| Summary |
Nowadays, more and more people go shopping on the way home or to the working place, rather than take a special trip for shopping. However, most of studies focus on maximizing the “covered” potential shoppers (making a dedicated trip), not maximizing the “captured” business flow (based on the pass-by trips). In addition, most of the previous studies aim to select the facility nodes from the potential facility site, without considering whether there are some existing facilities in the region. How would the newly introduced facilities affect the market share? Thus, the objective of this study is to efficiently determine the location of the facilities to maximize the captured customer flow in a competitive environment.
Given a specific market region, it is assumed there are some existing retailing facilities set up by the competitor, and the new operator plans to set up its own facilities to maximize its market share. From the academic point of view, this study is based on the competitive flow-capturing location allocation problem (FCLAP) and assumes that the potential customers can detour, away from their pre-determined route, to receive the service. The objective is to capture the most customers by the ideal facility allocation. To this problem, the model proposed in a past study is used. In addition, the Genetic Algorithm is used to design a solution algorithm that can serve as a better method to handle this problem.
In the numerical experiment, the proposed model and the solution algorithm have been tested with a middle-sized example network with 25 nodes respectively. The influence for the number of new facilities in the network has been examined. In designing the test problems, we simulate the locations of the existing facilities and the demand flows by random numbers. It is found the solution algorithm based on the genetic algorithm can achieve a better solution quality when compared with the greedy heuristic algorithm in the literature. Furthermore, the example network has been extended to examine the effect of problem scale. It is found that the genetic algorithm does perform better than the greedy heuristic algorithm with bigger networks. The test results show the developed model and solution approach can be used for dealing with the Flow Capturing Location-Allocation Problem and providing the decision support for the planners. |
