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In this study, a vendor-managed inventory model is developed for a single-vendor multiple-retailer single-warehouse (SV-MR-SV) supply chain problem based on the economic order quantity in which demands are stochastic and follow a uniform probability distribution. In order to reduce holding costs and to help balanced on-hand inventory cost between the vendor and the retailers, it is assumed that all inventory is held at a central warehouse with the lowest cost among the parties. The capacity of the central warehouse is limited. The objective is to find the warehouse replenishment frequency, the vendor\'s replenishment frequency, the order points, and the order quantities of the retailers such that the total inventory cost of the integrated supply chain is minimized. The proposed model is a mixed integer nonlinear programming problem (MINLP); hence, a genetic algorithm (GA) is utilized to solve this NP-hard problem. The parameters of the GA are calibrated using the Taguchi method to find better solutions. Some numerical illustrations are solved at the end to demonstrate the applicability of the proposed methodology and to evaluate the performance of the solution method.

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The purpose of this article is to investigate a two-echelon supply chain inventory problem consisting of a single-vendor and a single-buyer with controllable lead time and investment for quality improvements. This paper presents an integrated vendor-buyer inventory model in order to minimize the sum of the ordering cost, holding cost, setup cost, investment for quality improvement and crashing cost by simultaneously optimizing the optimal order quantity, process quality, lead time and number of deliveries the vendor to the buyer in one production run with the objective of minimizing total relevant cost. Here the lead-time crashing cost has been assumed to be an exponentially function of the lead-time length. The main contribution of proposed model is an efficient iterative algorithm developed to minimize integrated total relevant cost for the single vendor and the single buyer systems with controllable lead time reduction and investment for quality improvements. Graphical representation is also presented to illustrate the proposed model. Numerical examples are presented to illustrate the procedures and results of the proposed algorithm. Matlab coding is also developed to derive the optimal solution and present numerical examples to illustrate the model.

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The study determined optimum inventory levels for various bakery resources using the bread supply chain network in Onitsha City. Structured questionnaires were administered among bakery factories. The optimum design achieved through the optimization model was compared with the existing systems. Analysis of 90 bakeries with a combined capacity of 3960 revealed that total money N 564,408,477.28 is spent on energy annually. Of this amount, 66.75% is expended annually to meet diesel requirements, while firewood and petrol account for 22.57% and 10.66%, respectively. The results of the ABC analysis show that flour ranks as class A with over 78%, followed by sugar at 13%, whilst the remainder of the ingredients constitutes 9%. High operating costs was identified as a major factor militating against the growth of the sector. Consequently, baked bread is expensive and remuneration is very poor, making the industry less attractive. The implementation of optimization practice adds value leading to savings amounting to N 6,957.51, thus enhancing the supply chain competiveness. The annual supply chain performance measured by inventory turnover shows a frequency of 73 inventory turns. Since the bakeries contribute to ensuring food security, these findings, if implemented, will assuage the rising food insecurity in the nation.

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Today, the global business environment compels enterprises to consider rest of the world in their competitive strategy analysis where firms ignore external factors such as economic trends, competitive positions or technology advancement in other countries. While going truly global with supply chain management, a company develops product in the United States, produce in India and trade in Europe, and they have changed the traditional operation management & logistical activities. This change in trade and the modernization of transport infrastructures have elevated the importance of flow management to new levels. Manufacturers and researchers have noticed many problems concerning supply chain activities, and usually either a system or subcomponent in supply chains is discussed in the literature, but they fails to answer the rational (why, what, how) behind them. This paper addresses a review of the principles, bottlenecks and strategies of supply chain practices for organizations with an emphasis on the implications of Indian agri-food sector. Findings of this review reveal that the human & environmental issues, improved product visibility, food safety/quality and the associated economic benefits in sustainable agri-food supply chains can be achieved through innovation, collaboration, elimination of uncertainties and introducing global SCM practices into green & lean initiatives.

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The competition increscent urgency in the global economy causes many organizations focus more on novel management ideas in constructing, delivery services and their productions. In current conditions, supply chain and the importance of a suitable performance evaluating system in order to understand the current condition and program for its improvement, has a very special importance in creating surplus value for clients. In this research, the performance of 7 active supply chains in tile industry which have similar supply chain construction, including providers, producers, distributors and clients, by evaluating producers who perform a key role in chains and by using data envelopment analysis method are considered. According to effectiveness of inputs on outputs of organizations, outputs are considered as functions of inputs and finally the relative performance amounts of decision making units, the key companies of chain supply are computed.

