A multi-objective differential evolution algorithm for parallel batch processing machine scheduling considering electricity consumption cost

Zhou, Shengchao; Li, Xin; Du, Ni; Pang, Yan; Chen, Huaping

doi:10.1016/j.cor.2018.04.009

Cited by 88 publications

(35 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Based on the current solution, the third neighborhood structures S 3 is used to search for better solutions than the current solution, and it means that the solutions found can dominate the current solution. 13.…”

Section: Variable Neighborhood Searchmentioning

confidence: 99%

“…Scholars have carried out a lot of research on energy-efficiency scheduling under TOU electricity tariffs in different generic manufacturing systems. Simple manufacturing systems, such as single machine and parallel machine, have been studied by many researchers [10][11][12][13]. However, there are few reports on complex manufacturing systems, such as flow shop, flexible flow shop, job shop, flexible job shop, etc.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy Saving for Tissue Paper Mills by Energy-Efficiency Scheduling under Time-of-Use Electricity Tariffs

2021

View full text Add to dashboard Cite

Environmental concerns and soaring energy prices have brought huge pressure of energy saving and emission reduction to tissue paper mills. Electricity is one of the main energy sources of tissue paper mills. The production characteristics of tissue paper mills make it easy to decrease energy cost by using time-of-use (TOU) electricity tariffs. This study investigates the bi-objective energy-efficiency scheduling of tissue paper mills under time-of-use electricity tariffs, the objectives of which are makespan and energy cost. First, considering the processing energy cost, setup energy cost, and transportation energy cost, an energy cost model of a tissue paper mill under TOU electricity tariffs is established. Second, the energy-efficiency scheduling model under TOU electricity tariffs is built based on the energy cost model. Finally, on the basis of decomposition and teaching–learning optimization, this study proposes a novel multi-objective evolutionary algorithm and further combined with the variable neighborhood search to solve the problem. The case study results demonstrate that our study of tissue paper mill energy saving is feasible, and the proposed method has better performance than the existing methods.

show abstract

Section: Variable Neighborhood Searchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy Saving for Tissue Paper Mills by Energy-Efficiency Scheduling under Time-of-Use Electricity Tariffs

2021

View full text Add to dashboard Cite

show abstract

“…Modeled by a mixed integer programming Solved by heuristic algorithms, including four single-sequence based heuristics, a biased random-key genetic algorithm, and a hybrid bin loading algorithm Liao and Liao [7] Scheduling jobs in a flow shop with two batch processing machines Formulated as Improved Mixed Integer Linear (MILP) Programming models The solution was proposed as a MILP-based heuristic algorithm Lu et al [8] Scheduling of a multiproduct multi-stage batch processing system Modeled as a mixed integer linear programming (MILP) problem The model was divided into several sub-problems as the horizon was rolled forward, of which a fixand-relax strategy was applied Muthuswamy et al [9] Makespan minimization in a two-machine no-wait flow shop with batch processing machines A particle swarm optimization algorithm was proposed Sabzehparvar and Seyed-Hosseini [10] Multi-mode resource constrained project scheduling problem with mode dependent time lags Solved by Floyd-Warshall algorithm Tang and Liu [11] Two-machine flow shop scheduling involving a batching machine with transportation or deterioration consideration Solved by a heuristic algorithm and its worst-case performance was discussed Traumann and Schwindt [12] Scheduling a given set of operations in a multipurpose batch plant Modeled by a novel two-phase approach (algorithm) dealing with two types of constraints separately H. Zhou et al [13] Batch-processing machine scheduling with arbitrary release times and non-identical job sizes A particle swarm optimization algorithm was modified and applied S. Zhou et al [14] Parallel batch processing machine scheduling considering electricity consumption cost A multi-objective differential evolution algorithm was proposed imi : Amount of time needed to process the activity (batch) in execution mode m i (processing unit m i ) in scenario s. r s i1mi : Amount of time needed from machine #1 as a resource to process the activity (batch) in execution mode m i (processing unit m i ) in scenario s. r s i2mi : Amount of time needed from machine #2 as a resource to process the activity (batch) in execution mode m i (processing unit m i ) in scenario s.…”

Section: Mathematical Modelingmentioning

confidence: 99%

Modeling and optimization of batch production based on layout and cutting problems under uncertainty

Saeedi

Feizi

2021

RAIRO-Oper. Res.

