Theory of constraints (TOC) production and manufacturing performance

This paper is based on an empirical study of the relationship between Theory of Constraints (TOC) production and operational performance in manufacturing plants. The study uses a survey questionnaire to collect data from a sample of 61 European firms which have implemented the TOC approach. Analysis of variance (ANOVA) technique and regression models have been employed to test the research hypotheses. The results detect many differences and similarities in adoption of TOC practices across the countries and suggest that manufacturing managers should consider adopting some TOC practices instead of others. In particular the Drum-buffer-rope methodology, the development of a Master Production Schedule based on constraints and the use of Non-constraint resources with excess capacity are among the most important practices to enhance competitive performance of manufacturing plants

MODELING, OPTIMISATION AND ANALYSIS OF RE-ENTRANT FLOWSHOP JOB SCHEDULING WITH FUZZY PROCESSING TIMES

This paper presents a makespan minimization of -jobs -machines re-entrant flow shop scheduling problem (RFSP) under fuzzy uncertainties using Genetic Algorithm. The RFSP objective is formulated as a mathematical programme constrained by number of jobs and resources availability with traditional scheduling policies of First Come First Serve (FCFS) and the First Buffer First Serve (FBFS). Jobs processing times were specified by fuzzy numbers and modelled using triangular membership function representations. The modified centroid defuzzification technique was used at different alpha-cuts to obtain fuzzy processing times (FPT) of jobs to explore the importance of uncertainty. The traditional GA schemes and operators were used together with roulette wheel algorithm without elitism in the selection process based on job fuzzy completion times. A test problem of five jobs with specified Job Processing and Transit Times between service centres, Job Start Times and Job Due times was posed. Results obtained using the deterministic and fuzzy processing times were compared for the two different scheduling policies, FCFS and FBFS. The deterministic optimal makespan for FBFS schedule was 61.2% in excess of the FCFS policy schedule.  The results also show that schedules with fuzzy uncertainty processing times provides shorter makespans than those for deterministic processing times and those under FCFS performing better than those under FBFS policy for early jobs while on the long run the FBFS policy performs better. The results underscore the need to take account of comprehensive fuzzy uncertainties in job processing times as a trade-off between time and costs influenced by production makespan. http://dx.doi.org/10.4314/njt.v36i3.2

Multicriteria hybrid flow shop scheduling problem: literature review, analysis, and future research

This research focuses on the Hybrid Flow Shop production scheduling problem, which is one of the most difficult problems to solve. The literature points to several studies that focus the Hybrid Flow Shop scheduling problem with monocriteria functions. Despite of the fact that, many real world problems involve several objective functions, they can often compete and conflict, leading researchers to concentrate direct their efforts on the development of methods that take consider this variant into consideration. The goal of the study is to review and analyze the methods in order to solve the Hybrid Flow Shop production scheduling problem with multicriteria functions in the literature. The analyses were performed using several papers that have been published over the years, also the parallel machines types, the approach used to develop solution methods, the type of method develop, the objective function, the performance criterion adopted, and the additional constraints considered. The results of the reviewing and analysis of 46 papers showed opportunities for future researchon this topic, including the following: (i) use uniform and dedicated parallel machines, (ii) use exact and metaheuristics approaches, (iv) develop lower and uppers bounds, relations of dominance and different search strategiesto improve the computational time of the exact methods,  (v) develop  other types of metaheuristic, (vi) work with anticipatory setups, and (vii) add constraints faced by the production systems itself

Design and development of CSP techniques for finding robust solutions in job-shop scheduling problems with Operators

[ES] Se desarrolla una técnica CSP para buscar soluciones robustas en el problema job-shop scheduling. La técnica esta desarrollada en tres pasos. El primer paso resuelve el problema sin tener en cuenta operadores. El segundo paso introduce las restricciones de los operadores y obtiene soluciones teniendo en cuenta el makespan y la robustez. En el tercer paso se mejora la robustez redistribuyendo los buffers. Para probar las robustez de las soluciones obtenidas se aplican incidencias virtuales en las soluciones.[EN] A CSP technique have been developed for finding robust solutions in job-shop scheduling problems with operators. The technique is developed in three steps. The first step solve the problem without operators minimizing the makespan. The second step introduce the operator constraints and give solutions take into account makespan and robustness. The third step improve the robustness redistributing the buffer. Some virtual incidences are created and to check the robustness of the solutions.Escamilla Fuster, J. (2012). Design and development of CSP techniques for finding robust solutions in job-shop scheduling problems with Operators. http://hdl.handle.net/10251/18029Archivo delegad

Scheduling flow lines with buffers by ant colony digraph

This work starts from modeling the scheduling of n jobs on m machines/stages as flowshop with buffers in manufacturing. A mixed-integer linear programing model is presented, showing that buffers of size n - 2 allow permuting sequences of jobs between stages. This model is addressed in the literature as non-permutation flowshop scheduling (NPFS) and is described in this article by a disjunctive graph (digraph) with the purpose of designing specialized heuristic and metaheuristics algorithms for the NPFS problem. Ant colony optimization (ACO) with the biologically inspired mechanisms of learned desirability and pheromone rule is shown to produce natively eligible schedules, as opposed to most metaheuristics approaches, which improve permutation solutions found by other heuristics. The proposed ACO has been critically compared and assessed by computation experiments over existing native approaches. Most makespan upper bounds of the established benchmark problems from Taillard (1993) and Demirkol, Mehta, and Uzsoy (1998) with up to 500 jobs on 20 machines have been improved by the proposed ACO