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Closed-loop supply chain network design is a critical issue due to its impact on both economic and environmental performances of the supply chain. In this paper, we address the problem of designing a multi-echelon, multi-product and capacitated closed-loop supply chain network. First, a mixed-integer linear programming formulation is developed to maximize the total profit. The main contribution of the proposed model is addressing two economic viability issues of closed-loop supply chain. The first issue is the collection of sufficient quantity of end-of-life products are assured by retailers against an acquisition price. The second issue is exploiting the benefits of colocation of forward facilities and reverse facilities. The presented model is solved by LINGO for some test problems. Computational results and sensitivity analysis are conducted to show the performance of the proposed model.

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In this study, a new stochastic model is proposed to deal with a multi-product, multi-period, multi-stage, multi-site production and transportation supply chain planning problem under demand uncertainty. A two-stage stochastic linear programming approach is used to maximize the expected profit. Decisions such as the production amount, the inventory level of finished and semi-finished product, the amount of backorder and the quantity of products to be transported between upstream and downstream plants in each period are considered. The robustness of production supply chain plan is then evaluated using statistical and risk measures. A case study from a real textile and apparel industry is shown in order to compare the performances of the proposed stochastic programming model and the deterministic model.

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A supply chain is a set of facilities connected together in order to provide products to customers. The supply chain is subject to random failures caused by different factors which cause the unavailability of some sites. Given the current economic context, the management of these unavailabilities is becoming a strategic choice to ensure the desired reliability and availability levels of the different supply chain facilities. In this work, we treat two problems related to the field of supply chain, namely the design and unavailabilities management of logistics facilities. Specifically, we consider a stochastic distribution network with consideration of suppliers\' selection, distribution centres location (DCs) decisions and DCs’ unavailabilities management. Two resolution approaches are proposed. The first approach called non-integrated consists on define the optimal supply chain structure using an optimization approach based on genetic algorithms (GA), then to simulate the supply chain performance with the presence of DCs failures. The second approach called integrated approach is to consider the design of the supply chain problem and unavailabilities management of DCs in the same model. Note that, we replace each unavailable DC by performing a reallocation using GA in the two approaches. The obtained results of the two approaches are detailed and compared showing their effectiveness.

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A fundamental problem is the use of DEA in multistep or multilevel processes such as supply chain, lack of attention to processes’ internal communications in a way that the recent studies on DEA in the context of serial processes have focused on closed systems that the outputs of one level become the inputs of the next level and none of the inputs enter the mediator process. The present study aimed to examine the general dimensions of an open multilevel process. Here, some of the data such as inputs and outputs are supposed to leave the system while other outputs turn into the inputs of the next level. The new inputs can enter the next level as well. We expand this mode for network structures. The overall performance of such a structure is considered as a weighted average of sectors’ performance or distinct steps. Therefore, this suggested model in this study, not only provides the possibility to evaluate the performance of the entire network, but creates the performance analysis for each of the sub-processes. On the other hand, considering the data with undesirable structure leads to more correct performance estimation. In the real world, all productive processes do not comprise desirable factors. Therefore, presenting a structure that is capable of taking into account the undesirable structure is of crucial importance. In this study, a new model in the DEA by network structure is offered that can analyze the performance considering undesirable factors.

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This work investigates the effect of different inventory policies of a supply chain model using the system dynamics approach which belongs to the class of Vendor Managed Inventory (VMI), automatic pipeline, inventory and order based production control systems (VMI-APIOBPCS). This work helps management to investigate the effect of different policies such as adding the VMI system or third party logistic (TPL) on the whole cost of the supply chain. To this end, this work applies system dynamics in supply chain with two supplier and one retail channel which consists of VMI system. Moreover, this work studies the performance of the proposed model via three metrics: Bullwhip effect; satisfaction of the end-customer; the amount of the whole inventory of chain.

