Flow-shop problems, as a typical manufacturing challenge, have become an interesting area of research. The primary concern is that the solution space is huge and, therefore, the set of feasible solutions cannot be enumerated one by one. In this paper, we present an efficient solution strategy based on a genetic algorithm (GA) to minimize the makespan, total waiting time and total tardiness in a flow shop consisting of n jobs and m machines. The primary objective is to minimize the job waiting time before performing the related operations. This is a major concern for some industries such as food and chemical for planning and production scheduling. In these industries, there is a probability of the decay and deterioration of the products prior to accomplishment of operations in workstation, due to the increase in the waiting time. We develop a model for a flowshop scheduling problem, which uses the planner-specified weights for handling a multi-objective optimization problem. These weights represent the priority of planning objectives given by managers. The results of the proposed GA and classic GA are analyzed by the analysis of variance (ANOVA) method and the results are discussed.

Selection of industrial robots for the present day’s manufacturing organizations is one of the most difficult assignments due to the presence of a wide range of feasible alternatives. Robot manufacturers are providing advanced features in their products to sustain in the globally competitive environment. For this reason, selection the most suitable robot for a given industrial application now becomes a more complicated task. In this paper, four models of data envelopment analysis (DEA), i.e. Charnes, Cooper and Rhodes (CCR), Banker, Charnes and Cooper (BCC), additive, and cone-ratio models are applied to identify the feasible robots having the optimal performance measures, simultaneously satisfying the organizational objectives with respect to cost and process optimization. Furthermore, the weighted overall efficiency ranking method of multi-attribute decision-making theory is also employed for arriving at the best robot selection decision from the short-listed competent alternatives. In order to demonstrate the relevancy and distinctiveness of the adopted DEA-based approach, two real time industrial robot selection problems are solved.

Selecting from a pool of interdependent projects under certainty, when faced with resource constraints, has been studied well in the literature of project selection problem. After briefly reviewing and discussing popular modeling approaches for dealing with uncertainty, this paper proposes an approach based on chance constrained programming and utility theory for a certain range of problems and under some practical assumptions. Expected Utility Programming, as the proposed modeling approach, will be compared with other well-known methods and its meaningfulness and usefulness will be illustrated via two numerical examples and one real case.

Clustering cellular manufacturing plays an important role in many industrial engineering problems. This paper investigates the performance of two methods of heuristic and metaheuristics fuzzy clustering. The proposed method investigates heuristic well-known FCM and particle swarm optimization (PSO) on some well-known benchmarks. We use two criteria of J(P) as well as Xie-Beni to compare the results. Three parameters of PSO method is tuned using design of experiment and then the results of PSO are compared versus FCM method in terms of two mentioned criteria. The proposed models are run for each instance 10 different times and, using ANOVA test, the means of two methods are compared. While the results of ANOVA do not indicate any meaningful difference between PSO and FCM in terms of J(P), we have found some meaningful differences between PSO and FCM in terms of Xie-Beni criterion. In other words, PSO performs better than FCM in terms of Xie-Beni.

This paper presents a two-stage hybrid flow shop scheduling problem with setup and assembly operations. The proposed study of this paper considers one kind of product with a quantity of demand where each product is made by assembling a set of different parts. At first, the parts are manufactured in a two-stage hybrid flow-shop and then the parts are assembled into products on assembly stage. Setup operations are needed when a machine starts processing the parts or it changes items. The considered objective is minimizing the completion time of all products. Since the problem is classified as NP-hard class, a combinatorial algorithm is proposed. The proposed algorithm is a three-step procedure where we use heuristic, genetic algorithm (GA), simulated annealing (SA), NEH and Johnson’s algorithm. Three lower bounds are presented and improved to evaluate the proposed algorithms. An extensive computational experiment is conducted to compare the performances of the proposed algorithms.

In this paper, we present a Bayesian analysis of a data set selected from a Brazilian food company. This data set represents the times taken for different quality control analysts to test manufactured products arriving at the company’s quality control department. The samples selected from each batch contain mixtures of different products, which may be submitted to quality testing taking different times. From preliminary analysis of the data, it was observed that the histograms presented two clusters, indicating a mixture of distributions. A mixture of parametrical distributions was thus assumed in the presence of a covariate in order to analyze the data set and to establish standards to be used by the company for the times taken by the analysts. Inferences and predictions are obtained using a Bayesian approach with standard existing Markov Chain Monte Carlo (MCMC) methods.

The present work deals with the cutting forces and cutting temperature produced during turning of titanium alloy Ti-6Al-4V with PVD TiN coated tungsten carbide inserts under dry environment. The 1st order mathematical models are developed using multiple regression analysis and optimized the process parameters using contour plots. The model presented high determination coefficient (R2 = 0.964 and 0.989 explaining 96.4 % and 98.9 % of the variability in the cutting force and cutting temperature, which indicates the goodness of fit for the model and high significance of the model. The developed mathematical model correlates the relationship of the cutting force and temperature with the process parameters with good degree of approximation. From the contour plots, the optimal parametric combination for lowest cutting force is v 3 (75 m/min) – f 1 (0.25 mm/rev). Similarly, the optimal parametric combination for minimum temperature is v 1 (45 m/min) – f 1 (0.25 mm/rev). Cutting speed is found to be the most significance parameter on cutting forces followed by feed. Similarly, for cutting temperature, feed is found to be the most influencing parameter followed by cutting speed.

This paper deals with the development of an inventory model for time varying demand and constant demand; and time dependent holding cost and constant holding cost for case 1 and case 2 respectively. Previous models incorporating that the holding cost is constant for the entire inventory cycle. Mathematical model has been developed for determining the optimal order quantity, the optimal cycle time and optimal total inventory cost for both cases. Differential calculus is used for finding optimal solution. Numerical examples are given for both cases to validate the proposed model. Sensitivity analysis is carried out to analyze the effect of changes in the optimal solution with respect to change in various parameters.

Integrated cell formation and layout (CFLP) is an extended application of the group technology philosophy in which machine cells and cell layout are addressed simultaneously. The aim of this technological innovation is to improve both productivity and flexibility in modern manufacturing industry. However, due to its combinatorial complexity, the cell formation and layout problem is best solved by heuristic and metaheuristic approaches. As CFLP is prevalent in manufacturing industry, developing robust and efficient solution methods for the problem is imperative. This study seeks to develop a fuzzy simulated evolution algorithm (FSEA) that integrates fuzzy-set theoretic concepts and the philosophy of constructive perturbation and evolution. Deriving from the classical simulated evolution algorithm, the search efficiency of the major phases of the algorithm is enhanced, including initialization, evaluation, selection and reconstruction. Illustrative computational experiments based on existing problem instances from the literature demonstrate the utility and the strength of the FSEA algorithm developed in this study. It is anticipated in this study that the application of the algorithm can be extended to other complex combinatorial problems in industry.

In this paper, we present a new Imperialist Competitive Algorithm (ICA) to solve a bi-objective unrelated parallel machine scheduling problem where setup times are sequence dependent. The objectives include mean completion time of jobs and mean squares of deviations from machines workload from their averages. The performance of the proposed ICA (PICA) method is examined using some randomly generated data and they are compared with three alternative methods including particle swarm optimization (PSO), original version of imperialist competitive algorithm (OICA) and genetic algorithm (GA) in terms of the objective function values. The preliminary results indicate that the proposed study outperforms other alternative methods. In addition, while OICA performs the worst as alternative solution strategy, PSO and GA seem to perform better.