An Operational Scheduling Framework for Tanker-based Water Distribution System under Uncertainty

TL;DR

This paper presents a stochastic optimization framework for scheduling tanker-based water distribution under demand and travel time uncertainties, improving sustainability and efficiency in urban water supply systems.

Contribution

It introduces a two-stage stochastic model combining Monte Carlo simulation and scenario trees to optimize tanker operations considering uncertainties.

Findings

Enhanced demand fulfillment under uncertainty

Efficient scheduling reduces water wastage

Stochastic solutions outperform deterministic models

Abstract

Tanker water systems play critical role in providing adequate service to meet potable water demands in the face of acute water crisis in many cities globally. Managing tanker movements among the supply and demand sides requires an efficient scheduling framework that could promote economic feasibility, ensure timely delivery, and avoid water wastage. However, to realize such a sustainable water supply operation, inherent uncertainties related to consumer demand and tanker travel time need to accounted in the operational scheduling. Herein, a two-stage stochastic optimization model with a recourse approach is developed for scheduling and optimization of tanker based water supply and treatment facility operations under uncertainty. The uncertain water demands and tanker travel times are combinedly modelled in a computationally efficient manner using a hybrid Monte Carlo simulation and scenario tree approach. The maximum demand fulfillment, limited extraction of groundwater, and timely delivery of quality water are enforced through a set of constraints to achieve sustainable operation. A representative urban case study is demonstrated, results are discussed for two uncertainty cases (i) only demand, and (ii) integrated demand-travel time. Value of stochastic solution over expected value and perfect information model solutions are analyzed and features of the framework for informed decision-making are discussed.