Reza Ehtesham Rasi
Abstract
Scheduling is one of the key parameters to maintain competitive advantage of organizations, and can directly affect productivity, reduce production time and increase the profitability of an organization. Job shop scheduling problem (JSSP) seeks to find the optimal sequence of performing various jobs ...
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Scheduling is one of the key parameters to maintain competitive advantage of organizations, and can directly affect productivity, reduce production time and increase the profitability of an organization. Job shop scheduling problem (JSSP) seeks to find the optimal sequence of performing various jobs related to group of machines. The purpose of this paper is to provide a multi objective to optimize makespan, energy consumption and machine erosion in flexible JSSP. The problem of this paper is to assign each operation to a machine and to order the operations on the machines, such that the maximal completion time (makespan) of all operations is minimized. The obtained model belongs to NP-Hard class of optimization problems. In terms of overcoming NP-hardness of the proposed model and solve the complicated problem, a non-dominated sorting genetic algorithm (NSGAII) is employed. As there is no benchmark available in the literature, the non-dominated ranking genetic algorithm (NRGA) is developed to validate the results obtained and test problems are provided to show the applicability of the proposed methodology and evaluate the performance of the algorithms. In this study, to evaluate the performance of these algorithms, they were statistically analyzed using T-test. Ultimately, results of the selected model were ranked by applying the technique for order of preference by similarity to ideal solution (TOPSIS).
Ahmad Jafarnejad; Ghahreman Abdoli; Hannan Amoozad Mahdiraji; Saber Khalili Esbouei
Abstract
In recent years, the relationship between the concepts of operations management and finance management has been an attractive area of research among researchers. One of the emerging areas at the beginning of the 21st century in the literature of operations and supply chain management is the topic of ...
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In recent years, the relationship between the concepts of operations management and finance management has been an attractive area of research among researchers. One of the emerging areas at the beginning of the 21st century in the literature of operations and supply chain management is the topic of supply chain finance (SCF). SCF is a new concept that provides efficient financing of the supply chain, where all parties can balance the working capital and improve cash flow at a reduced cost by utilizing the buyer's or other parties' credit rating. Hence, in this study, an approach to optimize financing based on the Stackelberg model in a three-level supply chain, considering the circumstances in which the supplier is financially constrained for fulfillment the buyer's order and funded by the bank as another member of the supply chain based on the purchase order financing (POF) is discussed. For this purpose, a nonlinear mathematical programming model has been developed to maximize the payoff function of the partners.
Arman Sajedinejad; Erfan Hassannayebi; Mohammad Saviz Asadi Lari
Abstract
It is scientifically challenging to determine the inventory level all through the supply chain in such a way that the desired objectives such as effectiveness and responsiveness of the supply chain system can be obtained. Simulation is a means for solving various problems which cannot be solved ...
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It is scientifically challenging to determine the inventory level all through the supply chain in such a way that the desired objectives such as effectiveness and responsiveness of the supply chain system can be obtained. Simulation is a means for solving various problems which cannot be solved by regular exact models such as mathematical ones due to their complexity. The present paper is aimed at simulating lean multi-product supply chain system as well as optimization of the objectives of supply chain. Variables of the simulation model include two types of Kanbans namely withdrawal, and production to determine the inventory level, and batch size of delivery parts for each stage of supply chain. So, in this paper simulation model was developed for supply chains, taking into consideration the different production scenarios and were modeled and compared. A production scenario is adopted for each level of the chain in order to achieve the objectives. The use of meta-heuristic techniques leads us to optimization of these variables which helps decrease delay of both product delivery and inventory level of supply chain. In this case, Genetic Algorithm has applied to find the best variable values of each scenario (included in the right number of each Kanbans), aimed at decreasing the costs and delivery delays. An example based on a case study is given to illustrate the efficiency of the proposed approach. Considering each level of supply chain, the ratio between and among cost, inventory, and delivery delay variables were obtained.