Comparative Analysis of Heuristic Algorithms Used for Solving a Production and Maintenance Planning Problem (PMPP)

Zouhour Guiras,Nidhal Rezg,Alexandre Dolgui,Zied Hajej

doi:10.3390/app8071088

Abstract

In this work, we develop methods to assess the risk of profit–loss resulting from the choice of a computational method for solving a joint production and maintenance-planning problem. In fact, the optimal objective function is calculated via the use of algorithms and optimization methods. The use of these methods can have an impact on an event that can disrupt the optimal production and maintenance plan. To achieve our goals, we start with calculating the manufacturing system’s joint production and maintenance plans over a finite horizon using different methods. In the second part of the work, we propose analytical models to quantify the risk of profit–loss resulting from product returns and the integration of an imperfect maintenance policy. Numerical examples are conducted by adopting the different algorithms used. This study provides insights into the most efficient computational methods for the encountered problems. This research proposes new approaches to help and guide managers in the analysis and evaluation of their decisions.

Highlights

A risk is described as a “hazard”, a chance of bad consequences, loss, or exposure to mischance
Based on the results obtained (Table 7), using a medium data size, we observe that the differential evolution method provides the best production plan and the best number of preventive maintenance actions compared with the other algorithms in order to obtain the best cost with a minimal execution time that gives the minimal GAP
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

Summary

Introduction

A risk is described as a “hazard”, a chance of bad consequences, loss, or exposure to mischance (from the Concise Oxford English Dictionary, reported in [1]). Its assessment may be incorporated into the company’s planning and decision-making processes, encompassing profits, reliability, and other performance objectives [2]. Risk identification and assessment provided specific indications of managerial attention to achieve the performance objectives [2]. Their main objective was to illustrate the challenges where coordination within supply chain networks brings to risk management. In his book, Zio [5] introduced the application of the Monte Carlo simulation method for the analysis of system reliability and risk. Failure mode and effects analysis (FMEA) is a risk assessment tool that reduces potential failures in systems, process, designs, or services [6]. Hubbard et al [7] described a number of common scoring methods that are currently used to assess risk in a variety of different domains

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Conclusion