In this paper, a new formulation model for cellular manufacturing system (CMS) design problem is proposed. The proposed model of this paper considers assembly operations and product structure so that it includes the scheduling problem with the formation of manufacturing cells, simultaneously. Since the proposed model is nonlinear, a linearization method is applied to gain optimal solution when the model is solved using direct implementation of mixed integer programming. A new genetic algorithm (GA) is also proposed to solve the resulted model for large-scale problems. We examine the performance of the proposed method using the direct implementation and the proposed GA method. The results indicate that the proposed GA approach could provide efficient assembly and product structure for real-world size problems.

In recent years, death toll of natural and man-made disasters has increased at an appalling rate. Thus, disaster management and especially efficient management of humanitarian relief efforts seem to be essential. This paper presents a bi-objective mixed-integer mathematical model for Humanitarian Relief Logistics (HRL) operations planning, as an important part of the humanitarian relief efforts. This model determines optimal policies including location of warehouses, quantity of emergency relief items that should be held at each warehouse, and distribution plan to provide an emergency response pre-positioning strategy for disasters by considering two objectives. The first one minimizes the average response time and the second one minimizes the total operational cost including the fixed cost of establishing warehouses, the holding cost of unused supplies and the penalty cost of unsatisfied demand. The survival of pre-positioned supplies, demand amount and routes condition following an event are considered under uncertainty in the model solved by a robust scenario-based approach. The robust approach is applied to reduce the effects of fluctuations of the uncertain parameters with regards to all the possible future scenarios. The research demonstrates the applicability and usefulness of the proposed model on a case study on earthquake preparation in the Seattle area in USA. In addition, the work applies the Reservation Level Tchebycheff Procedure (RLTP) method to solve the bi-objective model in an interactive way with decision maker. This work provides practitioners, specifically planning teams, with a new approach to assist with disaster preparedness and to improve their logistics decisions.

Integrated supply chain includes different components of order, production and distribution and it plays an important role on reducing the cost of manufacturing system. In this paper, an integrated supply chain in a form of multi-objective decision-making problem is presented. The proposed model of this paper considers different parameters with uncertainty using trapezoid numbers. We first implement a ranking method to covert the fuzzy model into a crisp one and using multi-objective particle swarm optimization, we solve the resulted model. The results are compared with the performance of NSGA-II for some randomly generated problems and the preliminary results indicate that the proposed model of the paper performs better than the alternative method.

Distribution network design is an important issue in supply chain management and plays an important role in making new market development. Because of JIT philosophy, most of managers now have focused on designing appropriate distribution networks. Thus, categorizing distributors and selecting the best ones are crucial for companies. This paper provides a new method to categorize and select distributors. The fuzzy Adaptive Resonance Theory (ART) algorithm is utilized to categorize distributors according to their similarity. To improve the performance of the algorithm, we train the algorithm using the past data. Finally, a numerical example is illustrated to examine the validity of the proposed algorithm.

Hub covering problem is one of the most popular areas of research due to wide ranges of applications in different service or manufacturing industries. This paper considers a multi-objective hub covering location problem under congestion. The proposed study of this paper considers two objectives where the first one minimizes total transportation cost and the second one minimizes total waiting time for all hobs. The resulted multi-objective decision making problem is formulated as mixed integer programming. Simulated annealing is used to solve the resulted model and the performance of the proposed model is compared against two other alternative methods, particle sward optimization and NSGA-II. The results are compared in terms of four criteria including quality metric, mean ideal distance, diversification metric and spacing metric. The results indicate that the proposed model could perform better than the other two alternative methods in terms of quality metric but the results are somehow mix in terms of other three criteria.