1,721,014 research outputs found
A heuristic with a performance guarantee for the commodity constrained split delivery vehicle routing problem
No full textThe commodity constrained split delivery vehicle routing problem (C-SDVRP) is a routing problem where customer demands are composed of multiple commodities. A fleet of capacitated vehicles must serve customer demands in a way that minimizes the total routing costs. Vehicles can transport any set of commodities and customers are allowed to be visited multiple times. However, the demand for a single commodity must be delivered by one vehicle only. In this work, we developed a heuristic with a performance guarantee to solve the C-SDVRP. The proposed heuristic is based on a set covering formulation, where the exponentially-many variables correspond to routes. First, a subset of the variables is obtained by solving the linear relaxation of the formulation by means of a column generation approach which embeds a new pricing heuristic aimed to reduce the computational time. Solving the linear relaxation gives a valid lower bound used as a performance guarantee for the heuristic. Then, we devise a restricted master heuristic to provide good upper bounds: the formulation is restricted to the subset of variables found so far and solved as an integer program with a commercial solver. A local search based on a mathematical programming operator is applied to improve the solution. We test the heuristic algorithm on benchmark instances from the literature. The comparison with the state-of-the-art heuristics for solving the C-SDVRP shows that our approach significantly improves the solution time, while keeping a comparable solution quality and improving some best-known solutions. In addition, our approach is able to solve large instances with 100 customers and six commodities, and also provides very good quality lower bounds. Furthermore, an instance of the C-SDVRP can be transformed into a CVRP instance by simply duplicating each customer as many times as the requested commodities and by assigning as demand the demand of the single commodity. Hence, we compare heuristics for the C-SDVRP against the state-of-the-art heuristic for the Capacitated Vehicle Routing Problem (CVRP). The latter approach revealed to have the best performance. However, our approach provides solutions of comparable quality and has the interest of providing a performance guarantee
A column generation based heuristic for the generalized vehicle routing problem with time windows
Full text linkThe generalized vehicle routing problem with time windows (GVRPTW) is defined on a directed graph G=(V,A) where the vertex set V is partitioned into clusters. One cluster contains only the depot, where is located a homogeneous fleet of vehicles, each with a limited capacity. The other clusters represent customers. A demand is associated with each cluster. Inside a cluster, the vertices represent the possible locations of the customer. A time window is associated with each vertex, during which the visit must take place if the vertex is visited. The objective is to find a set of routes such that the total traveling cost is minimized, exactly one vertex per cluster is visited, and all the capacity and time constraints are respected. This paper presents a set covering formulation for the GVRPTW which is used to provide a column generation based heuristic to solve it. The proposed solving method combines several components including a construction heuristic, a route optimization procedure, local search operators and the generation of negative reduced cost routes. Experimental results on benchmark instances show that the proposed algorithm is efficient and high-quality solutions for instances with up to 120 clusters are obtained within short computation times
Mixed integer programming formulations for the generalized traveling salesman problem with time windows
No full textThe generalized traveling salesman problem with time windows (GTSPTW) is defined on a directed graph where the vertex set is partitioned into clusters. One cluster contains only the depot. Each vertex is associated with a time window, during which the visit must take place if the vertex is visited. The objective is to find a minimum cost tour starting and ending at the depot such that each cluster is visited exactly once and time constraints are respected, i.e., for each cluster, a single vertex is visited during its time window. In this paper, four mixed integer linear programming formulations for the GTSPTW are proposed and compared. They are based on different definitions of variables. All the formulations are compact, which means the number of decision variables and constraints is polynomial with respect to the size of the instance. Dominance relations between their linear relaxations are established theoretically. Computational experiments are conducted to compare the linear relaxations and branch-and-bound performances of the four formulations. The results show that two formulations are better than the other ones
A heuristic branch-cut-and-price algorithm for the ROADEF/EURO challenge on Inventory Routing
No full textThis paper is part of the special section devoted to the ROADEF/EURO challenge on inventory routing. We propose an extended formulation that we address with a heuristic branch-cut-and-price method. Among the difficulties that we had to face are a fractional objective function, the simultaneous generation of constraints and columns, and a complex pricing problem. We evaluate our approach on the benchmark instances proposed for the challenge
A tutorial on Branch-Price-and-Cut algorithms
