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35
A general heuristic for vehicle routing problems
 Computers & Operations Research
, 2007
"... We present a unified heuristic, which is able to solve five different variants of the vehicle routing problem: the vehicle routing problem with time windows (VRPTW), the capacitated vehicle routing problem (CVRP), the multidepot vehicle routing problem (MDVRP), the site dependent vehicle routing pr ..."
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Cited by 75 (3 self)
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We present a unified heuristic, which is able to solve five different variants of the vehicle routing problem: the vehicle routing problem with time windows (VRPTW), the capacitated vehicle routing problem (CVRP), the multidepot vehicle routing problem (MDVRP), the site dependent vehicle routing problem (SDVRP) and the open vehicle routing problem (OVRP). All problem variants are transformed to a rich pickup and delivery model and solved using the Adaptive Large Neighborhood Search (ALNS) framework presented in Ropke and Pisinger (2004). The ALNS framework is an extension of the Large Neighborhood Search framework by Shaw (1998) with an adaptive layer. This layer adaptively chooses among a number of insertion and removal heuristics, to intensify and diversify the search. The presented approach has a number of advantages: ALNS provides solutions of very high quality, the algorithm is robust, and to some extent selfcalibrating. Moreover, the unified model allows the dispatcher to mix various variants of VRP problems for individual customers or vehicles. As we believe that the ALNS framework can be applied to a large number of tightly constrained optimization problems, a general description of the framework is given, and it is discussed how the various components can be designed in a particular setting. The paper is concluded with a computational study, in which the five different variants of the vehicle routing problem are considered on standard benchmark tests from the literature. The outcome of the tests is promising as the algorithm is able to improve 183 best known solutions out of 486 benchmark tests. The heuristic has also shown promising results for a large class of vehicle routing problems with backhauls, as demonstrated in Ropke and Pisinger (2005).
Effective local search algorithms for routing and scheduling problems with general time window constraints
 TRANSPORTATION SCIENCE
, 2002
"... We propose local search algorithms for the vehicle routing problem with soft time window constraints. The time window constraint for each customer is treated as a penalty function, which is very general in the sense that it can be nonconvex and discontinuous as long as it is piecewise linear. In ou ..."
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Cited by 15 (3 self)
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We propose local search algorithms for the vehicle routing problem with soft time window constraints. The time window constraint for each customer is treated as a penalty function, which is very general in the sense that it can be nonconvex and discontinuous as long as it is piecewise linear. In our algorithm, we use local search to assign customers to vehicles and to find orders of customers for vehicles to visit. It employs an advanced neighborhood, called the cyclic exchange neighborhood, in addition to standard neighborhoods for the vehicle routing problem. After fixing the order of customers for a vehicle to visit, we must determine the optimal start times of processing at customers so that the total penalty is minimized. We show that this problem can be efficiently solved by using dynamic programming, which is then incorporated in our algorithm. We then report computational results for various benchmark instances of the vehicle routing problem. The generality of time window constraints allows us to handle a wide variety of scheduling problems. As such an example, we mention in this paper an application to a production scheduling problem with inventory cost, and report computational results for real world instances.
A variable neighborhood search heuristic for periodic routing problems
 European Journal of Operational Research
"... The aim of this paper is to propose a new heuristic for the Periodic Vehicle Routing Problem (PVRP) without time windows. The PVRP extends the classical Vehicle Routing Problem to a planning horizon of several days. Each customer requires a certain number of visits within this time horizon while the ..."
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Cited by 14 (1 self)
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The aim of this paper is to propose a new heuristic for the Periodic Vehicle Routing Problem (PVRP) without time windows. The PVRP extends the classical Vehicle Routing Problem to a planning horizon of several days. Each customer requires a certain number of visits within this time horizon while there is some flexibility on the exact days of the visits. Hence, one has to choose the visit days for each customer and to solve a VRP for each day. Our method is based on Variable Neighborhood Search (VNS). Computational results are presented, that show that our approach is competitive and even outperforms existing solution procedures proposed in the literature. Also considered is the special case of a single vehicle, i.e. the Periodic Traveling Salesman Problem (PTSP). It is shown that slight changes of the proposed VNS procedure is also competitive for the PTSP.
A Variable Neighborhood Search for the Capacitated Arc Routing Problem with Intermediate Facilities
"... The capacitated arc routing problem (CARP) focuses on servicing edges of an undirected network graph. A wide spectrum of applications like mail delivery, waste collection or street maintenance outlines the relevance of this problem. A realistic variant of the CARP arises from the need of intermediat ..."
