Using real-time information to reschedule jobs in a flowshop with variable processing times

Versión revisada. Embargo 36 mesesIn a time where detailed, instantaneous and accurate information on shop-floor status is becoming available in many manufacturing companies due to Information Technologies initiatives such as Smart Factory or Industry 4.0, a question arises regarding when and how this data can be used to improve scheduling decisions. While it is acknowledged that a continuous rescheduling based on the updated information may be beneficial as it serves to adapt the schedule to unplanned events, this rather general intuition has not been supported by a thorough experimentation, particularly for multi-stage manufacturing systems where such continuous rescheduling may introduce a high degree of nervousness in the system and deteriorates its performance. In order to study this research problem, in this paper we investigate how real-time information on the completion times of the jobs in a flowshop with variable processing times can be used to reschedule the jobs. In an exhaustive computational experience, we show that rescheduling policies pay off as long as the variability of the processing times is not very high, and only if the initially generated schedule is of good quality. Furthermore, we propose several rescheduling policies to improve the performance of continuous rescheduling while greatly reducing the frequency of rescheduling. One of these policies, based on the concept of critical path of a flowshop, outperforms the rest of policies for a wide range of scenarios.Ministerio de Ciencia e Innovación DPI2016-80750-

NEH-based heuristics for the permutation flowshop scheduling problem to minimize total tardiness

Since Johnson׳s seminal paper in 1954, scheduling jobs in a permutation flowshop has been receiving the attention of hundreds of practitioners and researchers, being one of the most studied topics in the Operations Research literature. Among the different objectives that can be considered, minimising the total tardiness (i.e. the sum of the surplus of the completion time of each job over its due date) is regarded as a key objective for manufacturing companies, as it entails the fulfilment of the due dates committed to customers. Since this problem is known to be NP-hard, most research has focused on proposing approximate procedures to solve it in reasonable computation times. Particularly, several constructive heuristics have been proposed, with NEHedd being the most efficient one, serving also to provide an initial solution for more elaborate approximate procedures. In this paper, we first analyse in detail the decision problem depending on the generation of the due dates of the jobs, and discuss the similarities with different related decision problems. In addition, for the most characteristic tardiness scenario, the analysis shows that a huge number of ties appear during the construction of the solutions done by the NEHedd heuristic, and that wisely breaking the ties greatly influences the quality of the final solution. Since no tie-breaking mechanism has been designed for this heuristic up to now, we propose several mechanisms that are exhaustively tested. The results show that some of them outperform the original NEHedd by about 25% while keeping the same computational requirements.Ministerio de Ciencia e Innovación DPI2010-15573/DPIMinisterio de Ciencia e Innovación DPI2013-44461-P/DP

Efficient heuristics for the hybrid flow shop scheduling problem with missing operations

In this paper, we address the hybrid flowshop scheduling problem for makespan minimisation. More specifically, we are interested in the special case where there are missing operations, i.e. some stages are skipped, a condition inspired in a realistic problem found in a plastic manufacturer. The main contribution of our paper is twofold. On the one hand we carry out a computational analysis to study the hardness of the hybrid flowshop scheduling problem with missing operations as compared to the classical hybrid flowshop problem. On the other hand, we propose a set of heuristics that captures some special features of the missing operations and compare these algorithms with already existing heuristics for the classical hybrid flowshop, and for the hybrid flowshop problem with missing operations. The extensive computational experience carried out shows that our proposal outperforms existing methods for the problem, indicating that it is possible to improve the makespan by interacting with the jobs with missing operations.Ministerio de Ciencia e Innovación DPI2016-80750-

An Efficient Constructive Heuristic to Balance Trade-Offs Between Makespan and Flowtime in Permutation Flow Shop Scheduling

Balancing trade-offs between production cost and holding cost is critical for production and operations management. Utilization of a production line affects production cost, which relates to makespan, and work-in-process (WIP) inventories in a production line affect holding cost, which relate to flowtime. There are trade-offs between two objectives, to minimize makespan and to minimize flowtime. Without addressing trade-off balancing issues in flow shop scheduling, WIP inventories are still high in manufacturing, generating unnecessary holding cost. However, utilization is coupled with WIP inventories. Low WIP inventory levels might lower utilization and generate high production cost. Most existing constructive heuristics focus only on single-objective optimization. In the current literature, the NEH heuristic proposed by Nawaz, Enscore, and Ham (1983) is the best constructive heuristic to minimize makespan, and the LR heuristic proposed by Liu and Reeves (2001) is the best to minimize flowtime. In this paper, we propose a current and future deviation (CFD) heuristic to balance trade-offs between makespan and flowtime minimizations. Based on 5400 randomly generated instances, 120 instances in Taillard’s benchmarks, and one-year historical records of operating room scheduling from University of Kentucky HealthCare (UKHC), our CFD heuristic outperforms the NEH and LR heuristics on trade-off balancing, and achieves the most stable performances from the perspective of statistical process control (SPC)

