We address a problem from a meat company, in which orders are produced in two stages, consisting of preparing meats on benches and allocating them to conveyors to be packed in disposable trays. In an environment where machines are unrelated, the company has to take daily decisions on the number and start time of working periods, the number of workers and their allocation to machines, and the scheduling of activities to satisfy the required orders. The objective of the problem is to minimize, in a lexicographic way, the number of unscheduled activities, the weighted tardiness, and the total production cost. To solve the problem, we propose a multi-start random constructive heuristic, which tests different combinations of number of workers in the machines and for each combination produces many different schedules of the orders. The results of our computational experiments over realistic instances show that the heuristic is effective and can support the company on its daily decisions.
Integrated Workforce Scheduling and Flexible Flow Shop Problem in the Meat Industry / Bolsi, B.; de Lima, V. L.; de Queiroz, T. A.; Iori, M.. - 631:(2021), pp. 594-602. (Intervento presentato al convegno IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2021 tenutosi a Nantes nel 2021) [10.1007/978-3-030-85902-2_63].
Integrated Workforce Scheduling and Flexible Flow Shop Problem in the Meat Industry
Bolsi B.
;Iori M.
2021
Abstract
We address a problem from a meat company, in which orders are produced in two stages, consisting of preparing meats on benches and allocating them to conveyors to be packed in disposable trays. In an environment where machines are unrelated, the company has to take daily decisions on the number and start time of working periods, the number of workers and their allocation to machines, and the scheduling of activities to satisfy the required orders. The objective of the problem is to minimize, in a lexicographic way, the number of unscheduled activities, the weighted tardiness, and the total production cost. To solve the problem, we propose a multi-start random constructive heuristic, which tests different combinations of number of workers in the machines and for each combination produces many different schedules of the orders. The results of our computational experiments over realistic instances show that the heuristic is effective and can support the company on its daily decisions.
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