For years, there has been increasing attention placed on the metal removal processes such as turning and milling operations; researchers from different areas focused on cutting conditions optimization. Cutting conditions optimization is a crucial step in Computer Aided Process Planning (CAPP); it aims to select optimal cutting parameters (such as cutting speed, feed rate, depth of cut, and number of passes) since these parameters affect production cost as well as production deadline. This paper deals with multipass turning operation optimization using a proposed Hybrid Genetic Simulated Annealing Algorithm (HSAGA). The SA-based local search is properly embedded into a GA search mechanism in order to move the GA away from being closed within local optima. The unit production cost is considered in this work as objective function to minimize under different practical and operational constraints. Taguchi method is then used to calibrate the parameters of proposed optimization approach. Finally, different results obtained by various optimization algorithms are compared to the obtained solution and the proposed hybrid evolutionary technique optimization has proved its effectiveness over other algorithms.

In general, planning and scheduling of production are treated separately under the hierarchical strategy. Then, over the time, the iterative strategy appeared which partially considers the scheduling constraints during planning, except that the latter remains unsatisfactory because there is no guarantee that these constraints are taken into account. For this, is born the integrated strategy which integrates planning and scheduling and aims to solve the problem and define a feasible production plan. Since capacity constraints don’t reflect reality in terms of resource availability, and they are not always considered, capacity becomes aggregated. To remedy this problem, it is necessary to integrate more precise constraints of scheduling at the planning level. Based on this observation, we propose in this article a new model that integrates planning and scheduling and considers the constraint of resource availability. In our model, the objective function optimizes the total cost of production for a mono-level job-shop problem. To solve this N-P difficult problem we use a stochastic approached method as genetic algorithm (GA).

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In this paper we present a multi-optimization technique based on genetic algorithms to search optimal cuttings parameters such as cutting depth, feed rate and cutting speed of multi-pass turning processes. Tow objective functions are simultaneously optimized under a set of practical of machining constraints, the first objective function is cutting cost and the second one is the used tool life time. The proposed model deals multi-pass turning processes where the cutting operations are divided into multi-pass rough machining and finish machining. Results obtained from Genetic Algorithms method are presented in Pareto frontier graphic; this technique helps us in decision making process. An example is presented to illustrate the procedure of this technique.

After focusing on Cost of Quality (CoQ), the companies must now move into integrating the CoQ into Supply Chain upstream and downstream, to address CoQ issues across Supply Chain Network Design (SCND) modeling. In this preliminary study, we propose a state of the art of the published works on models and classifications of SCND incorporating the CoQ existing in the literature. In the second part of this paper, we will present a review by studying and analyzing the application of some meta-heuristics to solve supply chain models. The reviewed meta-heuristics include: the genetic algorithm (GA), and simulated annealing (SA). Finally, we suggest directions for future research.