Abstract
The Test Laboratory Scheduling Problem (TLSP) is a real-world scheduling problem that extends the well-known Resource-Constrained Project Scheduling Problem (RCPSP) by several new constraints. Most importantly, the jobs have to be assembled out of several smaller tasks by the solver, before they can be scheduled. In this paper, we introduce different metaheuristic solution approaches for this problem. We propose four new neighborhoods that modify the grouping of tasks. In combination with neighborhoods for scheduling, they are used by our metaheuristics to produce high-quality solutions for both randomly generated and real-world instances. In particular, Simulated Annealing managed to find solutions that are competitive with the best known results and improve upon the state-of-the-art for larger instances. The algorithm is currently used for the daily planning of a large real-world laboratory.
Highlights
The task of testing of components and equipments is an expensive and time-consuming activity for many industrial companies
We consider a specific version of the testing problem, called the Test Laboratory Scheduling Problem (TLSP), which is an extension of the well-known ResourceConstrained Project Scheduling Problem (RCPSP)
We considered the real-world scheduling problem TLSP and we proposed metaheuristic approaches for this problem
Summary
The task of testing of components and equipments is an expensive and time-consuming activity for many industrial companies. We consider a specific version of the testing problem, called the Test Laboratory Scheduling Problem (TLSP), which is an extension of the well-known ResourceConstrained Project Scheduling Problem (RCPSP) For this problem, analogously to many other scheduling problems, we have to assign to each job a start time and a set of resources. We investigate the possibility of using a local search approach for the general problem To this aim, we develop four new complex neighborhoods that modify the grouping and combine them with neighborhoods affecting the schedule of the jobs. Properly tuned using a statistically principled tuning procedure, are compared among each other and with the results of Danzinger et al (2020) and Mischek and Musliu (2021) on a dataset composed of artificial and realworld instances. The algorithms described in this paper are used successfully in the daily scheduling of our industrial partner’s laboratory
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