فصلنامه مدیریت زنجیره تامین
پیاپی 68 (پاییز 1399)

One of the unrealistic assumptions of the economical production quantity model is the perfect quality the goods produced. To this end, following the previous attempts, in this paper it is assumed to have destructive inspections and existence of imperfect and rejected items after rework. In developed models, the new EPQ model has been calculated under 3 circumstances: 1- Imperfect items are not counted into the inventory after inspection, 2- The mentioned items are kept in the stock up to the end of production period within each cycle in EPQ model, 3- Aforementioned items are kept in stock up to the end of cycle. Moreover, we found out that if we use the traditional amounts instead of new ones, the total cost per unit time will increase with the same order. Finally, as sum of the fractions of imperfect and reworkable items ascend the EPQ and total cost per unit time will increase. A numerical example is applied to address the reliability of the results.

This research is a multi-product multi-period inventory control model, with inventory costs (including holding, ordering, shortage, and purchasing costs) which is provided under inflation and general discount policy with a randomized demand and has shortcomings (lost sales and lost backorder), and there are some limited and well-known programming periods. Also, the demand for different samples varies in different periods, so the ordering of a product in a particular period must reduce the number of deficiencies, and the shortage must also be allowed. This model also has some limitations in which the budget and storage space are limited. And the examinations are instantaneous. In this paper, a three-objective non-linear complex integer programming model including minimizing total inventory costs and total storage space and maximizing the level of assurance of inventory is introduced simultaneously. Then, multi-objective optimization algorithm of non-dominated sorting genetic algorithm and multi-objective is proposed to solve the problem. a numerical example is presented , which are used for small-size issues and obtaining the exact answer from the Lingo benefit program , And then the problem is executed in larger dimensions than the calculations of the meta-heuristic algorithms NSGAII in MATLAB software, and the computational results are obtained after the algorithm is applied to the sample problem.

Recently, an increase in the demand of petroleum derivatives has caused to pay particularattention to environmental issues of processes pertaining to its production, distribution andrecovery in addition to the need to manage its supply chain. In this regard, this study has designedand planned a green closed-loop supply chain network for an engine oil product underuncertainty. The proposed model encompasses two objectives in such a way the first objectivefunction aims at minimizing the costs of supply chain and the second one considersenvironmental effects of the related activities. In accordance with the multi objectives of themodel, the ε-constraint method is deployed to solve the concerned model. Likewise, the demandof the main product and the amount of recyclables from the demand markets are presumed to behemmed in by uncertainty. To cope with the considered uncertainty, the Mulvey optimizationmethod is exploited. Meanwhile, new methods for recycling and disposal of non-recyclablematerials are discussed in the proposed model. Eventually, the validation of the proposed modelis evaluated using data from Behran Oil Company.

Factors such as political issues, demand fluctuations, technological changes, financialinstability, natural disasters, and more importantly the competitive environment of the business,increase the uncertainty and risks in the supply chain. Management of such risks, namely,resilient supply chain, is one of the current challenges of organizations. Therefore, organizationsneed to design and understand the dynamics of factors affecting supply chain resiliency toformulate sustainability strategies. In this research, by investigating related literature, the relatedcomponents of the resilient supply chain and balanced scorecard have been identified to map thecauses and effects of resilient supply chain with the Meta-synthesis approach, which have beenconfirmed by experts. The results were categorized into three categories and ten dimensions with83 codes. Finally, by the help of the results and the relationships between them, the causal modelof the factors influencing resilient supply chain has been developed. The diagram prepared inthis research provides an overview for supply chain managers by considering the dynamics ofparameters affecting policy making. Effective key components to reduce recovery time andincrease resilience have been identified based on field studies, which have highlighted theimportance of risk management in resilience through supply chain agility, reengineering,integration and flexibility which is strengthened by investing in the development oforganization's growth and learning.

One of the key issues in inventory management is classification of items, it is necessary tohave various depots to produce various products in this age that variety and diversity of productsare the focus. However, this variety of items and diversity show the necessity of extracting somecriteria for precise and efficient classification more than ever. The aim of this study is to rankthe required criteria for classification of Fast-Moving items of Sarcheshmeh Copper. Datacollection tool was a standard self-made questionnaire, based on five-option Lickert scale andtriangular fuzzy numbers, and the criteria used in different articles in the field of multi-criteriaclassification of commonly used items from 1986 to 2016, as well as extraction of some criteriafrom Sarcheshmeh Copper. Cronbach's alpha reliability of this questionnaire was 0.923, and itsvalidity has been determined based on quantitative and qualitative face validity. The targetpopulation included 52 experts of various affairs of Sarcheshmeh Copper. Fuzzy geometric meanwas used for the integration of experts' opinion, and for defuzzification, triangular fuzzy numbersmean was used. Finally, it was determined that criteria ((accuracy of information)), ((availabilityof technical specifications)), ((scarcity)), ((downtime or stop)), respectively, are the mostimportant classification criteria of Fast-Moving items.

Supply Chain Management is one of the new approaches in supply chain management anda competitive advantage for organizations. The purpose of this research is to present a hybridmodel for supplier selection and order allocation under uncertainty in which a two-objectivenonlinear integer programming model is presented to optimize the general supply chain whereall parameters of objective functions and constraints are presented. It is considered uncertain.The method of solving this is that the appropriate weights of the relevant metrics and sub-criteriafor the producers are first obtained by using the organization's criteria through the multi-criteriadecision making method. These weights provide input to the proposed mathematical model. Themathematical model presented is solved as a case study using GAMS software. Then, two multiobjectivemeta-heuristic algorithms, including genetic algorithm and particle optimizationalgorithm with inappropriate sorting, are presented and their results are compared with accuratesoftware for validation. The responses of the meta-algorithms to the GAMS solutions fordifferent problems are less than 2.5%. These results show that the proposed algorithms convergeto the optimal and efficient solution