A branch and bound algorithm for a parallel machine scheduling problem in green manufacturing industry considering time cost and power consumption

The concept that green manufacturing can create social value has emerged around the world to reduce the amount of carbon emissions and preserve the ecological environment. Among the optimization problems faced by manufacturing firms, the parallel machine scheduling problem (PMSP) plays an important role both in theory and practice and has been intensively studied in recent decades. The traditional PMSP mainly focuses on time efficiency, such as makespan minimization. However, in response to global warming and other negative environmental impacts, it is important to pay attention to green manufacturing while considering the trade-off between social value (e.g. energy savings) and time costs. This paper considers the PMSP in green manufacturing considering the time cost and social value. In our model, the energy cost is related to the working speed of the machines. To efficiently solve this model, some properties are provided and proven and are also incorporated into the proposed branch and bound (BB) algorithm to limit the number of nodes in the BB tree. The algorithm is tested using a set of computational experiments involving with data obtained from an auto parts manufacturing company.

Automated summary

The concept of green manufacturing creating social value has gained global attention for its potential to reduce carbon emissions and protect the environment. The study focuses on the parallel machine scheduling problem (PMSP) in manufacturing, highlighting the need to consider social value alongside traditional efficiency measures. A model incorporating energy costs and social value is proposed, with a branch and bound algorithm developed to efficiently solve the problem and tested in computational experiments with data from an auto parts manufacturer.