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This study aims to provide a model for designing logistics networks with multiple products. In such a design model, it is important to determine the operation‐related parameters accurately.

An iterative process is used to design a logistics network. First, a mixed integer‐programming (MIP) model is used to determine the configuration of the network. Then, based on the output of MIP model, an inventory‐planning model for multiple products is developed to decide the lot size and ordering frequency for each product at each node of the logistics network, and a vehicle routing model is used to find the shortest product delivery routes from wholesalers to retailers. After that, the operation‐related parameters are recalculated and updated, and the configuration of the logistics network is re‐optimized. Such process proceeds until it converges. By this iterative process, the operation‐related parameters can be determined more accurately.

The iterative process proposed in the paper possesses following advantages: determining operation‐related parameters accurately, and adapting to changing market quickly.

A typical logistics network used to distribute multiple products is considered as target system to apply this model. This paper does not handle a real case study as an application example.

This paper proposes a modular design approach: different types of mathematics are used for different modules. The model developed in this paper is realizable as software tool in logistics management.

Purpose – The study sets out to explore the use of an iterative approach for designing distribution chain in an agile virtual environment; in an agile virtual environment, quick adaptation to changing market situation and automation of supply chain management processes are essential. Design/methodology/approach – The iterative approach consists of two parts: the strategic model and the tactical model. First, the strategic model (including number of distributors, location of distributors) is determined. Then, based on the output of the strategic model, the tactical model (i.e. the inventory planning at each node, and vehicle routeing between different nodes of the chain) is determined. After determining the tactical model, the operation‐related parameters from the tactical model are input into the strategic model again, and the configuration of distribution chain is re‐optimized. Such iterations proceed until the design result converges. Findings – The proposed iterative design process provides many advantages such as reuse of knowledge, adaptive to changing market conditions, modular design, and optimal results guaranteed by mixed mathematical usage. The proposed approach is also realizable as a supply chain management software tool. Originality/value – An important contribution of this study is the iterative process that uses three different types of mathematics. For determination of the strategic model mixed integer programming is used. Determination of the tactical model is effected using genetic algorithm and probability theory.

When an enterprise wants to design its distribution chain, it needs first to assess all possible distributors, then select the eligible ones to form the design model. This assessing process can be finished by distributor benchmarking. In this paper, a new approach is developed to benchmark distributors. The benchmarking process is done by the following three steps. First, all factors needed for benchmarking a distributor are identified by a systematic analysis. Second, an internet‐based information acquisition module is developed to get all needed information from possible distributors. Third, an inference module based on the combination of fuzzy logic and array‐based logic is developed to benchmark a distributor. As the information acquisition module is implemented via Internet, and the inference process for benchmarking a distributor is executed by computer applications, it is possible to realize online distributor benchmarking by the approach provided in this paper.

The purpose of this study is to identify patient experience unique to online pharmacy services (OPS) based on the characteristics of OPS (i.e. interactivity and virtuality) and to reveal how patient experience is derived from OPS and thereby enhances patient adherence from both online social support and patient experience perspectives.

The Stimulus-Organism-Response framework was used to conceptualize this study; both primary and secondary data for 296 validated participants were collected on a real OPS platform. A structural equation modeling approach combined with partial least squares was employed for the quantitative analysis.

Social presence and user engagement can be identified as patient experience in the OPS context; online emotional support has a stronger association with patients' social presence than it does with patients' user engagement; patients' social presence has a stronger association with their medication adherence than it does with diet adherence, while patients' user engagement has a stronger association with their diet adherence than it does with medication adherence; patients' medication knowledge negatively moderates the relationship between user engagement and diet adherence.

This study identifies patient experience in accordance with unique characteristics of OPS, and it reveals the nuanced underlying mechanisms by which online social support is associated with patient experience and by which patients' experience is associated with their adherence. This study enriches the literature on patient adherence, patient experience and OPS, providing insights for healthcare providers, OPS designers and policymakers.

Many practical control problems require achieving multiple objectives, and these objectives often conflict with each other. The existing multi-objective evolutionary reinforcement learning algorithms cannot achieve good search results when solving such problems. It is necessary to design a new multi-objective evolutionary reinforcement learning algorithm with a stronger searchability.

