Godfrey Chagwiza

Impact of New Water Sources on the Overall Water Network: An Optimisation Approach

A mathematical programming problem is formulated for a water network with new water sources included. Salinity and water hardness are considered in the model, which is later solved using the Max-Min Ant System (MMAS) to assess the impact of new water sources on the total cost of the existing network. It is efficient to include new water sources if the distances to them are short or if there is a high penalty associated with failure to meet demand. Desalination unit costs also significantly affect the decision whether to install new water sources into the existing network while softening costs are generally negligible in making such decisions. Experimental results show that, in the example considered, it is efficient to reduce number of desalination plants to remain with one central plant. The Max-Min Ant System algorithm seems to be an effective method as shown by least computational time as compared to the commercial solver Cplex.

Formulation of a nonlinear water distribution problem with water rationing: an optimisation approach

A mixed-integer nonlinear programming water distribution problem that incorporates water rationing is presented. The city of Bulawayo water distribution problem is implemented and solved using max–min ant system, genetic, tabu search, and simulated annealing algorithms with 100 runs performed for each algorithm. The results show that the city of Bulawayo can save

Parameter Improvement of the Soccer League Competition Algorithm by Introducing Stubborn Players: Application to Water Distribution Network

3158 a day. The max–min ant system produced the best optimal costs compared with the other algorithms. The least run time is obtained by implementing the tabu search algorithm. Water lost through hoarding during water-rationing periods contributes significantly to the total operational costs. Statistical analysis of the results obtained by different algorithms shows that the optimal costs obtained by tabu search, and simulated annealing algorithms are insignificantly different. Future research may be directed toward incorporating priority among water users and formulating a hybrid algorithm that uses both the max–min ant system and tabu search algorithms to solve such problems.

A new hybrid matheuristic optimization algorithm for solving design and network engineering problems

A modified soccer league algorithm is presented in this paper. The effect of stubborn fixed players is investigated and the algorithm is implemented to three benchmark water distribution networks. The modified algorithm is compared to several algorithms. The results show that the modified algorithm performs better than the soccer league competition algorithm, in particular, on the average number of evaluations required to find the optimal cost. Computational results show that the utility benefit of both the individual player and team is essential. The algorithm becomes more reliable when utility benefits are high and as the number of fixed players increases.

A New Plant Intelligent Behaviour Optimisation Algorithm for Solving Vehicle Routing Problem

Abstract A hybrid matheuristic algorithm is formulated by combining Grotschel–Holland and Max–Min Ant System algorithms to solve design and network engineering problems. In the majority of our examples, the hybrid algorithm seems promising. The matheuristic has the ability to increase exploration and it maximizes exploitation of various solutions and the certainty of exact methods to find a solution. Further, a recommended study direction is to improve the algorithm to solve higher-order polynomial type problems efficiently.

Optimisation of total water coverage in a network with priority using max-min ant system algorithm

A new plant intelligent behaviour optimisation algorithm is developed. The algorithm is motivated by intelligent behaviour of plants and is implemented to solve benchmark vehicle routing problems of all sizes, and results were compared to those in literature. The results show that the new algorithm outperforms most of algorithms it was compared to for very large and large vehicle routing problem instances. This is attributed to the ability of the plant to use previously stored memory to respond to new problems. Future research may focus on improving input parameters so as to achieve better results.

A Mixed Integer Programming Poultry Feed Ration Optimisation Problem Using the Bat Algorithm

Abstract A general mathematical programming algorithm is presented to optimise the total cost for the whole water network achieving total water coverage to all demand points in a water network with priority considering penalties associated with failure to meet the demand of two classes of consumers. The algorithm accommodates water leakages and pressure-driven demand and their effects on allocating water. Max-min ant system (MMAS) is used to solve the model using MIDACO 3.0 after interfacing MATLAB 7.0.4 and GAMS 23.9.4. The model is first solved using a particle swarm optimisation (PSO) algorithm. MMAS proved to be an effective water network optimisation tool with priority evidenced in an analysis of the Bulawayo water network by a lower total cost, inclusive of penalties for failure to meet demand for senior priority water users, which is reduced by 25.4%.

An Assessment of Factors Influencing Forest Harvesting in Smallholder Tobacco Production in Hurungwe District, Zimbabwe: An Application of Binary Logistic Regression Model

In this paper, a feed ration problem is presented as a mixed integer programming problem. An attempt to find the optimal quantities of Moringa oleifera inclusion into the poultry feed ration was done and the problem was solved using the Bat algorithm and the Cplex solver. The study used findings of previous research to investigate the effects of Moringa oleifera inclusion in poultry feed ration. The results show that the farmer is likely to gain US 0.89 more if Moringa oleifera is included in the feed ration. Results also show superiority of the Bat algorithm in terms of execution time and number of iterations required to find the optimum solution as compared with the results obtained by the Cplex solver. Results revealed that there is a significant economic benefit of Moringa oleifera inclusion into the poultry feed ration.

A generalised likelihood uncertainty estimation mixed-integer programming model: Application to a water resource distribution network

Deforestation is one of the major effects posed by the smallholder tobacco farming as the farmers heavily depend on firewood sourced from natural forest for curing tobacco. The research aims at assessing the factors that influence the harvesting of natural forest in the production of tobacco. Data is collected through the structured questionnaire from 60 randomly selected farmers. Binary logistic regression model is used to explain the significance of factors influencing natural forest harvesting. Results show that farmer experience, tobacco selling price, and agricultural training level negatively affect the harvesting of natural forests (to obtain firewood) for curing tobacco significantly (). However, gender, size of the household, tobacco yield, and level of education are insignificant () in influencing natural forest harvesting. Though farmers are exploiting the environment and at the same time increasing foreign currency earning through tobacco production, there is therefore a need to put in place policies that encourage sustainable forest product utilization such as gum plantations, subsidizing price of coal, and introducing fees, as well as penalties or taxes to the offenders.

Parasitic Optimisation Algorithm: a Lesson from Striga Asiatica (L) Kuntze Weed

A framework for incorporating uncertainty in water distribution models that uses generalised likelihood unbiased estimation (GLUE) and mixed-integer programming (MIP) is proposed and applied to a small water distribution system. Model parameters with uncertainty are first modelled in GLUEWIN and the mean estimates are used instead of single-point estimates. The MIP is solved in GAMS using CPlex solver with single-point estimates and then GLUE-generated estimates. There is a large difference between the results from GLUEMIP and those of the general MIP. It is therefore recommended that GLUEMIP framework be used so as to avoid penalties associated with failure to meet demand and for better planning.