No abstract

To improve system reliability without changing its nature, three methods are proposed. The first method uses more reliable components and the second method provides redundant components within the system. The third method is a combination of these two methods. The redundancy allocation problem (RAP) finds the appropriate mix of components and redundancies within a system to maximize its reliability or minimize its cost due to several constraints, such as cost, weight, and volume. This paper presents a methodology to solve the RAP, which is an NP-hard problem, modeled with discrete variables. In this paper, we use a metaheuristic to solve the RAP of a series-parallel system with a mix of components. Our metaheuristic offers a practical method with specific solution encoding, and combines a penalty function to solve large instances of the relaxed RAP, where different types of components can be used in parallel. The efficiency of the algorithm was tested through a set of well-known benchmark problems from the literature. Testing of the algorithm achieved satisfactory results in reasonable computing time.

Nowadays, the Tunisian hospital environment is a complex organization in which the safety of the patient is of primary concern to the authorities. In our study we focus on the Obstetrics and Gynecology Department of CHU of Sfax. It should be noted that no risk management study dealt with the hospital logistic chain in this institution. Hence, the purpose of this paper is to develop a strategy targeting the control of risks related to the patient care activities. The proposed approach consists of two phases. First, a qualitative survey, based on 20 semi-structured interviews, is carried out to identify the problems related to care and logistic activities of the Obstetrics and Gynecology Department in CHU Sfax. Second, the assessment of the identified risks in the hospital context is a multicriteria decision problem. To perform the evaluation of the 12 objectives depending on the identified risks, we have chosen the AHP (Analytic Hierarchy Process) for its simplicity and flexibility.

This work deals with Human Resource Scheduling Problem (HRSP) where fairness is a very important factor when assigning different shifts to the seafaring teams. This type of problem is part and partial of the NP-hard problems category. We propounded to work out this Seafaring Staff Scheduling Problem (SSSP) using one of the population-based meta-heuristics called Cuckoo Optimization Algorithm (COA), one of the newest, most robust and most popular bio-inspired algorithms. rnAffording schedules that ensure an enhanced staff rest to the company compared to the traditionally used ones was the main objective of the paper. The results indicate that this method outperforms the traditional one in solving this NP -hard problem. In addition, they prove the COA performance in the improvement of the objective function value compared to the previously proposed methods in the literature namely GRASP and ABC. Finally, the use of the COA in scheduling also increased the total posts to be assigned by one compared to the ABC method.