The occupational exposure to hazardous chemical agents is a problem acknowledged by international and European institutions and organizations. To assess and manage risks arising from work activities involving chemical agents, safety managers should rely on several tools, including models and algorithms, available in the literature. However, to the best of our knowledge, no models dealing with reducing chemical risk through the scheduling of work tasks have been defined. The aim of this paper is to propose an optimization tool to identify the best scheduling of tasks for minimizing occupational exposures to inhaled chemical substances. This tool is based on a two-phase algorithm, with each phase exploiting a different single period Mixed Integer Linear Programming (MILP) compact formulation encompassing real constraints such as the Threshold Limit Values (TLVs® ) for chemicals, in terms of TLV-TWA (Time-Weighted Average) and TLV-STEL (Short-Term Exposure Limit). Besides the TLV restrictions, the models include classical preemptive job scheduling constraints, such as the assignment of tasks to workers and preemption handling. In order to test our algorithm, we solved a real exposure scenario in a foundry by means of a MILP solver (Gurobi). The optimal scheduling of tasks, obtained in a reasonable amount of time, reduces the adverse effects due to occupational exposures to chemical agents.
A mathematical programming approach for minimizing occupational exposures to chemical agents
Stefana E.;Zanotti R.;Marciano F.;Mansini R.
2020-01-01
Abstract
The occupational exposure to hazardous chemical agents is a problem acknowledged by international and European institutions and organizations. To assess and manage risks arising from work activities involving chemical agents, safety managers should rely on several tools, including models and algorithms, available in the literature. However, to the best of our knowledge, no models dealing with reducing chemical risk through the scheduling of work tasks have been defined. The aim of this paper is to propose an optimization tool to identify the best scheduling of tasks for minimizing occupational exposures to inhaled chemical substances. This tool is based on a two-phase algorithm, with each phase exploiting a different single period Mixed Integer Linear Programming (MILP) compact formulation encompassing real constraints such as the Threshold Limit Values (TLVs® ) for chemicals, in terms of TLV-TWA (Time-Weighted Average) and TLV-STEL (Short-Term Exposure Limit). Besides the TLV restrictions, the models include classical preemptive job scheduling constraints, such as the assignment of tasks to workers and preemption handling. In order to test our algorithm, we solved a real exposure scenario in a foundry by means of a MILP solver (Gurobi). The optimal scheduling of tasks, obtained in a reasonable amount of time, reduces the adverse effects due to occupational exposures to chemical agents.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
