Dual-resource Constrained Job Shop Scheduling Research Articles

A multi-method approach to scheduling and efficiency analysis in dual-resource constrained job shops with processing time uncertainty

In dual-resource constrained job shop scheduling, jobs have to be assigned to workers and machines. While the problem is notoriously hard in terms of computational complexity, additional practical difficulties arise from data uncertainty and worker efficiency variation. In this paper, we propose a methodological pipeline combining mathematical optimization, simulation, and data analysis to cope with these aspects over time. Accounting for uncertain worker processing times, we employ an iterative two-stage optimization-simulation approach: A first-stage optimization model determines an assignment of workers to jobs; in the second stage, the scheduling of this assignment is evaluated operationally using sampled realizations of worker processing times. Both stages are executed in an alternating fashion until no further improvement is possible in the average realized makespan. At the end of a work day, realized worker efficiencies are then measured in a slacks-based data envelopment analysis on the operations level. Workers learn about their individual efficiency and are prompted to reduce inefficiencies. The resulting overall methodology not only provides robustified schedules for dual-resource constrained job shops under uncertainty, but also reduces the impact of uncertainty over time by motivating workers to operate on the efficient frontier. Computational results are conducted in several settings with both heuristic and exact solution procedures for the second-stage dual-resource constrained job shop scheduling problem. The results demonstrate the versatility of the outline with respect to addressing uncertainty and worker inefficiency.

Ageing workforce effects in Dual-Resource Constrained job-shop scheduling

Currently, the ageing population is getting higher attention in production systems due to the increasing percentage of the ageing workforce, which persists longer in the working environment. Older workers are more vulnerable because of a possible decrease in physical capacities but can have a higher experience level. It is important to evaluate these factors during production process decisions and adapt the working environment to such workers. In such a way, production systems performance and workers’ well-being are jointly achieved. In companies aiming to include these aspects, digitalisation and automation can provide tools that can be used at tactical, strategic or operational levels. For example, at the operational level, wearable sensors, smart devices or trackers can be used in analysing the working time, effort and physical fatigue of operators via human-energy expenditure. These measures can be used to assess the required recovery time. However, the most challenging step is to include these measures in the decision-making process to organise the working process for operators according to their profiles and health restrictions. In this paper, we show how fatigue can be incorporated into operational processes such as scheduling in the context of a Dual-Resource Constrained (DRC) job shop. We consider several models to integrate rest allowance into the scheduling process and present a heuristic approach to assign operations to workers and machines. The impact of rest allowance on system performance is measured and managerial insights are provided.

Optimization Algorithm Simulation for Dual-Resource Constrained Job-Shop Scheduling

Optimization Algorithm Simulation for Dual-Resource Constrained Job-Shop Scheduling

A branch population genetic algorithm for dual-resource constrained job shop scheduling problem

The manufacturing systems constrained by both machines and heterogeneous workers are referred to as Dual Resource Constrained (DRC) systems. DRC scheduling problem has attracted more and more attention in recent years. In order to address the Dual Resource Constrained Job Shop Scheduling Problem (DRCJSP) to minimize the makespan and cost, a meta-heuristic algorithm named Branch Population Genetic Algorithm (BPGA) is proposed in this paper. The proposed algorithm is a genetic algorithm (GA) based scheduling approach, and it introduces the branch population to accumulate and transfer evolutionary experience of parent chromosomes via pheromone. The branch population can strengthen the population diversity and accelerate convergence. Additionally, several mechanisms are applied to optimize the performance of BPGA. The elite evolutionary operator is utilized to optimize search ability by laying particular emphasis on the evolution of the elite population. The roulette selection operator based on sector segmentation is proposed to decrease the computational complexity and avoid the algorithm prematurity. The scheduling strategy based on compressed time window is proposed to improve the global scheduling performance. In our research, BPGA shows convergence to the Pareto front according to the Markov chain theory. Numerical experiments with randomly generated examples and case studies are analyzed to evaluate the performance of the proposed algorithm. Computational experiments show BPGA can provide the promising results for the DRCJSP.

A hybrid discrete particle swarm optimization for dual-resource constrained job shop scheduling with resource flexibility

In this paper, a novel hybrid discrete particle swarm optimization algorithm is proposed to solve the dual-resource constrained job shop scheduling problem with resource flexibility. Particles are represented based on a three-dimension chromosome coding scheme of operation sequence and resources allocation. Firstly, a mixed population initialization method is used for the particles. Then a discrete particle swarm optimization is designed as the global search process by taking the dual-resources feature into account. Moreover, an improved simulated annealing with variable neighborhoods structure is introduced to improve the local searching ability for the proposed algorithm. Finally, experimental results are given to show the effectiveness of the proposed algorithm.

A New Hybrid Optimization Algorithm and its Application in Job Shop Scheduling

In this paper, a dual-resource constrained job shop scheduling problem was studied by designing a hybrid genetic algorithm based on Genetic Algorithm (GA) and Simulated Annealing (SA). GA is used to search for a group of better solutions to the problem of minimizing production cost and then SA is applied to searching them for the best one. The combination of GA and SA utilizes the advantages of the two algorithms and overcomes their disadvantages. The operation-based encoding and an active schedule decoding method were employed. This hybrid genetic algorithm reasonably assigns the resources of machines and workers to jobs and achieves optimum on some performance. The results of numerical simulations, which are compared with those of other well-known algorithms, show better performance of the proposed algorithm.