Flexible jobshop scheduling problem with resource recovery constraints.

Objectives and methods of study: The general objective of this research is to study a scheduling problem found in a local brewery. The main problem can be seen as a parallel machine batch scheduling problem with sequence-dependent setup times, resource constraints, precedence relationships, and capacity constraints. In the first part of this research, the problem is characterized as a Flexible Job-shop Scheduling Problem with Resource Recovery Constraints. A mixed integer linear formulation is proposed and a large set of instances adapted from the literatura of the Flexible Job-shop Scheduling Problem is used to validate the model. A solution procedure based on a General Variable Neighborhood Search metaheuristic is proposed, the performance of the procedure is evaluated by using a set of instances adapted from the literature. In the second part, the real problem is addressed. All the assumptions and constraints faced by the decision maker in the brewery are taken into account. Due to the complexity of the problem, no mathematical formulation is presented, instead, a solution method based on a Greedy Randomize Adaptive Search Procedure is proposed. Several real instances are solved by this algorithm and a comparison is carried out between the solutions reported by our GRASP and the ones found through the procedure followed by the decision maker. The computational results reveal the efficiency of our method, considering both the processing time and the completion time of the scheduling. Our algorithm requires less time to generate the production scheduling (few seconds) while the decision maker takes a full day to do it. Moreover, the completion time of the production scheduling generated by our algorithm is shorter than the one generated through the process followed by the decision maker. This time saving leads to an increase of the production capacity of the company. Contributions: The main contributions of this thesis can be summarized as follows: i) the introduction of a variant of the Flexible Job-shop Scheduling Problem, named as the Flexible Job-shop Scheduling Problem with Resource Recovery Constraints (FRRC); ii) a mixed integer linear formulation and a General Variable Neighborhood Search for the FRRC; and iii) a case study for which a Greedy Randomize Adaptive Search Procedure has been proposed and tested on real and artificial instances. The main scientific products generated by this research are: i) an article already published: Sáenz-Alanís, César A., V. D. Jobish, M. Angélica Salazar-Aguilar, and Vincent Boyer. “A parallel machine batch scheduling problem in a brewing company”. The International Journal of Advanced Manufacturing Technology 87, no. 1-4 (2016): 65-75. ii) another article submitted to the International Journal of Production Research for its possible publication; and iii) Scientific presentations and seminars

Efficient heuristics for the parallel blocking flow shop scheduling problem

We consider the NP-hard problem of scheduling n jobs in F identical parallel flow shops, each consisting of a series of m machines, and doing so with a blocking constraint. The applied criterion is to minimize the makespan, i.e., the maximum completion time of all the jobs in F flow shops (lines). The Parallel Flow Shop Scheduling Problem (PFSP) is conceptually similar to another problem known in the literature as the Distributed Permutation Flow Shop Scheduling Problem (DPFSP), which allows modeling the scheduling process in companies with more than one factory, each factory with a flow shop configuration. Therefore, the proposed methods can solve the scheduling problem under the blocking constraint in both situations, which, to the best of our knowledge, has not been studied previously. In this paper, we propose a mathematical model along with some constructive and improvement heuristics to solve the parallel blocking flow shop problem (PBFSP) and thus minimize the maximum completion time among lines. The proposed constructive procedures use two approaches that are totally different from those proposed in the literature. These methods are used as initial solution procedures of an iterated local search (ILS) and an iterated greedy algorithm (IGA), both of which are combined with a variable neighborhood search (VNS). The proposed constructive procedure and the improved methods take into account the characteristics of the problem. The computational evaluation demonstrates that both of them –especially the IGA– perform considerably better than those algorithms adapted from the DPFSP literature.Peer ReviewedPostprint (author's final draft

A hybrid CFGTSA based approach for scheduling problem: a case study of an automobile industry

In the global competitive world swift, reliable and cost effective production subject to uncertain situations, through an appropriate management of the available resources, has turned out to be the necessity for surviving in the market. This inspired the development of the more efficient and robust methods to counteract the existing complexities prevailing in the market. The present paper proposes a hybrid CFGTSA algorithm inheriting the salient features of GA, TS, SA, and chaotic theory to solve the complex scheduling problems commonly faced by most of the manufacturing industries. The proposed CFGTSA algorithm has been tested on a scheduling problem of an automobile industry, and its efficacy has been shown by comparing the results with GA, SA, TS, GTS, and hybrid TSA algorithms

Scheduling Jobs in Flowshops with the Introduction of Additional Machines in the Future

This is the author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Elsevier and can be found at: http://www.journals.elsevier.com/expert-systems-with-applications/.The problem of scheduling jobs to minimize total weighted tardiness in flowshops,\ud with the possibility of evolving into hybrid flowshops in the future, is investigated in\ud this paper. As this research is guided by a real problem in industry, the flowshop\ud considered has considerable flexibility, which stimulated the development of an\ud innovative methodology for this research. Each stage of the flowshop currently has\ud one or several identical machines. However, the manufacturing company is planning\ud to introduce additional machines with different capabilities in different stages in the\ud near future. Thus, the algorithm proposed and developed for the problem is not only\ud capable of solving the current flow line configuration but also the potential new\ud configurations that may result in the future. A meta-heuristic search algorithm based\ud on Tabu search is developed to solve this NP-hard, industry-guided problem. Six\ud different initial solution finding mechanisms are proposed. A carefully planned\ud nested split-plot design is performed to test the significance of different factors and\ud their impact on the performance of the different algorithms. To the best of our\ud knowledge, this research is the first of its kind that attempts to solve an industry-guided\ud problem with the concern for future developments

Climbing depth-bounded adjacent discrepancy search for solving hybrid flow shop scheduling problems with multiprocessor tasks

This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The problem even in its simplest form is NP-hard in the strong sense. The great deal of interest for this problem, besides its theoretical complexity, is animated by needs of various manufacturing and computing systems. We propose a new approach based on limited discrepancy search to solve the problem. Our method is tested with reference to a proposed lower bound as well as the best-known solutions in literature. Computational results show that the developed approach is efficient in particular for large-size problems

A Variable Neighborhood MOEA/D for Multiobjective Test Task Scheduling Problem

Test task scheduling problem (TTSP) is a typical combinational optimization scheduling problem. This paper proposes a variable neighborhood MOEA/D (VNM) to solve the multiobjective TTSP. Two minimization objectives, the maximal completion time (makespan) and the mean workload, are considered together. In order to make solutions obtained more close to the real Pareto Front, variable neighborhood strategy is adopted. Variable neighborhood approach is proposed to render the crossover span reasonable. Additionally, because the search space of the TTSP is so large that many duplicate solutions and local optima will exist, the Starting Mutation is applied to prevent solutions from becoming trapped in local optima. It is proved that the solutions got by VNM can converge to the global optimum by using Markov Chain and Transition Matrix, respectively. The experiments of comparisons of VNM, MOEA/D, and CNSGA (chaotic nondominated sorting genetic algorithm) indicate that VNM performs better than the MOEA/D and the CNSGA in solving the TTSP. The results demonstrate that proposed algorithm VNM is an efficient approach to solve the multiobjective TTSP