Abstract
This paper introduces a new model and solution methodology for a real-world production scheduling problem arising in the electronics industry. The production environment is a high volume, just-in-time, make-to-order facility with volatile demand over many product families that are assembled on flexible lines. A distinguishing characteristic of the problem is the presence of non-traditional sequence-dependant setup costs, which complicate our ability to find high-quality solutions. The scheduling problem arose when product variety exceeded the mix that the existing lines could accommodate. A nonlinear integer programming formulation is presented for the problem of minimizing setup costs, and a greedy randomized adaptive search procedure (GRASP) is developed to find solutions. To select the GRASP parameter values, an efficient, space-filling experimental design method is used based on nearly orthogonal Latin hypercubes. The proposed methodology is tested on actual factory data and compared to a prior heuristic presented in the literature; our heuristic provides a cost savings in 7 out of the 10 cases examined, and an average improvement of 17.39 % which is shown to be highly statistically significant. This improvement is due in part to the introduction of a pre-processing step to determine preferential and non-preferential line assignment information.
Similar content being viewed by others
References
Allahverdi, A., Gupta, J. N. D., & Aldowaisan, T. (1999). A review of scheduling research involving setup consideration. OMEGA The International Journal of Management Science, 27(2), 219–239.
Allahverdi, A., Ng, C. T., Cheng, T. C. E., & Kovalyov, M. Y. (2008). A survey of scheduling problems with setup times or costs. European Journal of Operational Research, 187(3), 985–1032.
Bard, J. F. (1997). An analysis of a rail car unloading area for a consumer products manufacturer. Journal of the Operational Research Society, 48(9), 873–883.
Bitran, G. R., & Hax, A. C. (1977). On the design of hierarchical production planning systems. Decision Sciences, 8(1), 25–53.
Cioppa, T. M. (2002). Efficient nearly orthogonal and space-filling designs for high-dimensional complex models. Unpublished doctoral dissertation, Naval Postgraduate School, Monterey, CA.
Cioppa, T. M., & Lucas, T. W. (2007). Efficient nearly orthogonal and space-filling Latin hypercubes. Technometrics, 49(1), 45–55.
Feo, T. A., & Bard, J. F. (1989). Flight scheduling and maintenance base planning. Management Science, 35(12), 1415–1432.
Feo, T. A., & Resende, M. G. C. (1995). Greedy randomized adaptive search procedures. Journal of Global Optimization, 6(2), 109–134.
Feo, T. A., Venkatraman, K., & Bard, J. F. (1991). A GRASP for a difficult single machine scheduling problem. Computers & Operations Research, 18(8), 635–643.
Festa, P., & Resende, M. G. C. (2009a). An annotated bibliography of GRASP—Part I: algorithms. International Transactions in Operational Research, 16, 1–24.
Festa, P., & Resende, M. G. C. (2009b). An annotated bibliography of GRASP—Part II: applications. International Transactions in Operational Research, 16, 131–172.
Higgins, A. J., Hajkowicza, S., & Buib, E. (2008). A multi-objective model for environmental investment decision making. Computers & Operations Research, 35(1), 253–266.
Hirsch, M. J., Meneses, C. N., Pardalos, P. M., Ragle, M., & Resende, M. G. C. (2007). A continuous GRASP to determine the relationship between drugs and adverse reactions. Data Mining, Systems Analysis and Optimization in Biomedicine, 953, 106–121.
Kleijnen, J. P. C., Sanchez, S. M., Lucas, T. W., & Cioppa, T. M. (2005). State-of-the-art review: a user’s guide to the brave new world of designing simulation experiments. INFORMS Journal on Computing, 17(3), 263–289.
Kolisch, R. (2001). Make-to-order assembly management. Berlin: Springer.
Laporte, G., Riera-Ledesma, J., & Salazar-González, J. J. (2003). A branch-and-cut algorithm for the undirected traveling purchaser problem. Operations Research, 51(6), 940–951.
Loveland, J. L., Monkman, S. K., & Morrice, D. J. (2007). Dell uses new production scheduling heuristics to accommodate increased product variety. Interfaces, 37(3), 209–219.
Mavridou, T., Pardalos, P. M., Pitsoulis, L. S., & Resende, M. G. C. (1998). A GRASP for the biquadratic assignment problem. European Journal of Operational Research, 105, 613–621.
Monkman, S. K., Morrice, D. J., & Bard, J. F. (2005). Scheduling product families in a high volume, flexible, assemble-to-order factory. In G. Kendall, L. Lei & M. Pinedo (Eds.), Proceedings of the 2nd multidisciplinary international conference on scheduling: theory & applications (pp. 394–395).
