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Nowadays, organizations have to compete with different competitors in regional, national and international levels, so they have to improve their competition capabilities to survive against competitors. Undertaking activities on a global scale requires a proper distribution system which could take advantages of different transportation modes. Accordingly, the present paper addresses a location-routing problem on multimodal transportation network. The introduced problem follows four objectives simultaneously which form main contribution of the paper; determining multimodal routes between supplier and distribution centers, locating mode changing facilities, locating distribution centers, and determining product delivery tours from the distribution centers to retailers. An integer linear programming is presented for the problem, and a genetic algorithm with a new chromosome structure proposed to solve the problem. Proposed chromosome structure consists of two different parts for multimodal transportation and location-routing parts of the model. Based on published data in the literature, two numerical cases with different sizes generated and solved. Also, different cost scenarios designed to better analyze model and algorithm performance. Results show that algorithm can effectively solve large-size problems within a reasonable time which GAMS software failed to reach an optimal solution even within much longer times.

In today's competitive business, buying and returning products have become a common practice because of incompleteness of the products or the failure to meet the customer's satisfaction or reusing products. Before handling this cycle, companies need a proper logistics network because of its impact on the e ciency and responsiveness of the supply chain. In this research, a forward and reverse logistics network was proposed for product distribution and collection. The contribution of this paper is a multiperiod, multi-echelon integrated forward and reverse supply chain network design problem with transportation mode selection. Di erent decisions including determination of the optimum number and locations of facilities, facilities' opening time, and transportation mode selection were considered in this paper. Due to the multi-period nature of the problem, the problem is exible for future periods. A mixed integer nonlinear programming model was proposed for the introduced problem, considering di erent levels of facility capacities. As another contribution, a genetic algorithm was developed to cope with the problem's complexity, especially for large-sized instances. E ectiveness and reliability of the algorithm, evaluated by solving several random instances with the obtained numerical results and comparisons, con rmed the capability of the proposed algorithm to nd good solutions within an acceptable processing time period.

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