As e-commerce fulfillment complexity increases, intelligent warehouse systems centered on automated guided vehicles (AGVs) face increasingly stringent demands for resource allocation efficiency. The coordinated configuration of AGVs and workstations directly determines system productivity and operational costs. However, existing research often relies on deterministic assumptions or single optimization methods, which struggle to capture the impact of random factors—such as order arrivals and processing times—on system performance, and lack comprehensive analysis of resource coordination and optimal investment. To address this gap, this study proposes a joint optimization approach integrating stochastic frontier analysis (SFA) with mixed-integer nonlinear programming (MINLP), establishing an intelligent warehouse resource allocation model focused on the integrated optimization of AGVs and workstations. First, SFA is employed to separate environmental randomness from technical inefficiency, identifying the efficiency frontier under random disturbances and delineating the effective boundary of resource allocation. Second, an MINLP model is constructed under the constraint of this efficiency frontier to jointly optimize discrete investments and matching relationships between AGVs and workstations. By coupling efficiency evaluation with combinatorial optimization, this method overcomes the limitations of single approaches in handling uncertainty and achieving global optimization. Third, a case study at a large e-commerce company in China demonstrates that the model effectively identifies resource redundancy and imbalance: after optimization, the number of workstations and AGVs decreased by approximately 48% and 11%, respectively, achieving a reduction of resource inputs and a decline in weighted objective-function value, together with efficiency improvement. By introducing SFA into discrete resource optimization and deeply integrating it with MINLP, this research establishes a unified framework of efficiency frontier identification and resource optimization allocation, offering a novel methodology for designing intelligent warehouse systems under uncertain conditions.
Citation: Guanghui Fan, Weihua Liu, Chao Fan, Guodong Li, Hongxiao Zhang. A collaborative optimization model of efficiency frontier and resource in smart warehouses with stochastic disturbance[J]. Journal of Industrial and Management Optimization, 2026, 22(11): 5557-5587. doi: 10.3934/jimo.2026189
As e-commerce fulfillment complexity increases, intelligent warehouse systems centered on automated guided vehicles (AGVs) face increasingly stringent demands for resource allocation efficiency. The coordinated configuration of AGVs and workstations directly determines system productivity and operational costs. However, existing research often relies on deterministic assumptions or single optimization methods, which struggle to capture the impact of random factors—such as order arrivals and processing times—on system performance, and lack comprehensive analysis of resource coordination and optimal investment. To address this gap, this study proposes a joint optimization approach integrating stochastic frontier analysis (SFA) with mixed-integer nonlinear programming (MINLP), establishing an intelligent warehouse resource allocation model focused on the integrated optimization of AGVs and workstations. First, SFA is employed to separate environmental randomness from technical inefficiency, identifying the efficiency frontier under random disturbances and delineating the effective boundary of resource allocation. Second, an MINLP model is constructed under the constraint of this efficiency frontier to jointly optimize discrete investments and matching relationships between AGVs and workstations. By coupling efficiency evaluation with combinatorial optimization, this method overcomes the limitations of single approaches in handling uncertainty and achieving global optimization. Third, a case study at a large e-commerce company in China demonstrates that the model effectively identifies resource redundancy and imbalance: after optimization, the number of workstations and AGVs decreased by approximately 48% and 11%, respectively, achieving a reduction of resource inputs and a decline in weighted objective-function value, together with efficiency improvement. By introducing SFA into discrete resource optimization and deeply integrating it with MINLP, this research establishes a unified framework of efficiency frontier identification and resource optimization allocation, offering a novel methodology for designing intelligent warehouse systems under uncertain conditions.
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Guanghui Fan, Weihua Liu, Chao Fan, Guodong Li, Hongxiao Zhang. A collaborative optimization model of efficiency frontier and resource in smart warehouses with stochastic disturbance[J]. Journal of Industrial and Management Optimization, 2026, 22(11): 5557-5587. doi: 10.3934/jimo.2026189
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