Meta heuristic for Minimizing Makespan in a Flow-line Manufacturing Cell with Sequence Dependent Family Setup Times

This paper presents a new mathematical model for the problem of scheduling part families and jobs within each part family in a flow line manufacturing cell where the setup times for each family are sequence dependent and it is desired to minimize the maximum completion time of the last job on the last machine (makespan) while processing parts (jobs) in each family together. Gaining an optimal solution for this type of complex problem in large sizes in reasonable computational time using traditional approaches or optimization tools is extremely difficult. A meta-heuristic method based on Simulated Annealing (SA) is proposed to solve the presented model. Based on the computational analyses, the proposed algorithm was found efficient and effective at finding good quality solutions

Spatial Prediction of Slope Failures in Support of Forestry Operations Safety

This study produces a slope failure susceptibility map for evaluation of the Caspian Forest for its capacity to support road construction and timber harvesting. Fifteen data layers were used as slope failure conditioning factors, and an inventory map of recent failures was used to detect the most susceptible areas. Five different datasets of conditioning factors were constructed to compare the efficiency of one over the other in susceptibility assessment. Slope failure susceptibility maps were produced using an adaptive neuro-fuzzy interface system (ANFIS) and geographical information system (GIS). The accuracy of the maps was then evaluated by the area under curve (AUC). The validation results suggest that the ANFIS model with input conditioning factors of slope degree, slope aspect, altitude, and lithology performed the best (AUC=83.74%) among the various ANFIS models explored here. The five ANFIS models have performed reasonably well, and the maps allow development of prudent hazard mitigation plans for the safety in forestry operations

An Efficient Bi-objective Genetic Algorithm for the Single Batch-Processing Machine Scheduling Problem with Sequence Dependent Family Setup Time and Non-identical Job Sizes

This paper considers the problem of minimizing make-span and maximum tardiness simultaneously for scheduling jobs under non-identical job sizes, dynamic job arrivals, incompatible job families,and sequence-dependentfamily setup time on the single batch- processor, where split size of jobs is allowed between batches. At first, a new Mixed Integer Linear Programming (MILP) model is proposed for this problem; then, it is solved by -constraint method.Since this problem is NP-hard, a bi-objective genetic algorithm (BOGA) is offered for real-sized problems. The efficiency of the proposed BOGA is evaluated to be comparedwith many test problemsby -constraint method based on performance measures. The results show that the proposed BOGAis found to be more efficient and faster than the -constraint method in generating Pareto fronts in most cases

Developing a Permutation Method Using Tabu Search Algorithm: A Case Study of Ranking Some Countries of West Asia and North Africa Based on Important Development Criteria

The recent years have witnessed an increasing attention to the methods of multiple attribute decision making in solving the problems of the real world due to their shorter time of calculation and easy application. One of these methods is the ‘permutation method’ which has a strong logic in connection with ranking issues, but when the number of alternatives increases, solving problems through this method becomes NP-hard. So, meta-heuristic algorithm based on Tabu search is used to find optimum or near optimum solutions at a reasonable computational time for large size problems. This research is an attempt to apply the ‘permutation method’ to rank some countries of the West Asia and the North Africa based on the development criteria. Knowing the situation of each country as compared with other countries, particularly the respective neighbouring countries, is one of the most important standards for the assessment of performance and planning for the future activities

Designing an integrated production/distribution and inventory planning model of fixed-life perishable products

This paper aims to investigate the integrated production/distribution and inventory planning for perishable products with fixed life time in the constant condition of storage throughout a two-echelon supply chain by integrating producers and distributors. This problem arises from real environment in which multi-plant with multi-function lines produce multi-perishable products with fixed life time into a lot sizing to be shipped with multi-vehicle to multi-distribution-center to minimize multi-objective such as setup costs between products, holding costs, shortage costs, spoilage costs, transportation costs and production costs. There are many investigations which have been devoted on production/distribution planning area with different assumption. However, this research aims to extend this planning by integrating an inventory system with it in which for each distribution center, net inventory, shortage, FIFO system and spoilage of items are calculated. A mixed integer non-linear programming model (MINLP) is developed for the considered problem. Furthermore, a genetic algorithm (GA) and a simulated annealing (SA) algorithm are proposed to solve the model for real size applications. Also, Taguchi method is applied to optimize parameters of the algorithms. Computational characteristics of the proposed algorithms are examined and tested using t-tests at the 95% confidence level to identify the most effective meta-heuristic algorithm in term of relative percentage deviation (RPD). Finally, Computational results show that the GA outperforms SA although the computation time of SA is smaller than the GA

