Sustainable efficient energy is the key factor of any sustainable manufacturing system. This study addresses a multi-item sustainable economic energy efficient reliable manufacturing quantity (MSEEERMQ) model. The manufacturing system produces defective products during long-runs, where those products may be reworked under the optimum effect of energy and carbon footprint with some costs. As all products are not sold immediately, the holding cost increases based on time. The management decides the system design variable to reduce energy consumption cost and increase system reliability under some time-dependent holding costs, and the optimum energy such that the maximum profit of the production model is obtained with a system reliability as a decision variable. The inflation and time-value of money are considered to calculate the cost of the production model under efficient energy. Using control theory, an Euler–Lagrange method is employed to obtain the sustainable critical path, which gives the optimal solution of the model. There are two lemmas to prove the global optimal solution of the model through the control theory. There is an illustrative example to test the model. Under different conditions there are other two examples with graphical representation and sensitivity analysis. Numerical studies reveal that maximum profit is obtained at the optimal value of the decision variable.

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.

Several industries are facing many challenges in their production systems due to increasing customer demand. Customer demand is growing for products with innovative features that are flexible, good quality, and appealing. This paper presents a flexible production-inventory system that produces multiple parts of a product. Defective products may be produced during the production process. Those defective products are remanufactured immediately after inspection. Limited budget and space constraints are considered, along with product assembly. Based on different distribution functions, non-linear equations are calculated using the Kuhn–Tucker optimization technique. Numerical examples, a graphical representation, and sensitivity analysis are presented in this paper. The solution procedure evaluates the minimization of the total investment based on the ${\chi }^2$ distribution. This study examines electronic products those are more likely to be defective rather than perfect during production.