Nowadays, the e-commerce has brought a profound change for economy and society. With the further development of e-commerce, the uncertainty for the customers with the last-mile delivery has brought more and more attention for the planning in logistic. In this research, a SCMVRPPSTW model is constructed to make an application for logistic routing planning problem. Considering the stochasticity of the problem, two two-stage heuristic algorithms are proposed. For the first stage method, the first algorithm simply used an insertion heuristic to build the initial route while the second algorithm adding the genetic algorithm to escape the local optima and search for better result. The second stage solving method is based on an existing metaheuristic developed for the TOPTW, which can proposed a fast re-optimizing for the adjusted routing.
The approximate test problems are generated by revising the Solomon’s benchmarks test problems. The results from the algorithms were compared with the result from MIP model using Gurobi Solver running for 10800 seconds. In the small testing, the GA with ILS has more standard presents than insertion with ILS, while both heuristics are able to obtain optimal or near optimal solutions for the tested problems in an acceptable computational time.
There are several directions for the future study. In this study, routes are assumed to start as a distribution center and end at a collection center. Therefore, it is a single depot problem. For large companies, the goods may be stored in more than one collection center which vehicles can be
stationed. Thus, how to extend one depot to multiple depots is a direction of the further study.
Meanwhile, each vehicle is assumed to perform at most one route in the same planning period in this research. In some practical applications, the vehicle capacity is small or the planning period is large, performing more than one route per vehicle may be more appropriate for practical
implementation. In urban areas, where travel times are rather small, it is often the case that after performing short tours vehicles are reloaded and used again. Hence, how to extend one trip to multiple trips is also a direction of the future works.
Moreover, the study only consider the stochasticity of the customer for logistics problem and the traveling time for each routes is set as known. In real practice, the traveling time is usually not certain due to other time dependent properties of the network such as congestion levels, incident location, and construction zone on certain road segments. Sometimes when facing a traffic jam, the deliverymen change the routing while the delivery. Therefore, how to extend the problem with a dynamic planning is also a direction of the future works.
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