In this research, we investigate a transshipment scheduling problem in which a set of loading and unloading jobs must be performed at a single station of a transshipment terminal. We assume that there is a set of product types and an inventory storage center with a constant capacity for each. Each loading job decreases the inventory level of a particular product type and has a predetermined due date, while each unloading job increases the inventory level of a product type and has a given release date. An inbound truck delivers a set of products, which have the same release dates and must be unloaded, to the inventory storage center. In contrast, an outbound truck conveys a set of goods, which have identical completion times and must be loaded. We aim to minimize the total freight cost of trucks as well as the total weighted tardiness of products. To this end, we develop a linear integer programming model and a variable neighborhood search algorithm including a set of efficient local search procedures. Moreover, we run the two previously developed metaheuristics, i.e. a genetic algorithm and a particle swarm algorithm so as to solve the problem. Using a set of randomly generated test instances, we perform extensive computational experiments and statistical analyses to highlight the efficiency and superiority of the developed algorithm.

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