Open Science
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A Skill-Based Technician Routing and Scheduling Model for Sustainable Maintenance Interventions in Global Customer Service
The increasing geographical dispersion of industrial assets and the growing pressure toward environmental responsibility are reshaping global customer service maintenance operations. Although technician routing and scheduling problems have been widely studied, existing models predominantly empha-size cost efficiency and service level compliance, often neglecting the explic-it trade-off between operational performance and environmental sustainabil-ity, particularly in skill-based maintenance settings. This paper proposes a mixed-integer linear programming model for routing and scheduling multi-skilled technicians performing maintenance interventions in global customer service networks. Technician heterogeneity is captured through perfor-mance-based multipliers that affect intervention duration, reflecting differ-ent competency levels. This structure generates a critical sustainability trade-off: the most skilled technician may complete an intervention faster but be located farther from the site, implying higher travel-related environmental impact, whereas a closer but less skilled technician may increase service time and operational burden. The model jointly optimizes assignment, schedul-ing, travel, and return-to-base decisions while explicitly integrating envi-ronmental impact and operational costs within a unified objective function. Computational experiments show how the proposed approach supports bal-anced and sustainability-oriented maintenance planning decisions in global service networks.
Vai al RepositoryAn industry 5.0 value-oriented compromised programming model for supply chain reconfiguration against tariffs
Tariff shocks have emerged as a critical source of disruption for global supply chains, altering firms’ cost structures, competitive positioning, and strategic configuration choices. Despite the relevance of this phenomenon, research still lacks analytical models capable of guiding supply chain redesign in a way that aligns with Industry 5.0 values—namely the simultaneous pursuit of profitability, human-centricity, resilience, and environmental sustainability. This paper thus introduces a novel compromise-programming model that integrates these multidimensional objectives to support firms in reconfiguring their supply chains under tariff-induced pressure. The model is applied to a real automotive supply chain facing a tariff increase, offering empirically grounded managerial insights into how firms can adjust sourcing, pricing, and production decisions to mitigate economic losses while advancing broader Industry 5.0 priorities. The results illustrate the trade-offs and synergies that emerge when firms respond to tariff shocks holistically, and highlight the economic and social implications of adopting an Industry 5.0–aligned reconfiguration strategy.
Vai al RepositoryA Cluster Based Genetic Algorithm for Product Allocation Across Multiple Warehouse
In recent years, the growth of e-commerce has driven a trend toward order fulfillment strategies that draw products from multiple dispersed warehouses. This evolution has heightened the need for optimal product allocation to warehouse locations to minimize inter-warehouse shipment flows and reduce order completion times and costs. Despite the practical significance of this allocation problem, there is a lack of heuristic approaches capable of addressing large-scale, real-world instances. This paper proposes a novel genetic algorithm to solve the multi-warehouse product allocation problem, integrating tailored genetic operators and constraint-handling mechanisms to enhance solution quality. We evaluate the approach on an industrial case study drawn from an e-commerce company, comprising realistic demand and distribution scenarios. Computational experiments demonstrate that our genetic algorithm outperforms baseline methods in reducing total inter-warehouse flow, achieving significant improvements in logistical efficiency. These results clearly confirm the proposed method’s practical applicability and robustness for complex e-commerce fulfillment networks.
Vai al RepositoryCOCOA: A Model-Agnostic Framework for Forecasting Accessories Demand from Final Product Sales
Forecasting accessories demand is challenging because it is structurally dependent on final product sales but not deterministically derived from them. Existing approaches either treat accessories as independent time series or rely on attach-rate assumptions embedded in model-specific frameworks, limiting flexibility and generalizability. This paper proposes the COCOA-framework, a model-agnostic framework that exploits transaction-level co-occurrence and integrates forecasted final-product demand as future covariates within a multivariate model. The approach is evaluated through a real automotive case study involving 121 accessories with heterogeneous demand patterns, benchmarked against univariate models across multiple forecasting techniques and horizons, under both perfect and imperfect final-product forecasts. Results show significant and largely model-agnostic improvements for intermittent and lumpy demand, with gains attenuating but remaining present under realistic forecast uncertainty.
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