## Abstract

This paper considers a two-agent scheduling problem with linear resource-dependent processing times, in which each agent has a set of jobs that compete with that of the other agent for the use of a common processing machine, and each agent aims to minimize the weighted number of its tardy jobs. To meet the due date requirements of the jobs of the two agents, additional amounts of a common resource, which may be in discrete or continuous quantities, can be allocated to the processing of the jobs to compress their processing durations. The actual processing time of a job is a linear function of the amount of the resource allocated to it. The objective is to determine the optimal job sequence and resource allocation strategy so as to minimize the weighted number of tardy jobs of one agent, while keeping the weighted number of tardy jobs of the other agent, and the total resource consumption cost within their respective predetermined limits. It is shown that the problem is (Formula presented.) -hard in the ordinary sense, and there does not exist a polynomial-time approximation algorithm with performance ratio unless (Formula presented.); however it admits a relaxed fully polynomial time approximation scheme. A proximal bundle algorithm based on Lagrangian relaxation is also presented to solve the problem approximately. To speed up convergence and produce sharp bounds, enhancement strategies including the design of a Tabu search algorithm and integration of a Lagrangian recovery heuristic into the algorithm are devised. Extensive numerical studies are conducted to assess the effectiveness and efficiency of the proposed algorithms.

Original language | English |
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Pages (from-to) | 573-591 |

Number of pages | 19 |

Journal | Naval Research Logistics |

Volume | 67 |

Issue number | 7 |

DOIs | |

Publication status | Published - 1 Oct 2020 |

## Keywords

- fully polynomial time approximation scheme
- proximal bundle algorithm
- resource allocation
- two-agent scheduling

## ASJC Scopus subject areas

- Modelling and Simulation
- Ocean Engineering
- Management Science and Operations Research