Firms buy factor inputs in the factor market and sell value-added outputs to customers in the product market. Firms also engage in economic exchanges along (vertically related exchange) and across the value chain (horizontally related exchange). The combination of these valueadding and exchange functions determine a firm's performance. Another important determinant affecting firm performance relates to organisational growth in terms of capacity. This paper examines how the factor and product markets are related to organisational capacity and firm performance in the container shipping industry. The findings revealed that the price on the product market (i.e. freight rate) is positively related to the production capacity of the industry, but the price on the factor market (i.e. charter rate) does not significantly influence liner shipping firms' decision on their fleet size adjustment. This study also illustrates the relationship between firm size and level of vertical expansion for carriers to own ships rather than chartering ships from the factor market. Successful firms tend to grow. Expansion can be a strategy for firms to achieve performance gains. To understand vertical expansion in this industry, a regression equation model is developed for the reference of managers to predict the level of ordering of new ships by ocean carriers. In addition, this study used objective data to empirically test the relationship between organisational capacity and firm performance, shedding light on the profitability of ocean carriers.

There are several manufacturing applications in which a tool needs to move along a prescribed path performing machining operations. The path is typically described by a sequence of curves. For the entire process, the tool must move along each curve exactly once. For typical paths, significant lime may be wasted in the movement between the end point of one curve to the start vertex of the next one along which the tool must operate. Normally, this non-machining motion is a straight-line motion. A good process plan would minimize the time wasted on such motion. An excellent application of this problem is found in the increasingly popular Layered Manufacturing (LM) methods. We first introduce a Genetic Algorithm (GA)-based approach to solve this problem. Next, we present a new strategy using a combination of the Asymmetric Traveling Salesman Problem and Integer Programming (TSP-IP) to solve it. Based on the pros and cons of these approaches, two enhanced GA formulations are developed. We compare the performance of the different techniques, with a view to their application to real-time path planning in LM applications.

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