A genetic algorithm for capital budgeting problem with fuzzy parameters

When an organization utilizes modern technology in its manufacturing process, it needs to update and upgrade its facilities repetitively by efficient ways to stay with great productivity along with efficiency so. Capital Budgeting (CB) problem is one of the most important issues in decision makings about capital in the manufacturing management. Sometimes all variables and parameters are not necessarily deterministic and enough experiments are not available. Current study develops a chance constrained integer programming in the fuzzy environment for capital budgeting. Considering the complexity theory, a good answer could not be found in reasonable time, so that an intelligent Genetic Algorithm (GA) as a metaheuristic approach is provided to trace this problem with satisfying solutions. Thereupon, a fuzzy simulation-based genetic algorithm is provided for solving chance constrained integer programming model with fuzzy parameters

Prenatal and clinical characteristics of pregnant women infected with COVID-19 in Yazd, Iran: A multicenter cross-sectional study

Background: Coronavirus infection has caused widespread concern among mothers and physicians about the health of pregnant women and infants. Objective: The aim of this study was to evaluate the clinical and prenatal findings of pregnant women with coronavirus disease-2019 (COVID-19) virus. Materials and Methods: The present study was a descriptive study that was conducted in 6 mother and child care centers. In this study, 81 pregnant women with COVID-19 admitted to centers in the period from March 2020-September 2020 were studied. Clinical and prenatal findings of the pregnant mothers were recorded using a data collection form with details of demographic characteristics and these were analyzed. Results: The gestational age of the affected women was between 4 and 40 wk. 48 deliveries were performed and 25% of deliveries were preterm. Coronavirus infection was the cause of termination of pregnancy in 4 cases. The most common symptoms of women when visiting the medical centers were: dry cough (58.0%), muscle pain and myalgia (56.8%) and fever (51.9%). The most common laboratory findings in the women were: increased C-reactive protein (67.90%), lymphopenia (18.51%), decreased white blood cells (27.16%), and increased liver enzymes (18.51%). Regarding the status of the newborns, out of the 33 neonates examined, 3 neonates were diagnosed with COVID-19. Conclusion: The most common symptoms of pregnant women with COVID-19 are similar to those of other adults. In relation to neonatal infection, given that a number of the neonates tested positive, there appears to be evidence of vertical transmission, which requires further investigation. Key words: Coronavirus, COVID-19, Pregnancy, Clinical, Prenatal

The Effects of Botulinum Toxin Type A on Reducing Sialorrhea in Children with Cerebral Palsy: A Self-Controlled Clinical Trial

Background: Cerebral palsy stands as the main cause of mobility disability in childhood, and the accompanying sialorrhea exacerbates health and psychological issues for both the child and the family. We aimed to assess the effect of botulinum toxin type A on reducing sialorrhea in children with cerebral palsy.Methods: This self-controlled clinical trial was executed among children afflicted with cerebral palsy. The Teacher Drooling Scale was used as the data collection tool. The intervention involved the administration of botulinum toxin A, with a dosage ranging from 30 to 50 units in each parotid gland, skillfully guided by a radiologist using ultrasound. Sialorrhea scores were compared before and after the injection.Results: Our study included 21 children with cerebral palsy and sialorrhea. After the two post-injection weeks, a noteworthy drop was observed in the sialorrhea score (4.10±0.831) compared to the pre-injection score (4.71±0.463). The sialorrhea score until the ninth month after injection (1.121±3.43) was still significantly lower than the score before injection.Conclusion: The injection of botulinum toxin A emerges as a potent medication, significantly curtailing the drooling among patients with cerebral palsy. This finding can be used to prevent aspiration pneumonia and reduce social and psychological complications in this population

Project risk evaluation by using a new fuzzy model based on Elena guideline

The complexity and dynamics of the executive projects have coped contractors with substantial hazards and losses. Project risk management is a critical tool for authority to improve its performance and secure the success of the organization. However, a number of standards and approaches have been developed to formulate the projects based on their risks. The Elena guideline is a systematic standard developed by Iran Project Management Association. This guideline provides the full cycle of the risk management process. Risk evaluation is the key part of the risk management process. On the other hand, different techniques have been developed to model a risk evaluation problem. Fuzzy inference system is one of the most popular techniques that is capable of handling all types of the uncertainty involved in projects. This paper proposes a three-stage approach based on the fuzzy inference system under the environment of the Elena guideline to cope with the risky projects. Finally, an illustrative example of the risk evaluation is presented to demonstrate the potential application of the proposed model. The results show that the proposed model evaluates the risky projects efficiently and effectively