Designing Integrated Supply Chain Scheduling Problems

Research Objective:

With globalisation and advanced information technology, the supply chain SC) is becoming more flexible, diversified, customised and dynamic, constantly forcing original equipment manufacturers to shift their production strategy to make-to-order or hybrid. The asynchronous SC cannot handle these challenges, and thus integration at the operational level is essential, ensuring higher flexibility in upstream and downstream SCs. This complex but realistic scenario can be dealt with in four categories of flexibilities: supply flexibility, operational flexibility, sequence flexibility and process flexibility. The mathematical model with the included flexibilities is notoriously complex computationally. Thus, designing professional schedulers to schedule or reschedule promptly is also highly challenging for this time-sensitive, operationally integrated system. Thus, we have been designing integrated SC scheduling problems (ISCSPs), considering all the challenges and flexibilities discussed above, and schedulers to optimise these complex problems. This research aims to:

  1. design schedulers for classical job shop scheduling problems,
  2. integrate supply and production portfolios in response to multiple customer orders with delivery time windows,
  3. integrate environmental sustainability for the supply portfolio and batching decisions with the ISCSPs, and
  4. design dynamic ISCSPs. 

The most beneficial facet of the developed ISCSPs and schedulers is the visibility and meaningful managerial insights provided by the multi-portfolio solutions, fostering the responsive relationship among SC stages.

Achievements:

  1. Mahmud, S., Abbasi, A., Chakrabortty, R. K., & Ryan, M. J. (2021). Multi-operator communication based differential evolution with sequential Tabu Search approach for job shop scheduling problems. Applied Soft Computing, 108, 107470.
  2. Mahmud, S., Chakrabortty, R. K., Abbasi, A., & Ryan, M. J. (2022). Switching strategy-based hybrid evolutionary algorithms for job shop scheduling problems. Journal of Intelligent Manufacturing, 1-28.
  3. Mahmud, S., Chakrabortty, R. K., Abbasi, A., & Ryan, M. J. (2022). Swarm intelligent based metaheuristics for a bi-objective flexible job shop integrated supply chain scheduling problems. Applied Soft Computing, 108794.