Ethiopia is one of the largest charcoal-producing countries in Africa where its urban consumers burn over 3 million tons per year. The purpose of this study was to measure the amount of charcoal produced and its related environmental and socioeconomic impact in the study area. A total of 305 respondents were selected by using a simple random sampling technique. The amount of greenhouse gas emissions from charcoal production was analyzed based on the Intergovernmental Panel on climate change quantification techniques, and the impact of charcoal production on households’ income was analyzed using propensity score matching. The results revealed that the annual charcoal production rate and emission of carbon dioxide equivalent have an increasing trend at an alarming rate in the study area. From propensity score matching analysis, the economic impact of charcoal production has a positive difference of 0.43813162 as compared to nonproducers. Socioeconomic factors like land size, eucalyptus coverage, agricultural extension, market distance, and the number of oxen have a highly significant effect but variables like sex, family size, education status, credit services, and marital status had no significant effect on charcoal production. In general, even though charcoal production is economically having a positive impact on households’ annual aggregate income; it has disproportionality adverse effect on the environment like air pollution in addition to sophisticated respiratory health problems. Therefore, responsible institutions and planners should have focused on the multidimensional effect of traditional charcoal production on environmental issues and sophisticated health problems especially on employed laborers and nearby residents.
The objective of this research is to propose a methodology for multi-objective optimization of a mixed-model assembly line balancing problem with the stochastic environment. To do this a mathematical model representing the problems at hand is developed with objectives of minimizing cycle time and minimization of the number of workstations (which is of Type-E ALB problem). And two optimization meta-heuristics are considered to solve it, namely, Non-Dominated Sorting Genetic Algorithm- II (NSGA-II) and Multi-Objective Genetic Algorithm (MOGA). To test the performance of the algorithms three different size standard problems in Assemble-to-order types of industry are taken and five demand arrival scenarios are considered to incorporate the stochastic nature of the demand arrival for each model in all problems. Both the algorithms are coded and run using MATLAB® 2013a and are compared based on different performance measures. The results indicated that MOGA outperformed NSGA-II in most of the test problems. Nevertheless, both algorithms have resulted in significant improvements in the performance measures in Assemble-to-order types of industry dataset compared to the existing line configuration.
Keywords: Assembly Line, Multi-objective optimization, Single model, mixed-model, stochastic environment, Genetic Algorithm
In Ethiopia, rice is a recently introduced crop which is considered as the “Millennium crop” expected to hugely contribute food security. This paper seeks to measure the technical, allocative, and economic efficiency of rain-fed rice production and identify the factors that affect the efficiency of farmers in Fogera Districts of the Amhara Region. For the study, cross-sectional data were collected from a survey of 230 smallholder rice producers. The study used stochastic frontier production(SFA) and cost function to investigate the variations in the efficiency level of rice producers. The result indicated that the TE was higher as compared with the EE and AE. The average TE ranges between 24% and 93% with a mean of 70%. However, the mean of EE was 24.40 % and the AE 37.30%. Therefore, reduction of cost of production (such as improved input supply systems), warehouse facilities to keep produce and prevent the immediate sale of a product, introducing of a contract marketing system would improve the economic efficiency of the rice farming. Intervention on education and training on female-headed households, reducing family dependency, training of older farmers’ were vital to increase the EE of rice production.Similarly, improving the farmer’s education level to boost knowledge about new rice technology applications, and frequent training of farmers would enhance the TE of farmers in the study area.
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