Hybrid Salp Swarm Algorithm for Solving the Green Scheduling Problem in a Double-Flexible Job Shop

Abstract

Green scheduling is not only an effective way to achieve green manufacturing but also an effective way for modern manufacturing enterprises to achieve energy conservation and emission reduction. The double-flexible job shop scheduling problem (DFJSP) considers both machine flexibility and worker flexibility, so it is more suitable for practical production. First, a multi-objective mixed-integer programming model for the DFJSP with the objectives of optimizing the makespan, total worker costs, and total influence of the green production indicators is formulated. Considering the characteristics of the problem, three-layer salp individual encoding and decoding methods are designed for the multi-objective hybrid salp swarm algorithm (MHSSA), which is hybridized with the Lévy flight, the random probability crossover operator, and the mutation operator. In addition, the influence of the parameter setting on the MHSSA in solving the DFJSP is investigated by means of the Taguchi method of design of experiments. The simulation results for benchmark instances show that the MHSSA can effectively solve the proposed problem and is significantly better than the MSSA and the MOPSO algorithm in the diversity, convergence, and dominance of the Pareto frontier.

Flexible job shop scheduling problem under Industry 5.0: A survey on human reintegration, environmental consideration and resilience improvement

“…Berti et al (2021) also considers workers' age, influencing workers' fatigue and physical condition. Finally, the cost of workers can be considered, as in Li et al (2016a); Gong et al (2018a); Liu et al (2022). The cost to employ a worker depends on his skills and experiences.…”

Modeling an Optimal Environmentally Friendly Energy-Saving Flexible Workshop

“…As an energy-intensive field, manufacturing consumes nearly one third of the world's energy [25,26]. The study of energy saving shop scheduling is of great significance to the sustainable development of the industry [27,28]. Therefore, the questions of how to reduce the energy consumption of the workshop as much as possible and realize reasonable scheduling of processing equipment and AGVs under the condition of ensuring normal operation of the workshop and minimizing production time have become research hotspots.…”

Solving the Flexible Job Shop Scheduling Problem Using a Discrete Improved Grey Wolf Optimization Algorithm

“…Objective Type Method Algorithm Characteristic [41] Makespan Conversion IWOA ROV conversion rule [42] Makespan Conversion HHS LPV mapping rule [43] Makespan and total energy consumption Conversion GWO Ascending mapping [44] Makespan, total worker costs and total influence of the green production Conversion HSSA Ascending mapping [45] Makespan, critical machine load and total machine load Discretization HDFA Hamming Distance [46] Makespan, critical machine load and total machine load Discretization DPSO Crossover and mutation update operators [47] Makepsan, total flow time, critical machine load and total machine load Discretization DJaya DJaya update operator [48] Makepsan Discretization CROTS Discrete collision and decomposition reactions [49] Energy consumption and cost Discretization CSO Discrete seeking and tracing modes [50] Energy consumption and makespan Discretization DWWO…”