Operational Research in Engineering Sciences

Journal DOI: https://doi.org/10.31181/oresta190101s

(A Journal of Management and Engineering) ISSN 2620-1607 | ISSN 2620-1747 |

Scheduling with lot streaming in a two-machine re-entrant flow shop

Ferda Can Cetinkaya,
Department of Industrial Engineering, Çankaya University, Ankara, Turkey
Mehmet Duman,
NERITA, Near East University, TRNC, Mersin 10, Turkey

Abstract

Lot streaming is splitting a job-lot of identical items into several sublots (portions of a lot) that can be moved to the next machines upon completion so that operations on successive machines can be overlapped; hence, the overall performance of a multi-stage manufacturing environment can be improved. In this study, we consider a scheduling problem with lot streaming in a two-machine re-entrant flow shop in which each job-lot is processed first on Machine 1, then goes to Machine 2 for its second operation before it returns to the primary machine (either Machine 1 or Machine 2) for the third operation. For the two cases of the primary machine, both single-job and multi-job cases are studied independently. Optimal and near-optimal solution procedures are developed. Our objective is to minimize the makespan, which is the maximum completion time of the sublots and job lots in the single-job and multi-job cases, respectively. We prove that the single-job problem is optimally solved in polynomial-time regardless of whether the third operation is performed on Machine 1 or Machine 2. The multi-job problem is also optimally solvable in polynomial time when the third operation is performed on Machine 2. However, we prove that the multi-job problem is NP-hard when the third operation is performed on Machine 1. A global lower bound on the makespan and a simple heuristic algorithm are developed. Our computational experiment results reveal that our proposed heuristic algorithm provides optimal or near-optimal solutions in a very short time.

Keywords
scheduling, lot streaming, re-entrant flow shop, makespan.

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SCImago Journal & Country Rank

CiteScore for Management Science and Operations Research

8.1
2021CiteScore
 
 
89th percentile
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CiteScore for Engineering (miscellaneous)

8.1
2021CiteScore
 
 
93rd percentile
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