Workforce planning and production scheduling in a reconfigurable manufacturing system facing the COVID-19 pandemic

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F22%3A00361843" target="_blank" >RIV/68407700:21730/22:00361843 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.jmsy.2022.04.018" target="_blank" >https://doi.org/10.1016/j.jmsy.2022.04.018</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jmsy.2022.04.018" target="_blank" >10.1016/j.jmsy.2022.04.018</a>

Result language

angličtina

Original language name

Workforce planning and production scheduling in a reconfigurable manufacturing system facing the COVID-19 pandemic

Original language description

Due to the outbreak of the COVID-19 pandemic, the manufacturing sector has been experiencing unprecedented issues, including severe fluctuation in demand, restrictions on the availability and utilization of the workforce, and governmental regulations. Adopting conventional manufacturing practices and planning approaches under such circumstances cannot be effective and may jeopardize workers’ health and satisfaction, as well as the continuity of businesses. Reconfigurable Manufacturing System (RMS) as a new manufacturing paradigm has demonstrated a promising performance when facing abrupt market or system changes. This paper investigates a joint workforce planning and production scheduling problem during the COVID-19 pandemic by leveraging the adaptability and flexibility of an RMS. In this regard, workers' COVID-19 health risk arising from their allocation, and workers' preferences for flexible working hours are incorporated into the problem. Accordingly, first, novel Mixed-Integer Linear Programming (MILP) and Constraint Programming (CP) models are developed to formulate the problem. Next, exploiting the problem’s intrinsic characteristics, two properties of an optimal solution are identified. By incorporating these properties, the initial MILP and CP models are considerably improved. Afterward, to benefit from the strengths of both improved models, a novel hybrid MILP-CP solution approach is devised. Finally, comprehensive computational experiments are conducted to evaluate the performance of the proposed models and extract useful managerial insights on the system flexibility.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF15_003%2F0000466" target="_blank" >EF15_003/0000466: Artificial Intelligence and Reasoning</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Manufacturing Systems

ISSN

0278-6125

e-ISSN

1878-6642

Volume of the periodical

63

Issue of the periodical within the volume

April

Country of publishing house

AT - AUSTRIA

Number of pages

12

Pages from-to

563-574

UT code for WoS article

000808260400002

EID of the result in the Scopus database

2-s2.0-85131137285