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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/LL1902" target="_blank" >LL1902: Obohacování SMT řešičů pomocí strojového učení</a><br>

Návaznosti

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

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Engineering Applications of Artificial Intelligence

ISSN

0952-1976

e-ISSN

1873-6769

Svazek periodika

2023

Číslo periodika v rámci svazku

105676

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

18

Strana od-do

105676-105693

Kód UT WoS článku

000894966600011

EID výsledku v databázi Scopus

2-s2.0-85143908425