Multiple cycle economic lot and delivery-scheduling problem in a two-echelon supply chain

This paper addresses the problem of lot sizing, scheduling, and delivery of several items in a two-echelon supply chain over a finite planning horizon. Single supplier produces the items through a flexible flow line and delivers them directly to an assembly facility where the transfer of sub-lots between adjacent stages of supplier’s production system (i.e., lot streaming) is allowed in order to decrease the manufacturing lead time. At first, a mixed zero-one nonlinear programming model is developed based on the so-called basic period (BP) approach aiming to minimize the average setup, inventory holding, and delivery costs per unit time in the supply chain without any stock-out. The problem is very complex and cannot be solved to optimality especially for real-sized problems. Therefore, two efficient hybrid genetic algorithms (HGA) are proposed based on the power-of-two (PTHGA) and non-power-of-two (NPTHGA) variants of BP approach. The solution qualities of the proposed algorithms are compared with a proposed lower bound. Numerical experiments demonstrate that the NPTHGA outperforms the PTHGA algorithm with respect to the solution quality, but the PTHGA outperforms the NPTHGA with respect to the computation time.     

Predication of discharge coefficient of cylindrical weir-gate using adaptive neuro fuzzy inference systems (ANFIS)

Settlement of sediments behind weirs and accumulation of materials floating on water behind gates decreases the performance of these structures. Weir-gate is a combination of weir and gate structures which solves them Infirmities. Proposing a circular shape for crest of weirs to improve their performance, investigators have proposed cylindrical shape to improve the performance of weir-gate structure and call it cylindrical weir-gate. In this research, discharge coefficient of weir-gate was predicated using adaptive neuro fuzzy inference systems (ANFIS). To compare the performance of ANFIS with other types of soft computing techniques, multilayer perceptron neural network (MLP) was prepared as well. Results of MLP and ANFIS showed that both models have high ability for modeling and predicting discharge coefficient; however, ANFIS is a bit more accurate. The sensitivity analysis of MLP and ANFIS showed that Froude number of flow at upstream of weir and ratio of gate opening height to the diameter of weir are the most effective parameters on discharge coefficient.     

Not Waving,Drowning: Can Local Government Policies on Climate Change Adaptation and Disaster Resilience Make a Difference?

Climate change will increase the intensity, duration and/or frequency of some climate-related hazards. Responsibility for adapting to such impacts of climate change in Australia has, in the main, fallen on local governments which have paid varying degrees of attention to the issue. This paper takes an integrated approach to compare the climate adaptation and disaster resilience policies and plans of local governments of two low-lying coastal cities in Australia to understand whether (and how) local governments can make a difference. The findings indicate that local governments can significantly contribute to building resilience and adapting to climate-related hazards, however a number of factors such as the attitudes of local governments on climate change, environmental activism, and the recent experiences of climate-related disasters are instrumental for shaping a better local response. Local action also needs to be supported by a more integrated approach by all levels of government.     

A multi-objective scatter search for a mixed-model assembly line sequencing problem

A mixed-model assembly line (MMAL) is a type of production line where a variety of product models similar to product characteristics are assembled. There is a set of criteria on which to judge sequences of product models in terms of the effective utilization of this line. In this paper, we consider three objectives, simultaneously: minimizing total utility work, total production rate variation, and total setup cost. A multi-objective sequencing problem and its mathematical formulation are described. Since this type of problem is NP-hard, a new multi-objective scatter search (MOSS) is designed for searching locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, in terms of solution quality and diversity level, various test problems are made and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three prominent multi-objective genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed MOSS outperforms the existing genetic algorithms, especially for the large-sized problems.     

Sintering Behavior of Al/B4C-AlB12-Al2O3 Nanostructure Composite Synthesized by In Situ Processing Method

The aim of this article is to study the effect of time and temperature of sintering on the properties and microstructure of Al/B₄C-AlB₁₂-Al₂O₃ nanocomposite synthesized via in situ processing method. In this method, a mixture of Al-B₂O₃-C, as starting materials, was activated and reacted in a planetary ball mill under Argon atmosphere. The phase analysis, morphology, and particles size were studied by x-ray diffraction and scanning electron microscopy methods. The composite powders were compressed with uniaxial cold press and then were sintered at different temperatures (470-600 °C) for various times (30-90 min). The density, hardness, and compressive strength of sintered samples were investigated. The results revealed that by increasing the sintering temperature and sintering time, both hardness and compaction strength of nanocomposite reach to maximum at 560 °C. The results showed that the sample sintered at 560 °C for 90 min had highest sintered density (≈2590 kg/m³) and highest hardness value of ~88 BHN with compaction strength of ~313 MPa. Nanosize and homogeneous distribution of in situ formed ceramic particles were observed in the synthesized composite.     

A new analytical model to predict heavy oil production rate in the SAGD process

An analytical model for predicting the oil production rate in the steam-assisted gravity drainage (SAGD) process is presented in this article. The suggested correlation is found based on Butler's original work. It considers the most effective parameters of the process that emphasize the influence of gravity drainage and that are grouped together in the form of the Rayleigh's number. The present model introduces three coefficients (i, j, and k) into the equation, which are determined by minimizing an objective function based on the difference between the six experimental SAGD datasets and the calculated results. The tool chosen for the minimization is the genetic algorithm (GA). After the initial evaluation, the same approach is used for other reservoir characteristics to ensure the robustness of the new equation. Having considered various simulation outcomes with an average error of 8.9% makes this model a credible one for predicting the SAGD production rates. The novelty of the new predictive model lies within its unique approach, making it quite fast and applicable to a wide range of reservoirs with low associated estimation inaccuracies.