A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint
With the growing concern of energy shortage and environment pollution, the energy aware operation management problem has emerged as a hot topic in industrial engineering recently. An integrated model consisting of production scheduling, preventive maintenance (PM) planning, and energy controlling is established for the flow shops with the PM constraint and peak demand constraint. The machine’s on/off and the speed level selection are considered to save the energy consumption in this problem. To minimize the makespan and the total energy consumption simultaneously, a multi-objective algorithm founded on NSGA-II is designed to solve the model effectively. The key decision variables are coded into the chromosome, while the others are obtained heuristically using the proposed decoding method when evaluating the chromosome. Numerical experiments were conducted to validate the effectiveness and efficiency by comparing the proposed algorithm and the traditional rules in manufacturing plant. The impacts of constraints on the Pareto frontier are also shown when analyzing the tradeoff between two objectives, which can be used to explicitly assess the energy consumption.
A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint
With the growing concern of energy shortage and environment pollution, the energy aware operation management problem has emerged as a hot topic in industrial engineering recently. An integrated model consisting of production scheduling, preventive maintenance (PM) planning, and energy controlling is established for the flow shops with the PM constraint and peak demand constraint. The machine’s on/off and the speed level selection are considered to save the energy consumption in this problem. To minimize the makespan and the total energy consumption simultaneously, a multi-objective algorithm founded on NSGA-II is designed to solve the model effectively. The key decision variables are coded into the chromosome, while the others are obtained heuristically using the proposed decoding method when evaluating the chromosome. Numerical experiments were conducted to validate the effectiveness and efficiency by comparing the proposed algorithm and the traditional rules in manufacturing plant. The impacts of constraints on the Pareto frontier are also shown when analyzing the tradeoff between two objectives, which can be used to explicitly assess the energy consumption.
A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint
Weiwei Cui (author) / Biao Lu (author)
2020
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
A Genetic Algorithm for Flow Shop Scheduling with Assembly Operations to Minimize Makespan
| Online Contents | 2014
A Genetic Algorithm for Flow Shop Scheduling with Assembly Operations to Minimize Makespan
| Springer Verlag | 2014
British Library Online Contents | 1993