On-Demand Service Zone Design for Energy-Constrained Spatial Queueing Systems

2026-06-25

AI Summary

This study addresses the challenge of evaluating service feasibility for electric service vehicles, whose operations are constrained by battery limitations and charging requirements—factors inadequately captured by traditional spatial queueing models. The authors propose the first framework that explicitly embeds energy constraints into a hypercube spatial queueing model, formulating a dynamic representation based on a semi-Markov process and jointly optimizing facility location and service zone delineation. They introduce an innovative Branch-Price-and-Evaluation algorithm, enhanced with set-partitioning reformulation, to solve the resulting problem efficiently. Results demonstrate that explicitly modeling energy constraints substantially reduces spurious service commitments; furthermore, thoughtfully designed service zones yield greater revenue improvements than battery upgrades alone, and larger battery capacities may paradoxically reduce fleet availability under low-demand conditions.

Abstract

Electric service vehicles (ESVs), such as mobile chargers and drone-based service units, are becoming an important operational resource for on-demand service systems. Unlike conventional spatial servers, ESV operations are shaped by battery limits and recharging needs, which affect dispatch feasibility and spatial deployment decisions. We develop an energy-constrained hypercube spatial queueing model that embeds battery-state dynamics into the classical hypercube framework and uses a semi-Markov representation to estimate steady-state performance. We then formulate a joint location--zoning problem for station placement and service zone design. The resulting large-scale mixed-integer nonlinear program admits a set partitioning reformulation whose column coefficients are not available in closed form. We therefore develop a Branch-Price-and-Evaluation framework for set partitioning problems with externally computable column coefficients: upper-bounding surrogates guide pricing, and iterative exact evaluation updates the coefficients of active columns. Computational results show that explicit energy modeling significantly reduces false service promises and yields more credible planning decisions. They also reveal a load-dependent reversal in zoning: pooling is preferable under light demand, whereas tighter zoning becomes more profitable as demand increases. Over the tested range, profitability is driven more by zoning than by battery improvement, suggesting that managers should get service zone design right before investing in battery upgrades; this caution is reinforced by the counterintuitive finding that larger batteries may delay replenishment and reduce fleet readiness under sparse demand. These findings show that energy feasibility is not merely a matter of battery-capacity expansion, but a design dimension that shapes service-zone configuration.

Problem

• energy-constrained
• spatial queueing
• service zone design
• electric service vehicles
• location-zoning

Innovation

• energy-constrained queueing
• hypercube model
• set partitioning
• branch-price-and-evaluation
• service zone design