Evolutionary multi-stage financial scenario tree generation

Multi-stage financial decision optimization under uncertainty depends on a careful numerical approximation of the underlying stochastic process, which describes the future returns of the selected assets or asset categories. Various approaches towards an optimal generation of discrete-time, discrete-state approximations (represented as scenario trees) have been suggested in the literature. In this paper, a new evolutionary algorithm to create scenario trees for multi-stage financial optimization models will be presented. Numerical results and implementation details conclude the paper

Sensitivity of multi-product two-stage economic lotsizing models and their dependency on change-over and product cost ratio's

This study considers the production and inventory management problem of a two-stage semi-process production system. In case both production stages are physically connected it is obvious that materials are forced to flow. The economic lotsize depends on the holding cost of the end-product and the combined change-over cost of both production stages. On the other hand this 'flow shop' is forced to produce at the speed of the slowest stage. The benefit of this approach is the low amount of Work In Process inventory. When on the other hand, the involved stages are physically disconnected, a stock of intermediates acts as a decoupling point. Typically for the semi-process industry are high change-over costs for the process oriented first stage, which results in large lotsize differences for the different production stages. Using the stock of intermediates as a decoupling point avoids the complexity of synchronising operations but is an additional reason to augment the intermediate stock position. The disadvantage of this model is the high amount of Work-In-Process inventory. This paper proposes the 'synchronised planning model' realising a global optimum instead of the combination of two locally optimised settings. The mathematical model proves (for a two-stage single-product setting) that the optimal two-stage production frequency corresponds with the single EOQ solution for the first stage. A sensitivity study reveals, within these two-stage lotsizing models, the economical cost dependency on product and change-over cost ratio‟s. The purpose of this paper is to understand under which conditions the „joined setup‟ or the „two-stage individual eoq model‟ remain close to the optimal model. Numerical examples prove that the conclusions about the optimal settings remain valid when extending the model to a two-stage multi-product setting. The research reveals that two-stage individually optimized EOQ lotsizing should only be used when the end-product stage has a high added value and small change-over costs, compared to the first stage. Physically connected operations should be used when the end-product stage has a small added value and low change-over costs, or high added value and large change-over costs compared to the first production stage. The paper concludes with suggesting a practical common cycle approach to tackle a two-stage multi-product production and inventory management problem. The common cycle approach brings the benefit of a repetitive and predictable production schedule

A Novel Energy Efficient Adsorption Drying with Zeolite For Food Quality Product: A Case Study in Paddy and Corn Drying

Nowadays, the importance of powdered food products as for example soups, sauces, dried yeasts, and herbal medicine is increasing for consumer convenience. Mostly, these products have been produced with drying process either, direct sunlight, conventional, or modern dryer. The direct sunlight dryer depends on the daily weather extremely both in the product drought and process continuity. Meanwhile,conventional dryer results high energy consumption as well as low product quality due to the introduction of hot air. In addition, modern dryer process can improve the product quality, but the energy efficiency was fair. This paper discusses the design and application of adsorption dryer with zeolite for food. Here, the air as drying medium was dehumidified by zeolite to enhance the driving force. Thus, the drying can be well conducted in low or medium temperature. The dryer was designed in single and multi stage system. Result showed that energy efficiency of single stage dryer was 70 - 72% (10% higher than that of conventional dryer). While in multi stage, the energy efficiency can reach 80% (for two stage) and 90% (for three stage). In corn and paddy drying, the dryer with zeolite can speed up drying time and retain the nutrition and physical quality

Throughput and Collision Analysis of Multi-Channel Multi-Stage Spectrum Sensing Algorithms

Multi-stage sensing is a novel concept that refers to a general class of spectrum sensing algorithms that divide the sensing process into a number of sequential stages. The number of sensing stages and the sensing technique per stage can be used to optimize performance with respect to secondary user throughput and the collision probability between primary and secondary users. So far, the impact of multi-stage sensing on network throughput and collision probability for a realistic network model is relatively unexplored. Therefore, we present the first analytical framework which enables performance evaluation of different multi-channel multi-stage spectrum sensing algorithms for Opportunistic Spectrum Access networks. The contribution of our work lies in studying the effect of the following parameters on performance: number of sensing stages, physical layer sensing techniques and durations per each stage, single and parallel channel sensing and access, number of available channels, primary and secondary user traffic, buffering of incoming secondary user traffic, as well as MAC layer sensing algorithms. Analyzed performance metrics include the average secondary user throughput and the average collision probability between primary and secondary users. Our results show that when the probability of primary user mis-detection is constrained, the performance of multi-stage sensing is, in most cases, superior to the single stage sensing counterpart. Besides, prolonged channel observation at the first stage of sensing decreases the collision probability considerably, while keeping the throughput at an acceptable level. Finally, in realistic primary user traffic scenarios, using two stages of sensing provides a good balance between secondary users throughput and collision probability while meeting successful detection constraints subjected by Opportunistic Spectrum Access communication

Synchronous and Sequential Strategies in the Process Design of Cascade Equipment

Cascade or multistage equipment is characterized by the repetition of similar equipment elements in series. Process design, resulting into the main geometric and kinematic dimensions of the equipment, makes use of different strategies. These strategies, based on a process description, the (equality- and inequality) constraints and the number of degrees of freedom of the mathematical system, which describes the process, can be divided in synchronous- and sequential procedures. In a synchronous strategy no a priori requirements are made as to the distribution of a given process variable over the stages, so that the equipment dimensions are obtained simultaneously. In contrast to this a sequential strategy makes use of a priori statements resulting into stage-to-stage calculations and a decreasing number of degrees of freedom. The general theory presented with detailed information on process description, constraints and degrees of freedom, has been applied to the process design of a multi-stage centrifugal compressor

On Backstops and Boomerangs: Environmental R&D under Technological Uncertainty

The literature on environmental R&D frequently studies innovation as a two-stage process, with a single R&D event leading from a conventional polluting technology to a perfectly clean backstop. We allow for uncertainty in innovation in that the new technology may turn out to generate a new pollution problem. R&D may therefore be optimally undertaken more than once. Using and externding recent results from multi-stage optimal control theory, we provide a full characterization of the optimal pollution and R&D policies. The optimal R&D program is strictly sequential and has an endogenous stopping point. Uncertainty drives total R&D effort and its timing.stock pollution, backstop technology, multi-stage optimal control, pollution thresholds, uncertainty

Fuzzy Interval-Valued Multi Criteria Based Decision Making for Ranking Features in Multi-Modal 3D Face Recognition

Soodamani Ramalingam, 'Fuzzy interval-valued multi criteria based decision making for ranking features in multi-modal 3D face recognition', Fuzzy Sets and Systems, In Press version available online 13 June 2017. This is an Open Access paper, made available under the Creative Commons license CC BY 4.0 https://creativecommons.org/licenses/by/4.0/This paper describes an application of multi-criteria decision making (MCDM) for multi-modal fusion of features in a 3D face recognition system. A decision making process is outlined that is based on the performance of multi-modal features in a face recognition task involving a set of 3D face databases. In particular, the fuzzy interval valued MCDM technique called TOPSIS is applied for ranking and deciding on the best choice of multi-modal features at the decision stage. It provides a formal mechanism of benchmarking their performances against a set of criteria. The technique demonstrates its ability in scaling up the multi-modal features.Peer reviewedProo