Inclined slipway optimization in a shipyard

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In most of the world’s shipyards, ships over 2500 t are launched using the inclined slipway method. The biggest problem in shipyards, efficient use of inclined slipway capacities where the blocks turn into mega-blocks, directly affects the purchases of new projects. The scope of this work is inclined slipway layout optimization work among ongoing projects at a shipyard and the placement of possible proposal projects in the current project flow with the best solution. Within the scope of the study, first, the slipway restrictions and then the slipway activities were determined. Minimum and maximum limits have been established for restrictions. Constraints have been created for the start and end dates for slipway use, the occupancy time of the slipway, the occupancy capacity of the slipway, the placement of the ship in the slipway cells and the width and length of the ships. Accordingly, the interdependencies and durations of the activities were determined. The presence of four cells was assumed with a slipway angle of 5° and a slipway vessel settlement area of 25 × 100 m. Among the 19 ongoing projects in a shipyard, four proposal projects were tried to be placed with the best layout and cost. In this study, three algorithms were tried. First, the ‘greedy’ algorithm and, second, the greedy and local search algorithms were run together, and the latest variable neighborhood search (VNS) algorithm was applied. Afterward, the greedy + local search algorithm and VNS algorithm were tested and the optimization objective function was determined. For this, slipway capacity calculation, excess capacity calculation and cost calculation were carried out. By performing these calculations, it was ensured that the cost and the time schedule were optimized so that the activities stayed between the start and end dates. Although the greedy algorithm was tried for project 4, the best result was obtained by the greedy + local search and VNS algorithms. As a result of the local search application, the authors applied the greedy algorithm on project 3; the cost was lower. The best result for project 2 was achieved with greedy + local search, and the greedy + local search and VNS algorithms for project 1 were run separately, and the cost was minimized.