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| Content Provider | Springer Nature Link |
|---|---|
| Author | Di Gaspero, Luca Gärtner, Johannes Kortsarz, Guy Musliu, Nysret Schaerf, Andrea Slany, Wolfgang |
| Copyright Year | 2007 |
| Abstract | The min-Shift Design problem (MSD) is an important scheduling problem that needs to be solved in many industrial contexts. The issue is to find a minimum number of shifts and the number of employees to be assigned to these shifts in order to minimize the deviation from workforce requirements. Our research considers both theoretical and practical aspects of the min-Shift Design problem. This problem is closely related to the minimum edge-cost flow problem (MECF), a network flow variant that has many applications beyond shift scheduling. We show that MSD reduces to a special case of MECF and, exploiting this reduction, we prove a logarithmic hardness of approximation lower bound for MSD. On the basis of these results, we propose a hybrid heuristic for the problem, which relies on a greedy heuristic followed by a local search algorithm. The greedy part is based on the network flow analogy, and the local search algorithm makes use of multiple neighborhood relations. An experimental analysis on structured random instances shows that the hybrid heuristic clearly outperforms our previous commercial implementation. Furthermore, it highlights the respective merits of the composing heuristics for different performance parameters. |
| Starting Page | 79 |
| Ending Page | 105 |
| Page Count | 27 |
| File Format | |
| ISSN | 02545330 |
| Journal | Annals of Operations Research |
| Volume Number | 155 |
| Issue Number | 1 |
| e-ISSN | 15729338 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2007-07-07 |
| Publisher Place | Boston |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Workforce scheduling Hybrid algorithms Local search Greedy heuristics Theory of Computation Combinatorics Operations Research/Decision Theory |
| Content Type | Text |
| Resource Type | Article |
| Subject | Management Science and Operations Research |
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