Flow shop scheduling

Routing (electronic design automation)

Algorithm

Engineering

Tardiness

Embedded system

Scheduling (production processes)

Operating system

Computer science

Mathematical optimization

Acoustics

Integer (computer science)

Integer programming

Business

Computer network

Physics

Operations research

Mathematics

Bottleneck

Schedule

Programming language

Finance

Permutation (music)

Optimization problem

Bilevel optimization

Order (exchange)

Job shop scheduling

Omega

With the fourth-generation industrial revolution, manufacturing industries are focusing on dynamic, fully autonomous, and more customer-oriented production systems. This customer-oriented change converts classically static customer demand into that which is dynamic and real-time, as no prior information regarding customer demand is known in advance. This paper focuses on real-time order acceptance and scheduling (r-OAS) for a data-enabled permutation flow shop. To compensate for the shortage in prevailing approaches that make bottleneck-based decisions or assume that the intermediate buffers among workstations are infinite, an r-OAS scheme is generated based on a data-driven representation, which can concisely predict the dynamic production status of flow shops and the corresponding makespan of a job with finite intermediate buffer constraints. Using this representation, real-time job release planning (r-JRP) can be coupled with r-OAS to minimize various operational costs of flow shops (i.e., the costs of the work-in-process, earliness, and tardiness). In terms of the inherent interactive mechanism between r-OAS and r-JRP, in which r-OAS generates a decision space for r-JRP and r-JRP then feeds the lowest operational costs back for use in r-OAS decision-making, a bilevel interactive optimization (BIO) is formulated to simultaneously address the two subproblems based on the Stackelberg game. The r-OAS acts as the leader, while r-JRP acts as the follower. The BIO is a type of nonlinear integer programming, and a bilevel tabu-enumeration heuristic algorithm is developed to solve it. The efficiency of the BIO is verified through a practical case study. The results show that the BIO can increase the net revenue of flow shops by 2.97%, compared to the bottleneck-based approach, and by 2.45% and 0.92%, respectively, compared to step-by-step methodologies.

Real-time order acceptance and scheduling for data-enabled permutation flow shops: Bilevel interactive optimization with nonlinear integer programming