جستجوی مقالات مرتبط با کلیدواژه « supplier selection » در نشریات گروه « صنایع »

The construction supply chain is presently the focus of considerable interest among numerous project-related businesses. Strong project management is essential for the effective completion of a project, since restricted budgets and time constraints are considered for each project. The research uses multi-objective linear programming to create a mathematical model of the building supply chain. The primary aims of the present investigation are to limit the expenses associated with logistics and to diminish the release of greenhouse gases caused by transportation. Given the reality of managing several projects concurrently, the model provided comprises a network of projects. Following the completion of each project, an inspection is arranged to assess its level of success. Estimating the costs of a project relies on several variables. In reality, there are always uncertainties highlighted in several studies about the uncertainty of cost and time parameters. This research incorporates many characteristics concurrently to simulate real-world settings and address the issue of uncertainty. The expression of uncertainty for all costs, activity length, inspection, supplier capacity, and resource demand are represented by triangular fuzzy numbers. Ultimately, the precision of the model's performance has been verified using a numerical illustration.

Supplier selection of urban train signaling equipment is one of the main problems in urban railways due to the serious impact of this equipment on the safety of travelers. The supplier's selection is a multi-criteria decision-making (MCDM) problem, in which the preferences over criteria are highly dependent on the opinions of decision-makers (DMs). The focus of the present study is to provide an appropriate model of multi-criteria group decision making in a probabilistic setting to effectively combine DMs' judgments. For this purpose, the Bayesian hierarchical model with the best-worst method (BWM) is used to determine the weights of criteria and the Vise Keriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is used to prioritize suppliers. Bayesian BWM aggregates the opinions of all DMs at once, instead of averaging the individual opinions of the DMs that underlie MCDM methods. In this method, the probability of preference of one criterion over one another is calculated using Markov-chain Monte Carlo (MCMC), in addition to the weight of criteria, so that the confidence between pairs of criteria is revealed and ranking of criteria become more certain. Given that the average obtained confidence levels are 0.95, the validity of the Bayesian BWM results is confirmed. For different values of the VIKOR parameter, the third supplier will have the highest rating and the fifth supplier will have the lowest rating. The introduced multi-criteria decision model in this research will help the decision-makers of the urban railway company and other organizations with several suppliers to be able to select the best supplier by considering the relevant criteria.

Economic order/production quantity (EOQ/EPQ) models for imperfect items have received great attention in the last decade. The main common feature of these models is the incorporation of the imperfect items into the problem. While in real-world problems, buyers usually work with multiple suppliers, the focus of the existing literature has been on a single-supplier variant of the problem. In this study, we formulate an EOQ model for items with imperfect quality when considering multiple suppliers, which is a particular type of supplier selection and EOQ models. The initial formulated problem is a mixed integer nonlinear programming that aims to maximize the total annual profit of the buyer such that customer demand is completely met and suppliers’ capacity constraints are satisfied. Since proposed model is nonlinear, first by using the unique property of model, it is converted to mixed integer linear programming then solved by GAMS/CPLEX software to obtain optimal solution. We then use a numerical example to illustrate the problem, and conduct a sensitivity analysis to study the sensitivity of the objective function and the decision variables to the imperfect rate of the items supplied by the selected suppliers.

Nowadays companies measure suppliers on the basis of a variety of factors and criteria that affect the supplier's selection issue. This paper intended to identify the key effective criteria for selection of green suppliers through an efficient algorithm callediterative process mining or i-PM. Green data were collected first by reviewing the previous studies to identify various environmental criteria. Then, the suppliers were evaluated and ranked on the basis of those criteria. The score table derived for the green criteria was one of the inputs to the algorithm. Moreover, membership functions and minimum support values ​​were specified for each criterion as another input to the algorithm. The supplier ranking index was also obtained based on the score assigned to supplier's performance. Then, the hidden relationships between data were discovered and association rules were achieved and analyzed to identify the most important green criterion for selecting green suppliers.

Supplier selection, order allocation and production planning are important and challenging decisions in supply chain management. There are many studies on mentioned topics separately. In this paper, a multi-objective mathematical model proposed to optimize a sustainable supplier selection problem with order allocation and production planning simultaneously. This study considers a multi-supplier, multi-product, multi-item and multi-period supply chain. The designed mathematical model seeks to maximize total profit and minimize unsatisfied demand and total risk along with enforcing sustainability criteria in selecting suppliers. Supplier selection is a virtual process in every manufacturing company. On the other hand, this research considers all the important aspects of this problem. Therefore, the proposed framework can be implemented in many different companies like electronic, food, chemical industry. The proposed model is solved utilizing two metaheuristic algorithms including NSGA II and MOPSO. Moreover, algorithms are tuned utilizing Taguchi analysis. Furthermore, ten sample problems are generated and results are compared to identify the best algorithm for the proposed model.