No full textThis paper provides a tutorial on Branch-Price-and-Cut (BPC) algorithms for a generic class of problems whose objective is to find a set of feasible paths in a graph while optimising a given objective function. The tutorial is split into two main parts. First, we describe the building blocks of a BPC algorithm: the Branch-and-Bound algorithm, the column generation procedure and the Branch-and-Cut algorithm. Then, we focus on the description of a BPC algorithm for the class of problems we consider. Precisely, we present the classical and advanced techniques that should be embedded in an efficient algorithm. Particular attention is devoted to the solution of the pricing problem in the case where it is formulated as an Elementary Shortest Path Problem with Resource Constraints. The aim of the tutorial is pedagogical. Hence, its intended reader is someone facing the first implementation of a BPC algorithm. Implementation tips and examples accompany the techniques and concepts to ease their comprehension. Precisely, the examples are based on the Capacitated Vehicle Routing Problem, which is a well-known problem belonging to the class we consider
A Branch-Price-and-Cut algorithm for the Multi-Commodity two-echelon Distribution Problem
No full textIn the Multi-Commodity two-echelon Distribution Problem (MC2DP), multiple commodities are distributed in a two-echelon distribution system involving suppliers, distribution centres and customers. Each supplier may provide different commodities and each customer may request several commodities as well. In the first echelon, capacitated vehicles perform direct trips to transport the commodities from the suppliers to the distribution centres for consolidation purposes. In the second echelon, each distribution centre owns a fleet of capacitated vehicles to deliver the commodities to the customers through multi-stop routes. Commodities are compatible, i.e., they can be mixed in the vehicles. Finally, customer requests can be split by commodities, that is, a customer can be visited by several vehicles, but the total amount of each commodity has to be delivered by a single vehicle. The aim of the MC2DP is to minimize the total transportation cost to satisfy customer demands. We propose a set covering formulation for the MC2DP where the exponential number of variables relates to the routes in the delivery echelon. We develop a Branch-Price-and-Cut algorithm (BPC) to solve the problem. The pricing problem results in solving an Elementary Shortest Path Problem with Resource Constraints (ESPPRC) per distribution centre. We tackle the ESPPRC with a label setting dynamic programming algorithm which incorporates ng-path relaxation and a bidirectional labelling search. Pricing heuristics are invoked to speed up the procedure. In addition, the formulation is strengthened by integrating capacity cuts and two families of valid inequalities specific for the multiple commodities aspect of the problem. Our approach solves to optimality 439 over the 736 benchmark instances from the literature. The optimality gap of the unsolved instances is 2.1%, on average
A branch-and-cut algorithm for the generalized traveling salesman problem with time windows
No full textThe generalized traveling salesman problem with time windows (GTSPTW) is defined on a directed graph where the vertex set is partitioned into clusters. One cluster contains only the depot. Each vertex is associated with a time interval, the time window, during which the visit must take place if the vertex is visited. The objective is to find a minimum cost tour starting and ending at the depot such that each cluster is visited exactly once and time constraints are respected, i.e., for each cluster, one vertex is visited during its time window. In this paper, two integer linear programming formulations for GTSPTW are provided as well as several problem-specific valid inequalities. A branch-and-cut algorithm is developed in which the inequalities are separated dynamically. To reduce the computation times, an initial upper bound is provided by a simple and fast heuristic. We propose different sets of instances characterized by their time window structures. Experimental results show that our algorithm is effective and instances including up to 30 clusters can be solved to optimality within one hour
Adaptive large neighborhood search for the commodity constrained split delivery VRP
No full textThis paper addresses the commodity constrained split delivery vehicle routing problem (C-SDVRP) where customers require multiple commodities. This problem arises when customers accept to be delivered separately. All commodities can be mixed in a vehicle as long as the vehicle capacity is satisfied. Multiple visits to a customer are allowed, but a given commodity must be delivered in one delivery.
In this paper, we propose a heuristic based on the adaptive large neighborhood search (ALNS) to solve the C-SDVRP, with the objective of efficiently tackling medium and large sized instances. We take into account the distinctive features of the C-SDVRP and adapt several local search moves to improve a solution. Moreover, a mathematical programming based operator (MPO) that reassigns commodities to routes is used to improve a new global best solution.
Computational experiments have been performed on benchmark instances from the literature. The results assess the efficiency of the algorithm, which can provide a large number of new best-known solutions in short computational times
A note on the lifted Miller-Tucker-Zemlin subtour elimination constraints for routing problems with time windows
No full textWe propose lifted versions of the Miller–Tucker–Zemlin subtour elimination constraints for routing problems with time windows (TW). The constraints are valid for problems such as the travelling salesman problem with TW, the vehicle routing problem with TW, the generalized travelling salesman problem with TW, and the general vehicle routing problem with TW. They are corrected versions of the constraints proposed by Desrochers and Laporte (1991)
- …
