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The purpose of this paper is to consider various possible constraints of the problem of production and maintenance planning control for a multi-machine under subcontracting constraint, in order to bring the manufacturer industry closer to real mode. In this paper, we present an efficient and feasible optimal solution, by comparing optimization procedures.

Our manufacturing system is composed of parallel machines producing a single product, to satisfy a random demand under a given service level. In fact, the demand is greater than the total capacity of the set of machines; hence there rises a necessity of subcontracting to complete the missing demand. In addition, we consider that the unit cost of subcontracting is a variable depending on the quantity subcontracted. As a result, we have developed a stochastic optimal control model. Then, to solve the problem we compared three optimization methods: (exact/approximate), the genetic algorithm (GA), the Pattern Search (PS) and finally fmincon. Thus, we validate our approach via a numerical example and a sensitivity analysis.

This paper defines an internal production plan, a subcontracting plan and an optimal maintenance strategy. The optimal solution presented in this paper significantly improves the ability of the decision maker to consider larger instances of the integrated model. In addition, the decision maker can answer the following question: Which is the most optimal subcontractor to choose?

The approach developed deals with the case of the real-mode manufacturing industry, taking into consideration different constraints and determining decision variables which allow it to expand the profits of the manufacturing industry in different domains such as automotive, aeronautics, textile and pharmacies.

This paper is one of the few documents dealing with the integrated maintenance in subcontracting constraint production which considers the complex aspect of the multi-machine manufacturing industry. We also dealt with the stochastic aspect of demand and failures. Then, we covered the impact of the unit cost variation of subcontracting on the total cost. Finally, we shed light on a comparison between three optimization methods in order to arrive at the most optimal solution.

The purpose of this paper is to recover the waste heat of flue gas heat exchanger (FGHE) as efficiently as possible and avoid the acid dew corrosion of that.

A novel flue gas waste heat recovery system was proposed in the paper. The dynamic mathematical models of key equipment in that were established based on theory and experiment method. The proportion integration differentiation-differentiation (PID-P) cascade control method based on particle swarm optimization algorithm was used to control the outlet temperature of FGHE. The dynamic characteristics of the flue gas heat exchange system were simulated by the particle swarm optimization algorithm with different fitness functions.

The PID-P temperature controller parameters can be quickly and effectively obtained by the particle swarm optimization algorithm based on the fitness function of integral time absolute error (ITAE). The overshoot, rise time and adjusting time of the novel system are 2, 83 and 105s, respectively. Compared with the traditional two-step tuning (T-ST) method, the novel system is better in dynamic and steady-state performance. The overshoot and the adjustment time of the system are reduced by 44% and 328s, respectively. ITAE is a performance evaluation index for control system with good engineering practicability and selectivity.

The dynamic mathematical model of key equipment in the new flue gas waste heat recovery system is established and the system's control strategies and methods are explored.

The purpose of this paper is to recommend a validated numerical model for simulation the flue gases heat recovery recuperators. Due to fulfill of this demand, the influences of ash fouling characteristics during the transient/steady-state simulation and optimization of a 3D complex heat exchanger equipped with inner plain fins and side plate fins are studied.

For the particle dispersion modeling, the discrete phase model is applied and the flow field has been solved using SIMPLE algorithm.

According to obtained results, for the recuperator equipped with combine inner plain and side plate fins, determination of ash fouling characteristics is really important, effective and determinative. It is clear that by underestimating the ash fouling characteristics, the achieved results are wrong and different with reality.

Finally, the configuration with inner plain fins with characteristics of: d_i =5 mm, d_o = 6 mm, d_g = 2 mm, d_k = 3 mm and NIPFT = 9 and side plate fins with characteristics of: T_F = 3 mm, P_F = 19 mm, NSPF = 17·2 = 34, W_F = 10 mm, H_F = 25 mm, L_F = 24 mm and ß = 0° is introduced as the optimum model with the best performance among all studied configurations.