Metaheuristics optimization via memory to solve the profitable arc tour problem (original) (raw)
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Hybrid metaheuristics for the profitable arc tour problem
Journal of the Operational Research Society, 2011
The profitable arc tour problem is a variant in the vehicle routing problems. It is included in the family of the vehicle routing with profit problems in which a set of vehicle tours are constructed. The objective is to find a set of cycles in the vehicle tours that maximize the collection of profits minus travel costs, subject to constraints limiting the length of cycles that profit is available on arcs. To solve this variant we adopted two metaheuristics based on adaptive memory. We show that our algorithms provide good results in terms of solution quality and running times.
Adaptive Memory Procedure to solve the Profitable Arc Tour Problem
Int. J. Comb. Optim. Probl. Informatics, 2010
In this paper we propose an Adaptive Memory Procedure to solve the Profitable Arc Tour Problem (PATP). The PATP is a variant of the well-known Vehicle Routing Problems in which a set of vehicle tours are constructed. The objective is to find a set of cycles in the vehicle tours that maximize the collection of profits minus travel costs, subject to constraints limiting the length of cycles that profit is available on arcs. Computational experiments show that our algorithms provide good results in terms of quality of solution and running times.
Tabu Search Metaheuristic Embedded in Adaptive Memory Procedure for the Profitable Arc Tour Problem
13th IFAC Symposium on Information Control Problems in Manufacturing, 2009
This paper describes a tabu search heuristic embedded in adaptive memory procedure for the Profitable Arc Tour Problem (PATP). The PATP is a variant of the well-known Vehicle Routing Problem in which a set of vehicle tours are constructed. The objective is to find in the graph a set of cycles that maximize the collection of profits minus travel costs, which is in turn subject to constraints limiting the length of cycles that profit is available on arcs. We propose a tabu search algorithm for the solution of the PATP. The tabu search heuristic is embedded in an adaptive memory procedure that alternates between small and large neighborhood stages during the solution improvements phase. Computational experiments are made in randomly generated instances given by .
2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009
This paper describes a tabu search heuristic embedded in adaptive memory procedure for the Profitable Arc Tour Problem (PATP). The PATP is a variant of the well-known Vehicle Routing Problem in which a set of vehicle tours are constructed. The objective is to find in the graph a set of cycles that maximize the collection of profits minus travel costs, which is in turn subject to constraints limiting the length of cycles that profit is available on arcs. We propose a tabu search algorithm for the solution of the PATP. The tabu search heuristic is embedded in an adaptive memory procedure that alternates between small and large neighborhood stages during the solution improvements phase. Computational experiments are made in randomly generated instances given by .
The Profitable Arc Tour Problem: Solution with a Branch-and-Price Algorithm
Transportation Science, 2005
In this article, we introduce a new arc routing problem that we call the profitable arc tour problem. This problem is defined on a graph in which profits and travel costs are associated with the arcs. The objective is to find a set of cycles in the graph that maximizes the collection of profit minus travel costs, subject to constraints limiting the number of times that profit is available on arcs and the maximal length of cycles. The problem is related both to constrained flow problems and to vehicle-routing problems. We tackle it from this standpoint and propose a branch-and-price algorithm for its solution. In the column-generation phase, the issue of the collection decisions while traveling through the arcs is addressed. In the branching phase, the fact that viewing solutions in terms of flow variables regularly induces an integer flow matrix leads us to introduce a branching method called the flow-splitting method. Finally, the relationships of this problem with constrained flow...
