Integrated lot-sizing and scheduling: Mitigation of uncertainty in demand and processing time by machine learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F23%3A00361517" target="_blank" >RIV/68407700:21730/23:00361517 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.engappai.2022.105676" target="_blank" >https://doi.org/10.1016/j.engappai.2022.105676</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.engappai.2022.105676" target="_blank" >10.1016/j.engappai.2022.105676</a>

Result language

angličtina

Original language name

Integrated lot-sizing and scheduling: Mitigation of uncertainty in demand and processing time by machine learning

Original language description

Production rescheduling is one of the most challenging problems in production management, in which some parameters, such as customer demand and job processing time, are subject to uncertainty during the planning horizon. This paper develops a scheduling and rescheduling method for the simultaneous lot-sizing and job shop scheduling problem, considering sequence-dependent setup times and capacity constraints. The objective is to find a trade-off between safety levels and production costs focusing on schedulability and optimality as the two most important performance indicators in the face of uncertainty. Therefore, a new adjustable formulation based on satisfiability modulo theories has been developed to tackle the demand and processing time uncertainty. Then, a combination of neural networks and a K-means based heuristic was applied to calibrate the model by determining the profitable value of the safety levels as a strategy to increase the robustness of the schedule. We also developed a Monte Carlo simulation to assess the performance of the proposed algorithm and compare it with other approaches addressed in the literature. The computational results show that the proposed algorithm is efficient and promising in protecting the schedulability of the model, taking the optimality criterion into account.

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/LL1902" target="_blank" >LL1902: Powering SMT Solvers by Machine Learning</a><br>

Continuities

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

Publication year

2023

Confidentiality

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

Name of the periodical

Engineering Applications of Artificial Intelligence

ISSN

0952-1976

e-ISSN

1873-6769

Volume of the periodical

2023

Issue of the periodical within the volume

105676

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

18

Pages from-to

105676-105693

UT code for WoS article

000894966600011

EID of the result in the Scopus database

2-s2.0-85143908425