1,721,044 research outputs found
An Optimization Framework for Combining the Petroleum Replenishment Problem with the Optimal Bidding in Combinatorial Auctions
Full text linkLocation-based techniques for the synergy approximation in combinatorial transportation auctions
No full textA Utility Optimization Model for the Real Time Pricing of Electricity under Uncertain Demand
No full textUsing combinatorial auctions for the procurement of occasional drivers in the freight transportation: A case-study
No full textThe logistics companies should continuously invent novel solutions to oppose fierce competition. Introducing occasional drivers in the context of freight transportation can represent a valuable option. This paper is concerned with reinforcing the distribution capability of a real-life company by using the novel paradigm of crowdshipping, besides employing its fleet of vehicles. Crowdshipping consists involving occasional drivers, which are to be selected from the public through the use of the combinatorial auction paradigm. We propose a mathematical model that integrates the decisions related to the vehicle routing with the winner determination problem for the occasional drivers’ selection. The objective is to minimize the overall transportation cost of using the company's available fleet plus the cost of employing external drivers. We also propose two heuristic methods to solve real-life distribution instances; one is based on the decomposition method and the other on a cost-comparison greedy approach. The validity of the model, as well as the heuristic methods, has been verified by solving a real case study related to an online bookstore with door-to-door delivery in Oman. Our computational results show savings that reach 30 with respect to the solution implemented by the company. © 2021 Elsevier Lt
Solving the Flood Propagation Problem with Newton Algorithm on Parallel Systems
Full text linkIn this paper we propose a parallel implementation for the flood propagation method Flo2DH. The model is built on a finite element spatial approximation combined with a Newton algorithm that uses a direct LU linear solver. The parallel implementation has been developed by using the standard MPI protocol and has been tested on a set of real world problems
Solving the Periodic Edge Routing Problem in the Municipal Waste Collection
No full text In many municipal waste collection systems, it is necessary to extend the planning horizon to more than one working day. This can happen, for example, in the collection of some recyclable articles. In this case, some of the streets must be served every day but others need only once every two days service. In this paper, we focus on planning the routing of the collection vehicles while extending the planning horizon to two working days. We propose a simple, but effective, heuristic approach and we carry out extensive computational experiments to evaluate its performance. We also apply our method to solve a real-case application related to the collection of recyclable wastes in a small Italian city. </jats:p
Dynamic Pricing of Electricity in the Retail Markets
No full textThis paper aims at defining a dynamic and flexible tariff structure for a distribution company that protects the retail consumers against the excessive fluctuations of the wholesales market prices. We propose a two-stage pricing
scheme that sets in a first-stage a time-of-use tariff that is corrected later by a dynamic component once the real-time demand has been observed. A personalized tariff scheme may be offered by a distribution company to each dynamic customer by allowing him to choose the appropriate robustness level expressed in terms of variability between the first- and the second-stage decisions. The arising limited recourse model has been tested on realistic test problems, by using a slight modification of a recently proposed interior point solution framework
Solving the Asymmetric Traveling Salesman Problem with Periodic Constraints
No full textIn this article we describe a heuristic algorithm to solve
the asymmetrical traveling salesman problem with periodic
constraints over a given m-day planning horizon.
Each city i must be visited ri times within this time horizon,
and these visit days are assigned to i by selecting
one of the feasible combinations of ri visit days with the
objective of minimizing the total distance traveled by the
salesman. The proposed algorithm is a heuristic that
starts by designing feasible tours, one for each day of
the m-day planning horizon, and then employs an improvement procedure that modifies the assigned combination to each of the cities, to improve the objective
function. Our heuristic has been tested on a set of test
problems purposely generated by slightly modifying
known test problems taken from the literature. Computational
comparisons on special instances indicate encouraging
results
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