Parameterisation of demand-driven material requirements planning: a multi-objective genetic algorithm

Demand-Driven Material Requirements Planning (DDMRP) is a recent inventory management method that has generated considerable interest in both academia and industry. Many recent papers have demonstrated the superiority of DDMRP over classical methods like MRP or Kanban, an observation confirmed by co...

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Vydané v:International journal of production research Ročník 61; číslo 15; s. 5134 - 5155
Hlavní autori: Damand, David, Lahrichi, Youssef, Barth, Marc
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Taylor & Francis 03.08.2023
Taylor & Francis LLC
Predmet:
DDMRP
Genetic algorithms
Inventory
Inventory management
lot sizing
Material requirements planning
metaheuristic
multi-objective
Multiple objective analysis
NSGA-II
on-hand inventory
Optimization
OTD
Parameter identification
Parameterization
ISSN:0020-7543, 1366-588X
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Popis
Shrnutí:Demand-Driven Material Requirements Planning (DDMRP) is a recent inventory management method that has generated considerable interest in both academia and industry. Many recent papers have demonstrated the superiority of DDMRP over classical methods like MRP or Kanban, an observation confirmed by companies that have implemented DDMRP. However, DDMRP depends on many parameters that affect its performance. Only general rules are given by the authors of the method to fix these parameters but no algorithm. The present paper aims to fill this gap by proposing a multi-objective optimisation algorithm to fix a set of eight identified parameters. The suggested genetic algorithm is coupled with a simulation algorithm that computes the objective functions. Two opposing objective functions are considered: first, the maximisation of orders delivered on-time to the customer and, second, the minimisation of on-hand inventory. A set of data instances was generated to test the suggested method. Fronts of non-dominated solutions are found for all these instances.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2022.2098074