A computational evaluation of constructive and improvement heuristics for the blocking flow shop to minimize total flowtime

This paper focuses on the blocking flow shop scheduling problem with the objective of total flowtime minimisation. This problem assumes that there are no buffers between machines and, due to its application to many manufacturing sectors, it is receiving a growing attention by researchers during the last years. Since the problem is NP-hard, a large number of heuristics have been proposed to provide good solutions with reasonable computational times. In this paper, we conduct a comprehensive evaluation of the available heuristics for the problem and for related problems, resulting in the implementation and testing of a total of 35 heuristics. Furthermore, we propose an efficient constructive heuristic which successfully combines a pool of partial sequences in parallel, using a beam-search-based approach. The computational experiments show the excellent performance of the proposed heuristic as compared to the best-so-far algorithms for the problem, both in terms of quality of the solutions and of computational requirements. In fact, despite being a relative fast constructive heuristic, new best upper bounds have been found for more than 27% of Taillard’s instances.Ministerio de Ciencia e Innovación DPI2013-44461-P/DP

Iterative beam search algorithms for the permutation flowshop

We study an iterative beam search algorithm for the permutation flowshop (makespan and flowtime minimization). This algorithm combines branching strategies inspired by recent branch-and-bounds and a guidance strategy inspired by the LR heuristic. It obtains competitive results, reports many new-best-so-far solutions on the VFR benchmark (makespan minimization) and the Taillard benchmark (flowtime minimization) without using any NEH-based branching or iterative-greedy strategy. The source code is available at: https://gitlab.com/librallu/cats-pfsp

An effective iterated greedy algorithm for the mixed no-idle flowshop scheduling problem

In the no-idle flowshop, machines cannot be idle after finishing one job and before starting the next one. Therefore, start times of jobs must be delayed to guarantee this constraint. In practice machines show this behavior as it might be technically unfeasible or uneconomical to stop a machine in between jobs. This has important ramifications in the modern industry including fiber glass processing, foundries, production of integrated circuits and the steel making industry, among others. However, to assume that all machines in the shop have this no-idle constraint is not realistic. To the best of our knowledge, this is the first paper to study the mixed no-idle extension where only some machines have the no-idle constraint. We present a mixed integer programming model for this new problem and the equations to calculate the makespan. We also propose a set of formulas to accelerate the calculation of insertions that is used both in heuristics as well as in the local search procedures. An effective iterated greedy (IG) algorithm is proposed. We use an NEH-based heuristic to construct a high quality initial solution. A local search using the proposed accelerations is employed to emphasize intensification and exploration in the IG. A new destruction and construction procedure is also shown. To evaluate the proposed algorithm, we present several adaptations of other well-known and recent metaheuristics for the problem and conduct a comprehensive set of computational and statistical experiments with a total of 1750 instances. The results show that the proposed IG algorithm outperforms existing methods in the no-idle and in the mixed no-idle scenarios by a significant margin.Quan-Ke Pan is partially supported by the National Science Foundation of China 61174187, Program for New Century Excellent Talents in University (NCET-13-0106), Science Foundation of Liaoning Province in China (2013020016), Basic scientific research foundation of Northeast University under Grant N110208001, Starting foundation of Northeast University under Grant 29321006, and Shandong Province Key Laboratory of Intelligent Information Processing and Network Security (Liaocheng University). Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "RESULT - Realistic Extended Scheduling Using Light Techniques" with reference DPI2012-36243-C02-01 co-financed by the European Union and FEDER funds and by the Universitat Politecnica de Valencia, for the project MRPIV with reference PAID/2012/202.Pan, Q.; Ruiz García, R. (2014). An effective iterated greedy algorithm for the mixed no-idle flowshop scheduling problem. Omega. 44:41-50. https://doi.org/10.1016/j.omega.2013.10.002S41504

Iterated-greedy-based algorithms with beam search initialization for the permutation flowshop to minimize total tardiness

