Flow Shop Scheduling Problem (FSSP) has significant application in the industry, and therefore it has been extensively addressed in the literature using different optimization techniques. Current research investigates Permutation Flow Shop Scheduling Problem (PFSSP) to minimize makespan using the Hybrid Evolution Strategy (HES _SA ). Initially, a global search of the solution space is performed using an Improved Evolution Strategy (I.E.S.), then the solution is improved by utilizing local search abilities of Simulated Annealing (S.A.). I.E.S. thoroughly exploits the solution space using the reproduction operator, in which four offsprings are generated from one parent. A double swap mutation is used to guide the search to more promising areas in less computational time. The mutation rate is also varied for the fine-tuning of results. The best solution of the I.E.S. acts as a seed for S.A., which further improved the results by exploring better neighborhood solutions. In S.A., insertion mutation is used, and the cooling parameter and acceptance-rejection criteria induce randomness in the algorithm. The proposed HES _SA algorithm is tested on well-known NP-hard benchmark problems of Taillard (120 instances), and the performance of the proposed algorithm is compared with the famous techniques available in the literature. Experimental results indicate that the proposed HES _SA algorithm finds fifty-four upper bounds for Taillard instances, while thirty-eight results are further improved for the Taillard instances.

Industries have to produce high quality products with low cost because of the competitive environment and customer demand. To cope with the increasing demand of customized products at low cost, the concept of cellular manufacturing systems (CMS) has been introduced under the umbrella of lean manufacturing. Industries are facing three major problems in CMS; selection of product families, cell formation and products scheduling. This paper deals with the products scheduling problem in CMS. As it is a non-deterministic polynomial-time (NP) hard problem, a hybrid particle swarm optimization algorithm with Nawaz, Enscore, Ham-NEH (NEPSO) embedded with local search is proposed to find optimized sequence results for two conflicting performance measures (work in process and machine cell utilization). Here, particle swarm optimization (PSO) is integrated with an NEH algorithm to quickly achieve better optimal sequence. For this purpose, the solution obtained from the NEH algorithm is used as a seed for PSO optimization. A mathematical model is presented for conflicting performance measures; minimization of work in process (WIP) and maximization of average machine cell utilization. A case study of automotive manufacturing cells was conducted. Results of the NEPSO were compared with the existing method, standard PSO, genetic algorithm (GA), and NEH algorithm, showing that the NEPSO performed better in term of problem optimization of the cellular layouts.

Production now requires the management of production processes and operations on the basis of customers’ demand to ensure the best combination of technology and humans in the system. The role of the humans in the production process is very significant for the production and quality of the product. The production system depends upon technology and human factors and is highly influenced by the working conditions of the workers, that is, work load, physical, dealings, job timings and so forth. In the current global economy, minimizing production costs is a serious priority for the industries. However, the costs of bad working conditions increase the intensity of the average stress among employees to cause extra costs by affecting the workers’ efficiency and products’ quality, which is invisible in the eyes of decision makers. This research identifies the cost of workers’ stress by developing a linkage between the economic benefits of the firms and the social upgrading of the workers. A numerical example of a production based system is performed to represent the real-time application of the proposed model. A sensitivity analysis is also carried out to quantify the impact of average stress among workers on the production system. Sequential quadratic programming is used to optimize the given nonlinear model for production planning. The optimal results influence ergonomics awareness and the relationship with the safety culture among managers in a firm. It is concluded that efficient and effective production cannot be possible without considering the working conditions of humans in the firm. Managerial insights are also generated from the implications of the results and sensitivity analysis.