جستجوی مقالات مرتبط با کلیدواژه « Supply Chain » در نشریات گروه « برق »

This paper presents a multi-objective mathematical model which aims to optimize and harmonize a supply chain to reduce costs, improve quality, and achieve a competitive advantage and position using meta-heuristic algorithms. The purpose of optimization in this field is to increase quality and customer satisfaction and reduce production time and related prices. The present research simultaneously optimized the supply chain in the multi-product and multi-period modes. The presented mathematical model was firstly validated. The algorithm's parameters are then adjusted to solve the model with the multi-objective simulated annealing (MOSA) algorithm. To validate the designed algorithm's performance, we solve some examples with General Algebraic Modeling System (GAMS). The MOSA algorithm has achieved an average error of %0.3, %1.7, and %0.7 for the first, second, and third objective functions, respectively, in average less than 1 minute. The average time to solve was 1847 seconds for the GAMS software; however, the GAMS couldn't reach an optimal solution for the large problem in a reasonable computational time. The designed algorithm's average error was less than 2% for each of the three objectives under study. These show the effectiveness of the MOSA algorithm in solving the problem introduced in this paper.

Reducing the manufacturing costs of the products is considered of the main factors for lead-acid battery (LAB) manufacturers 'sustainability and survival in the automotive industry. Then in order to meaningfully decrease the due costs and enhance the competitive potential of the manufacturers in this field, it’s important to choose the suitable sites for recycling centers in this industry's supply chain. In the present study, data envelopment analysis (DEA) has been utilized for evaluating and choosing the recycling centers as befitting for the supply chain of LABs in the automotive industry. Pursuant to DEA and a set of criteria, as being critical for decision makers in this filed, various locations are evaluated. As this method prescribes, the locations are ranked using the maximum scores of efficiencies and after that, more appropriate centers are picked and the inappropriate ones get removed. A LAB supply chain related case study in the automotive industry has been employed for evaluating the influencing power and efficiency of proposed method and following that, a number of beneficial management results have been extracted.

Application of an absolute supply chain model does not invalidate the possibility of few defective items in a supplied lot, therefore it becomes essential to conduct an inspection process for segregating the defective items, subsequently such segregated items are sold at discounted price. Shortages mainly occur with sudden demand or erratic production capacity, and player’s decisions are influenced by it. In this paper, the shortage is considered as a seller’s decision variable and demand is receptive to selling price and marketing expenditure of the buyer. Player’s interaction will be reviewed and determined as non-cooperative Stackelberg game. Further, a supply chain model is endured to substantiate the interaction and democracy among buyer and seller in the supply chain and is pitched by non-cooperative game theoretical approaches. The Stackelberg game approach is used in the non-cooperative method where one player acts as leader and another as follower. Hereafter, unanimous numerical examples along with sensitivity analysis are exhibited to compare amidst two different models with and without shortages to demonstrate the significance of the paper.

Refunding and bundling reservation are known as two popular methods to increase profit where in recent years have gained attention of researchers. One main application of refunding policy emerges for online product sale methods, where consumers can be refunded by returning goods which are not favorite according to their interest. Examining three scenarios including refunding, bundle reservation and refunding along with bundle reservation policies, we will investigate a model for each corresponding scenario. We try to compare two refund and bundle reservation pricing policies in a two-level supply chain including one manufacturer and one wholesaler, and we provide a combined model including two products. The demand is constant and also the population-related information about the division of the population into two types of consumers, strategic consumers (consumers who can predict the second stage discount) and myopic consumers (consumers who can not predict the second stage discount) are available. In addition, the percentage of consumers who refund the product due to regret, the inability to install the product or other reasons, is constant and is independent of the amount of refund. We show that the combined model is optimal and has a higher profit margin than any other policy alone.

In the current situation,taking into consideration the environmental and social issues are related with the production and distribution of products in supplychain systems,due tothe global concerns related with emitting lots of greenhouse gaseswithin the manufacturing process and overlooking the major needs of publicThis paper proposes a newmultiobjectivemodel in the area ofclosed loop supplychainproblem integrated with lot sizing by considering lean,agility and sustainability factors simultaneously.In this regard,responsiveness, environmental,social and economic aspects are regarded in the model besides the capacity and service level constraints.Inaddition,strategic and operational backup decisions are developed to increase the resiliency of the system against disruption of the facilities and routs simultaneously.Next,a robust possibilistic programming approach is applied to handle the uncertainty of the model.To increase the responsiveness of the system,a fuzzyc-means clusteringmethod isapplied to select the potential locations based on the proximity to local customers.In the following, a new hybrid metaheuristic algorithm comprised of a PMOPSO algorithm and aMOSEO is developed to deal with large size problems efficiency and to assess the impact of using a single-based initial solution as the income for the second phase of the proposed hybrid algorithm.To ensure about the effectiveness of the proposed hybrid algorithm,the results of this algorithm arecompared with a NSGA-II.