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Cited by 9 (0 self)
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The capacitated arc routing problem (CARP) focuses on servicing edges of an undirected network graph. A wide spectrum of applications like mail delivery, waste collection or street maintenance outlines the relevance of this problem. A realistic variant of the CARP arises from the need of intermediate facilities (IFs) to load up or unload the service vehicle and from tour length restrictions. The proposed Variable Neighborhood Search (VNS) is a simple and robust solution technique which tackles the basic problem as well as its extensions. Particularly, it outperforms all known heuristics on four sets of benchmark instances.
An iterated local search algorithm for the timedependent vehicle routing problem with time windows
 Discrete Optimization
"... We proposed an iterated local search (ILS) algorithm for the timedependent vehicle routing problem with time windows in [1], and below are the detailed computational results of our ILS. We use Solomon’s benchmark instances [4] and Gehring and Homberger’s benchmark instances [2], which have been wid ..."
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Cited by 9 (0 self)
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We proposed an iterated local search (ILS) algorithm for the timedependent vehicle routing problem with time windows in [1], and below are the detailed computational results of our ILS. We use Solomon’s benchmark instances [4] and Gehring and Homberger’s benchmark instances [2], which have been widely used in the literature. We compare our computational results with the previous best known values as of June 2, 2004, presented in the web site
Reactive Search Optimization: Learning while Optimizing
"... The final purpose of Reactive Search Optimization (RSO) is to simplify the life for the final user of optimization. While researchers enjoy designing algorithms, testing alternatives, tuning parameters and choosing solution schemes — in fact this is part of their daily life — the final users ’ inter ..."
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Cited by 6 (3 self)
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The final purpose of Reactive Search Optimization (RSO) is to simplify the life for the final user of optimization. While researchers enjoy designing algorithms, testing alternatives, tuning parameters and choosing solution schemes — in fact this is part of their daily life — the final users ’ interests are different: solving a problem in the
A variable neighborhood search for the periodic vehicle routing problem with time windows
 PROCEEDINGS OF THE 9TH EU/MEETING ON METAHEURISTICS FOR LOGISTICS AND VEHICLE ROUTING
, 2008
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A branchandpricebased large neighborhood search algorithm for the vehicle routing problem with time windows. Networks
"... Given a fleet of vehicles assigned to a single depot, the vehicle routing problem with time windows (vrptw) consists of determining a set of feasible vehicle routes to deliver goods to a set of customers while minimizing, first, the number of vehicles used and, second, total distance traveled. A la ..."
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Cited by 3 (0 self)
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Given a fleet of vehicles assigned to a single depot, the vehicle routing problem with time windows (vrptw) consists of determining a set of feasible vehicle routes to deliver goods to a set of customers while minimizing, first, the number of vehicles used and, second, total distance traveled. A large number of heuristic approaches for the vrptw have been proposed in the literature. In this paper, we present a large neighborhood search algorithm that takes advantage of the power of branchandprice which is the leading methodology for the exact solution of the vrptw. To ensure diversification during the search, this approach uses different procedures for defining the neighborhood explored at each iteration. Computational results on the Solomon’s and the Gehring and Homberger’s benchmark instances are reported. Compared to the best known methods, the proposed algorithm produces better solutions, especially on the largest instances where the number of vehicles used is significantly reduced.
A hybrid solution approach for readymixed concrete delivery
 Transportation Science
, 2009
"... Companies in the concrete industry are facing the following scheduling problem on a daily basis: concrete produced at several plants has to be delivered at customers ’ construction sites using a heterogeneous fleet of vehicles in a timely, but costeffective manner. As the ordered quantity of concre ..."
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Companies in the concrete industry are facing the following scheduling problem on a daily basis: concrete produced at several plants has to be delivered at customers ’ construction sites using a heterogeneous fleet of vehicles in a timely, but costeffective manner. As the ordered quantity of concrete typically exceeds the capacity of a single vehicle several deliveries need to be scheduled in order to fulfill an order. The deliveries cannot overlap and the time between consecutive deliveries has to be small. Our solution approach effectively integrates optimization and heuristic techniques. Information is passed back and forth between an integer multicommodity flow optimization component and a variable neighborhood search component in order to find highquality solutions in a reasonable amount of time. Even though both components are capable of producing feasible solutions, the integrated approach is far more effective. Computational experiments show that our hybrid method outperforms a commercially available solution approach based on simulated annealing by more than 20 percent on average.