Abstract
Uncertainty is common in ship construction. However, few studies have focused on scheduling problems under uncertainty in shipbuilding. This paper formulates the scheduling problem of panel block construction as a multiobjective fuzzy flow shop scheduling problem (FSSP) with a fuzzy processing time, a fuzzy due date, and the just-in-time (JIT) concept. An improved multiobjective particle swarm optimization called MOPSO-M is developed to solve the scheduling problem. MOPSO-M utilizes a ranked-order-value rule to convert the continuous position of particles into the discrete permutations of jobs, and an available mapping is employed to obtain the precedence-based permutation of the jobs. In addition, to improve the performance of MOPSO-M, archive maintenance is combined with global best position selection, and mutation and a velocity constriction mechanism are introduced into the algorithm. The feasibility and effectiveness of MOPSO-M are assessed in comparison with general MOPSO and nondominated sorting genetic algorithm-II (NSGA-II).
Highlights
Large bulk carriers, tankers, and container ships are characterized by large block coefficients and long parallel middle bodies
Note that the ROV rule is more available for a flow shop scheduling problem with a relatively large number of jobs (e.g., n = 10, 20, or larger), for in such cases the problem that different positions of the particles map to the same permutations of jobs is pretty rare and has little or no adverse effect on the effectiveness of the optimization algorithms
Real-time production data are used to test the performance of the proposed algorithm
Summary
Tankers, and container ships are characterized by large block coefficients and long parallel middle bodies. Several studies on multiobjective fuzzy job shop scheduling problems (JSSPs) are applicable because FSSPs are a special case of JSSPs. Sakawa and Kubota [10] employed genetic algorithms to solve a multiobjective fuzzy JSSP with a fuzzy processing time and a fuzzy due date. The objectives of multiobjective fuzzy JSSPs include minimizing the maximum fuzzy completion time, minimizing the number of tardy jobs, maximizing the minimum agreement index of the fuzzy due date and fuzzy completion time, and maximizing the average agreement index These objectives can be considered in multiobjective fuzzy FSSPs. Multiobjective fuzzy FSSPs can be considered to be similar to a host of actual flow shop production cases. We formulate the scheduling problem of panel block construction as a multiobjective fuzzy FSSP with a fuzzy processing time, a fuzzy due date, and the JIT idea.
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