In this paper, two inventory models with starting shortages and without shortages for perishable products in supply chain are proposed. The demand for perishable products is dependent on price and stock. Supply chain is composed of one manufacturer, one distribution center, and one retailer. The objective of these two models is to maximize the average profit per unit time by determining the optimal replenishment cycle, frequency, and quantity. The property of optimal solutions for two cases of two models is discussed to verify the existence of optimal solutions. Algorithms for searching optimal solutions are presented. In order to investigate the effect of parameters on optimal solutions and obtain some management insights, computational experiments with sensitivity analyses are carried out. Finally, conclusions and future researches are provided.

This paper aimed to design a sustainable agile retail supply chain using multi-objective optimization methods. To this end, a mathematical model was presented for the sustainable agile supply chain with five objectives, including "minimizing costs", "minimizing unanswered demand", "maximizing the quality of goods purchased from suppliers," "maximizing social responsibility or social benefits", and "minimizing environmental impacts". The NSGA-II, PESA and SPEA-II algorithms were used to solve the proposed model, which were run in MATLAB software. After collecting data from the SAIPA Company’s supply chain, the model was solved using the three algorithms. The results indicate that the SPEA-II algorithm produces more high quality responses, compared to the other two algorithms. Furthermore, the SPEA-II algorithm was found to be among the Pareto Front responses. A decrease of environmental impacts had no effect on the problem responses due to the lack of a specific structure in the current system.

One of the major challenges being faced by manufacturing companies today is the issue of addressing various aspects of perishable products in a supply chain environment. To address this issue, the integrated lot size problem for a perishable product has been investigated in the present work. The problem is modeled as a single vendor multiple buyer system. A variant of the truckload discount scheme is applied and the proposed model is formulated as a mixed integer program (MIP). The traditional warehouses are replaced by ‘cross-docks’ and situations in which, cross-docking would be more beneficial are highlighted. The problem of fleet selection is also addressed and various strategies to minimize the vendor cost are also highlighted for centralized and decentralized supply chains. Sensitivity analysis is then carried out on various input parameters such as setup cost at plant, variable transportation cost, fixed transportation cost, setup cost per order, holding cost and, lost cost, which underscores the significant impact of economies of scale in transportation on the total supply chain cost. Analysis of lead time-cost trade-off reveals that alternate modes of transportation could be explored, which significantly reduces the lead time of transportation, thereby minimizing the total supply chain cost.

Due to high competition, companies tend to improve their market share by applying different selling mechanisms such as online to offline commerce as an efficient selling mechanism in which the companies sells their products via both online and real stores. This study deals with a selling problem for two complementary supply chains including a supplier and a shopping center where the commodities are sold by a virtual and a traditional shopping center who present items as complementary shopping centers. It is assumed that market demand depends on price and service level so that they can purchase the item via both shopping centers based on their priorities. Also to analyze the reactions of the chain’s partners, different games such as Nash and Stackelberg game are deemed. The aim is to obtain the closed-form solutions of the decision variables of the networks’ members in order to maximize their profits where the prices of different selling periods at each echelon of the chains are the decision variables of the model. The closed-form solutions are derived and the solutions are examined by a numeric example. In following, several sensitivity analyses on key factors are performed to determine efficient ones on the variables and profits.

The uncertainty of demand and lead time in inventory management has posed challenges for the supply chain management. The purpose of this paper is to optimize the total profit and customer service level of supply chain by robust parameter design of inventory policies. This paper proposes to use system dynamics simulation, Taguchi method and Response Surface Methodology (RSM) to model a multi-echelon supply chain. Based on the sequential experiment principle, Taguchi method combining location and dispersion modeling method is adopted to locate the optimum area quickly, which is very efficient to optimize the responses in discrete levels of parameters. Then, fractional factorial design and full factorial design are used to recognize significant factors. Finally, RSM is used to find the optimal combinations of factors for profit maximization and customer service level maximization in continuous levels of parameters. Furthermore, a discussion of multi-response optimization is addressed with different weight of each response. Confirmation experiment results have shown the effectiveness of the proposed method.

