Dynamic programming approach for solving the open shop problem

Abstract

This paper deals with the open shop scheduling problem (OSP) with makespan minimization. An exact dynamic programming algorithm is proposed for solving the OSP to optimality. This approach is applied to the OSP for the first time. Computational results show that the proposed algorithm is able to solve moderate benchmark instances.

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Appendix

Appendix

Table 7 Processing times of benchmark instance tai4-1

We study an example, which explains how to create forbidden intervals. Similarly as by Guéret and Prins (1999), we investigate benchmark instance j3-per0-1, see Table 7. Also, we set \(bound=1174\).

The algorithm selects operation \(O_{2,2}\), which has the longest processing time \(p^*=511\). Due to a symmetry, \(O_{2,2}\) cannot start later than

$$\begin{aligned} \dfrac{bound-1-p^*}{2}=331. \end{aligned}$$

Thus, the forbidden interval of operation \(O_{2,2}\) is [332, 1174).

Due to the symmetry, operations \(O_{1,2},O_{2,1},O_{2,3},O_{3,2}\) cannot be completed in the interval \([332,p^*)\). Thus, we have For example, if \(O_{1,2}\) started at 23, then this operation would be completed at 332, which belongs to the forbidden interval.

Operations Forbidden intervals due to a symmetry
\(O_{1,2}\) [23, 511)
\(O_{2,1}\) [232, 511)
\(O_{2,2}\) [332, 1174)
\(O_{2,3}\) [0, 511)
\(O_{3,2}\) [152, 511)

The subset-sum values are:

Name Subset-sums
\(S_{M_1}\) 0 100 436 464 536 564 900 1000
\(S_{M_2}\) 0 180 309 489 511 691 820 1000
\(S_{M_3}\) 0 227 384 389 611 616 773 1000
\(S_{J_1}\) 0 227 309 464 536 691 773 1000
\(S_{J_2}\) 0 100 389 489 511 611 900 1000
\(S_{J_3}\) 0 180 384 436 564 616 820 1000

Subset-sum values in bold can be removed due to a symmetry.

Using algorithm proposed by Guéret and Prins (1999), we obtain forbidden intervals for both the starting and completion times. These intervals have directly been derived from the subset-sum values.

Table 8 Forbidden intervals derived from the subset-sum values

By joining all previously obtained forbidden intervals, we obtain the resulting forbidden intervals for the starting times of operations.

Table 9 Forbidden intervals of benchmark instance j3-per0-1

For example, the forbidden intervals are obtained for the starting time of operation \(O_{3,2}\) in the following way. The interval [152, 511) is forbidden due to a symmetry. From Table 8 it follows that

$$\begin{aligned}{}[174,180)\cup [483,511)\cup [685,691)\cup [994,1000) \end{aligned}$$

and

$$\begin{aligned}{}[174,180)\cup [354,384)\cup [790,820)\cup [994,1000) \end{aligned}$$

are also the forbidden intervals.

Since \(p_{3,2}=180\) and Table 8 also represents the forbidden intervals for the completion times, we have that intervals

$$\begin{aligned}{}[174,204)\cup [610,640)\cup [814,820) \end{aligned}$$

and

$$\begin{aligned}{}[303,331)\cup [505,511)\cup [814,820) \end{aligned}$$

are forbidden.

Finally, since operation \(O_{3,2}\) has to be completed before \(bound=1174\), we have that [994, 1174) is also forbidden. Thus, we have obtained the full forbidden intervals for operation \(O_{3,2}\), see Table 9.

See Table 10.

Table 10 Detailed results of the effectiveness of the dominance test

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Ozolins, A. Dynamic programming approach for solving the open shop problem. Cent Eur J Oper Res 29, 291–306 (2021). https://doi.org/10.1007/s10100-019-00630-3

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Keywords

  • Open shop scheduling
  • Makespan
  • Dynamic programming