Channel Selection and Coordination in Dual-Channel Supply Chains

This paper investigates the influence of channel structures and channel coordination on the supplier, the retailer, and the entire supply chain in the context of two single-channel and two dual-channel supply chains. We extensively study two Pareto zone concepts: channel-adding Pareto zone and contract-implementing Pareto zone. In the channel-adding Pareto zone, both the supplier and the retailer benefit from adding a new channel to the traditional single-channel supply chain. In the contract-implementing Pareto zone, it is mutually beneficial for the supplier and the retailer to utilize the proposed contract coordination policy. The analysis suggests the preference lists of the supplier and the retailer over channel structures with and without coordination are different, and depend on parameters like channel base demand, channel operational costs, and channel substitutability

Mutual benefits of two multicriteria analysis methodologies: A case study for batch plant design

This paper presents a MultiObjective Genetic Algorithm (MOGA) optimization framework for batch plant design. For this purpose, two approaches are implemented and compared with respect to three criteria, i.e., investment cost, equipment number and a flexibility indicator based on work in process (the so-called WIP) computed by use of a discrete-event simulation model. The first approach involves a genetic algorithm in order to generate acceptable solutions, from which the best ones are chosen by using a Pareto Sort algorithm. The second approach combines the previous Genetic Algorithm with a multicriteria analysis methodology, i.e., the Electre method in order to find the best solutions. The performances of the two procedures are studied for a large-size problem and a comparison between the procedures is then made

Multiobjective optimization for multiproduct batch plant design under economic and environmental considerations

This work deals with the multicriteria cost–environment design of multiproduct batch plants, where the design variables are the size of the equipment items as well as the operating conditions. The case study is a multiproduct batch plant for the production of four recombinant proteins. Given the important combinatorial aspect of the problem, the approach used consists in coupling a stochastic algorithm, indeed a genetic algorithm (GA) with a discrete-event simulator (DES). Another incentive to use this kind of optimization method is that, there is no easy way of calculating derivatives of the objective functions, which then discards gradient optimization methods. To take into account the conflicting situations that may be encountered at the earliest stage of batch plant design, i.e. compromise situations between cost and environmental consideration, a multiobjective genetic algorithm (MOGA) was developed with a Pareto optimal ranking method. The results show how the methodology can be used to find a range of trade-off solutions for optimizing batch plant design

Gender income disparity in the USA: analysis and dynamic modelling

We analyze and develop a quantitative model describing the evolution of personal income distribution, PID, for males and females in the U.S. between 1930 and 2014. The overall microeconomic model, which we introduced ten years ago, accurately predicts the change in mean income as a function of age as well as the dependence on age of the portion of people distributed according to the Pareto law. As a result, we have precisely described the change in Gini ratio since the start of income measurements in 1947. The overall population consists of two genders, however, which have different income distributions. The difference between incomes earned by male and female population has been experiencing dramatic changes over time. Here, we model the internal dynamics of men and women PIDs separately and then describe their relative contribution to the overall PID. Our original model is refined to match all principal gender-dependent observations. We found that women in the U.S. are deprived of higher job positions. This is the cause of the long term income inequality between males and females in the U.S. It is unjust to women and has a negative effect on real economic growth. Women have been catching up since the 1960s and that improves the performance of the U.S. economy. It will take decades, however, to full income equality between genders. There are no new defining parameters included in the model except the critical age, when people start to lose their incomes, was split into two critical ages for low-middle incomes and the highest incomes, which obey a power law distribution. Such an extension becomes necessary in order to match the observation that the female population in the earlier 1960s was practically not represented in the highest incomes

Modeling the evolution of age-dependent Gini coefficient for personal incomes in the U.S. between 1967 and 2005

This study validates the microeconomic model defining the evolution of personal incomes in the U.S. Because of a large portion of population not reporting any income, any comprehensive modeling of the overall personal income distribution (PID) is complicated. Age-dependent PIDs allow overcoming this shortcoming since the portion of population without income is very low (Gini coefficient, personal income distribution, age, mean income, microeconomic modelling, USA, real GDP, macroeconomics

The Cash Flow Advantages of 3PLs as Supply Chain Orchestrators

With an increasingly open global economy and advanced technologies, some third-party logistics providers (3PLs), such as Eternal Asia, have emerged as supply chain orchestrators, linking buyers with manufacturers worldwide. In addition to their traditional transportation services, these orchestrators provide procurement and financial assistance to buyers in the supply network, especially small- and medium-sized enterprises (SMEs) in developing countries. Oftentimes, the 3PLs can obtain payment delay arrangements from the financially stronger manufacturers, which in turn can be partially extended to the SME buyers, alleviating their high costs of capital. To illustrate the efficiency improvements of the aforementioned practice, we use a model to explicitly capture the cash-flow dynamics in a supply chain consisting of a manufacturer, a buyer, and a 3PL firm and explore the conditions under which this innovation benefits all parties in the supply chain so that the business model is sustainable. We characterize these conditions and show that the supply chain profit can be higher under leadership by the 3PL than by the manufacturer. The intermediary role of the 3PL is crucial, in that its benefit may vanish if the manufacturer chooses to directly grant payment delay to the buyers. We demonstrate that the benefit is more likely to occur with more buyers. We further identify the unique Nash bargaining solution for the transportation time and the payment delay grace period

Catalytic-Dielectric Barrier Discharge Plasma Reactor For Methane and Carbon Dioxide Conversion

A catalytic - DBD plasma reactor was designed and developed for co-generation of synthesis gas and C2+ hydrocarbons from methane. A hybrid Artificial Neural Network - Genetic Algorithm (ANN-GA) was developed to model, simulate and optimize the reactor. Effects of CH4/CO2 feed ratio, total feed flow rate, discharge voltage and reactor wall temperature on the performance of catalytic DBD plasma reactor was explored. The Pareto optimal solutions and corresponding optimal operating parameters ranges based on multi-objectives can be suggested for catalytic DBD plasma reactor owing to two cases, i.e. simultaneous maximization of CH4 conversion and C2+ selectivity, and H2 selectivity and H2/CO ratio. It can be concluded that the hybrid catalytic DBD plasma reactor is potential for co-generation of synthesis gas and higher hydrocarbons from methane and carbon dioxide and showed better than the conventional fixed bed reactor with respect to CH4 conversion, C2+ yield and H2 selectivity for CO2 OCM process

Optimal design of batch plants under economic and ecological considerations: Application to a biochemical batch plant

This work deals with the multicriteria cost-environment design of multiproduct batch plants, where the design variables are the equipment item sizes as well as the operating conditions. The case study is a multiproduct batch plant for the production of four recombinant proteins. Given the important combinatorial aspect of the problem, the approach used consists in coupling a stochastic algorithm, indeed a Genetic Algorithm (GA) with a Discrete Event Simulator (DES). To take into account the conflicting situations that may be encountered at the earliest stage of batch plant design, i.e. compromise situations between cost and environmental considerations, a Multicriteria Genetic Algorithm (MUGA) was developed with a Pareto optimal ranking method. The results show how the methodology can be used to find a range of trade-off solutions for optimizing batch plant design