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Location and Scheduling Trucks at Cross Docking Systems

Esmaeeli, Ehasn | 2016

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 48256 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Hajji, Alireza
  7. Abstract:
  8. Given the increased competition in supply chains and enlargement of their scale along with increasingly significant role of distribution systems, design and optimization of novel systems with comprehensive approaches in a timely manner has become important more than ever. One of such modern, efficient systems is cross docking in which the distribution network is omitted and thus operations are carried out faster and the productivity is increased. In this system, processes like packaging is done in cross docking and the goods and products are directly sent to customers. This study investigates a long-term and a short-term approach to the design of in two distinct problems. In the first problem, as a long-term approach, a facility location planning is considered. The solution of the first problem is then used in the second problem for short-term decision making for the transportation vehicles routing problem. In the first problem, the objective function is designed as minimization of costs while in the second problem the objective function is defined to minimize costs as well as emissions from the vehicles. To effectively solve the proposed mathematical model, a metaheuristic hybrid algorithm based on genetic algorithm, multi-objective genetic and ants colony algorithm is developed and as demonstrated by the results, this hybrid approach significantly increases the performance of the algorithm. Furthermore, in order to improve the performance of the algorithm, in the ant colony algorithm, modifications have been made to the evaporation of the pheromone in order to achieve the best performance
  9. Keywords:
  10. Ant Colony Algorithm ; Cross Docking ; Genetic Algorithm ; Supply Chain ; Non-Dominate Sorting Genetic Algorithm (NSGAII) Method

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