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Third party logistics service providers (3PLs) have an important role in supply chain management. Increasing cooperation with 3PLs is expanding in today’s business environment. Hence, 3PLs need to have an efficient distribution network to meet customer demands. Nevertheless, few researches have tried to propose a solution for distribution network problems of 3PLs. The optimization problem which is discussing in our study is solved in two stages. At the first stage, the assignment problem which includes assigning the order of the vehicles is solved with mixed integer programming by using GAMS 21.6/CPLEX. The output of the first stage is used as an input in the second stage. In this stage routes are determined for vehicles by developing a genetic algorithm by using C#.

Third party logistic (3PL) has become more important for logistic sector in recent years. Companies want to reduce the costs and provide customer satisfaction exactly. They don’t want to deal with logistics problems, so they prefer special firms for some or all of their logistics operations. Therefore, a third party logistics (3PL) business is emerging and developing rapidly to fulfill the demands for advanced logistics services, in such fields as, transportation, warehousing, freight consolidation and distribution, product marking, labeling and packaging, inventory management, cross docking, product returns, order management, and logistics information systems (Rabinovich, Windle, Dresner, & Corsi, 1999). There are different definitions for 3PL in the literature. Some broad definition for 3PL are “the use of external companies to perform logistics functions that have traditionally been performed within an organization” (Lieb, 1992) and “an external organization that performs all or part of a company’s logistics functions” (Coyle, Bardi, & Langley, 2003). Bask (2001) describes 3PL as ‘‘relationships between interfaces in the supply chains and third-party logistics providers, where logistics services are offered, from basic to customized ones, in a shorter or long term relationship, with the aim of effectiveness and efficiency”. The vehicle routing problem (VRP) can be described as the problem of designing optimal delivery or collection routes from one or several depots to a number of geographically scattered cities or customers, subject to side constraints. The VRP plays a central role in the fields of physical distribution and logistics. The vehicle routing problem lies at the heart of distribution management (Laporte, 1992). A vehicle routing problem (VRP) is one of visiting a set of customers using a fleet of vehicles, respecting constraints on the vehicles, customers, drivers, and so on. The goal is to produce a low cost routing plan specifying for each vehicle, the order of the customer visits they make. Industrial VRPs tend to be large, and so local search techniques are used extensively as they scale well and can produce reliably good solutions (Shaw, 1998). Genetic Algorithms (GAs) have seen widespread application to various combinatorial optimization problems, including certain types of vehicle routing problem, especially where time windows are included (Baker & Ayechew, 2003). GAs employ search procedures based on the mechanics of natural selection and survival of the fittest. In the GAs, which use multiple point search instead of single point search and work with the coded structure of variables instead of actual variables themselves, the only information required is the objective function thereby making the searching for global optimum simpler (Das, 2002). Many researchers have conducted studies to solve vehicle routing problems using mathematical models and heuristic algorithms. These are some examples of recent studies: Hadjar and Soumis (2009) used a branch-and-price approach price approach to solve the multiple depot vehicle scheduling problem with time windows (MDVSPTW). They developed a dynamic time window reduction technique. This technique is used at every node of the branch-and-price tree to tighten the time window. Chen, Hsueh, and Chang (2009) proposed a nonlinear mathematical model to consider production scheduling and vehicle routing with time windows for perishable food products. They used a constrained Nelder–Mead method and a heuristic method for the vehicle routing with time window to solve the problem. Zachariadis et al. (2009a) proposed a metaheuristic methodology for the capacitated vehicle routing problem with two-dimensional loading constraints. Tabu search and Guided local search are used together in this methodology. The aim of the paper is to find the minimum cost routes, starting and terminating at a central depot. Hemmelmayr et al. (2009) developed a new heuristic for the periodic vehicle routing problem without time window. The method is based on variable neighborhood search. In the periodic vehicle routing problem there is a planning horizon of several days and the customers must be visited more than once. Fleszar, Osman, and Hindi (2009) proposed a variable neighborhood search heuristic for open vehicle problem. Proposed