6. Solving the MT-VRPB using a Collaborative Sequential Mat-heuristic approach
6.1. The Mat-heuristic Approaches
6.1.1. Matheuristics for VRPs: Brief Literature Review
One of the early studies is by Foster and Ryan (1976) in which an improvement heuristic that incorporates the solution of a mixed-integer linear programming (MILP) model is proposed for the VRP. In this study, a set partitioning formulation for the VRP is presented first and then a matheuristic algorithm is proposed. At first phase, a set of petal routes is generated using a heuristic construction method known farthest away cheapest insertion method. The reason behind calling them petal routes is because of their resemblance to petals as they are rooted at the depot. In the second phase, set partitioning formulation is solved on the set of routes obtained at first phase. The algorithm was tested on data set containing fifteen instances ranging in size from 21 to 100 customers. The computational results show improvements when compared with previously published results.
Fisher and Jaikumar (1981) proposed a cluster-first-route-second method. In this algorithm, heuristic is used to select so-called seed customers and then in order to assign the remaining customers to the seed customers, an assignment problem is solved to optimality at first phase. Where each seed customer pinpoints a cluster of customers associated with it. At second phase, a Travelling Salesman Problem (TSP) is solved on
137 each cluster to obtain the final set of routes. The algorithm was tested on 12 VRP standard problem instances and outperformed previously published studies.
In 1995, Bramel and Simchi-Levi proposed a method similar to that of Fisher and Jaikumar (1981) for the VRP. This methodology is based on the routing problem type formulation as a Capacitated Concentrator Location Problem (CCLP). The basic idea in this algorithm is to identify seed customers in order to estimate the cost of assigning each customer to each seed customer and then solve a CCLP to determine the customer clusters. Hence, after determining the clusters, a TSP is solved on each cluster to obtain the solution. The computational results show that this algorithm outperforms all published heuristics when tested on a set of standard test problems.
Rochat and Taillard (1995) proposed a matheuristic algorithm for the VRP. In the first phase, a heuristic based on local search algorithm is used to solve the VRP. Hence all routes obtained at this phase are stored in a set P. At second phase, in order to choose best routes from set P, a set partitioning model is solved to optimality. The algorithm was tested on various problem instances from the literature and solutions of 40 instances are improved compared to previous published work.
Kelly and Xu (1999) proposed a set partitioning based heuristic for the VRP. This algorithm is similar to that of Foster and Ryan (1976). In the first phase, different solutions are obtained using a simple and fast construction heuristics. In the second phase, a set partitioning model is solved in order to select the best routes from the set of all routes. The algorithm is tested on VRP benchmark instances. The computational results show that this algorithm found same solutions in most cases when compared with the best known published results.
138 De Franceschi et al. (2006) proposed a new ILP-based refinement heuristic for vehicle routing problems. In this algorithm, the initial solution is constructed by taking the best known solution in the literature. Then chains of customers are removed from the solution; hence, a large number of chains is organized from the removed chains of customers and various insertion points are pinpointed in the partial solution. Then chains of removed customers are inserted in the insertion points by solving the MILP model to optimality. The algorithm is tested on two data sets from the literature. The results presented show that the algorithm found better solutions in some cases when compared with the best known in the literature.
Archetti and Speranza (2008) proposed an optimization-based heuristic for the split delivery vehicle routing problem (SDVRP). An integer program based on the extension of the classical set-covering model is used as master program and the tabu search works as a subordinate (slave) method. That is, tabu search is used once and frequency counters are used to analyse the obtained set of solutions by tabu search. This is done in order to specify the number of times a particular edge was part of a solution and to point out whether particular customers’ demand was split. όurthermore, the solutions came across by the tabu search in which a customer that is never or rarely split has been served by a single vehicle in high-quality solution. Likewise the edges which are encountered frequently during tabu search are likely indicated as a part of near-optimal solution. A set of promising routes R has been generated by the frequency counters and desirability measures are used in order to sort out this set. Finally, a subset of routes r has been taken from the set of promising routes R and based on that subset of routes, a set-covering problem is solved iteratively. The computational results show that the
139 initial solutions obtained by the tabu search are improved by the proposed method in all test instances except one.
Schmid at el. (2009) proposed a hybrid solution approach based on the integer multicommodity network flow (MCNF) component and variable neighbourhood search (VNS) for the ready-mixed concrete delivery problem. The proposed hybrid approach belongs to the collaborative category of matheuristics. Therefore the information between an integer MCNF component and VNS is exchanged in a bi-directional way in order to obtain the high quality solutions. First the MCNF component is solved, that is initialized with a randomly generated set of patterns. Then VNS is used to further improve the best solution iteratively in order to enrich the pool of patterns used by the MCNF. Finally the MCNF component is used again to obtain even better solutions.
Computational experiments are done using a real-life data taken from a concrete company. The obtained computational results show that the proposed hybrid approach performed better when compared with the solution obtained by a commercial approach (based on simulated annealing meta-heuristic) developed specially for this type of problems.
Rei et al. (2010) presented a hybrid algorithm the single VRP with stochastic demands.
This methodology employs both local branching heuristic and Monte Caro sampling in order to divide the solution space in sub-regions; hence obtaining sub-problems. A MILP model is then used to solve the sub-problems. A sub-set of instances ranging in size from 60 to 90 customers are tested using this methodology. The algorithm proved quite effective in terms of solution quality.
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