Coordinated Rescheduling of Precast Production


Schedule disturbances are common and inevitable in the process of precast production. Not only is it necessary for the precaster to repair existing production schedule to accommodate these unexpected changes, it is also critical that the precaster and the contractor reach agreement on a new delivery schedule. However, the current practice of rescheduling is rudimentary in terms of computer support and depends largely on human experience. Without a proper exploration of the possibilities to resolve the schedule disturbances, both parties are likely to adopt overly conservative assumptions to optimize their own interests. A more beneficial approach would be to incorporate specific requirements from both parties and support negotiation through computer-aided approaches to the generation of a range of alternatives meeting these requirements. This research has proposed and developed a coordinated production reactive scheduling model for this purpose. The fundamental basis of the model involves the formulation of the precast production rescheduling problem as a multiobjective optimization problem, in a way that includes the objectives from both the precaster and the contractor. A multiobjective genetic algorithm is applied in the global search procedure for a rich set of alternative repaired schedules. This search exploits the use of a solution representation that gives the best sequence and the corresponding heuristics needed to resolve the disturbances. The results from several examples in a case study have demonstrated the utility of the procedure developed, principally in automating the generation of alternative schedules that involve different degrees of trade-off between the objectives. Unlike the commonly adopted approach to solve multiobjective optimization problems, this has been achieved without the need to pre-determine weights for the objectives. Comparisons between several GA-centric optimization techniques show that a variation of non-dominated sorting genetic algorithm with the elitist strategy proposed in this research is more consistent in locating non-dominated solutions along the Pareto front regardless of different mold utilization levels in production schedules. As a further enhancement to the proposed model, a local search process is implemented to conduct incremental exploration of the search space in specific areas identified by either the precaster or the contractor. The basic idea is to improve existing repaired schedules iteratively by searching for alternatives with specific characteristics in the neighborhoods of solutions on the Pareto front. This capability would be useful when minimal adjustments are needed for the alternatives generated by the global search in the first phase. The encouraging results obtained from the case study suggest that the proposed Min-Max Conflicts heuristic is capable of finding specific schedules by exploiting domain knowledge associated with specific constraints; furthermore, the local search can be completed within a reasonable amount of computational time. Together, the alternative schedules generated by the global search procedure as well as the specific schedules from the local search procedure provide the precaster and the contractor useful insight into the trade-offs between their objectives as they negotiate a new delivery schedule. Keywords: rescheduling, schedule coordination, multiobjective optimization, genetic algorithms, local search, precast production.