An energy-efficient scheduling and rescheduling method for production and logistics systems†

被引:41
作者
Nouiri, Maroua [1 ]
Bekrar, Abdelghani [1 ]
Trentesaux, Damien [1 ]
机构
[1] Univ Polytech Hauts France, UMR CNRS 8201, LAMIH, F-59313 Le Mont Houy, Valenciennes, France
关键词
scheduling; rescheduling; sustainability; flexible job shop; physical internet; effectiveness; energy efficiency; perturbations; JOB-SHOP; SUPPLY CHAIN; OPTIMIZATION; SYSTEM; MODEL; STRATEGIES; MANAGEMENT; ALGORITHM; SEQUENCE;
D O I
10.1080/00207543.2019.1660826
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Scheduling can be defined as the allocation of available resources over time while optimising a set of criteria like early completion time of task, holding inventory, etc. The complexity of the scheduling problem, already known to be high, increases if dynamic events and disruptions are considered. In addition, in production and logistics, designers of scheduling systems must consider sustainability-related expectations. This paper presents an energy-efficient scheduling and rescheduling method (named Green Rescheduling Method, GRM). GRM aims at the solving of the dynamic scheduling problem under the condition of a certain level of routing flexibility enabling the reassignment of tasks to new resources. The key performance indicators integrated into the proposed GRM are effectiveness and efficiency-oriented. Applications concern the domains of production and logistics. In order to assess the proposed approach, experimentations have been made and results illustrate the applicability of GRM to build efficient and effective scheduling and rescheduling both for flexible manufacturing systems and inventory distribution systems in a physical internet network. A mathematical formulation for flexible job shop problem with energy consumption is also proposed using mixed Integer programming to evaluate the performance of the predictive part of GRM.
引用
收藏
页码:3263 / 3283
页数:21
相关论文
共 55 条
[1]   Rescheduling job shops under random disruptions [J].
Abumaizar, RJ ;
Svestka, JA .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 1997, 35 (07) :2065-2082
[2]  
Al-Hinai N., 2012, WORLD ACAD SCI ENG T, V64, P996
[3]   Model predictive control for inventory management in biomass manufacturing supply chains [J].
Alvarez-Rodriguez, Dayron Antonio ;
Normey-Rico, Julio Elias ;
Costa Flesch, Rodolfo Cesar .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2017, 55 (12) :3596-3608
[4]  
[Anonymous], 2006, 13 CIRP INT C LIF CY
[5]  
Bai J., 2009, 2009 INT WORKSH INT, P1, DOI [10.1109/IWISA.2009.5072720, DOI 10.1109/IWISA.2009.5072720]
[6]   Dynamic Clan Particle Swarm Optimization [J].
Bastos-Filho, C. J. A. ;
Carvalho, D. F. ;
Figueiredo, E. M. N. ;
de Miranda, P. B. C. .
2009 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, 2009, :249-254
[7]  
Chaari T, 2014, 2014 INTERNATIONAL CONFERENCE ON ADVANCED LOGISTICS & TRANSPORT (ICALT 2014), P229, DOI 10.1109/ICAdLT.2014.6866316
[8]   Simulation for PI-Hub Cross-Docking Robustness [J].
Chargui, Tarik ;
Bekrar, Abdelghani ;
Reghioui, Mohamed ;
Trentesaux, Damien .
SERVICE ORIENTATION IN HOLONIC AND MULTI-AGENT MANUFACTURING, 2018, 762 :317-328
[9]   A Physical Internet-enabled Building Information Modelling System for prefabricated construction [J].
Chen, Ke ;
Xu, Gangyan ;
Xue, Fan ;
Zhong, Ray Y. ;
Liu, Diandian ;
Lu, Weisheng .
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, 2018, 31 (4-5) :349-361
[10]   A location-inventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints [J].
Dai, Zhuo ;
Aqlan, Faisal ;
Zheng, Xiaoting ;
Gao, Kuo .
COMPUTERS & INDUSTRIAL ENGINEERING, 2018, 119 :338-352