Carlo Maria Aloe

Cultural and archaeological tourism is increasingly acknowledged as a catalyst for regional development, particularly in areas beyond mainstream travel circuits. While itinerary planning has attracted academic attention, the optimization of multi-period cultural routes across dispersed archaeological landscapes remains underexplored. This paper proposes a novel bi-objective optimization model–belonging to the class of Vacation Planning Problems and formulated as a variant of the Team Orienteering Problem–for constructing multi-period archaeological itineraries. The model maximizes cultural value and minimizes travel distance, subject to some logistics constraints. A real-world application in Calabria, Italy–a region rich in archaeological heritage but limited in tourism infrastructure–demonstrates the capability of the model to generate efficient, thematically coherent routes. The findings underscore the potential of operations research methods to enhance sustainable tourism planning in marginal cultural landscapes.

As consumer awareness grows and regulations regarding the quality and safety of perishable goods become stricter, careful management of environmental conditions throughout the supply chain is becoming essential. Among these factors, storage temperature plays a crucial role in preserving the physicochemical characteristics of products. Therefore, an effective approach to ensure quality and safety up to the final customer is to continuously monitor the temperature within warehouses, using specific location-mapping techniques and stocking optimization methods. This study proposes a dynamic optimization model for the storage location assignment problem, integrating both temperature and humidity constraints into the placement of stock-keeping units. The model operates under a multi-period, multi-product framework and leverages real-time sensor data to account for spatial temperature stratification and environmental variability within the warehouse, contributing to the reduction in the energy consumption. Two alternative optimization strategies are explored: one focused on minimizing thermal and humidity stress, and another targeting the reduction in average storage cycle time. A detailed what-if analysis is conducted across three scenarios, varying warehouse fill rates and incoming load volumes, in order to prove the effectiveness of the proposed model in a real-data context. The results show that the approach minimizing environmental stress consistently outperforms traditional methods in quality-related metrics, maintaining superior objective function values.

Cultural and archaeological tourism is increasingly acknowledged as a catalyst for regional development, particularly in areas beyond mainstream travel circuits. While itinerary planning has attracted academic attention, the optimization of multi-period cultural routes across dispersed archaeological landscapes remains underexplored. This paper proposes a novel bi-objective optimization model—belonging to the class of Vacation Planning Problems and formulated as a variant of the Team Orienteering Problem—for constructing multi-period archaeological itineraries. The model maximizes cultural value and minimizes travel distance, subject to some logistics constraints. A real-world application in Calabria, Italy—a region rich in archaeological heritage but limited in tourism infrastructure—demonstrates the capability of the model to generate efficient, thematically coherent routes. The findings underscore the potential of operations research methods to enhance sustainable tourism planning in marginal cultural landscapes.

As consumer awareness grows and regulations regarding the quality and safety of perishable goods become stricter, careful management of environmental conditions throughout the supply chain is becoming essential. Among these factors, storage temperature plays a crucial role in preserving the physicochemical characteristics of products.Therefore, an effective approach to ensure quality and safety up to the final customer is to continuously monitor the temperature within warehouses, using specific location-mapping techniques and stocking optimisation methods. This study proposes a dynamic optimization model for the Storage Location Assignment Problem (SLAP), integrating both temperature and humidity constraints into the placement of stock-keeping units (SKUs). The model operates under a multi-period, multi-product framework and leverages real-time sensor data to account for spatial temperature stratification and environmental variability within the warehouse. Two alternative optimization strategies are explored: one focused on minimizing thermal and humidity stress, and another targeting the reduction of average storage cycle time. A detailed what-if analysis is conducted across three scenarios, varying warehouse fill rates and incoming load volumes, in order to prove the effectiveness of the proposed model in real-data context.