A BRANCH-AND-BOUND ALGORITHM FOR TWO-COMPETING-AGENT SINGLE-MACHINE SCHEDULING PROBLEM WITH JOBS UNDER SIMULTANEOUS EFFECTS OF LEARNING AND DETERIORATION TO MINIMIZE TOTAL WEIGHTED COMPLETION TIME WITH NO-TARDY JOBS

Authors

DOI:

https://doi.org/10.23055/ijietap.2021.28.6.7723

Keywords:

Multi agent scheduling, Variable processing times, Learning effect, Deterioration effect, Branch-and-bound algorithm

Abstract

Recent scheduling studies focus on variable job-processing times and multi-agent problems simultaneously, but none of them studied with jobs under the simultaneous effect of learning and deterioration. This paper studies a two-competing-agent single-machine scheduling problem with jobs under simultaneous learning and deterioration effect. The goal is to find an optimal solution to minimize the total weighted completion time for the first agent, subject to the restriction that no tardy job is allowed for the second agent. According to our current literature knowledge, this paper will be the first one with these specifications. For this problem, a two-stage methodology is developed in the study. In the first stage, heuristics are proposed to find the near-optimal solution of which is used as input for the second stage. For the second stage, a branch-and-bound algorithm along with several dominances and a lower bound is developed to find the optimal solution. Computational experiments are provided to further measure the performance of the proposed algorithms.

Author Biography

Duran Toksari, Erciyes University

PROF DR. INDUSTRIAL ENGINEERING DEPARTMENT- FACULTY OF ENGINEERING

Published

2022-01-19

How to Cite

Danaci, T., & Toksari, D. (2022). A BRANCH-AND-BOUND ALGORITHM FOR TWO-COMPETING-AGENT SINGLE-MACHINE SCHEDULING PROBLEM WITH JOBS UNDER SIMULTANEOUS EFFECTS OF LEARNING AND DETERIORATION TO MINIMIZE TOTAL WEIGHTED COMPLETION TIME WITH NO-TARDY JOBS. International Journal of Industrial Engineering: Theory, Applications and Practice, 28(6). https://doi.org/10.23055/ijietap.2021.28.6.7723

Issue

Section

Production Planning and Control