The just-in-time job-shop scheduling problem with distinct due-dates for operations

Ahmadian, Mohammad Mahdi; Salehipour, Amir

doi:10.1007/s10732-020-09458-6

Cited by 9 publications

(4 citation statements)

References 31 publications

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“…(2018) are only a few examples of adapting matheuristics for combinatorial optimization problems. We refer the interested reader to Ahmadian and Salehipour (2022, 2020) for recent applications of the matheuristic algorithms.…”

Section: The Proposed Solution Methodsmentioning

confidence: 99%

An optimization method for characterizing two groups of data

Salehipour

2022

Int Trans Operational Res

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Feature selection aims to choose a subset of features, out of the set of candidate features, such that the selected set best represents the whole in a particular aspect. We propose an optimization model for the problem of selecting features in the presence of two groups of data. The objectives include minimizing the number of selected features and maximizing similarities within the same group and differences between different groups. We use the lexicographic method and prove several properties of the problem. We show that obtaining even feasible solutions for the problem can be challenging. Therefore, we propose matheuristic algorithms. We test our algorithms on both randomly generated and real‐world datasets. The computational results indicate that the proposed matheuristics deliver quality solutions in a reasonable amount of time.

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Section: The Proposed Solution Methodsmentioning

confidence: 99%

An optimization method for characterizing two groups of data

Salehipour

2022

Int Trans Operational Res

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“…In this section, we propose an R&S matheuristic algorithm for solving problem

1|(a_j, L_j, b_j)|C_{\max }

. The R&S has been shown to deliver quality solutions for challenging scheduling problems (Ahmadian et al., 2020; Khatami and Salehipour, 2020; Ahmadian and Salehipour, 2021; Ahmadian et al., 2021; Ahmadian and Salehipour, 2022). The operation of the R&S algorithm is similar to that of the fix‐and‐optimize algorithm of Helber and Sahling (2010).…”

Section: The Solution Methodsmentioning

confidence: 99%

The coupled task scheduling problem: an improved mathematical program and a new solution algorithm

Khatami

Salehipour

2022

Int Trans Operational Res

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The general single machine coupled task scheduling problem with the objective function of minimizing the makespan, which is strongly NP‐hard, aims to schedule a set of coupled task jobs on one machine such that the completion time of the last job is minimized. We propose a new mixed‐integer program (MIP) for the problem. We also propose a relax‐and‐solve (R&S) matheuristic algorithm as the solution method. We show that the new MIP outperforms the available models and improves the quality of solutions. Also, the proposed MIP significantly improves the average gap to the best known feasible solution of an existing binary search algorithm. We show that our R&S matheuristic produces new best solutions for almost 50% of the instances.

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“…They used the mathematical programming model to generate an optimal schedule for a given sequence of operations. Ahmadian and Salehipour [2] presented a matheuristic algorithm to solve JIP-JSP, which operates by decomposing the problem into smaller sub-problems and solving the subproblems using a commercial optimization solver to obtain optimal or near-optimal schedules. Ahmadian et al [3] developed a variable neighbourhood search algorithm to solve JIT-JSP.…”

Section: Literature Reviewmentioning

confidence: 99%

Minimizing the total weighted earliness and tardiness for a sequence of operations in job shops

Girish

Habibullah

J³

2022

RAIRO-Oper. Res.

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This paper proposes exact algorithms to generate optimal timing schedules for a given sequence of operations in job shops to minimize the total weighted earliness and tardiness. The algorithms are proposed for two job shop scheduling scenarios, one involving due dates only for the last operation of each job and the other involving due dates for all operations on all the jobs. Computational experiments on benchmark problem instances reveal that, in the case of the scheduling scenario involving due dates only for the last operation of each job, the proposed exact algorithms generate schedules faster than those generated using a popular optimization solver. In the case of the scheduling scenario involving due dates for all operations on all the jobs, the exact algorithms are competitive with the optimization solver in terms of computation time for small and medium size problems.

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The just-in-time job-shop scheduling problem with distinct due-dates for operations

Cited by 9 publications

References 31 publications

An optimization method for characterizing two groups of data

An optimization method for characterizing two groups of data

The coupled task scheduling problem: an improved mathematical program and a new solution algorithm

Minimizing the total weighted earliness and tardiness for a sequence of operations in job shops

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