## 5 Conclusions  
In order to resolve the often-conflicting objectives of system reliability and profit maximization, an organization should establish appropriate maintenance guidelines that take into consideration costs associated with both production activities and equipment failures, the latter of which include e.g., costs due to lost production. In fact, there is no profit without risk. Therefore, decision-makers always try to study the effectiveness of their adopted strategies, and want to know how their decisions lead to a loss of profit. Companies seek to generate more profit, without having to regret their decision.  
In this context, this research can serve as a decision support to quantify the profit loss following decisions made. Furthermore, in real life, decision-makers may involve this research in their work strategies. Each company has a system that helps them make predictions before the manufacturing process. The proposed models can be integrated into the employed systems, taking into account the impact of the computational methods used. The work proposes a set of mathematical tools that help evaluate decision-making in a random and risky environment, and can guide decision-makers in their choices. Therefore, it is important to have approaches and tools to help managers make judicious and efficient decisions in the face of this type of uncertainty.  
The proposed approach aims to analyze the risk of profit-loss following a decision made on the manufacturing system under uncertainties. A single operation manufacturing system is considered, which produces a single product in order to satisfy a random multi-period demand over a finite horizon \(H\). A mathematical model is presented to formulate the model. The first objective is to find the optimal production and maintenance plans with different optimization algorithms to deduce among the most appropriate computational algorithm for our problem. Based on the results of each method, the second objective of this study is to propose analytical models that evaluate the financial losses for some work strategies by using the optimal production and maintenance plans found by the presented methods. The goal is to evaluate the impact of the results of these computational methods on the calculation of profit loss for the two cases presented in the paper. The first case studied is the integration of an imperfect preventive maintenance policy. The second is the integration of a returned product strategy in random production periods. The implemented numerical examples show that the risk of profit-loss differs from one use of computational method to another. This shows that the use of an algorithm can influence profit and cause loss of profit.  
Our work is a new contribution, which treats the impact of optimization algorithms on decisions made following incidents that may disrupt the systems. However, it is not exempt from limitations. In fact, in the literature, several approaches can solve the problem type proposed in the paper. However, this work deals with only four approximate methods, which are compared with the solution found by an exact method. The choice of these algorithms is based on the most used ones in such types of problems, and thus were easily adapted to our analytical model.  
For future research, we will study the impact of the decisions made by decision-makers on various industrial problems. We will also study the impact of the decision on the optimization of assembly and disassembly systems, under different uncertainties.  
[PERSON], [PERSON], [PERSON] and [PERSON] elaborated this work. [PERSON] and [PERSON] developed the mathematical model and simulation program. [PERSON] wrote the paper. [PERSON] supervised this work and contributed to revise this paper. [PERSON] and [PERSON] conducted this research, supervised, and revised this work.  
This research received no external funding.  
The authors declare no conflict of interest.