Application of Dispersive Liquid-Liquid Microextraction in Separation and Preconcentration of Silver prior its Determination by Flame Atomic Absorption Spectrometry

A simple, sensitive, and rapid dispersive liquid-liquid microextraction (DLLME) method is developed for the preconcentration of silver ions prior to determination by flame atomic absorption spectrometry (FAAS). In this work, 1-phenyl-1,2-propanedione-2-oximethiosemicarbazone (PPDOT), chloroform, and methanol are used as the complexing agent, extraction solvent, and disperser solvent, respectively. The effects of different analytical parameters on the complex formation and the extrac-tion efficiency are investigated and optimized. The effects of interfering ions on the determination of silver(I) are also examined. Under the optimized conditions, a linear calibration curve was achieved in the range of 0.60−120.0 µg L−1, with the detection limit of 0.61 µg L−1. The pre-concentration factor calculated as the ratio of the slopes of the calibration graphs with and without the pre-concentration was 35.5. The relative standard deviations (RSDs) for the silver(I) determinations were below 3 %. The proposed separation procedure was successfully applied to the determination of silver(I) in natural water and photographic film samples with satisfactory results (recoveries > 95 %)

Resolving forward-reverse logistics multi-period model using evolutionary algorithms

© 2016 Elsevier Ltd In the changing competitive landscape and with growing environmental awareness, reverse logistics issues have become prominent in manufacturing organizations. As a result there is an increasing focus on green aspects of the supply chain to reduce environmental impacts and ensure environmental efficiency. This is largely driven by changes made in government rules and regulations with which organizations must comply in order to successfully operate in different regions of the world. Therefore, manufacturing organizations are striving hard to implement environmentally efficient supply chains while simultaneously maximizing their profit to compete in the market. To address the issue, this research studies a forward-reverse logistics model. This paper puts forward a model of a multi-period, multi-echelon, vehicle routing, forward-reverse logistics system. The network considered in the model assumes a fixed number of suppliers, facilities, distributors, customer zones, disassembly locations, re-distributors and second customer zones. The demand levels at customer zones are assumed to be deterministic. The objective of the paper is to maximize the total expected profit and also to obtain an efficient route for the vehicle corresponding to an optimal/near optimal solution. The proposed model is resolved using Artificial Immune System (AIS) and Particle Swarm Optimization (PSO) algorithms. The findings show that for the considered model, AIS works better than the PSO. This information is important for a manufacturing organization engaged in reverse logistics programs and in running units efficiently. This paper also contributes to the limited literature on reverse logistics that considers costs and profit as well as vehicle route management

Reducing lead time risk through multiple sourcing: the case of stochastic demand and variable lead time

This paper studies a buyer sourcing a product from multiple suppliers under stochastic demand. The buyer uses a (Q, s) continuous review, reorder point, order quantity inventory control system to determine the size and timing of orders. Lead time is assumed to be deterministic and to vary linearly with the lot size, wherefore lead time and the associated stockout risk may be influenced by varying the lot size and the number of contracted suppliers. This paper presents mathematical models for a multiple supplier single buyer integrated inventory problem with stochastic demand and variable lead time and studies the impact of the delivery structure on the risk of incurring a stockout during lead time

Developing a resilient supply chain in complex product systems through investment in reliability and cooperative contracts

In recent years, finding mitigation strategies for supply chain disruptions has become one of the most critical challenges for businesses. This issue is crucial for complex product industries because of their role in the modern economy, few suppliers, and their need for high investment in research and development (R&D). This paper studies a resilient supply chain in complex product systems to overcome its specific challenges through supplier reliability enhancement and cooperative contracts. Utilising a game theoretic approach and analytical models, this paper aims to improve the supply chain performance from the resilience perspective while considering R&D investment, supplier learning effect, buyer fairness concern, and market sensitivity to the product’s technology. Investment in supplier reliability enhancement with different contracts is proposed to mitigate disruption risks for a two-echelon supply chain. Analytical mathematical models have been developed, and a simulation approach has been used in optimisation. The results show how proposed contracts effectively increase supply chain performance from financial and resilience perspectives. Moreover, the market sensitivity to the product’s technological level and the sensitivity to the price could adversely affect performance. The buyer’s fairness concern also improves the profit loss while decreasing the service level slightly

Economic pricing of complex products in a competitive closed-loop supply chain network under uncertainty: A case study of CoPS industry

The development of technology, globalization of the economy and the unpredictable behavior of customers have eventuated in a dynamic and competitive environment in the complex product systems (CoPS) market. Besides, CoPS economic pricing is one of the key factors that dramatically reduces production costs and increases competitiveness. In this regard, this paper unveils a hybrid data envelopment analysis (DEA)-fuzzy mathematical model for economic pricing of CoPS in a competitive closed-loop supply chain network under uncertainty. In the first stage, different CoPS suppliers are evaluated exploiting a DEA model based on a set of economic, technical, and geographical criteria. The advantage of this evaluation is choosing appropriate suppliers, and reducing the complexity of the original model. Next, using a robust optimization model, the strategic and tactical decisions are simultaneously determined, providing a fully optimal solution to the model. In the concerned model, the costs and capacities of facilities are considered to be hemmed in by uncertainty. Eventually, to evaluate the proposed approach, a case study is conducted to derive the important managerial results. The numerical results corroborate that the presented robust model is capable of providing a stable structure under different realizations

A Novel Robust Network Data Envelopment Analysis Approach for Performance Assessment of Mutual Funds under Uncertainty

Mutual fund (MF) is one of the applicable and popular tools in investment market. The aim of this paper is to propose an approach for performance evaluation of mutual fund by considering internal structure and financial data uncertainty. To reach this goal, the robust network data envelopment analysis (RNDEA) is presented for extended two-stage structure. In the RNDEA method, leader-follower (non-cooperative game) and robust optimization approaches are applied in order to modeling network data envelopment analysis (NDEA) and dealing with uncertainty, respectively. The proposed RNDEA approach is implemented for performance assessment of 15 mutual funds. Illustrative results show that presented method is applicable and effective for performance evaluation and ranking of MFs in the presence of uncertain data. Also, the results reveal that the discriminatory power of robust NDEA approach is more than the discriminatory power of deterministic NDEA models

Optimizing whole supply chain benefit versus buyer's benefit through supplier selection

A number of mathematical models have been developed for modeling the supplier selection problem. Most of these models have considered the buyer's viewpoint and maximized only the buyer's benefit. This does not necessarily lead to an optimal situation for all members of a supply chain. Co-ordination models have been presented to optimize the benefits of all the members and alignment of decisions between entities of a supply chain. In this paper, the issue of coordination between one buyer and multiple potential suppliers in the supplier selection process has been considered. On the other hand, in the objective function of the model, the total cost of the supply chain is minimized rather than only the buyer's cost. The total cost of the supply chain includes the buyer's cost and suppliers' costs. The model has been solved by applying mixed-integer nonlinear programming. Finally, the proposed method is illustrated by a numerical example.Supplier selection Supply chain coordination Nonlinear programming