Milk-run methodology is proposed to manage the procurement of orders from suppliers. The heuristic solution methods in the literature generally apply stepwise approach to route and load the vehicles. In this study we propose a hybrid genetic local search algorithm which simultaneous solves vehicle routing and order loading problems. This is the main contribution of the study. We consider volume and weight capacities (multi capacitated) of different types of transportation vehicles (heterogeneous fleet). Because of high adaptability and easy utilization, genetic algorithms are the most preferred approach of meta-heuristics. The chromosome structure of the proposed genetic algorithm is constituted by random numbers to eliminate infeasibility. The best chromosome of each generation is improved using local search method during the algorithm runs. We applied the algorithm to a real manufacturing company that produces welding robots and other process automation equipment. The results showed the effectiveness of the algorithm.

SIMULTANEOUS ROUTING AND LOADING METHOD FOR MILK-RUN USING HYBRID GENETIC SEARCH ALGORITHM / Yılmaz Eroğlu, D.; Rafele, Carlo; Cagliano, ANNA CORINNA; Murat, S. S.; Ippolito, M.. - STAMPA. - (2014), pp. 48-57. (Intervento presentato al convegno XII International Logistics and Supply Chain Congress tenutosi a Istanbul, Turkey nel 30-31 October 2014).

SIMULTANEOUS ROUTING AND LOADING METHOD FOR MILK-RUN USING HYBRID GENETIC SEARCH ALGORITHM

RAFELE, Carlo;CAGLIANO, ANNA CORINNA;
2014

Abstract

Milk-run methodology is proposed to manage the procurement of orders from suppliers. The heuristic solution methods in the literature generally apply stepwise approach to route and load the vehicles. In this study we propose a hybrid genetic local search algorithm which simultaneous solves vehicle routing and order loading problems. This is the main contribution of the study. We consider volume and weight capacities (multi capacitated) of different types of transportation vehicles (heterogeneous fleet). Because of high adaptability and easy utilization, genetic algorithms are the most preferred approach of meta-heuristics. The chromosome structure of the proposed genetic algorithm is constituted by random numbers to eliminate infeasibility. The best chromosome of each generation is improved using local search method during the algorithm runs. We applied the algorithm to a real manufacturing company that produces welding robots and other process automation equipment. The results showed the effectiveness of the algorithm.
File in questo prodotto:
File Dimensione Formato  
Eroglu et al_2014_paper.pdf

accesso aperto

Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 1.19 MB
Formato Adobe PDF
1.19 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2581148
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo