Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption

In the context of supply chain sustainability, Physical Internet (PI or π ) was presented as an innovative concept to create a global sustainable logistics system. One of the main components of the Physical Internet paradigm consists in encapsulating products in modular and standardized PI-container...

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Veröffentlicht in:Sustainability Jg. 11; H. 11; S. 3127
Hauptverfasser: Chargui, Tarik, Bekrar, Abdelghani, Reghioui, Mohamed, Trentesaux, Damien
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.06.2019
MDPI
Schlagworte:
Automatic
Computer Science
Docks
Engineering Sciences
Genetic algorithms
Handbooks
Heuristic
Integer programming
International conferences
Internet
Logistics
Operations Research
Optimization
Other
Scheduling
Supply chains
Sustainability
Trucks
United Kingdom > UK
France
United States > US
Physical Internet
hybrid meta-heuristics
Lexicographic Goal Programming
Rail–Road
truck scheduling
cross-docking
sustainability
energy consumption
Multi-Objective Programming
ISSN:2071-1050, 2071-1050
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Zusammenfassung:In the context of supply chain sustainability, Physical Internet (PI or π ) was presented as an innovative concept to create a global sustainable logistics system. One of the main components of the Physical Internet paradigm consists in encapsulating products in modular and standardized PI-containers able to move via PI-nodes (such as PI-hubs) using collaborative routing protocols. This study focuses on optimizing operations occurring in a Rail–Road PI-Hub cross-docking terminal. The problem consists of scheduling outbound trucks at the docks and the routing of PI-containers in the PI-sorter zone of the Rail–Road PI-Hub cross-docking terminal. The first objective is to minimize the energy consumption of the PI-conveyors used to transfer PI-containers from the train to the outbound trucks. The second objective is to minimize the cost of using outbound trucks for different destinations. The problem is formulated as a Multi-Objective Mixed-Integer Programming model (MO-MIP) and solved with CPLEX solver using Lexicographic Goal Programming. Then, two multi-objective hybrid meta-heuristics are proposed to enhance the computational time as CPLEX was time consuming, especially for large size instances: Multi-Objective Variable Neighborhood Search hybridized with Simulated Annealing (MO-VNSSA) and with a Tabu Search (MO-VNSTS). The two meta-heuristics are tested on 32 instances (27 small instances and 5 large instances). CPLEX found the optimal solutions for only 23 instances. Results show that the proposed MO-VNSSA and MO-VNSTS are able to find optimal and near optimal solutions within a reasonable computational time. The two meta-heuristics found optimal solutions for the first objective in all the instances. For the second objective, MO-VNSSA and MO-VNSTS found optimal solutions for 7 instances. In order to evaluate the results for the second objective, a one way analysis of variance ANOVA was performed.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2071-1050
2071-1050
DOI:10.3390/su11113127