Optimal due date assignment and resource allocation in a group technology scheduling environment

Batch scheduling of identical jobs with controllable processing times

“…We conclude that the proposed solution is feasible under KKT, and thus the optimal number of batches and the batch sizes are: K n ¼ 4; n n 1 ¼ 17:40. From (16), n n j ¼ n n 1 ÀðjÀ1Þs; j ¼ 2; …; K n : n n 2 ¼ 12:40; n n 3 ¼ 7:40 and n n 4 ¼ 2:40: The optimal cost (flowtimeþcompression) is given by (17) ð∑C þ cN c Þ n ¼…”

“…Some of the early papers are: Van Wassenhove and Baker [4], Nowicki and Zdrzalka [5], Janiak and Kovalyov [6], Wan et al [7], Hoogeveen and Woeginger [8], Janiak et al [9], Shakhlevich and Strusevich [10], Wang [11] Akturk et al [12] and Wang and Xia [13]. More recently, Tseng et al [14] studied a single machine setting with controllable processing times and an objective of minimum total tardiness; Turkcan et al [15] studied a setting of parallel machines and objective functions of minimum manufacturing cost and total weighted earliness and tardiness; Shakhlevich et al [16] focused on the trade-off between the maximum cost (which is a function of the completion times) and the total compression cost; Shabtay et al [17] addressed due date assignment problems in a group technology environment; Gurel et al [18] considered failures of the machine and repair time, and focused on an anticipative scheduling approach; Wan et al [19] studied the problem of scheduling jobs of two-agents on a single machine with controllable processing times; Choi et al [20] focused on minimizing weighted completion time subject to an upper bound on the maximum compression cost; Leyvand et al [21] considered just-in-time scheduling on parallel machines; Yin and Wang [22] studied a model combining controllable processing times and learning; Wang and Wang [23] addressed the single machine problem of minimizing the total resource consumption subject to an upper bound on the total weighted flowtime; Wei et al [24] focused on a model in which the job processing times are a function of both resource consumption and the job starting times; Uruk et al [25] studied a two-machine flowshop problem with flexible operations and controllable processing times; and Wang and Wang [26] focused on convex resource dependent processing times and job deterioration.…”

<title>Study on application of production flow analysis in manufacturing enterprise based on fuzzy clustering algorithm</title>

“…Therefore, parts should be classified to form processing parts family according to the process similarity of parts. [2][3][4][5][6] This is a group of machine tool equipment for processing this group of parts. Therefore, the group production system is according to the principle of modular.…”

Group Scheduling with Two Competing Agents on a Single Machine

“…Many advantages have been pointed out through the wide applications of GT. For instance, changeover between different parts are simplified, thereby reducing the costs or time involved; parts spend less time waiting, which results in less work-in-process inventory; parts tend to move through production in a direct route, and hence the manufacturing lead time is reduced (see, e.g., Ham et al, 1985;Webster and Baker, 1995;Allahverdi et al, 2008;Shabtay et al, 2010;Li et al, 2011, etc).…”