(530c) A Simultaneous Process Scheduling and Personnel Allocation Framework for Industrial-Scale Multipurpose Facilities

Process scheduling aims to allocate tasks to resources at specific time intervals with the aim to meet an objective, e.g. minimization of operating costs, turnaround time or maximization of profits. Often, the processing times of the resources (e.g. machines) are either specified a priori or estimated from the amount of material to be processed in the machines [1]. However, there are industrial applications where the processing time also depend on the skills of the technical staff operating the machine. This condition is often found in manufacturing facilities that involve human intervention, e.g. weighting of samples before processing or start-up of machines. A more skilled (senior) operator may perform an activity in processing times that are shorter than those required by a junior (inexperienced) operator thus impacting operations management and plant productivity. Consequently, process scheduling and personal allocation decisions must be simultaneously considered, i.e. assign operators to resources while performing the optimal allocation of tasks to resources. A naive approach to address this problem consists in solving both problems sequentially. While this approach is simple and easy to implement, it may lead to sub-optimal or even infeasible solutions [2]. Therefore, integrated approaches have been proposed in the literature to address this problem [2], [3], [4]; however, industrial-scale applications aiming at solving personnel allocation and process scheduling are rather limited [5], [6].

The aim of this study is to present a mathematical formulation that integrates process scheduling and personnel allocation decisions for job shop large-scale plants. This formulation is based on network flows and comprises a large number of variables and constraints, which requires large computational efforts. To circumvent this issue, we propose in this work an alternative solution method with optimal guarantees that evaluates solutions faster for some groups of instances. The performance of the proposed formulation and methods was tested using instances based on an actual industrial-scale facility previously introduced in the literature [7], [8]. Optimal integrated schedules were obtained using a rolling horizon framework and compared to the case where personnel allocation decisions are not considered together within the process scheduling. The results show that significant improvements can be achieved when using the proposed integrated approach.

References

[1] Ierapetritou M., Floudas C. Effective continuous-time formulation for short-term scheduling. 1. Multipurpose batch processes. Industrial & Engineering Chemistry Research. 1998; 37(11): 4341-4359.

[2] Daniels R.L., Mazzola J.B. Flow shop scheduling with resource flexibility. Operations Research. 1994;42:504–522.

[3] Artigues C., Gendreau M., Rousseau L., Vergnaud A. Solving an integrated employee timetabling and job-shop scheduling problem via hybrid branch-and-bound. Computers & Operations Research. 2009;36:2330–2340.

[4] Guyon O., Lemaire P., Pinson E., Rivreau D. Solving an integrated job-shop problem with human resource constraints. Annals of Operations Research. 2014;23:147–171.

[5] Schulze M., Zimmermann J. Staff and machine shift scheduling in a German potash mine. Journal of Scheduling. 2017;20:1–22.

[6] Ahmadi-Javid A., Hooshangi-Tabrizi P. Integrating employee timetabling with scheduling of machines and transporters in a job-shop environment: A mathematical formulation and an anarchic society optimization algorithm. Computers & Operations Research. 2017;84:73–91.

[7] Lagzi S., Fukasawa R., Ricardez-Sandoval L. A multitasking continuous time formulation for short-term scheduling of operations in multipurpose plants. Computers & Chemical Engineering. 2017;97:135–146.

[8] Lagzi S., Lee D.Y., Fukasawa R., Ricardez-Sandoval L. A computational study of continuous and discrete time formulations for a class of short-term scheduling problems for multipurpose plants. Industrial & Engineering Chemistry Research. 2017;56(31):8940–8953.