Dataset for the energy-aware CDD scheduling problem.

Description

Benchmark dataset and energy-instance generation Since no publicly available benchmark instances currently exist for the considered energy-aware multi-objective common due-date scheduling problem, the well-established benchmark dataset of Biskup and Feldman (2001) was extended in order to incorporate energy-related operational characteristics. The original benchmark instances are available through OR-Library and have been widely used in the earliness/tardiness scheduling literature. The original dataset provides, for each job (j) the processing time (p_j) together with the earliness and tardiness penalty parameters (〖 α〗_j,β_j). To model energy-aware manufacturing conditions, an additional job-dependent energy parameter (〖 Power〗_j) was introduced, representing the relative electricity demand of each job during processing. Consequently, each benchmark instance is characterized by the quadruple: (p_j,〖 α〗_j,〖 β〗_j,〖 Power〗_j) The Biskup and Feldman (2001)’s dataset can be found in (https://people.brunel.ac.uk/~mastjjb/jeb/orlib/schinfo.html ). The energy coefficients (〖 Power〗_j) were independently generated from a discrete uniform distribution in the interval ([1,8]), thus creating heterogeneous energy-consumption profiles across jobs. This modeling approach reflects realistic industrial environments in which different machining operations exhibit substantially different electricity requirements due to variations in spindle load, cutting intensity, tooling conditions, material hardness, and processing complexity. 〖 Power〗_j coefficients are given in file: <PowerData.txt> Furthermore, four electricity-pricing scenarios were considered in order to emulate different industrial energy environments, ranging from stable pricing conditions to highly volatile and uncertainty-driven electricity markets. These scenarios represent: (i) stable energy conditions, (ii) moderately volatile environments, (iii) highly stressed energy markets, and (iv) stochastic electricity-price conditions. The detailed specification of these pricing scenarios is presented in Section 5.1.

Institutions

• University of Patras

  West Greece, Pátrai

Categories

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