On insertion tie-breaking rules in heuristics for the permutation flowshop scheduling problem

The most efficient approximate procedures so far for the flowshop scheduling problem with makespan objective – i.e. the NEH heuristic and the iterated greedy algorithm – are based on constructing a sequence by iteratively inserting, one by one, the non-scheduled jobs into all positions of an existing subsequence, and then, among the so obtained subsequences, selecting the one yielding the lowest (partial) makespan. This procedure usually causes a high number of ties (different subsequences with the same best partial makespan) that must be broken via a tie-breaking mechanism. The particular tie-breaking mechanism employed is known to have a great influence in the performance of the NEH, therefore different procedures have been proposed in the literature. However, to the best of our knowledge, no tie-breaking mechanism has been proposed for the iterated greedy. In our paper, we present a new tie-breaking mechanism based on an estimation of the idle times of the different subsequences in order to pick the one with the lowest value of the estimation. The computational experiments carried out show that this mechanism outperforms the existing ones both for the NEH and the iterated greedy for different CPU times. Furthermore, embedding the proposed tie-breaking mechanism into the iterated greedy provides the most efficient heuristic for the problem so far.Ministerio de Ciencia e Innovación DPI2010-15573/DP

A particle swarm optimisation for the no-wait flow shop problem with due date constraints.

Peer ReviewedThis paper considers the no-wait flow shop scheduling problem with due date constraints. In the no-wait flow shop problem, waiting time is not allowed between successive operations of jobs. Moreover, a due date is associated with the completion of each job. The considered objective function is makespan. This problem is proved to be strongly NP-Hard. In this paper, a particle swarm optimisation (PSO) is developed to deal with the problem. Moreover, the effect of some dispatching rules for generating initial solutions are studied. A Taguchi-based design of experience approach has been followed to determine the effect of the different values of the parameters on the performance of the algorithm. To evaluate the performance of the proposed PSO, a large number of benchmark problems are selected from the literature and solved with different due date and penalty settings. Computational results confirm that the proposed PSO is efficient and competitive; the developed framework is able to improve many of the best-known solutions of the test problems available in the literature

An effective hybrid ant lion algorithm to minimize mean tardiness on permutation flow shop scheduling problem

This article aimed to develop an improved Ant Lion algorithm. The objective function was to minimize the mean tardiness on the flow shop scheduling problem with a focus on the permutation flow shop problem (PFSP). The Hybrid Ant Lion Optimization Algorithm (HALO) with local strategy was proposed, and from the total search of the agent, the NEH-EDD algorithm was applied. Moreover, the diversity of the nominee schedule was improved through the use of swap mutation, flip, and slide to determine the best solution in each iteration. Finally, the HALO was compared with some algorithms, while some numerical experiments were used to show the performances of the proposed algorithms. It is important to note that comparative analysis has been previously conducted using the nine variations of the PFSSP problem, and the HALO obtained was compared to other algorithms based on numerical experiments

An investigation into minimising total energy consumption and total completion time in a flexible job shop for recycling carbon fiber reinforced polymer

The increased use of carbon fiber reinforced polymer (CFRP) in industry coupled with European Union restrictions on landfill disposal has resulted in a need to develop relevant recycling technologies. Several methods, such as mechanical grinding, thermolysis and solvolysis, have been tried to recover the carbon fibers. Optimisation techniques for reducing energy consumed by above processes have also been developed. However, the energy efficiency of recycling CFRP at the workshop level has never been considered before. An approach to incorporate energy reduction into consideration while making the scheduling plans for a CFRP recycling workshop is presented in this paper. This research sets in a flexible job shop circumstance, model for the bi-objective problem that minimise total processing energy consumption and makespan is developed. A modified Genetic Algorithm for solving the raw material lot splitting problem is developed. A case study of the lot sizing problem in the flexible job shop for recycling CFRP is presented to show how scheduling plans affect energy consumption, and to prove the feasibility of the model and the developed algorithm

Permutation Flowshop Scheduling with Earliness and Tardiness Penalties

We address the permutation flowshop scheduling problem with earliness and tardiness penalties (E/T) and common due date of jobs. Large number of process and discrete parts industries follow flowshop type of production process. There are very few results reported for multi-machine E/T scheduling problems. We show that the problem can be sub-divided into three groups- one, where the due date is such that all jobs are necessarily tardy; the second, where the due date is such that it is not tight enough to act as a constraint on scheduling decision; and the third is a group of problems where the due date is in between the above two. We develop analytical results and heuristics for problems arising in each of these three classes. Computational results of the heuristics are reported. Most of the problems in this research are addressed for the first time in the literature. For problems with existing heuristics, the heuristic solution is found to perform better than the existing results.