Abstract
Maximizing benefit from budget allocation is a major challenge for municipalities in the modern era. This is especially significant when it comes to infrastructure network management such as water distribution networks. The main challenges of water distribution networks are leakage and leak repairs. Municipalities commonly use first-in-first-out approaches to determine which leaks to allocate budget for first. Yet, the deterioration of leaks is not linear through time and requires a more in-depth assessment of the condition of the leak. Therefore, this article presents two prioritization approaches for the scheduling of leaks while incorporating deterioration over time. This paper proposes and compares two optimization techniques: (1) a well-known genetic algorithm and (2) a novel approach named the Lazy Serpent Algorithm. The Lazy Serpent Algorithm has proved capable of surpassing the genetic algorithm in determining a more optimal order by using much less computation time. The Lazy Serpent Algorithm helps municipalities better distribute their resources to maximize their desired benefits.
Highlights
The concept of lazy serpent was inspired by the problem of leak repair scheduling and resource allocation for repair projects
Itit finishes finishesall allthe theavailable available deterioratingevents eventsfirst firstand and goes goes upwards upwards until until it it meets meets aa constraint, shows the results presented by the basic lazy serpent with deterioration preference as follows: starting events
The Lazy Serpent Algorithm was inspired by the need to solve a combinatorial optimization problem with a variety of constraints
Summary
The concept of lazy serpent was inspired by the problem of leak repair scheduling and resource allocation for repair projects. In this report leak repair projects are considered independent events, i.e., unrelated to each other. Some researchers attempted to distinguish independent event scheduling approaches from regular scheduling and dubbed them the name “priority algorithms.”. Davis [1] identified priority algorithms and their role in solving scheduling problems. The report identified multiple algorithms capable of solving prioritization problems, mainly greedy algorithms, genetic algorithms, adaptive priority algorithms, and dynamic programming. An early approach for optimizing independent event schedules was presented by Colorni et al [2].
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