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Morocco is facing climate change, as shown by national studies conducted to date. The purpose of our research work is, on one hand, to develop an experimental protocol; simulating the Moroccan climate, through exposing concretes to accelerated aging in different cycles of temperature variation (+5 to +40 C°) and humidity (60–98% RH) and on another hand, to determine the effect of exposure to temperature and humidity variation cycles on the durability indicators (of concrete [with and without thermal cure]).

For this purpose, three classes of concrete were studied (C35, C40 and C55). Each class of concrete was considered first with no addition of silica fume (SF) and then with a 10% addition of SF. The concrete samples underwent three types of conditioning before performing the tests. The control concretes (Ct) were demoulated after 24 h and stored under stable temperature and humidity conditions (20 ± 2 °C and 80% ± 5 RH). Treated concretes (CV) demouled after 24 h and exposed to 300 cycles of 12 h of temperature and moisture variation in a climate chamber. And finally, concretes that have undergone a heat cure (CTV) for 5 h at 90 °C, were then removed from the mold and exposed to temperature and moisture variations cycles identical to treated concrete (CV).

The results obtained show that aging accelerated by temperature change, and humidity improves durability indicators compared to Ct. Concretes that have undergone a thermal cure, followed by accelerated aging, show an improvement in durability indicators between 50 and 200 cycles, but the performance of concrete decreases after 200 exposure cycles. The addition of SF plays a beneficial role in the durability of concrete in the three exposure environments.

The originality of the work is, to develop an experimental protocol, simulating the Moroccan climate, through exposing concretes to accelerated aging in different cycles of temperature variation (+5 to +40 C°) and humidity (60–98% RH) and on another hand, to determine the effect of exposure to temperature and humidity variation cycles on the durability indicators of conventional concrete (with and without thermal cure).

This paper aims to present a new model for solving the integrated production planning and scheduling. Usually, the two decision levels are treated sequentially because of their complexity. Scheduling depends on the lot sizes calculated at the tactical level and ignoring scheduling constraints generates unrealistic and inconsistent decisions. Therefore, integrating more detail scheduling constraint in production planning is important for managing efficiently operations. Therefore, an integrated model was developed, and two evolutionary optimization approaches were suggested for solving it, namely, genetic algorithm (GA) and the hybridization of simulated annealing (SA) with GA HSAGA. The proposed algorithms have some parameters that must be adjusted using Taguchi method. Therefore, to evaluate the proposed algorithm, the authors compared the results given by GA and the hybridization. The SA-based local search is embedded into a GA search mechanism to move the GA away from being closed within local optima. The analysis shows that the combination of simulated annealing with GA gives better solutions and minimizes the total production costs.

The paper opted for an approached resolution method particularly GA and simulated annealing. The study represents a comparison between the results found using GA and the hybridization of simulated annealing and GA. A total of 45 instances were studied to evaluate job-shop problems of different sizes.

The results illustrate that for 36 instances of 45, the hybridization of simulated annealing and GA HSAGA has provided best production costs. The efficiency demonstrated by HSAGA approach is related to the combination between the exploration ability of GA and the capacity to escape local optimum of simulated annealing.

This study provides a new resolution approach to the integration of planning and scheduling while considering a new operational constrain. The model suggested aims to control the available capacity of the resources and guaranties that the resources to be consumed do not exceed the real availability to avoid the blocking that results from the unavailability of resources. Furthermore, to solve the MILP model, a GA is proposed and then it is combined to simulated annealing.