Characterizing Uncertainty in Correlated Response Variables for Pareto Front Optimization

Current research provides a method to incorporate uncertainty into Pareto front optimization by simulating additional response surface model parameters according to a Multivariate Normal Distribution (MVN). This research shows that analogous to the univariate case, the MVN understates uncertainty, leading to overconfident conclusions when variance is not known and there are few observations (less than 25-30 per response). This research builds upon current methods using simulated response surface model parameters that are distributed according to an Multivariate t-Distribution (MVT), which can be shown to produce a more accurate inference when variance is not known. The MVT better addresses uncertainty in the parameters which can affect the frequency of treatments appearing on the Pareto front resulting in potentially different proposed solution spaces from that of the MVN

Calibrating and Evaluating Dynamic Rule-Based Transit-Signal-Priority Control Systems in Urban Traffic Networks

Setting the traffic controller parameters to perform effectively in real-time is a challenging task, and it entails setting several parameters to best suit some predicted traffic conditions. This study presents the framework and method that entail the application of the Response Surface Methodology (RSM) to calibrate the parameters of any control system incorporating advanced traffic management strategies (e.g., the complex integrated traffic control system developed by Ahmed and Hawas). The integrated system is a rule-based heuristic controller that reacts to specific triggering conditions, such as identification of priority transit vehicle, downstream signal congestion, and incidents by penalizing the predefined objective function with a set of parameters corresponding to these conditions. The integrated system provides real time control of actuated signalized intersections with different phase arrangements (split, protected and dual). The premise of the RSM is its ability to handle either single or multiple objective functions; some of which may be contradicting to each other. For instance, maximizing transit trips in a typical transit priority system may affect the overall network travel time. The challenging task is to satisfy the requirements of transit and non-transit vehicles simultaneously. The RSM calibrates the parameters of the integrated system by selecting the values that can produce optimal measures of effectiveness. The control system was calibrated using extensive simulation-based analyses under high and very high traffic demand scenario for the split, protected, and dual control types. A simulation-based approach that entailed the use of the popular TSIS software with code scripts representing the logic of the integrated control system was used. The simulation environment was utilized to generate the data needed to carry on the RSM analysis and calibrate the models. The RSM was used to identify the optimal parameter settings for each control type and traffic demand level. It was also used to determine the most influential parameters on the objective function(s) and to develop models of the significant parameters as well as their interactions on the overall network performance measures. RSM uses the so-called composite desirability value as well as the simultaneous multi-objective desirabilities (e.g., the desirability of maximizing the transit vehicles throughput and minimizing the average vehicular travel time) estimates of the responses to identify the best parameters. This study also demonstrated how to develop “mathematical” models for rough estimation of the performance measures vis-à-vis the various parameter values, including how to validate the optimal settings. The calibrated models are proven to be significant. The optimal parameters of each control type and demand level were also checked for robustness, and whether a universal set of relative parameter values can be used for each control type. For the high traffic demand level, the optimal set of parameters is more robust than those of the very high traffic demand. Besides, the dual actuated controller optimal setting under the very high traffic demand scenario is more robust (than other control types settings) and shows the best performance

Cassava Biomaterial Innovations for Industry Applications

Breakthrough innovations can spur growth in the modern era industry to realise sustainability and high returns on investments. Nowadays, biobased innovations for application in diverse industry sectors are considered as future pillars to counter resource depletion and ensure positive environmental impacts. Cassava is a strong flagship biomaterial promoting solution for resource-efficient use and green environment. Innovative industrial application of cassava biomaterials enriches literature, presenting cassava as a versatile and unrivalled crop that is cardinal for more sustainable environment and biodegradable industrial products. Work on novel cassava biomaterials, which are low-cost, unexploited and with zero competition for food supply, are included. Using an integrated sustainable process, it shows how to indirectly reduce waste streams, through their effective use, guaranteeing zero carbon footprints and acting as a non-traditional strategy for equilibrium atmosphere and active packaging systems. Applications of Cassava biomaterial in food, as food supplements and in packaging systems are also covered in this chapter

Evaluation of novel bitter cassava film for equilibrium modified atmosphere packaging of cherry tomatoes