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In this paper, a comprehensive model is proposed to design a network for multi-period, multi-echelon, and multi-product inventory controlled the supply chain. Various marketing strategies and guerrilla marketing approaches are considered in the design process under the static competition condition. The goal of the proposed model is to efficiently respond to the customers’ demands in the presence of the pre-existing competitors and the price inelasticity of demands. The proposed optimization model considers multiple objectives that incorporate both market share and total profit of the considered supply chain network, simultaneously. To tackle the proposed multi-objective mixed-integer nonlinear programming model, an efficient hybrid meta-heuristic algorithm is developed that incorporates a Taguchi-based non-dominated sorting genetic algorithm-II and a particle swarm optimization. A variable neighborhood decomposition search is applied to enhance a local search process of the proposed hybrid solution algorithm. Computational results illustrate that the proposed model and solution algorithm are notably efficient in dealing with the competitive pressure by adopting the proper marketing strategies.

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In this study, a bi-objective model for integrated planning of production-distribution in a multi-level supply chain network with multiple product types and multi time periods is presented. The supply chain network including manufacturers, distribution centers, retailers and final customers is proposed. The proposed model minimizes the total supply chain costs and transforming time of products for customers in the chain. The proposed model is in the class of linear integer programming problems. The complexity of the problem is large and in the literatur, this problem has been shown to be NP-hard. Therefore, for solving this problem, two multi objective meta-heuristic approaches based on Pareto method including non-dominated Sorting Genetic Algorithm-II (NSGA-II) and non-dominated Ranking Genetic Algorithm (NRGA) have been suggested. Since the output of meta- heuristic algorithms are highly dependent on the input parameters of the algorithm, Taguchi method (Taguchi) is used to tune the parameters. Finally, in order to evaluate the performance of the proposed solution methods, different test problems with different dimensions have been produced and the performances of the proposed algorithms on the test problems have been analyzed.

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This paper presents an optimized diamond structured automobile supply chain network towards a robust Business Continuity Management model. The model is necessitated by the nature of the automobile supply chain. Companies in tier two are centralized and numerically limited and have to supply multiple tier one companies with goods and services. The challenge with this supply chain structure is the inherent risks in the supply chain. Once supply chain disruption takes place at tier 2 level, the whole supply chain network suffers huge loses. To address this challenge, the paper replaces Risk Analysis with Risk Ranking and it introduces Supply Chain Cooperation (SCC) to the traditional Business Continuity Plan (BCP) concept. The paper employed three statistical analysis techniques (correlation analysis, regression analysis and Smart PLS 3.0 calculations). In this study, correlation and regression analysis results on risk rankings, SCC and Business Impact Analysis were significant, ascertaining the value of the model. The multivariate data analysis calculations demonstrated that SCC has a positive total significant effect on risk rankings and BCM while BIA has strongest positive effects on all BCP factors. Finally, sensitivity analysis demonstrated that company size plays a role in BCM.

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The main aim of the study is to examine the influence of both external and internal actors on green supply chain management (GSCM) practices. By the way, this paper is to provide a first-hand understanding about the procedures taken by Turkish business firms and their logistics providers and supply chain business firms to operate in an environmental friendly supply chain. The result of this research is very important to promote performance of Turkish business relations in green perspectives. To find the right answers, comprehensive questionnaire forms were designed. We collect data from 2014 May to 2015 July through the Turkish companies. More than 180 companies have contributed in this great research. We analyzed data by statistical methods. One of the important results is the third party logistics service providers influence firms have a proactive green behavior.

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Coordinating the supply chain is among the most important subjects that is extensively addressed in the related literature. If a supply chain is to be coordinated, it is equivalent to say that we must solve a problem related to competition and cooperation. The game theory is obviously one of the most effective methods to solve such problems, in which the players of the supply chain are assumed to engage in cooperative and non-cooperative games. The current study aims to coordinate a two-level supply chain consisting of a manufacturer and a retailer. This will be achieved using cooperative advertisement along with pricing decisions such that the manufacturer offers a price discount to the retailer and the demand is affected by pricing and advertisement. Cooperative advertisement is a coordinated effort made by all the members of the supply chain to increase the customer demand, in which the retailer does the local advertisement and the manufacturer pays for a portion or all the costs of the retailer advertisement. We consider two models for manufacturer-retailer relation using the game theory: the manufacturer-Stackelberg and the retailer-Stackelberg games with asymmetric power distribution.