A knowledge-based approach for multi-factory production systems. (January 2017)

• Record Type: Journal Article
• Authors: Karimi, N.; Davoudpour, H.
• Is Part Of: Computers & operations research. Volume 77(2017)
• Page Start: 72
• Page End: 85
• Publication Date: 2017-01
• Subjects: Multi-factory production systems -- Batch delivery -- Mixed-integer programming -- Knowledge-based approach -- Imperialist competitive algorithm; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2016.07.003
• Languages: English
• ISSNs: 0305-0548

Abstract

Abstract: This paper investigates scheduling of jobs with deadlines across a serial multi-factory supply chain which involves minimizing sum of total tardiness and total transportation costs. Jobs can be transported among factories and can be delivered to the customer in batches which have limited capacity. The aim of this optimization problem is threefold: (1) determining the number of batches, (2) assigning jobs to batches, and (3) scheduling the batches production and delivery in each factory. The proposed problem formulated as a mixed-integer linear program. Then the model's performance is analyzed and evaluated through two examples. Moreover, a knowledge-based imperialistic competitive algorithm (KBICA) is also presented to find an approximate optimum solution for the problem. Computational experiments of the proposed problem investigate the efficiency of the method through different sizes of the test problems. Highlights: The coordination of production and delivery scheduling is investigated. A Mixed Integer Linear Programming model is presented. The mathematical model's performance is analyzed through two examples. A high performing knowledge-based approach is presented.