario, in this article, an automated self-adaptive multi-agent system has been proposed. The proposed system will help SMEs to take an appropriate decision to mitigate the uncertainty in supply chain. The developed system exploits the “Outsourcing of operations” feature by its agents to conclude the manufacturing processes faster and reduce the idle time of certain machines with less human intervention. The communication protocol for agents has been described to give an insight of their communication. It was explained how various agents like outsourcing, material planning and supplier selection will come into action at different stages and take appropriate outsourcing decisions. Therefore, this multi-agent architecture will facilitate small scale manufacturing industries to execute their manufacturing processes and complex logistics issues efficiently. The execution of proposed architecture has been described in the simulated case study.     

A closed loop supply chain under retail price and product quality dependent demand

The importance of remanufacturing of used products, maintaining the same quality as new ones has been widely recognized in practice. In this paper, we address a closed loop supply chain where the manufacturer manufactures a product with a decent quality acceptable to customers, and sells it through a retailer in the market. A third party collects the used products from consumers and sends to the manufacturer. The manufacturer re-manufactures the used product and the quality of remanufactured product is the same as that of the manufactured one. For such a closed loop supply chain, we construct five different scenarios – centralized, decentralized or Nash game and three different Stackelberg games, led by the manufacturer, retailer and third party respectively. We compare the optimal results to find out the best decentralized scenario out of the four mentioned. A numerical example is taken to study the impacts of product quality on demand as well as optimal decisions and investigate sensitivity of some key model-parameters.