Abdolreza Roshani

This study focuses on the combined lot-sizing and scheduling challenge within a closed-loop supply chain that prioritizes sustainable production by incorporating returned products. The proposed system facilitates both the production of new items from raw materials and the remanufacturing of recovered goods using two parallel machines with limited capacity. These machines operate independently and manage operations with sequence-dependent setup times and costs. A mixed-integer programming (MIP) model is formulated to minimize total costs, which include production, remanufacturing, setup, inventory holding, backlog, and energy consumption. The model's performance is validated using a recognized benchmark instance from existing literature, highlighting its ability to produce efficient and cost-effective schedules.

The target of this paper is to present a novel mathematical model to design a resilient and energy efficiency additive
manufacturing supply chain. This model minimizes the total cost of designing SC by selecting the optimal location and type of
3D printers to meet customer demand through active facilities and penalizing lost demand if necessary. To evaluate the
efficiency of the proposed algorithm, several experimental instances are solved and the results for a selected case is reported.
The results obtained for the selected case demonstrate that the proposed model can effectively reduce energy costs, with only
a slight increase in expected shipment costs while the fixed location costs and expected penalty costs remains unchanged.

The capacitated dynamic lot sizing problem with product returns in a closed remanufacturing system is addressed in this paper. The system is designed to satisfy the demands of different classes of single level products by remanufacturing end-life returned products. A single machine with a limited capacity in each time period is used to perform the remanufacturing operations. A mathematical programming formulation is proposed for the considered problem: the proposed model minimizes the sum of remanufacturing costs over a finite planning horizon. The problem is a generalized version of the classical capacitated dynamic lot, sizing problem, and thus it is NP-hard itself. Therefore, a simulated annealing algorithm, with an efficient neighborhood generation which takes into account the constraints of the problem, is proposed as a solution approach. To evaluate the efficiency of the proposed algorithm, a set of experimental instances are generated and solved. The comparison between the results obtained with the proposed simulated annealing approach and the ones generated by the CPLEX solver shows the effectiveness of the proposed algorithm. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Multi-Manned assembly line balancing problems are usually occurred in plants producing large-sized high-volume products such as automobiles and trucks. In this paper, a cost oriented objective function is presented for a multi-manned assembly line balancing problem. This kind of objective function may be used to balance final assembly lines of products in which manufacturing process is very labor intensive. A mixed-integer mathematical programming model is proposed to solve the problem optimally. The proposed formulation has been used to solve some small size problems by considering; both time-oriented and cost-oriented objective functions. The experiments show that, given the same precedence graph of multi manned assembly line with a Same cycle time, two different optimal solutions can be actually found when switching from time-oriented to cost-oriented objective functions, and vice versa. This difficulty increases the complexity of the cost-oriented multi manned assembly line balancing problems with respect to the multi-manned assembly line balancing problems addressed in the literature. (C) 2015, IFAC (International Federation or Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Multi-manned assembly lines are often designed to produce large-sized products, such as automobiles, trucks and buses. In this type of production lines, usually there are multi To manned workstations where a group of workers simultaneously performs different, operations On the Same individual product. One of the problems, that managers of such production lines usually encounter, is to produce the optimal number of items using a fixed number of workstations, without adding new ones in order to meet the market, demand. In this paper, such a class of assembly line balancing problems, named multi-manned assembly line balancing problems type II, has been addressed. Since the problem is NP-hard, a meta-heuristic approach based On a simulated annealing algorithm has been developed to solve the problem. The performance of the proposed algorithm has been tested On a set of test problems taken from the literature; the results show that the algorithm performs well. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Scheduling for job shop is very important in both fields of production management and combinatorial optimization. Since the problem is well known as NP-Hard class, many metaheuristic approaches are developed to solve the medium and large scale problems. One of the main elements of these metaheuristics is the solution seed structure. Solution seed represent the coding structure of real solution. In this paper, a new solution seed for job shop scheduling is presented. This solution seed is compared with a famous solution seed presented for the job shop scheduling. Since the problem is well known as NP-Hard class, a Tabu search algorithm is developed to solve large scale problems. The proposed solution seed are examined using an example and tabu search algorithm.