323,029 research outputs found
Energy-efficient frozen food transports: the Refrigerated Routing Problem
No full textGiven the growing importance of cold chains and the need to promote sustainable processes, energy efficiency in refrigerated
transports is investigated at operational level. The Refrigerated Routing Problem is defined, involving multi-drop deliveries
of palletised unit loads of frozen food from a central depot to clients. The objective is to select the route with minimum fuel
consumption for both traction and refrigeration. The problem formulation considers speed variation due to traffic congestion
phenomena, as well as decreasing load on board along the route as successive clients are visited. Transmission load for
exposure of the vehicle to outdoor temperatures and infiltration load at door opening are modelled, taking into account
outdoor conditions varying along the day and the year. The resulting multi-period problem is modelled and solved by
means of Constraint Programming. Test scenarios come from a real local network for frozen bread dough distributed to
supermarkets. Results show how fuel minimisation leads to the selection of different routes in comparison to the traditional
total travel distance or time objectives. Energy savings are affected by demand distribution among the clients, departure
time, number of visits per tour, seasonality and location of the delivery network
The on-demand warehousing problem
No full textWarehouses are key elements of supply chain networks, and great attention is paid to increase their efficiency. Highly volatile space requirements are enablers of innovative resource sharing concepts, where warehouse capacities are traded on online platforms. In this context, our paper introduces the on-demand warehousing problem from the perspective of platform providers. The objective prioritises demand–supply matching with maximisation of the number of transactions. If there is a tie, the secondary objective maximises the number of suppliers matched with at least one customer and the number of customers that have matches within a specific threshold with respect to the minimum achievable cost. Besides the mathematical integer programming formulation, a myopic list-based heuristic and an efficient matheuristic approach are presented and benchmarked against the performance of a commercial optimisation solver. The impact of several parameters on the platform's objective is analysed. A particularly relevant finding is that the pricing flexibility on the demand side does not necessarily imply higher payments to the supply side. All data instances are made available publicly to encourage more researchers to work on this timely and challenging topic
Metaheuristic Algorithms for UAV Trajectory Optimization in Mobile Networks
No full textWe consider a mobile network in which traditional static terrestrial base stations are not capable of completely serving the existing user demand, due to the huge number of connected devices. In this setting, an equipped Unmanned Aerial Vehicle (UAV) can be employed to provide network connection where needed in a flexible way, thereby acting as an unmanned aerial base station. The goal is to determine the best UAV trajectory in order to serve as many users as possible. The UAV can move at different speeds and can serve users within its communication range, although the data rate depends on the positions of UAV and users. In addition, each user has a demand (e.g., the number of bits the user wants to download/upload from/to the network) and a time window during which requires the service. We propose a Biased Random-Key Genetic Algorithm (BRKGA) and a Simulated Annealing Algorithm (SAA), and compare them on realistic instances with more than 500 users in different settings
Correction to: Solving a home energy management problem by Simulated Annealing (Optimization Letters, (2020), 10.1007/s11590-020-01545-8)
No full textSolving a home energy management problem by Simulated Annealing
Precipitation distribution in Friuli - Venezia Giulia: average amounts and cluster analysis
No full textTREND ANALYSIS OF MEAN MONTHLY MAXIMUM AND MINIMUM SURFACE TEMPERATURES OF THE 1951-1990 PERIOD IN FRIULI-VENEZIA GIULIA
No full textRAINFALL OVER FRIULI-VENEZIA GIULIA - HIGH AMOUNTS AND STRONG GEOGRAPHICAL GRADIENTS
No full textThe precipitation distribution over Friuli-Venezia Giulia - the easternmost region of Northern Italy extending from the Adriatic Sea to the Alps - has been studied. Monthly rainfall data over the region and the bordering areas of Veneto and Slovenia during the period from 1951 to 1986 have been analyzed by standard statistical methods, including cluster analysis. The overall results emphasize a distribution with rainfall increasing from the sea to the prealpine areas. The highest precipitations were recorded over the Musi-Canin range, with average values exceeding 3 200 mm per year. Noteworthy is the unforeseen subdivision of the region by the clustering procedure by means of the Angot index
Simulated Annealing for the Home Healthcare Routing and Scheduling Problem
No full textHome healthcare services are carried out by trained caregivers who visit the patient’s home, perform their service operations that depend on the patient’s need (e.g., medical care or just instrumental activities of daily living), and then move to the next patient. We consider the home healthcare scheduling and routing problem, in the formulation proposed by Mankowska et al. (2014), which includes synchronization among services and time windows for patients. For this problem, we propose a local search approach based on a novel neighborhood operator and guided by the Simulated Annealing metaheuristic. We show that our approach, properly tuned in a statistically-principled way, is able to outperform state-of-the-art methods on most of the original instances made available by Mankowska et al
Solving a home energy management problem by Simulated Annealing
No full textWe consider the energy scheduling problem for a domestic setting proposed and modeled by Della Croce et al. (Comput Ind Eng 109:169–178, 2017). We solve it by means of a Simulated Annealing approach based on a complex neighborhood structure. We perform an extensive and statistically-principled tuning phase using F-Race, given that the solver is dependent on a set of parameters, which comprises the classical ones of Simulated Annealing and others related to the neighborhood structure. The experimental analysis shows that our solver outperforms all four methods proposed in the original work by Della Croce et al. in almost all instances
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