عنوان مقاله [English]
In today's competitive world of distribution, companies are trying to reduce total costs by decreasing their expenses at every step of operations. One of these costs is the transportation cost. On the other hand, customers expect better and faster services and faster loading and transportation of goods and services are the ways to satisfy this request. One of the ways to achieve faster loading and transportation is to use cross-docks. A cross-dock is a warehouse, which is used to have a more efficient distribution within a supply chain. In this warehousing strategy, goods are usually stored in the cross-dock for less than 24 hours and several docks are assigned for loading (unloading) goods on (from) the trucks, which depart (arrive) from (at) the cross-dock.
One of the purposes of using cross-docks in supply chains is to reduce the distribution costs by managing the material flow. In addition, the purpose of cross-dock management is to reduce the operational and distribution costs, which gradually result to reducing the total cost of a supply chain. There are several problems in cross-dock management. Two of which are more important than
others are: dock assignment and truck routing. Having considered these problems
simultaneously, we can significantly reduce the total cost. In this paper, we
address a dock assignment and truck routing problem within cross-docks and
propose a mixed integer mathematical model for the problem. Also according to
the importance of customer's visiting time, in the proposed model customers
time windows also are considered. Regarding the NP-Hardness of the mentioned
problem, we propose a meta-heuristic algorithm based on Simulated Annealing
(SA). For evaluating the performance of the proposed algorithm, we solve
several problems with small dimension with proposed algorithm, a Tabu Search
(TS) algorithm and exact method (GAMS software). In addition, several problems
with large dimension solved by SA and TS and results are compared. These comparisons demonstrate the outperformance of the proposed Simulated Annealing (SA) algorithm.