View full text Add to dashboard Cite

This paper presents a modeling and optimization of batch production based on layout, cutting and project scheduling problems by considering scenario planning. In order to solve the model, a novel genetic algorithm with an improvement procedure based on variable neighborhood search (VNS) is presented. Initially, the model is solved in small sizes using Lingo software and the combined genetic algorithm; then, the results are compared. Afterwards, the model is solved in large sizes by utilizing the proposed algorithm and simple genetic algorithm. The main findings of this paper show: 1) To prove the validity of the proposed method, a case study has been solved by employing the classical method (employing Lingo 11) and the results were compared to the ones developed by the proposed algorithm. Since the results are the same in both cases, the suggested algorithm is valid and able to achieve optimal and near-optimal solutions. 2) The combined genetic algorithm is more effective in achieving optimal boundaries and closer solutions in all cases compared to classical genetic algorithm. In other words, the main finding of this paper is a combined genetic algorithm to optimize batch production modeling problems, which is more efficient than the methods provided in the literature.

show abstract

“…Giglio et al [45] solved an integrated lot sizing and energy-efficient job shop scheduling problem using a relax-and-fix heuristic algorithm; they show that their method can reduce energy consumption, machines idle times, and the overall cost of the system. Some researchers also considered the optimal scheduling method with time-sharing prices [46,47]; however, that belongs to another topic where only the time period with low electric charge is considered, and this goes out of the scope of this research. For the IPPS problem, owing to complexity in the integration of process planning and scheduling, studies on the IPPS problem with energy-saving criteria appear to be limited according to existing publications.…”

Section: Literature Reviewmentioning

confidence: 99%

Realizing Energy Savings in Integrated Process Planning and Scheduling

2019

View full text Add to dashboard Cite

The integration of scheduling and process planning can eliminate resource conflicts and hence improve the performance of a manufacturing system. However, the focus of most existing works is mainly on the optimization techniques to improve the makespan criterion instead of more efficient uses of energy. In fact, with a deteriorating global climate caused by massive coal-fired power consumption, carbon emission reduction in the manufacturing sector is becoming increasingly imperative. To ease the environmental burden caused by energy consumption, e.g., coal-fired power consumption in use of machine tools, this research considers both makespan as well as environmental performance criteria, e.g., total power consumption, in integrated process planning and scheduling using a novel multi-objective memetic algorithm to facilitate a potential amount of energy savings; this can be realized through a better use of resources with more efficient scheduling schemes. A mixed-integer linear programming (MILP) model based on the network graph is formulated with both makespan as well as total power consumption criteria. Due to the complexity of the problem, a multi-objective memetic algorithm with variable neighborhood search (VNS) technique is then developed for this problem. The Kim’s benchmark instances are employed to test the proposed algorithm. Moreover, the TOPSIS decision method is used to determine the most satisfactory non-dominated solution. Several scenarios are considered to simulate different machine automation levels and different machine workload levels. Computational results show that the proposed algorithm can strike a balance between the makespan criterion and the total power consumption criterion, and the total power consumption can be affected by machine tools with different automation levels and different workloads. More importantly, results also show that energy saving can be realized by completing machining as early as possible on a machine tool and taking advantage of machine flexibility.

show abstract

A multi-objective differential evolution algorithm for parallel batch processing machine scheduling considering electricity consumption cost

Cited by 88 publications

References 51 publications

Energy Saving for Tissue Paper Mills by Energy-Efficiency Scheduling under Time-of-Use Electricity Tariffs

Energy Saving for Tissue Paper Mills by Energy-Efficiency Scheduling under Time-of-Use Electricity Tariffs

Modeling and optimization of batch production based on layout and cutting problems under uncertainty

Realizing Energy Savings in Integrated Process Planning and Scheduling

Contact Info

Product

Resources

About