An agent-based heuristics optimisation model for production scheduling of make-to-stock connector plates manufacturing systems

Faris Madi, Ammar Al-Bazi*, Steve Buckley, John Smallbone, Karl Foster

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The manufacturing systems' success directly relates to their accurate, reliable and flexible schedules, including how production is planned and scheduled and which constraints are considered in generating the schedules. The study's objective arises from the need to generate an optimal production scheduling system in a connecting plates manufacturing company that works on a Make-To-Stock basis. This research investigates the impact of demand and operational constraints on production schedules, including the facility capacity, operators and machines availability, raw materials availability, inventory level and warehouse capacity. A multi-agent-based optimisation model is developed to face the complexity of considering demand and operational constraints and reflects their impact on generating a reliable production schedule. This model involves a proposed heuristic algorithm that considers demand and operations constraints in such a manufacturing environment and optimises the production schedule based on these restrictions/requirements. A real-life case study based on a connecting plates manufacturer company is used as a test bench of the proposed agent-based heuristic optimisation model. The proposed algorithm is compared with other related approaches to check its superiority based on key criteria, including inventory levels, missed/unsatisfied orders and total production time. Results show that the proposed heuristics algorithm reduced the number of missed orders by 34% compared with similar approaches.
Original languageEnglish
JournalSoft computing
Early online date21 Dec 2023
Publication statusE-pub ahead of print - 21 Dec 2023

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  • Demand and operational constraints
  • Heuristics optimisation
  • Make-to-Stock environment
  • Multi agent-based model
  • Production scheduling


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