This is a research article on Equilibrium modified atmosphere packaging (EMAP) technology that offers the possibility to maintain produce postharvest quality and extend its shelf-life.Equilibrium modified atmosphere packaging (EMAP) technology offers the possibility to maintain produce postharvest quality and extend its shelf-life. However, EMAP stability depends on well-tuned packaging design parameters to match environmental conditions. This study defined the design requirements of a biobased film EMAP that can preserve the quality and prolong the shelf-life of fresh cherry tomatoes under recommended and simulated abuse supply chain conditions. Optimum EMAP was evaluated based on headspace gas composition at 10–20 °C, 75–95% RH and verified by determining quality changes of packed cherry tomatoes in using a continuous or micro-perforated (0.27 μm) bio-based intact bitter cassava (IBC) film. This was compared with a non-bio-based polymer film (oriented polypropylene, OPP). The IBC film attained equilibrium O2 (2–3%) after 180 h at 10 °C, with 0 and 1 perforation, for 75 and 95% RH while OPP film maintained a downward O2 fall. Continuous and micro-perforated IBC film did not show any major differences in equilibrium headspace O2, thus perforation can be neglected. Based on desirability optimisation results, biobased IBC film demonstrated a better optimized EMAP system in attaining recommended gas and stretching cherry tomato shelf-life as compared to non-biobased (OPP) film. The application of bio-based IBC film offers new possibilities in packaging fresh produce under an equilibrium modified atmosphere without compromising its quality

Coupling soft computing, simulation and optimization in supply chain applications : review and taxonomy

Supply chain networks are typical examples of complex systems. Thereby, making decisions in such systems remains a very hard issue. To assist decision makers in formulating the appropriate strategies, robust tools are needed. Pure optimization models are not appropriate for several reasons. First, an optimization model cannot capture the dynamic behavior of a complex system. Furthermore, most common practical problems are very constrained to be modeled as simple tractable models. To fill in the gap, hybrid optimization/simulation techniques have been applied to improve the decision-making process. In this paper we explore the near-full spectrum of optimization methods and simulation techniques. A review and taxonomy were performed to give an overview of the broad field of optimization/simulation approaches applied to solve supply chain problems. Since the possibilities of coupling them are numerous, we launch a discussion and analysis that aims at determining the appropriate framework for the studied problem depending on its characteristics. Our study may serve as a guide for researchers and practitioners to select the suitable technique to solve a problem and/or to identify the promising issues to be further explored

Experimental Investigation and Optimization of Cutting Parameters in Plasma Arc Cutting

Experimental investigation of plasma arc cutting has been carried out using AISI 4140 and AISI 304 stainless steel as work-piece. The process parameters were considered as follows: feed rate, cutting current, cutting speed, gas pressure, voltage and torch height. The response parameters were chosen as follows: material removal rate (MRR), surface roughness (SR), right bevel angle (RBA), chamfer, dross, kerf width and heat affected zone (HAZ) which are the main cut quality characteristics of plasma arc cutting operation. The optimization of the process parameters have been carried out using desirability function, grey based principal component analysis (PCA) hybrid approach, genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA) and teaching-learning-based-optimization (TLBO) algorithm coupled with response surface methodology (RSM). A regression model was developed that represents the relationship between independent and dependent variables based on RSM. This type of novel approach has been proposed to evaluate and estimate the influence of plasma arc machining parameters on the quality of cut. This user-friendly mathematical approach is straight forward and the results thus obtained have also been validated by running confirmatory tests. The premise attributes provide beneficial knowledge for managing the machining parameters to enhance the preciseness of machined parts by plasma arc cutting. The obtained results indicate that the TLBO approach was significantly affected by the machining parameters directly with easy operability and economically

Multi-response optimization of magnetic field assisted EDM through desirability function using response surface methodology

Magnetic field assisted electrical discharge machining (MFAEDM) is the modification of in conventional EDM process by use of magnetic field on EN-31. This article explain the application of response surface methodology to analyzes the effect of various process parameters such as Ton, Toff and Ip on performance measures such as material removal rate (MRR), electrode wear rate (EWR) and overcut (OC). Analysis of variance was used to check the adequacy of response surface model and significance of process parameters on performance measures. Multi-objective desirability function has been applied to obtain the optimal process parameter settings. Thereafter, machined surface of EN-31 characterized through SEM and EDX. The novelty of this paper is to improve the strategies for flushing the debris which remain clogged in standard EDM in-between machining gap that will interrupts the regular discharge conditions and reduces cutting rate as well as deteriorate the surface characteristics