1,721,392 research outputs found
Sperimentazione e confronto di algoritmi per il problema del centro assoluto di una rete
Franco Angeli Editor
Sperimentazione e confronto di algoritmi per il problema del centro assoluto di una rete
Franco Angeli Editor
Operations management in distribution networks within a smart city framework
This article studies a vehicle routing problem with environmental constraints that are motivated by the requirements for sustainable urban transport. The empirical research presents a fleet planning problem that takes into consideration both minimum cost vehicle routes and minimum pollution. The problem is formulated as a mixed integer linear programming model and experimentally validated using data collected from a real situation: a grocery company delivering goods ordered via e-channels to customers spread in the urban and metropolitan area of Genoa smart city. The proposed model is a variant of the vehicle routing problem tailored to include environmental issues and street limitations. Its novelty regards also the use of real data instances provided by the B2C grocery company. Managerial implications are the choice of both the routes and the number and type of vehicles. Results show that commercial distribution strategies achieve better results in term of both business and environmental performance, provided the smart mobility goals and constraints are included into the distribution model from the beginning
Grocery distribution plans in urban networks with street crossing penalties
Following the emergency caused by the Covid-19 pandemic, there is the need, among other measures, to modify urban mobility plans in order to reduce the use of collective public transport, reducing the crowding of people while also preventing traffic congestion through discouraging the use of private vehicles. From this perspective, retail companies operating within cities must also reorganize themselves, considering both the unpredictable requirements of environmental sustainability and the new mobility needs calling for the promotion of bicycles and electric scooters. In this context, we deal with the need to determine minimum cost routes in urban areas for delivering orders placed through e-channels. More precisely, we face a variant of the green vehicle routing problem of heterogeneous fleets, in which the objective function includes environmental impact cost components that differ by vehicle type. Moreover, as a novel issue, attention must be paid to avoid crossing and passing close to bicycle lanes; therefore, penalties are associated with the transit of vehicles near bicycle lanes. To address this problem, we propose a mixed integer linear programming model and a matheuristic associated with it. The proposed approach is then used to analyze different scenarios derived from the transportation network of the city of Milan, Italy. Milan is one of the smartest cities in Europe from the mobility point of view but also one of the most affected by the Covid-19 pandemic, and the municipality is making a big investment to promote the use of bicycles
A biased random-key genetic algorithm for the knapsack problem with forfeit sets
This work addresses the Knapsack Problem with Forfeit Sets, a recently introduced variant of the 0/1 Knapsack Problem considering subsets of items associated with contrasting choices. Some penalty costs need to be paid whenever the number of items in the solution belonging to a forfeit set exceeds a predefined allowance threshold. We propose an effective metaheuristic to solve the problem, based on the Biased Random-Key Genetic Algorithm paradigm. An appropriately designed decoder function assigns a feasible solution to each chromosome, and improves it using some additional heuristic procedures. We show experimentally that the algorithm outperforms significantly a previously introduced metaheuristic for the problem
The optimal value range problem for the Interval (immune)Transportation Problem
We address the problem of finding the range of the optimal cost of a transportation problem when supply and demand vary over an interval. We consider the specific version of a transportation problem with supply inequality constraints and demand equality constraints under the assumption that the transportation costs are immune against the transportation paradox. We investigate some theoretical properties of the problem which constitute the basis of a novel solution algorithm. Our results show that the proposed algorithm hugely outperforms the best existing solution approaches
Liquid noble gases for dark matter searches: an updated survey
An updated technical and methodological comparison of liquid noble gas experiments is presented with particular attention to the low energy physics application of double-phase noble gas detectors in direct Dark Matter investigations
Going Beyond Counting First Authors in Author Co-citation Analysis
The present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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