Current struggles for customer satisfaction in make-to-order companies focus on product customization and on-time delivery. For better management of demand-mix variability, production activities are ...typically configured as flexible job shops. The advent of information technology and process automatization has given rise to very specific training requirement for workers, which indeed turns production scheduling into a dual-resource constrained problem. This paper states a novel dual-resource constrained flexible job-shop problem (DRCFJSP) whose performance considers simultaneously makespan and due date-oriented criteria, where eligibility and processing time are both dependent on worker expertise. Our research comes from an automobile collision repair shop with re-scheduling needs to react to real-time events like due date changes, delay in arrival, changes in job processing time and rush jobs. We have developed constructive iterated greedy procedures that performs efficiently on the large-scale bi-objective DRCFJSP arisen (good schedules in < 5 s), hence providing planners with the required responsiveness in their scheduling of repairing orders and allocation of workers at the different work centres. In addition, computational experiments were conducted on a test bed of smaller DRCFJSP instances generated for benchmarking purposes. Off-the-shelf resolution for an 80% of the medium-sized instances is not fruitful after 9000 s.
•We model a multi-objective energy-efficient scheduling with transportation times.•We develop an enhanced genetic algorithm to get Pareto solutions.•The proposed algorithm is effective in solving the ...EFJSP and is capable of obtaining better Pareto solutions than the multi-objective genetic algorithm.•An energy-saving scheduling strategy could be considered by reducing the transportation time.
Manufacturing enterprises nowadays face huge environmental challenges because of energy consumption and associated environmental impacts. One of the effective strategies to reduce energy consumption is by employing intelligent scheduling techniques. Production scheduling can have significant impact on energy saving in manufacturing system from the operation management point of view. Resource flexibility and complex constraints in flexible manufacturing system make production scheduling a complicated nonlinear programming problem. To this end, a multi-objective optimization model with the objective of minimizing energy consumption and makespan is formulated for a flexible job shop scheduling problem with transportation constraints. Then, an enhanced genetic algorithm is developed to solve the problem. Finally, comprehensive experiments are carried out to evaluate the performance of the proposed model and algorithm. The experimental results revealed that the proposed model and algorithm can solve the problem effectively and efficiently. This may provide a basis for the decision makers to consider energy-efficient scheduling in flexible manufacturing system.
The problem of maximizing total early work in a two‐machine flow‐shop, in which n jobs are to be scheduled subject to a common due date d, has been recently studied in the scheduling literature. An ...O(n2d4) time dynamic programming algorithm was presented first for the weighted case, and then for the unweighted case another O(n2d2) running time dynamic programming algorithm was proposed and converted into an On4ε2$$ O\left(\frac{n^4}{\varepsilon^2}\right) $$ time fully polynomial time approximation scheme (FPTAS). By establishing new problem properties, we present an O(nd2) time dynamic programming algorithm and an On3ε2$$ O\left(\frac{n^3}{\varepsilon^2}\right) $$ time FPTAS for the unweighted problem. We generalize the problem to a distributed setting of m parallel two‐machine flow‐shops, develop an O(nd3m) time dynamic programming algorithm, an On3m+1ε3m$$ O\left(\frac{n^{3m+1}}{\varepsilon^{3m}}\right) $$ time FPTAS, and three integer linear programming (ILP) formulations for it. Computational experiments are conducted to appraise the proposed ILP models.
This paper addresses the stable scheduling of multi-objective problem in flexible job shop scheduling with random machine breakdown. Recently, numerous studies are conducted about robust scheduling; ...however, implementing a scheme which prevents a tremendous change between scheduling and after machine breakdown (preschedule and realized schedule, respectively) can be critical for utilizing available resources. The stability of the schedule can be detected by a slight deviation of start and completion time of each job between preschedule and realized schedule under the uncertain conditions. In this paper, two evolutionary algorithms, NSGA-II and NRGA, are applied to combine the improvement of makespan and stability simultaneously. A simulation approach is used to evaluate the state and condition of the machine breakdowns. After the introduction of the evaluation criteria, the proposed algorithms are tested on a variety of benchmark problems. Finally, through performing statistical tests, the algorithm with higher performance in each criterion is identified.
•A methodology for addressing multi objective flexible job shop scheduling problem is proposed.•Stability and makespan considered to optimize simultaneously in presence of machine breakdown.•NRGA, NSGAII, and simulation used to tackle the problem.•In three criteria NSGAII is leading algorithm and for two criteria NRGA is the leading one.
This paper proposes a new dynamic algorithm based on simulation approach and multi-objective optimization to solve the FJSP with transportation assignment. The objectives considered in scheduling ...jobs and transportation tasks in a flexible job shop manufacturing system include makespan, robot travel distance, time difference with due date and critical waiting time. The results obtained from the computational experiments have shown that the proposed approach is efficient and competitive.
The traditional shop floor scheduling problem mainly focuses on the static environment, which is unrealistic in actual production. To solve this problem, this paper proposes a digital twin-driven ...shop floor adaptive scheduling method. Firstly, a digital twin model of the actual production line is established to monitor the operation of the actual production line in real time and provide a real-time data source for subsequent scheduling; secondly, to address the problem that the solution quality and efficiency of the traditional genetic algorithm cannot meet the actual production demand, the key parameters in the genetic algorithm are dynamically adjusted using a reinforcement learning enhanced genetic algorithm to improve the solution efficiency and quality. Finally, the digital twin system captures dynamic events and issues warnings when dynamic events occur in the actual production process, and adaptively optimizes the initial scheduling scheme. The effectiveness of the proposed method is verified through the construction of the digital twin system, extensive dynamic scheduling experiments, and validation in a laboratory environment. It achieves real-time monitoring of the scheduling environment, accurately captures abnormal events in the production process, and combines with the scheduling algorithm to effectively solve a key problem in smart manufacturing.
This paper investigates an energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines (HFSP-UPM) with the energy-saving strategy of turning off and on. We first analyse the ...energy consumption of HFSP-UPM and formulate five mixed integer linear programming (MILP) models based on two different modelling ideas namely idle time and idle energy. All the models are compared both in size and computational complexities. The results show that MILP models based on different modelling ideas vary dramatically in both size and computational complexities. HFSP-UPM is NP-Hard, thus, an improved genetic algorithm (IGA) is proposed. Specifically, a new energy-conscious decoding method is designed in IGA. To evaluate the proposed IGA, comparative experiments of different-sized instances are conducted. The results demonstrate that the IGA is more effective than the genetic algorithm (GA), simulating annealing algorithm (SA) and migrating birds optimisation algorithm (MBO). Compared with the best MILP model, the IGA can get the solution that is close to an optimal solution with the gap of no more than 2.17% for small-scale instances. For large-scale instances, the IGA can get a better solution than the best MILP model within no more than 10% of the running time of the best MILP model.
Flexible job shop scheduling problem (FJSP) has been extensively investigated and objectives are often related to time. Energy-related objective should be considered fully in FJSP with the advent of ...green manufacturing. In this study, FJSP with the minimisation of workload balance and total energy consumption is considered and the conflicting between two objectives is analysed. A shuffled frog-leaping algorithm (SFLA) is proposed based on a three-string coding approach. Population and a non-dominated set are used to construct memeplexes according to tournament selection and the search process of each memeplex is done on its non-dominated member. Extensive experiments are conducted to test the search performance of SFLA and computational results show the conflicting between two objectives of FJSP and the promising advantages of SFLA on the considered FJSP.
•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.
