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Hybrid sorting immune simulated annealing technique (HSISAT), a Meta -heuristic is proposed for solving the multi objective flexible job-shop scheduling problem (FJSP). The major objectives are distributing the time of machines among the set of operations and scheduling them to minimize the criterion (makespan, total workload and maximum workload). The processing time is sorted for isolating the critical machines and immune simulated annealing (ISA) is applied to increase the convergence speed. Several case studies have been taken from the literature to demonstrate the convergence speed of the proposed algorithm. The computational results have proved that the proposed hybrid algorithm is an effective approach to solve the multi-objective FJSP.

Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in computer-integrated manufacturing environment. A problem in traditional CAPP system is that the multiple planning tasks are treated in a linear approach. This leads to an overconstrained overall solution space, and the final solution is normally far from optimal or even nonfeasible. A single sequence of operations may not be the best for all the situations in a changing production environment with multiple objectives such as minimizing number of setups, maximizing machine utilization, and minimizing number of tool changes. In general, the problem has combinatorial characteristics and complex precedence relations, which makes the problem more difficult to solve. The main contribution of this work is to develop an intelligent CAPP system for shop-floor use that can be used by an average operator and to produce globally optimized results. In this paper, the feasible sequences of operations are generated based on the precedence cost matrix (PCM) and reward-penalty matrix (REPMAX) using superhybrid genetic algorithms-simulated annealing technique (S-GENSAT), a hybrid metaheuristic. Also, solution space reduction methodology based on PCM and REPMAX upgrades the procedure to superhybridization. In this work, a number of benchmark case studies are considered to demonstrate the feasibility and robustness of the proposed super-hybrid algorithm. This algorithm performs well on all the test problems, exceeding or matching the solution quality of the results reported in the literature. The main contribution of this work focuses on reducing the optimal cost with a lesser computational time along with generation of more alternate optimal feasible sequences. Also, the proposed S-GENSAT integrates solution space reduction, hybridization, trapping out of local minima, robustness, and convergence; it consistently outperformed both a conventional genetic algorithm and a conventional simulated annealing algorithm.

Traditional project management tools and other revised tools are limited in representation of the problem and in dealing situation dynamically. This paper details the use of Petri nets as a graphical and mathematical modeling and simulation tool in project management. In this context, the benefits of Petri nets are indicated. A Petri net aided software, a PETRI-PM is developed to model, simulate and analyze the project. Extensions to make Petri nets suitable for project management applications are proposed. The use of a PPC-matrix for token movements is proposed. The paper also discusses the implications of the model and the analysis it supports. The usefulness of the software is exemplified with a case study.