Because of non-deterministic polynomial time hardness of job-shop scheduling problem (JSP), approximate optimization based on meta-heuristics has been actively discussed. Considering position of planners in production sites, it is desirable to develop a method in which their know-how is respected. An approach for meeting this requirement is to set the schedule generated by a planner as the initial solution and then gradually improve the solution by repeating a search in its neighborhood so that he/she can follow and thoroughly examine the improved solution. For this reason, this research is focused on scheduling using simulated annealing (SA). Because SA has a disadvantage that good solutions cannot be obtained efficiently if the initial solution has not been given appropriately, methods for solving this problem have been proposed for JSPs aiming at minimizing makespan. In high-mix low-volume manufacturing, it is also important to minimize production lead time to reduce work-in-process inventory. This research takes up production lead time defined as the time between the starting and the finishing times of a job considering strong constraint on places for putting worksin-process in production of large equipment, and deals with development of an efficient method using SA for JSPs aiming at minimizing the average value of the production lead times. Two methods of neighborhood limitation in SA for reducing the evaluation value were developed by focusing on waiting time of operations. It was proven that using one of the proposed methods in SA with appropriate probabilities is effective to JSPs of a certain size by numerical examples.

This paper is concerned with performing job shop scheduling considering discrete uncertainty based on proactive approach. In production of large products, there often are cases where a work-in-process needs to undergo a reworking or a reprocessing depending on the result of an inspection process. Generally speaking, it is natural to cope with this type of uncertainty based on reactive approach, since it is discrete and makes a drastic change on the original production scenario. However, this may result in an unsatisfactory schedule which requires unreasonable overtime works or fails to keep due dates, etc., because the initial schedule is generated without considering the uncertainty and is updated considering only the information that is certain at the time of updating it. For this reason, we developed the following method based on proactive approach: (i) a set of consistent schedules each of which corresponds to a production scenario determined by an inspection result is generated by using genetic algorithm so that the weighted sum of overtime work, tardiness and makespan or earliness is minimized; (ii) the production starts based on the initial schedule that corresponds to the scenario in which no reworkings/reprocessings are necessary, and then the schedule is switched by the one which corresponds to the new scenario each time when it turns out that a reworking/reprocessing needs to be carried out. Comparison to reactive scheduling approach using numerical examples showed effectiveness of the proposed method.

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