Yugoslav Journal of Operations Research, 2020
In this work we deal with a generalized variant of the multi-vehicle covering tour problem (m-CTP). The m-CTP consists of minimizing the total routing cost and satisfying the entire demand of all customers, without the restriction of visiting them all, so that each customer not included in any route is covered. In the m-CTP, only a subset of customers is visited to fulfil the total demand, but a restriction is put on the length of each route and the number of vertices that it contains. This paper tackles a generalized variant of the m-CTP, called the multi-vehicle multi-covering Tour Problem (mm-CTP), where a vertex must be covered several times instead of once. We study a particular case of the mm-CTP considering only the restriction on the number of vertices in each route and relaxing the constraint on the length (mm-CTP-p). A hybrid metaheuristic is developet by combining Genetic Algorithm (GA), Variable Neighborhood Descent method (VND), and a General Variable Neighborhood Searc...
A memetic algorithm for the multiperiod vehicle routing problem with profit
European Journal of Operational Research, 2013
In this paper, we extend upon current research in the vehicle routing problem whereby labour regulations affect planning horizons, and therefore, profitability. We call this extension the multiperiod vehicle routing problem with profit (mVRPP). The goal is to determine routes for a set of vehicles that maximizes profitability from visited locations, based on the conditions that vehicles can only travel during stipulated working hours within each period in a given planning horizon and that the vehicles are only required to return to the depot at the end of the last period. We propose an effective memetic algorithm with a gianttour representation to solve the mVRPP. To efficiently evaluate a chromosome, we develop a greedy procedure to partition a given giant-tour into individual routes, and prove that the resultant partition is optimal. We evaluate the effectiveness of our memetic algorithm with extensive experiments based on a set of modified benchmark instances. The results indicate that our approach generates high-quality solutions that are reasonably close to the best known solutions or proven optima, and significantly better than the solutions obtained using heuristics employed by professional schedulers.
Behaviour of a Hybrid ILS Heuristic on the Capacitated Profitable Tour Problem
2018
In the present paper, we study the behaviour of a hybrid Iterative Local Search heuristic (ILS). A Large Neighborhood Search heuristic (LNS) and a Variable Neighborhood Descent with Random neighborhood ordering (RVND) are used in the local search phase of the proposed ILS. The approach is evaluated on a well-known variant of the Vehicle Routing Problem (VRP) called Capacitated Profitable Tour Problem (CPTP). In this variant, the vehicles are no longer required to visit all the customers. However, a specific profit is obtained each time a customer is visited. The goal of the CPTP is to design routes with maximum difference between collected profits and routing costs, which satisfy the capacity constraint of the vehicles. The experimental study consists in comparing different combinations of ILS, LNS and RVND. The computational results show that the hybridization of the three heuristics leads to better solutions. Furthermore, comparisons with a Variable Neighborhood Search and two Tabu Searches from the literature indicates that our hybrid approach is competitive.
A variable neighborhood search for solving the multi-vehicle covering tour problem
Electronic Notes in Discrete Mathematics, 2015
In this article, we consider a transportation problem with different kinds of locations: V , T , and W. The set T ⊂ V consists of vertices that must be visited through the use of potential locations in V and W consists of locations that must be covered. The problem consists in minimizing vehicle routes on a subset of V including T. We develop a variable neighborhood search heuristic based on a variable neighborhood descent in which a set of locations must be visited, whereas another subset must be close enough to the planned routes. We tested and compared our algorithm on datasets based on TSP Library instances.
Two new heuristic algorithms for Covering Tour Problem
2015
Covering Tour Problem (CTP) is the generalized form of Traveling Salesman Problem (TSP), which has found different applications in the designing of distribution networks, disaster relief, and transportation routing. The purpose of this problem is to determine the Hamiltoniancyclewiththe lowest costusinga subset of all the nodes, such that the other nodes would be in a distance shorter than the pre-specified one, from at least one visited node. In this paper, two new heuristic algorithms called MDMC and AGENI are offered to solve CTP. In order to assess the performance of the proposed algorithms in small scale, several test problems are accurately solved and the results compared with those from the proposed heuristic algorithms. Also, in large scales, the results of each of proposed algorithms are compared with the three heuristic algorithms existing in the literature. Finally, the effect of neighborhood searcheson the performance of the proposed algorithms will be investigated. The ...