Incorporating order acceptance, pricing and equity considerations in the scheduling of cloud manufacturing systems: matheuristic methods

被引:26
作者
Vahedi-Nouri, Behdin [1 ]
Tavakkoli-Moghaddam, Reza [1 ]
Hanzalek, Zdenek [2 ]
Arbabi, Hamidreza [1 ]
Rohaninejad, Mohammad [2 ]
机构
[1] Univ Tehran, Coll Engn, Sch Ind Engn, POB 11155-4563, Tehran, Iran
[2] Czech Tech Univ, Czech Inst Informat Robot & Cybernet, Ind Informat Dept, Prague, Czech Republic
关键词
Scheduling; cloud manufacturing; order acceptance; pricing; equity; matheuristic method; OF-THE-ART; ALGORITHM; SERVICE; MODEL; ALLOCATION; DECISIONS; SELECTION; TIME;
D O I
10.1080/00207543.2020.1806370
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Rooted from the Industry 4.0 principles, Cloud Manufacturing (CMfg) is a novel customer-oriented manufacturing norm, which can assist enterprises to withstand in the nowadays highly volatile and competitive market. CMfg systems comprise two separate parties, namely, customers and factories, with independent individuals. In this regard, considering the utilities of both customers and factories and establishing the equity amongst their individuals are of particular importance for the survival and flourishment of CMfg systems. Furthermore, due to the limited capacity of resources, tightness of due dates, and customers' cost expectations, all orders may not be accepted in CMfg systems. Accordingly, this paper aims to explore a scheduling problem in a CMfg system. A multi-objective mathematical model is presented for the problem, which can determine the acceptance or rejection of orders, set prices, and schedule them in an integrated manner to maximise the customers and factories' utilities, and enhance the equity among their members. Due to the high complexity of the problem, two matheuristic methods based on the Multi-Objective Grey Wolf Optimizer (MOGWO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are developed. An extensive computational experiment is carried out to validate the proposed matheuristic methods and evaluate their performance. Moreover, some guidance is presented for managers by conducting a sensitivity analysis.
引用
收藏
页码:2009 / 2027
页数:19
相关论文
共 55 条
[1]   Cloud manufacturing service selection optimization and scheduling with transportation considerations: mixed-integer programming models [J].
Akbaripour, Hossein ;
Houshmand, Mahmoud ;
van Woensel, Tom ;
Mutlu, Nevin .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2018, 95 (1-4) :43-70
[2]   Tardiness minimization on parallel machines [J].
Azizoglu, M ;
Kirca, O .
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 1998, 55 (02) :163-168
[3]   Multiprocessor scheduling with rejection [J].
Bartal, Y ;
Leonardi, S ;
Marchetti-Spaccamela, A ;
Sgall, J ;
Stougie, L .
SIAM JOURNAL ON DISCRETE MATHEMATICS, 2000, 13 (01) :64-78
[4]   Solving comprehensive dynamic job shop scheduling problem by using a GRASP-based approach [J].
Baykasoglu, Adil ;
Karaslan, Fatma S. .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2017, 55 (11) :3308-3325
[5]   Optimised scheduling in human-robot collaboration - a use case in the assembly of printed circuit boards [J].
Bogner, Karin ;
Pferschy, Ulrich ;
Unterberger, Roland ;
Zeiner, Herwig .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2018, 56 (16) :5522-5540
[6]   Cloud Manufacturing as a new type of Product-Service System [J].
Charro, Alberto ;
Schaefer, Dirk .
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, 2018, 31 (10) :1018-1033
[7]   A cooperative approach to service booking and scheduling in cloud manufacturing [J].
Chen, Jian ;
Huang, George Q. ;
Wang, Jun-Qiang ;
Yang, Chen .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2019, 273 (03) :861-873
[8]   Joint product variety, pricing and scheduling decisions in a flexible facility [J].
Chen, Pengyu ;
Xu, He ;
Li, Yongquan ;
Zeng, Li .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2017, 55 (02) :606-620
[9]   A reinforcement learning based approach for multi-projects scheduling in cloud manufacturing [J].
Chen, Shengkai ;
Fang, Shuiliang ;
Tang, Renzhong .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2019, 57 (10) :3080-3098
[10]   Handling multiple objectives with particle swarm optimization [J].
Coello, CAC ;
Pulido, GT ;
Lechuga, MS .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2004, 8 (03) :256-279