•Manufacturing companies are facing the emergent challenges to meet the demand of sustainable manufacturing.•Energy-efficient dynamic scheduling is a NP-hard problem presented in manufacturing ...systems.•A novel particle swarm optimization algorithm based on Hill function is presented to minimize makespan and energy consumption.•The relationship between makespan and energy consumption is conflicting.•The results show that the proposed algorithm outperforms the behavior of state of the art algorithms.
Due to increasing energy requirements and associated environmental impacts, nowadays manufacturing companies are facing the emergent challenges to meet the demand of sustainable manufacturing. Most existing research on reducing energy consumption in production scheduling problems has focused on static scheduling models. However, there exist many unexpected disruptions like new job arrivals and machine breakdown in a real-world production scheduling. In this paper, it is proposed an approach to address the dynamic scheduling problem reducing energy consumption and makespan for a flexible flow shop scheduling. Since the problem is strongly NP-hard, a novel algorithm based on an improved particle swarm optimization is adopted to search for the Pareto optimal solution in dynamic flexible flow shop scheduling problems. Finally, numerical experiments are carried out to evaluate the performance and efficiency of the proposed approach.
There is a lack of studies on joint optimisation of flexible job-shop scheduling problem (FJSP) considering energy consumption and production efficiency in the machining-assembly system. Thus, in ...this paper, we propose a methodology for multi-objective optimisation of energy-aware flexible job-shop scheduling during machining and assembly operations. First, a mixed integrated mathematical model is developed to improve production efficiency and minimise energy consumption. Then, a novel heuristic algorithm integrated particle swarm optimisation (PSO) and genetic algorithm (GA) is developed to address the established multi-objective problem. Moreover, numerical examples are carried out to verify the validity and performance of the solving methods in achieving energy awareness in the manufacturing system. Computational results are presented to demonstrate the advantage of solving the problem compared with the exact method and common heuristic algorithms, and the trade-off between production efficiency and energy efficiency is analysed to make the final decision for managers.
The traditional flexible job shop scheduling problem (FJSP) considers machine flexibility but not worker flexibility. Given the influence and potential of human factors in improving production ...efficiency and decreasing the cost in practical production systems, we propose a mathematical model of an extended FJSP with worker flexibility (FJSPW). A hybrid artificial bee colony algorithm (HABCA) is presented to solve the proposed FJSPW. For the HABCA, effective encoding, decoding, crossover and mutation operators are designed, and a new effective local search method is developed to improve the speed and exploitation ability of the algorithm. The Taguchi method of Design of Experiments is used to obtain the best combination of key parameters of the HABCA. Extensive computational experiments carried out to compare the HABCA with some well-performing algorithms from the literature confirm that the proposed HABCA is more effective than these algorithms, especially on large-scale FJSPW instances.
In this paper, a comparison and validation of various priority rules for the job shop scheduling problem under different objective functions is made. In a first computational experiment, 30 priority ...rules from the literature are used to schedule job shop problems under two flow time-related and three tardiness-related objectives. Based on this comparative study, the priority rules are extended to 13 combined scheduling rules in order to improve the performance of the currently best-known rules from the literature. Moreover, the best-performing priority rules on each of these five objective functions are combined into hybrid priority rules in order to be able to optimise various objectives at the same time. In a second part of the computational experiment, the robustness on the relative ranking of the performance quality is checked for the various priority rules when applied on larger problem instances, on the extension of multiple machines possibilities per job as well as on the introduction of sequence-dependent setup times. Moreover, the influence of dynamic arrivals of jobs has also been investigated to check the robustness on the relative ranking of the performance quality between static and dynamic job arrivals. The results of the computational experiments are presented and critical remarks and future research avenues are suggested.
At present, a lot of references use discrete event simulation to evaluate the fitness of evolved rules, but which simulation configuration can achieve better evolutionary rules in a limited time has ...not been fully studied. This study proposes three types of hyper-heuristic methods for coevolution of the machine assignment rules (MAR) and job sequencing rules (JSR) to solve the DFJSP, including the cooperative coevolution genetic programming with two sub-populations (CCGP), the genetic programming with two sub-trees (TTGP) and the genetic expression programming with two sub-chromosomes (GEP). After careful parameter tuning, a surrogate simulation model is used to evaluate the fitness of evolved scheduling policies (SP). Computational simulations and comparisons demonstrate that the proposed surrogate-assisted CCGP method (CCGP-SM) shows competitive performance with other evolutionary approaches using the same computation time. Furthermore, the learning process of the proposed methods demonstrates that the surrogate-assisted GP methods help accelerating the evolutionary process and improving the quality of the evolved SPs without a significant increase in the length of SP. In addition, the evolved SPs generated by the CCGP-SM show superior performance as compared with existing rules in the literature. These results demonstrate the effectiveness and robustness of the proposed method.
•Transportation time incorporate in flexible job shop scheduling.•We mathematically formulate the problem by two mixed integer linear programming models.•We propose a novel imperialist competitive ...algorithm hybridized by a simulated annealing.•The proposed algorithm is evaluated by comparing against two high performing algorithms.
