Smooth implementation and controlling conflicting goals of a project with the usage of all related resources through organization is inherently a complex task to management. At the same time deterministic models are never efficient in practical project management (PM) decision problems because the related parameters are frequently fuzzy in nature. The project execution time is a major concern of the involved stakeholders (client, contractors and consultants). For optimization of total project cost through time control, here crashing cost is considered as a critical factor in project management. The proposed approach aims to formulate a multi objective linear programming model to simultaneously minimize total project cost, completion time and crashing cost with reference to direct, indirect cost in the framework of the satisfaction level of decision maker with fuzzy goal and fuzzy cost coefficients.. To make such problems realistic, triangular fuzzy numbers and the concept of minimum accepted level method are employed to formulate the problem. The proposed model leads decision makers to choose the desired compromise solution under different risk levels and the project optimization problems have been solved under multiple uncertainty conditions. The Analytical Hierarchy Process is used to rank multiple objectives to make the problem realistic for the respective case. Here minimum operator and AHP based weighted average operator method is used to solved the model and the solution is obtained by using LINGO software Manuscript profile

The multi-objective optimization for a multi-product multi-period four-echelon supply chain network consisting of manufacturing plants, distribution centers (DCs) and retailers each with uncertain services and uncertain customer nodes are aimed in this paper. The two objectives are minimization of the total supply chain cost and maximization of the average number of products dispatched to customers. The decision variables are the number and the locations of reliable DCs and retailers, the optimum number of items produced by plants, the optimum quantity of transported products, the optimum inventory of products at DCs, retailers and plants, and the optimum shortage quantity of the customer nodes. The problem is first formulated into the framework of a constrained multi-objective mixed integer linear programming model. After that, the problem is solved by using meta-heuristic algorithms that are Multi-objective Genetic Algorithm (MOGA), Fast Non-dominated Sorting Genetic Algorithms (NSGA-II) and Epsilon Constraint Methods via the MATLAB software to select the best in terms of the total supply chain cost and the total expected number of products dispatched to customers simultaneously. At the end, the performance of the proposed multi-objective optimization model of multi-product multi-period four-echelon supply chain network design is validated through three realizations and an innumerable of various analyses in a real world case study of Bangladesh. The obtained outcomes and their analyses recognize the efficiency and applicability of the proposed model under uncertainty. Manuscript profile