An adaptive simulated annealing algorithm-based approach for assembly line balancing and a real-life case study

Type-2 Assembly Line Balancing Using Reactive Tabu Search

“…Data set includes seven problems with task numbers differing between 29 and 111: Buxey (29,8,7,14), Sawyer (30,8,7,14), Gunther (35,10,6,15), Kilbridge (45,9,3,11), Tonge (70, 23,3,25), Arcus1 (83,20,3,22), Arcus2 (111,25,3,27). The numbers in the parenthesis indicate the task numbers, the number of test instances contained in the relevant ALBP, minimum and maximum number of workstations of test instances in the relevant ALBP, respectively.…”

“…Triki et al [24] presented a GA which is hybridised with a local search procedure to solve an extension of SALBP-2 named 'Task Restrictions Assembly Line Balancing Problem' of type 2. Güden and Meral [25] proposed an adaptive SA approach which can be used to solve any deteriministic ALBP including SALBP-2. Their computational experiments on several SALB test problems from the literature showed that for most of the intances optimal solutions are obtained.…”

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“…(1-4) [9] Where; j= 1, 2 ….. k (represent the task number) = the quantity demand of model i = time to perform task j for model i (min), p= the number of models to be produced during the period: and i is used to identify the model, i = 1, 2 ….. p. TT j : represents the total time required for task j to accomplish all models that represents the time of the combined model which is showed in figure 6. Where; n = represent the number of stations.…”

An improved immune algorithm for simple assembly line balancing problem of type 1

“…Computational results on both showed that the method was efficient. Guden and Meral 27 proposed an adaptive simulated annealing method to solve the real life SALBP-1 of a dishwasher producer which consisted of approximately 300 tasks, 400 precedence relations per product model. Performance of the algorithm was tested on several problem instances from the literature and found to be satisfactory.…”