The permutation flow shop scheduling problem is one of the most studied operations research related problems. Literally, hundreds of exact and approximate algorithms have been proposed to optimise several objective functions. In this paper we address the total tardiness criterion, which is aimed towards the satisfaction of customers in a make-to-order scenario. Although several approximate algorithms have been proposed for this problem in the literature, recent contributions for related problems suggest that there is room for improving the current available algorithms. Thus, our contribution is twofold: First, we propose a fast beam-search-based constructive heuristic that estimates the quality of partial sequences without a complete evaluation of their objective function. Second, using this constructive heuristic as initial solution, eight variations of an iterated-greedy-based algorithm are proposed. A comprehensive computational evaluation is performed to establish the efficiency of our proposals against the existing heuristics and metaheuristics for the problem.Ministerio de Ciencia e Innovación DPI2013-44461-PMinisterio de Ciencia e Innovación DPI2016-80750-

A beam-search-based constructive heuristic for the PFSP to minimise total flowtime

In this paper we present a beam-search-based constructive heuristic to solve the permutation flowshop scheduling problem with total flowtime minimisation as objective. This well-known problem is NP-hard, and several heuristics have been developed in the literature. The proposed algorithm is inspired in the logic of the beam search, although it remains a fast constructive heuristic. The results obtained by the proposed algorithm outperform those obtained by other constructive heuristics in the literature for the problem, thus modifying substantially the state-of-the-art of efficient approximate procedures for the problem. In addition, the proposed algorithm even outperforms two of the best metaheuristics for many instances of the problem, using much lesser computation effort. The excellent performance of the proposal is also proved by the fact that the new heuristic found new best upper bounds for 35 of the 120 instances in Taillard’s benchmark.Ministerio de Ciencia e Innovación DPI2013-44461-PMinisterio de Ciencia e Innovación DPI2016-80750-

Integrating labor awareness to energy-efficient production scheduling under real-time electricity pricing : an empirical study

With the penetration of smart grid into factories, energy-efficient production scheduling has emerged as a promising method for industrial demand response. It shifts flexible production loads to lower-priced periods to reduce energy cost for the same production task. However, the existing methods only focus on integrating energy awareness to conventional production scheduling models. They ignore the labor cost which is shift-based and follows an opposite trend of energy cost. For instance, the energy cost is lower during nights while the labor cost is higher. Therefore, this paper proposes a method for energy-efficient and labor-aware production scheduling at the unit process level. This integrated scheduling model is mathematically formulated. Besides the state-based energy model and genetic algorithm-based optimization, a continuous-time shift accumulation heuristic is proposed to synchronize power states and labor shifts. In a case study of a Belgian plastic bottle manufacturer, a set of empirical sensitivity analyses were performed to investigate the impact of energy and labor awareness, as well as the production-related factors that influence the economic performance of a schedule. Furthermore, the demonstration was performed in 9 large-scale test instances, which encompass the cases where energy cost is minor, moderate, and major compared to the joint energy and labor cost. The results have proven that the ignorance of labor in existing energy-efficient production scheduling studies increases the joint energy and labor cost, although the energy cost can be minimized. To achieve effective production cost reduction, energy and labor awareness are recommended to be jointly considered in production scheduling. (C) 2017 Elsevier Ltd. All rights reserved

Efficient heuristic algorithms for the blocking flow shop scheduling problem with total flow time minimization

This paper proposes two constructive heuristics, i.e. HPF1 and HPF2, for the blocking flow shop problem in order to minimize the total flow time. They differ mainly in the criterion used to select the first job in the sequence since, as it is shown, its contribution to the total flow time is not negligible. Both procedures were combined with the insertion phase of NEH to improve the sequence. However, as the insertion procedure does not always improve the solution, in the resulting heuristics, named NHPF1 and NHPF2, the sequence was evaluated before and after the insertion to keep the best of both solutions. The structure of these heuristics was used in Greedy Randomized Adaptive Search Procedures (GRASP) with variable neighborhood search in the improvement phase to generate greedy randomized solutions. The performance of the constructive heuristics and of the proposed GRASPs was evaluated against other heuristics from the literature. Our computational analysis showed that the presented heuristics are very competitive and able to improve 68 out of 120 best known solutions of Taillard’s instances for the blocking flow shop scheduling problem with the total flow time criterionPeer ReviewedPostprint (author’s final draft