After the completion of a job on a machine, it needs to be transported to the next machine, actually taking some time. However, the transportation times are commonly neglected in the literature. This paper incorporates the transportation times between the machines into the flexible job-shop scheduling problem. We mathematically formulate the problem by two mixed integer linear programming models. Since the problem is NP-hard, we propose an adaptation of the imperialist competitive algorithm hybridized by a simulated annealing-based local search to solve the problem. Various operators and parameters of the algorithm are calibrated using the Taguchi method. The presented algorithm is assessed by comparing it against two other competitive algorithms in the literature. The computational results show that this algorithm has an outstanding performance in solving the problem.
In the last 25 years, extensive research has been carried out addressing the flexible job shop scheduling (JSS) problem. A variety of techniques ranging from exact methods to hybrid techniques have ...been used in this research. The paper aims at presenting the development of flexible JSS and a consolidated survey of various techniques that have been employed since 1990 for problem resolution. The paper comprises evaluation of publications and research methods used in various research papers. Finally, conclusions are drawn based on performed survey results.
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•A distributed no-wait flexible flow shop scheduling problem (DNWFFSP) is studied.•A machine selection and three greedy factory assignment rules are designed.•Several dispatching ...rules are presented to assign the priority to each job.•Some constructive heuristics are presented for solving the DNWFFSP.•A simple version of variable neighborhood descent with five neighborhoods is proposed.
The distributed shop scheduling has gained a great deal of attention due to its high-efficient collaboration and production. This paper studies a distributed no-wait flexible flow shop scheduling problem (DNWFFSP) with the makespan criterion, where each factory includes an identical flexible flow shop with no-wait constraint that requires each job to be processed without any waiting time between consecutive stages. A mixed-integer linear programming model for the DNWFFSP is presented. A machine selection method based on the first earliest available machine rule is presented. Three greedy factory assignment rules incorporating NEH (Nawaz-Enscore-Ham) heuristic are developed to allocate jobs into factories. Fourteen dispatch rules involving simple sorting and decomposition method are developed to construct a priority sequence of jobs. Thus, several constructive heuristics are obtained by combining dispatch rules and factory assignment rules. Furthermore, a constructive heuristic version of variable neighborhood descend is designed to address the DNWFFSP. One of constructive heuristics is selected to generate an initial solution, and five neighborhood search methods are used as the local search procedure. A comprehensive numerical comparison and statistical analysis are carried out to investigate the performance of the proposed approaches. The distributed NEH2 with smallest largest-medium rule obtains better results than other constructive heuristics for large-size instances.
To decrease the load of electricity grid during the peak period, time-of-use electricity price has been implemented in industries to shift the production activities from the peak to the off-peak ...period. This paper addresses the flexible job shop scheduling under time-of-use electricity prices (FJSPTOUEP) to minimize both makespan and total electricity cost (TEC) simultaneously. We present the mixed integer programming model and propose a hybrid multi-objective evolutionary algorithm based on decomposition (HMOEA/D) to solve the problem. To generate an initial population with certain quality and diversity, several rules are used together. In the framework of MOEA/D, a cooperative search operator is designed to generate new solutions by exchanging information of neighbours. To improve the quality of solutions, two local intensification operators are designed by analysing the critical path of the schedule. An adaptive selection strategy is designed based on the reference point for well using the local search operators to enhance exploitation ability. Moreover, according to the characteristics of time-of-use electricity prices, an adjustment strategy is proposed to reduce electricity cost for further improving solutions. Computational results and statistical comparisons show that both the local intensification and adjustment strategy are effective. It also shows that the proposed HMOEA/D is more effective than other optimization algorithms in solving the FJSPTOUEP.
•Flexible job shop scheduling under time-of-use electricity prices is studied.•Multi-objective evolutionary algorithm based on decomposition is proposed.•Adaptive selection strategy and adjustment strategy are designed.•Effectiveness is demonstrated by numerical comparisons.
•Formulate the integrated scheduling of intelligent handling equipment as a BHFS-BFLB.•Establish a compact MILP model for the investigated problem.•Develop an adaptive large neighborhood search ...algorithm for the problem.•Experimental results demonstrate the superiority of the proposed ALNS.
Although a large amount of intelligent handling equipment has been widely used in automated container terminals, few studies have explored the coordination optimization among them, which is critical to the overall operational efficiency of terminals. This paper investigates the integrated scheduling of intelligent handling equipment at automated container terminals, with challenging characteristics such as bidirectional flows and limited buffers. Besides, other characteristics such as blocking, machine eligibility, and separable sequence-dependent setup times also complicate the problem, making it difficult to find an optimal or near-optimal solution. This study formulates the problem as the blocking hybrid flow shop scheduling problem with bidirectional flows and limited buffers, and then develops a compact mixed-integer linear programming model that can obtain the optimal scheme on small-scale cases. To increase the size of solvable problems and improve the performance of the solution, an adaptive large neighborhood search is presented, which takes a high-quality coding scheme that specifies the equipment used by each job in various stages. Experiments have been conducted and computational results demonstrate the superiority of the proposed algorithm in terms of solution quality and convergence speed.
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