solution is based on reversing segments of sub-routes and exchanging segments between routes. Li et al. (2009) proposed a Lagrangian relaxation based-heuristic for the real-time vehicle rerouting problems with time windows. In real-time vehicle rerouting problems there are service disruption because of vehicle breakdowns. So some vehicles must be rerouted. Fuellerer et al. (2009) developed an effective heuristic based on ant colony optimization for the two-dimensional loading vehicle routing problem. There is a combination of two problems: Loading of the freight into the vehicles and routing the vehicles successfully. Zachariadis et al. (2009b) tried to propose an effective hybrid metaheuristic algorithm. The algorithm is a combination of tabu search and guided local search methodologies. There is simultaneous delivery and pick-up service in this type of vehicle routing problem. Genetic Algorithm (GA) is a popular algorithm for solving vehicle routing problems. Some of the studies are as follows: The study of Wang and Lu (2009) primarily focused on solving a capacitated vehicle routing problem (CVRP) by applying a novel hybrid genetic algorithm (HGA) capable of practical use for manufacturers. The search mechanism embedded in the GA focuses on the breeding process in evolution on crossover and mutation operators that are applied using probability setting to approach a close-to-optimal solution. Liu et al. (2009) studied the fleet size and mix vehicle routing problem (FSMVRP), in which the fleet is heterogeneous and its composition to be determined. They design and implement a genetic algorithm (GA) based heuristic. On a set of twenty benchmark problems it reaches the best-known solution 14 times and finds one new best solution. It also provides a competitive performance in terms of average solution. The paper of Prins (2009) presented two memetic algorithms (genetic algorithms hybridized with a local search) able to solve both the VFMP (The vehicle fleet mix problem) and the HVRP (The heterogeneous fleet VRP). They are based on chromosomes encoded as giant tours, without trip delimiters, and on an optimal evaluation procedure which splits these tours into feasible trips and assigns vehicles to them. Ho et al. (2008) studied the MDVRP (multi-depot vehicle routing problem) because the number of depots is not limited to one in many real-world situations. Besides routing and scheduling, the grouping problem is also considered in the MDVRP. Because the MDVRP integrates three hard optimization problems, a Hybrid genetic algorithm (HGA) rather than a simple GA was developed. The paper of Prins (2004) bridged the gap by presenting a relatively simple but effective hybrid GA. The framework of this research is the development of effective metaheuristics for hard combinatorial optimization problems met in vehicle routing. It is surprising to notice in the literature the absence of effective genetic algorithms (GA) for the vehicle routing problem contrary to node routing problems with time windows or arc routing problems. The study of Baker and Ayechew (2003) considers the application of a genetic algorithm (GA) to the basic vehicle routing problem (VRP), in which customers of known demand are supplied from a single depot. There are limited numbers of papers about vehicle routing problems of 3PLs. Some examples can be given as follows: Krajewska and Kopfer (2009) used a tabu search algorithm for solving the integrated transportation planning problem. Zäpfel and Bögl (2008) consider short-range weekly planning on the part of postal companies that must decide about pickup tours and delivery tours for fluctuating volume (number of shipments), with time windows for the demand points, in consideration of variable vehicle capacities and personnel planning, and including outsourcing decisions for tours and drivers for 3PLs. Tan et al. (2006) developed a hybrid multi-objective evolutionary algorithm for truck and trailer vehicle routing problem. The purpose of this study is to minimize the routing distance and the number of trucks. The remainder of this paper is organized as follows. The theoretic descriptions for genetic algorithms are presented in Section 2. The proposed methodology is described in Section 3. Section 3 also, explains a numerical example in Istanbul. Finally, the results and the conclusion are presented in Section 4.

In this study, a new methodology is developed to determine optimal routes for vehicles with different capacities and different costs for logistic companies. In the model, the customer orders have been met substantially with the aim of low-cost route suggestions. Mixed integer programming has been used for the assignment of the orders to the vehicles with the different capacities. Subsequently the suitable routes have been determined with the help of genetic algorithm method. It is seen that, this solution is such a considerably acceptable solution with the low cost. At the same time, it is seen that finding the solution is getting hard if the size of the problem increases. But this problem is deliberated; the optimal solution can be useful for today’s logistics companies.