Palestine is water-stressed and prone to possible shocks in water supply. However, about one-third to one-half of the delivered water in the Palestinian territories is lost in the distribution network, highlighting th...
详细信息
Palestine is water-stressed and prone to possible shocks in water supply. However, about one-third to one-half of the delivered water in the Palestinian territories is lost in the distribution network, highlighting the necessity of a cost-effective supply policy that also minimizes corrosion and health-related risks by achieving a maximum level of water quality. There must be a decision-making system to help managers review and measure the status quo and develop optimal allocation decisions to resolve the potential problems with supply and quality as much as possible. This research presents optimal resource allocation for the drinking water supply system (DWSS) to minimize the unit cost of supply in the system and the chloride concentration of the water supplied simultaneously. A bespoke biobjective mixed-integer linear programming (BOMILP) model was developed. The weighted sum method (WSM) based on the positive ideal solution (PIS) approach is utilized to tackle the biobjectiveness of the model in a Pareto sense. The model was implemented and validated using a case study of Gaza. The results suggest that the share of desalinated water resources in the supply network will increase by about 50% in the 2030-2035 period. Furthermore, we found that the unit cost of supply is highly sensitive to a decrease in resource capacities. Finally, the average quality of water supplied can deteriorate rapidly if demand surges. The model can successfully handle the complexity of effective utilization of resources and yield optimal decisions for policymakers.
In this study, an exact algorithm, called the search-and-remove (SR) algorithm, is proposed to compute the Pareto frontier of biobjective mixed-integer linear programming problems. At each stage of the algorithm, effi...
详细信息
In this study, an exact algorithm, called the search-and-remove (SR) algorithm, is proposed to compute the Pareto frontier of biobjective mixed-integer linear programming problems. At each stage of the algorithm, efficient slices (all integer variables are fixed in a slice) are searched with the dichotomic search algorithm and found slices are recorded and excluded from the decision space with the help of Tabu constraints. The algorithm is also enhanced with lower and upper bounds, which are updated at each stage of the algorithm. The SR algorithm continues until it is proved that all efficient slices of the biobjective mixed-integer linear programming (BOMILP) problem are found. The algorithm finally returns a set of potentially efficient slices including all efficient slices of the problem. Then, an upper envelope finding algorithm merges the Pareto frontiers of these slices to the Pareto frontier of the original problem. A computational analysis is performed on several benchmark problems and the performance of the algorithm is compared with state of the art methods from the literature. (C) 2018 Elsevier B.V. All rights reserved.
In this study, we develop a new criterion space search algorithm to find the Pareto frontier of biobjective mixed-integer linear programming problems. Our algorithm starts with the solution of an individual objective ...
详细信息
In this study, we develop a new criterion space search algorithm to find the Pareto frontier of biobjective mixed-integer linear programming problems. Our algorithm starts with the solution of an individual objective function and then sequentially finds all Pareto line segments and points, which are the elements of the Pareto frontier, of biobjective mixed-integer linear programming problems. At each iteration of the algorithm, one line segment (or one isolated point) of the Pareto frontier is detected. If there is no new Pareto line segment available, the algorithm ends. We provide numerical examples and present performance results of the algorithm over several test problems. (C) 2016 Elsevier Ltd. All rights reserved.
暂无评论