Framework for sustainable TVET-Teacher Education Program in Malaysia Public Universities

Studies had stated that less attention was given to the education aspect, such as teaching and learning in planning for improving the TVET system. Due to the 21st Century context, the current paradigm of teaching for the TVET educators also has been reported to be fatal and need to be shifted. All these disadvantages reported hindering the country from achieving the 5th strategy in the Strategic Plan for Vocational Education Transformation to transform TVET system as a whole. Therefore, this study aims to develop a framework for sustainable TVET Teacher Education program in Malaysia. This study had adopted an Exploratory Sequential Mix-Method design, which involves a semi-structured interview (phase one) and survey method (phase two). Nine experts had involved in phase one chosen by using Purposive Sampling Technique. As in phase two, 118 TVET-TE program lecturers were selected as the survey sample chosen through random sampling method. After data analysis in phase one (thematic analysis) and phase two (Principal Component Analysis), eight domains and 22 elements have been identified for the framework for sustainable TVET-TE program in Malaysia. This framework was identified to embed the elements of 21st Century Education, thus filling the gap in this research. The research findings also indicate that the developed framework was unidimensional and valid for the development and research regarding TVET-TE program in Malaysia. Lastly, it is in the hope that this research can be a guide for the nations in producing a quality TVET teacher in the future

Methods for Scheduling Problems Considering Experience, Learning, and Forgetting Effects

[EN] Workers with different levels of experience and knowledge have different effects on job processing times. By taking into account 1) the sum-of-processing-time; 2) the job-position; and 3) the experience of workers, a more general learning model is introduced for scheduling problems. We show that this model generalizes existing ones and brings the consideration of learning and forgetting effects closer to reality. We demonstrate that some single machine scheduling problems are polynomially solvable under this general model. Considering the forgetting effect caused by the idle time on the second machine, we construct a learning-forgetting model for the two-machine permutation flow shop scheduling problem with makespan minimization. A branch-and-bound method and four heuristics are presented to find optimal and approximate solutions, respectively. The proposed heuristics are evaluated over a large number of randomly generated instances. Experimental results show that the proposed heuristics are effective and efficient.This work was supported in part by the National Natural Science Foundation of China under Grant 61572127 and Grant 61272377, in part by the Key Research and Development Program in Jiangsu Province under Grant BE2015728, in part by the Collaborative Innovation Center of Wireless Communications Technology and the Key Natural Science Fund for Colleges and Universities in Jiangsu Province under Grant 12KJA630001, and in part by the Collaborative Innovation Center of Wireless Communications Technology. The work of R. Ruiz was supported by the Spanish Ministry of Economy and Competitiveness through Project "SCHEYARD-Optimization of Scheduling Problems in Container Yards" under Grant DPI2015-65895-R. This paper was recommended by Associate Editor A. Janiak.Li, X.; Jiang, Y.; Ruiz García, R. (2018). Methods for Scheduling Problems Considering Experience, Learning, and Forgetting Effects. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 48(5):743-754. https://doi.org/10.1109/TSMC.2016.2616158S74375448

MILP-based local search procedures for minimizing total tardiness in the No-idle Permutation Flowshop Problem

We consider the No-idle Permutation Flowshop Scheduling Problem (NPFSP) with a total tardiness criterion. We present two Mixed Integer Linear Programming (MILP) formulations based on positional and precedence variables, respectively. We study six local search procedures that explore two different neighborhoods by exploiting the MILP formulations. Our computational experiments show that two of the proposed procedures strongly outperform the state-of-the-art metaheuristic. We update 63% of the best known solutions of the instances in Taillards’ benchmark, and 77% if we exclude those instances for which we proved that the previous best known solutions are optimal