Multi-factory Scheduling Research Articles

Real-time multi-factory scheduling in Industry 4.0 with virtual alliances

The ever-changing and dynamic market environment requires applying job shop systems based on real-time data. The establishment of physical-virtual systems in the production process has led to the emergence of intelligent factories. Compared with those employing traditional production methods, such factories manufacture products with higher quality, higher production speed, and other economic benefits. Regarding the virtual connections of factories, events such as the arrival of new jobs, and machine breakdowns, are identified by Radio Frequency Identification System between different production units, and related decisions are made quickly and carefully. In this intelligent job shop scheduling, it is assumed that independent factories create virtual production networks in which, each factory focuses on its own interests. Regarding the importance of this issue in today’s industry, this research explores the real-time scheduling problem in multi-agent production networks distributed in smart factories. Since this problem is a combination of static scheduling and real-time scheduling, a bi-objective model of mixed-integer linear programming is first developed. An approach is then proposed to solve the dynamic real-time scheduling problem. In addition, a learning-based memetic algorithm for solving large-size bi-objective instances is proposed due to the NP-hardness of the considered problem. Afterward, the results of the proposed algorithm are compared with the hybrid Pareto-based tabu search algorithm. The computational results show that in large-size instances, the proposed algorithm outperforms the competing algorithm.

Multi-objective multi-factory scheduling

This paper introduces a multi-factory scheduling problem with heterogeneous factories and parallel machines. This problem, as a major part of supply chain planning, includes the finding of a suitable factory for each job and the scheduling of the assigned jobs at each factory, simultaneously. For the first time, this paper studies multi-objective scheduling in the production network in which each factory has its customers and demands can be satisfied by itself or other factories. In other words, this paper assumes that jobs can transfer from the overloaded machine in the origin factory to the factory, which has fewer workloads by imposing some transportation times. For simultaneous minimization of the sum of the earliness and tardiness of jobs and total completion time, after modeling the scheduling problem as a mixed-integer linear program, the existing multi-objective techniques are analyzed and a new one is applied to our problem. Since this problem is NP-hard, a heuristic algorithm is also proposed to generate a set of Pareto optimal solutions. Also, the algorithms are proposed to improve and cover the Pareto front. Computational experiences of the heuristic algorithm and the output of the model implemented by CPLEX over a set of randomly generated test problems are reported.

Network configuration multi-factory scheduling with batch delivery: A learning-oriented simulated annealing approach

This paper introduces the network configuration multi-factory scheduling with the batch delivery problem (NCMFSBDP) in supply chain (SC). There is a set of given jobs that are transported among factories through a sufficient number of batches with limited capacity. In the assumed SC network, some in-process jobs may have the same processing routes and can be delivered together within the same batch to reduce the transportation costs. On the other hand, the batch delivery decision may cause an increase in the tardiness and holding costs. Therefore, in such a complicated network, the aim of NCMFSBDP is to find the optimum scheduling and batch delivery solutions to minimize the total transportation, tardiness and holding costs. The proposed problem is formulated as a mixed-integer programming model. Due to the NP-hardness of the problem, in addition to the standard simulated annealing (SA) and the cloud theory-based SA, this paper develops a learning-oriented SA based on Q-learning algorithm to deal with the large-sized problems, quickly. Then, a novel hybrid cloud theory-based learning-oriented SA (HCLSA) is proposed. The algorithms are examined dealing with several randomly generated test instances versus genetic algorithm. According to the statistical analysis, it is shown that the proposed HCLSA is superior to the other examined algorithms in terms of both solution’s quality and execution time. Finally, some analytical insights are conducted about the effects of reinforcement learning on controlling parameters of SA during the search process.

A Benders decomposition algorithm for multi-factory scheduling problem with batch delivery

The multi-factory supply chain problem is investigated to determine the production and transportation scheduling of jobs which are allowed to be transported by batches. This is a mixed-integer optimization problem, which could be challenging to solve. The problem incorporates two parts: (1) assigning jobs to appropriate batch, and (2) scheduling jobs of batches for production and transportation. Based on the problem structure and because of its NP-hardness characteristics, Benders decomposition is recognized as a suitable approach. This approach decomposes the problem into assignment master problem and scheduling sub-problem. This would facilitate the solution procedure. By comparing performance of the proposed algorithm with an exact approach: Branch and Bound, It is achieved that it is able to find the near optimal solution in smaller computational times than the Branch and Bound.

Graph colouring-based algorithm to parallel jobs scheduling on parallel factories

This paper studies the multi-factory scheduling problem where a number of different individual factories join together to form a production network. In this problem, it is assumed each job has an original factory that initially ordered it, jobs can transport between the different factories in overloaded situations and the speed of machines in each factory can be different. Here, it is also assumed that the jobs may use more than one machine at the same time which is known as parallel jobs scheduling. This research extends a two-phase algorithm. In the first phase, the jobs according to need of machines decompose into the several job sets via semidefinite programming and a rounding algorithm in the graph. Then, in the second phase for scheduling the job sets obtained from first phase, we propose a discrete particle swarm optimisation (PSO). Finally, an extensive numerical study is conducted to assess the performance of proposed algorithm.

A survey of multi-factory scheduling

Because of current globalization trend, production has shifted from the single factory production to multi-factory production network. To become competitive in today's rapidly changing market requirements, factories have shifted from a centralized to a more decentralized structure, in many areas of decision making including scheduling. In multi-factory production network, each factory can be considered as an individual entity which has different efficiency and is subject to different constraints, for example, machine advances, worker cost, tax, close to suppliers, and transportation facilities, etc. Since limited resources make scheduling an important decision in the production, for several decades, researchers focused on determining an efficient schedule to improve the productivity. The recent remarkable attention in distributed production management in both academia and the industry has demonstrated the significance of multi-factory scheduling. For the first time, this paper provides a review on the multi-factory machine scheduling. For this, first, the paper classifies and reviews the literature according to shop environments, including single machine, parallel machines, flowshop, job shop, and open shop. Then the reviewed literature is quantified and measured. At the end, the paper concludes by presenting some problems receiving less attention than the others and proposes several research opportunities in the field.