Mathematical Optimization Models in the Sugarcane Harvesting Process

Over the past few decades, due to environmental and economic factors, the sugarcane has been considered a versatile and important plant to the several countries. The energy-sugar-ethanol agro-industries are seeking to take advantage of all its material, with the main products produced being renewable energy, sugar and ethanol. In this chapter, we propose to present a review of the important works that use mathematical and computational tools, aiming to optimize the sugarcane harvesting, in the past 30 years

Material flow cost accounting practices and resource efficiencies in South African sugar industry.

Doctor of Philosophy in Accounting. University of KwaZulu-Natal, Durban, 2019.Given the backdrop of inefficiencies and declining productivity in the South African sugar industry, this study examined material flow cost accounting (MFCA) as a decision-making toolkit for improving resource efficiency in the industry. This was considered with three distinct objectives, namely: to establish which factors determine the quality of sucrose in sugarcane production; to demonstrate the potential environmental and economic benefits of cleaner production processes and technologies in the sugar milling industry, and to examine the effectiveness of adopting the MFCA framework approach as a decision-making tool in the supply chain to improve overall performance of the sugar industry. Data were collected from a panel of the six sugar milling firms that are operating in the South African sugar milling industry. For the first and second objectives, the panel auto regressive distributive lag (P-ARDL) estimating technique was adopted while models from literature were employed to access the efficiency of the implementation of MFCA as an important alternative to the conventional accounting process in the third objective. A system generalized method of moments (GMM) estimation technique was also used to estimate the impact of sucrose content on profitability. As well, a random effect regression model was employed to examine the relationship between material flow cost accounting and resource efficiency. Besides the aforementioned methods, detailed conceptual issues relating to cleaner production were identified and addressed. Taking the sugar cane industry in South Africa as the study focus, an alternative measure that enhances the quality of sugar, particularly that of sucrose, was investigated. Findings from the study revealed that certain factors, such as transportation and loading delay, not only contribute to losses in sucrose, but also affect the farmers’ yields due to increase in deterioration of cane sugar. Specifically, the result of objective one revealed that, both in the short- and long- runs, most of the variables investigated have the tendency of increasing the sucrose level in sugar cane while an increase in other variables would decrease sucrose level altogether. However, the impact of soil water content (100mm) appears not to be statistically significant on sucrose production in the short- and long-runs. Of special interest is stalk growth (of sugar cane) and average temperature, as their values are more significantly germane as regards to the quantity of sucrose obtained for sugar cane processing in South Africa. The study further used a structural equation model to examine the relationship between cleaner production and firm performance, which was measured by environmental, operational and financial performance. The hypothesis tested supported that cleaner production had a positive and significant influence on the environmental, operational and financial performance of the firms in the sugar industry. Results from the last two objectives of the study provide evidence to support the conclusion that the effective MFCA implementation process supports increased efficiency in the sugar cane industry as well as cleaner production. The study also found that sucrose content has a positive and significant impact on the profitability of the firms. As well, the evidence showed that material flow cost accounting has a positive relationship with resource efficiency. This study, therefore, recommends the proficient use of MFCA among the South African industries as they possess the quality of classifying product cost from waste cost, hence, improving profitability and organizational efficiency. The contribution of this study lies in the researcher’s capability to model the MFCA process for minimizing the applicable costs of the sugar industry for optimal performance

The improvement of strategic crops production via a goal programming model with novel multi-interval weights

Nowadays, the need to increase agricultural production has becomes a challenging task for most of the countries. Generally, there are many resource factors which affect the deterioration of production level, such as low water level, desertification, soil salinity, low on capital, lack of equipment, impact of export and import of crops, lack of fertilizers, pesticide, and the ineffective role of agricultural extension services which are significant in this sector. The main objective of this research is to develop fuzzy goal programming (FGP) model to improve agricultural crop production, leading to increased agricultural benefits (more tons of produce per acre) based on the minimization of the main resources (water, fertilizer and pesticide) to determine the weight in the objectives function subject to different constraints (land area, irrigation, labour, fertilizer, pesticide, equipment and seed). FGP and GP were utilized to solve multi-objective decision making problems (MODM). From the results, this research has successfully presented a new alternative method which introduced multi-interval weights in solving a multi-objective FGP and GP model problem in a fuzzy manner, in the current uncertain decision making environment for the agricultural sector. The significance of this research lies in the fact that some of the farming zones have resource limitations while others adversely impact their environment due to misuse of resources. Finally, the model was used to determine the efficiency of each farming zone over the others in terms of resource utilization

A Fuzzy Credibility-Based Chance-Constrained Optimization Model for Multiple-Objective Aggregate Production Planning in a Supply Chain under an Uncertain Environment

In this study, a Multiple-Objective Aggregate Production Planning (MOAPP) problem in a supply chain under an uncertain environment is developed. The proposed model considers simultaneously four different conflicting objective functions. To solve the proposed Fuzzy Multiple-Objective Mixed Integer Linear Programming (FMOMILP) model, a hybrid approach has been developed by combining Fuzzy Credibility-based Chance-constrained Programming (FCCP) and Fuzzy Multiple-Objective Programming (FMOP). The FCCP can provide a credibility measure that indicates how much confidence the decision-makers may have in the obtained optimal solutions. In addition, the FMOP, which integrates an aggregation function and a weight-consistent constraint, is capable of handling many issues in making decisions under multiple objectives. The consistency of the ranking of objective’s important weight and satisfaction level is ensured by the weight-consistent constraint. Various compromised solutions, including balanced and unbalanced ones, can be found by using the aggregation function. This methodology offers the decision makers different alternatives to evaluate against conflicting objectives. A case experiment is then given to demonstrate the validity and effectiveness of the proposed formulation model and solution approach. The obtained outcomes can assist to satisfy the decision-makers’ aspiration, as well as provide more alternative strategy selections based on their preferences