Monkman, S. K., Morrice, D. J., & Bard, J. F. (2008). A production scheduling heuristic for an electronics manufacturer with sequence dependent set-up costs. European Journal of Operational Research, 187(3), 1100–1114.
Pinto, J. M., & Grossmann, I. E. (1998). Assignment and sequencing models for the scheduling of process systems. Annals of Operations Research, 81, 433–466.
Shen, Q., Chen, H., & Chu F. (2008). Model and algorithm for an inventory routing problem in crude oil transportation. Journal of Advanced Manufacturing Systems, 7(2), 297–301.
Author information
Authors and Affiliations
Corresponding author
Appendix: Triangle inequality proof
Appendix: Triangle inequality proof
Using the same notation as in Sect. 3, let G=(V,A) be an undirected graph representing an instance of the assignment and sequencing components of the full scheduling problem. Recall that the nodes in V correspond to each possible set of families that can be on a single line at the same time and the arcs in A correspond to feasible transitions between nodes. Two nodes i and j are connected if and only if they differ by a single family. The arc cost c ij is equivalent to the number of parts that must be placed on the line plus those that must be removed when some family in node i is removed and the new family is added, where the new family is the family in node j that is not already on the line. Parts not needed for any family in node j must be removed implying the symmetric relationship c ij =c ji .
Also note that the node set V does not contain the depot. The constraints that define the problem restrict the path of any line to only go through the depot node O once, and the arcs leaving the depot are uniquely predetermined by the families on lines at the beginning of the shift. Therefore, it is not possible for node O to appear at an intermediate point along a path which prevents the use of a path through node O to obtain a lower cost than the cost of a path whose only difference is the exclusion of node O. The triangle inequality (TI) then only needs to hold for graph G.
Proposition 1
The triangle inequality with respect to the cost matrix c=(c ij ) holds for the graph G=(V,A).
Proof
Without loss of generality, consider the three-node subgraph in Fig. 6 for the five families A, B, C, D, E. We need to show that c 13≤c 12+c 23 (the other cases in the subgraph admit identical arguments).
The arc cost c ij is the sum of the setup costs for each individual part, where the setup cost for part p is \(c^{p}_{ij}\). It is therefore sufficient to prove that
is true for every positive integer n.
For n=1, which is the case where a single part contributes to the setup costs:
For the TI to be violated for two paths 1→3 and 1→2→3, we must have \(c_{13}^{1} > c_{12}^{1} + c_{23}^{1}\) which is only possible if \(c_{13}^{1} = 1\) and \(c_{12}^{1} = c_{23}^{1} = 0\). For any single part, there are two possible cases that can result in \(c_{13}^{1} = 1\): either family C needs the part and family D does not, or family D needs the part and family C does not. In addition, we need to consider whether or not family E requires the part which, in combination with the first two cases, gives us four cases.
Case 1: Families A, B, D and E do not require the part and family C does require the part.
Since one of the families in node 1 requires the part, and no family in node 2 requires the part, the part must be removed from the line on arc 1→2 so \(c_{12}^{1} = 1\) and the TI is not violated.
Case 2: Families A, B and D do not require the part and families C and E do require the part.
In this case families in both nodes 1 and 2 require the part so \(c_{12}^{1} = 0\). However, none of the families in node 3 require the part so the part must be removed on arc 2→3, so \(c_{23}^{1} = 1\) and the TI is not violated.
Case 3: Families A, B, C and E do not require the part and family D does require the part.
In this case none of the families in nodes 1 or 2 require the part so \(c_{12}^{1} = 0\). However, family D does require the part so the part must be added on arc 2→3, so \(c_{23}^{1} = 1\) and the TI is not violated.
Case 4: Families A, B, and C do not require the part and families D and E do require the part.
Since none of the families in 1 require the part, but a family in 2 requires the part, the part does need to be added on arc 1→2 so \(c_{12}^{1} = 1\) and the TI is not violated.
Therefore there is no possible case for which the TI can be violated when n=1.
Now we assume the TI holds for k:
where k is a positive integer.
So for the TI to hold we need to prove that
In light of (3), the inequality in (4) reduces to \(c_{13}^{k + 1} \le c_{12}^{k + 1} + c_{23}^{k + 1}\) which we now must show is true. However, this reduced inequality is equivalent to the case where n=1, which we have shown above to be true.
Hence, by the principle of mathematical induction, (2) is true for every positive integer n and therefore the TI holds for G. □
Rights and permissions
About this article
Cite this article
Heath, S.K., Bard, J.F. & Morrice, D.J. A GRASP for simultaneously assigning and sequencing product families on flexible assembly lines. Ann Oper Res 203, 295–323 (2013). https://doi.org/10.1007/s10479-012-1167-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-012-1167-5