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The equation of unified knowledge says that S = f (A,P) which means that the practical solution to a given problem is a function of the existing, empirical, actual realities and the future, potential, best possible conditions of general stable equilibrium which both pure and practical reason, exhaustive in the Kantian sense, show as being within the realm of potential realities beyond any doubt. The first classical revolution in economic thinking, included in factor “P” of the equation, conceived the economic and financial problems in terms of a model of ideal conditions of stable equilibrium but neglected the full consideration of the existing, actual conditions. That is the main reason why, in the end, it failed. The second modern revolution, included in factor “A” of the equation, conceived the economic and financial problems in terms of the existing, actual conditions, usually in disequilibrium or unstable equilibrium (in case of stagnation) and neglected the sense of right direction expressed in factor “P” or the realization of general, stable equilibrium. That is the main reason why the modern revolution failed in the past and is failing in front of our eyes in the present. The equation of unified knowledge, perceived as a sui generis synthesis between classical and modern thinking has been applied rigorously and systematically in writing the enclosed American‐British economic, monetary, financial and social stabilization plans. In the final analysis, a new economic philosophy, based on a synthesis between classical and modern thinking, called here the new economics of unified knowledge, is applied to solve the malaise of the twentieth century which resulted from a confusion between thinking in terms of stable equilibrium on the one hand and disequilibrium or unstable equilibrium on the other.

The purpose of this paper is to explain the difference and connection between the network big data analysis technology and the psychological empirical research method.

This study analyzed the data from laboratory setting first, then the online sales data from Taobao.com to explore how the influential factors, such as online reviews (positive vs negative mainly), risk perception (higher vs lower) and product types (experiencing vs searching), interacted on the online purchase intention or online purchase behavior.

Compared with traditional research methods, such as questionnaire and behavioral experiment, network big data analysis has significant advantages in terms of sample size, data objectivity, timeliness and ecological validity.

Future study may consider the strategy of using complementary methods and combining both data-driven and theory-driven approaches in research design to provide suggestions for the development of e-commence in the era of big data.

The purpose of this second part of this special issue is to contribute to a better understanding of the nature of Soviet society. It is not possible to analyse such a society in all its complexities within the space of one study. There are, however, some economic relations which determine society's major features. We believe that commodity‐production relations in the Soviet Union are of this type.

This paper aims to explore the impacts of the recent development of Korean free trade agreements (FTAs) on its seaborne trade volumes. The paper firstly estimates the changes in cargo value flows caused by Korea-EU FTA, Korea-USA FTA and Korea-ASEAN FTA using a global computable general equilibrium model named Global Trade Analysis Project (GTAP) and its most recent database - version 7 with 2004 as the base year. Then a set of systematic conversion factors transferring trade value flows to volume flows of different types of commodities is calibrated according to the United Nations COMTRADE database and is used to convert the GTAP trade value flows into volume flows. Having indentified maritime cargo flows by different commodity types, this paper attempts to draw implications for maritime logistics policy in order to facilitate the trade of Korean merchandises and to propose key competitive strategy for the maritime container transport networking and logistics service providers in the Korean logistics industry.

The purpose of this paper is to provide an understanding of the openness and specific techniques through which illicit funds generated by criminals are moved, transferred and laundered in the financial arrangement retained using high-value portable commodities in the UK and internationally.

This paper presents exploratory findings from research conducted between 2011 and 2013 in the UK. The research undertaken sought to identify the process, steps and vulnerabilities behind money laundering via high-value portable commodities and highlight the explicit facilitators enabling this method of money laundering to take place.

Despite significant research into money laundering typologies, the use of high-value portable commodities has remained largely untouched regardless of the increased implementation of anti-money laundering policies and procedures seeking to halt the depositing of illicit cash into the global financial system. This paper demonstrates how high-value portable commodities are extremely vulnerable to money laundering despite the large-scale anti-money laundering efforts focused on combating money laundering across a broad range of sectors.

This paper is of value to government policymakers, regulators and financial institutions considering future preventative measures. It is also of value to financial investigators and law enforcement agencies intent on investigating money laundering. While the paper relies on data from the UK, the overall findings are such that wherever high-value portable commodities are present, so too does the opportunity for money laundering through the financial arrangement retained by high-value portable commodities.

This paper presents new research on the direct link between high-value portable commodities and money laundering in the UK despite significant research having previously taken place to identify and develop money laundering typologies.

The need to design materials handling systems optimally has grown as the cost of material handling equipment has continued to increase. For some facilities, this need is critical due to product obsolescence and quality. Simulation modelling based and factorial experimental design are used to help accentuate some of the decision options regarding the optimum number of carts, the optimum schedule and the minimum acceptable amount of lateness (or tardiness) allowed in the system. The methodology proposed and the experimental design utilized permitted the evaluation of different design options. Using cost and tardiness as the primary criteria, some design alternatives were identified that minimized overall operations and logistics costs thereby satisfying the system′s performance goals.

When a large-scale outbreak such as the COVID-19 pandemic happens, organizations that are responsible for delivering relief may face a lack of both provisions and human resources. Governments are the primary source for the humanitarian supplies required during such a crisis; however, coordination with humanitarian NGOs in handling such pandemics is a vital form of public-private partnership (PPP). Aid organizations have to consider not only the total degree of demand satisfaction in such cases but also the obligation that relief goods such as medicine and foods should be distributed as equitably as possible within the affected areas (AAs).

Given the challenges of acquiring real data associated with procuring relief items during the COVID-19 outbreak, a comprehensive simulation-based plan is used to generate 243 small, medium and large-sized problems with uncertain demand, and these problems are solved to optimality using GAMS. Finally, post-optimality analyses are conducted, and some useful managerial insights are presented.

The results imply that given a reasonable measure of deprivation costs, it can be important for managers to focus less on the logistical costs of delivering resources and more on the value associated with quickly and effectively reducing the overall suffering of the affected individuals. It is also important for managers to recognize that even though deprivation costs and transportation costs are both increasing as the time horizon increases, the actual growth rate of the deprivation costs decreases over time.

In this paper, a novel mathematical model is presented to minimize the total costs of delivering humanitarian aid for pandemic relief. With a focus on sustainability of operations, the model incorporates total transportation and delivery costs, the cost of utilizing the transportation fleet (transportation mode cost), and equity and deprivation costs. Taking social costs such as deprivation and equity costs into account, in addition to other important classic cost terms, enables managers to organize the best possible response when such outbreaks happen.

Pre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.

In this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.

Managers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.

The model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.