In the supply chain of Fast-moving consumer goods, logistics costs are a main part of the expenses. In low levels of these chains, we usually face with a Vehicle Routing Problem. In practice, due to the high cost of service in many cases, some customers are not chosen for serving. Restrictions associated with the investment in many cases makes impossible serve to some potential clients. In this problem, design a supply chain network, including a location-allocation problem in the warehouse, Multiple Depot Vehicle Routing Problem at the level of distribution and customer selection at the retail level in several periods of time is considered. In this issue, in addition to certain methods that can be used in small sizes, Meta-heuristic algorithms to solve large-scale model have been used. With the aim of improving performance, if not improve in a few diversifications, algorithms are temporarily enhanced. In GA this change leads to improve 1.2 percent and 1.6 percent about the mean of solutions and the best solution and in SA algorithm, this change is led to improving by 2.1% and 2.4% in average solutions and the best solutions. Finally, this approach about a real problem in Sepahan Oil Company is employed.

Despite the significance of full coordination of N-echelon supply chains in real-world decision-making situations, the relevant literature has rarely addressed this issue. Furthermore, there is a scarcity of mathematical models in the supply chain management literature for partially coordinated cases. To address these shortcomings, this study concerns both the full and partial coordination in serial N-echelon supply chains facing stochastic demand. In particular, three general cases including decentralized (no coordination), sub-supply chain coordination (partial coordination) and centralized (full coordination) cases are examined to support decisions on ordering and pricing. In addition, this study adds to the literature by investigating how to coordinate a serial N-echelon supply chain through a new spanning profit sharing contract, which can coordinate the entire supply chain through only one contract. Furthermore, we analytically prove the occurrence of externality benefit in partially coordinated cases, which is a paradoxical phenomenon suggesting that small coalitions are unstable. Two numerical examples extracted from the literature are given to verify the effectiveness and validity of the proposed contracts and models. The results show that the proposed contracts can be applied in a rather simple and convenient way and is reliable enough for use in real-world applications.

Against the background of supply chains, this paper constructs a class Hotelling model to describe and explore sequential auctions of close substitutes with slightly more general associated valuations. In this generalized model, both close substitutes and bidders are hypothetically distributed in the interval [0, 1], types of bidders are continuous, and each bidder’s valuations for close substitutes are not independent. And with the aid of this model, equilibriums are explored and efficiencies of the auctions are analyzed under second-price sealed-bid auction formats. Further considering two typical information policies, we investigate some concrete bids and revenues of the efficient sequential auctions while bidders’ valuations are linear functions of distances between them and close substitutes. Results show that efficiencies of the sequential auctions are conditional, and influences of information policies on revenues of the auctions are related to both numbers of bidders and locations of items.

This study investigated a production-inventory model with defective items under a two-part trade credit where the agreement of conditionally freight concession is considered in the integration supply chain. We assume the inspection process is conducted by the retailer before selling incoming items. All the defective items are discovered, stored and then sold as a single batch to a secondary market at a decreased price. Furthermore, shortages are allowed and completely backlogged for the retailer. The purpose of this study is to determine the optimal number of shipments per production cycle for the supplier, and the optimal length of time wherein there is no inventory shortage and replenishment cycle for the retailer such that the total profit function has a maximum value. In theoretical analysis, the existence and uniqueness of the optimal solutions are shown and an algorithm is developed to find the optimal solutions. Furthermore, numerical examples are presented to demonstrate the solution procedures and a sensitivity analysis of the optimal solutions regarding all parameters are also carried out.

A dynamic integrated solution for three main problems through integrating all metrics using SCOR are proposed in this research. This dynamic solution comprises strategic decisions in high-level, operational decisions in low-level and alignment of these two decision levels. In this regard, a human intelligence-based process for high level decisions and machine-intelligence based decision support systems (DSSs) for low-level decisions is then proposed using a novel approach. The operational presented model considers important supply chain features thoroughly such as different echelons, several suppliers, several manufacturers and several products during multiple periods. A multi-objective mathematical programming model is then developed to yield the operational decisions with Pareto efficient performance values and solved using a well-known meta-heuristic algorithm, i.e., NSGAII where its parameters is tuned using Taguchi method. Afterward, an intermediate machine-intelligence module is used to determine the best operational solution based on the strategic decision maker’s idea. The efficiency of the proposed framework is shown through numerical example where a sensitivity analysis is then conducted over the obtained results so as to show the impact of the strategic scenario planning on the considered supply chain’s performance.