The multi-objective reinforcement learning algorithm proposed in this paper is based on the evolutionary computation framework. In each generation, this study uses the long-short-term selection method to select parent policies. The long-term selection is based on the improvement of policy along the predefined optimization direction in the previous generation. The short-term selection uses a prediction model to predict the optimization direction that may have the greatest improvement on overall population performance. In the evolutionary stage, the penalty-based nonlinear scalarization method is used to scalarize the multi-dimensional advantage functions, and the nonlinear multi-objective policy gradient is designed to optimize the parent policies along the predefined directions.

The penalty-based nonlinear scalarization method can force policies to improve along the predefined optimization directions. The long-short-term optimization method can alleviate the exploration-exploitation problem, enabling the algorithm to explore unknown regions while ensuring that potential policies are fully optimized. The combination of these designs can effectively improve the performance of the final population.

A multi-objective evolutionary reinforcement learning algorithm with stronger searchability has been proposed. This algorithm can find a Pareto policy set with better convergence, diversity and density.

This study examines how informal business networks achieve marketing goals in socially uncertain contexts. Drawing from multiple historical sources, Shangbangs, a type of business network that thrived in pre-1949 China, are analyzed.

The Critical Historical Research Method (CHRM) undergirds a study of Shangbangs’ historicity (i.e. their socio-historically embedded multiplicity, including organizational forms, activities and connotations.

As informal regional, professional, project-based, special-product-based or mixed marketing networks, Shangbangs relied on “flexible specialization” and coupled multiple business needs to market goods and services, business organizations, specific social values and, when necessary, to debrand business rivals.

This analysis extends theories about marketing networks by probing their subtypes, diverse marketing activities, multipronged channels and relationship building with social entities (including underground societies, business associations and guilds) in response to pre-1949 China’s market uncertainties. Substantiating an alternative approach to “flexible specialization” and marketing innovations within the pre-1949 Chinese economy shows how a parallel theoretical framework can complement western-based marketing theories.

This first comprehensive analysis of Shangbangs, an innovative historical Chinese marketing network outside the conventional market-corporate dichotomy, can inform theory building for marketing strategy-making and management conditioned by social contexts.

The purpose of this paper is to investigate the relationships between business risks and credit choices of 400 farm households surveyed in Shaanxi province in October 2007 in the Yangling district. More specifically, this paper investigates whether or not rural farm households in China balance business risks from agricultural production with financial risk from the use of debt.

The data were collected through a survey of 400 farm households in Shaanxi province conducted in October 2007. Four separate regressions are run using a credit measure as the dependent variable and measures of profitability, risk, risk aversion, and demography, and debt source (formal versus informal lending) as independent variables.

The model shows evidence of risk balancing. That is, there is strong evidence that Chinese farmers reduce credit use and financial risk, as business risks increase.

The results suggest that Chinese policy makers could encourage the use of finacial leverage and prudent debt use by offering risk reducing programs such as crop insurance, weather insurance, or price insurance.

This paper uses a unique survey form to collect production risk data as well as gather information on credit use and sources. Data were collected so that risk measures could easily be computed using a triangular distribution. Furthermore, this is believed to be the first empirical validation of the risk balancing hypothesis.

Deepor Beel is one of the Ramsar Site and a wetland of great biodiversity, situated in the south-western part of Guwahati, Assam. With urban development at its forefront city of Guwahati, Deepor Beel is under constant threat. The study aims to calculate the lake water volume from the water surface area and the underwater terrain data using a triangulated irregular network (TIN) volume model.

The lake water surface boundaries for each year were combined with field-observed water level data to generate a description of the underwater terrain. Time series LANDSAT images of 2001, 2011 and 2019 were used to extract the modified normalized difference water index (MNDWI) in GIS domain.

The MNDWI was 0.462 in 2001 which reduced to 0.240 in 2019. This shows that the lake water storage capacity shrank in the last 2 decades. This leads to a major problem, i.e. the storage capacity of the lake has been declining gradually from 20.95 million m³ in 2001 to 16.73 million m³ in 2011 and further declined to 15.35 million m³ in 2019. The fast decline in lake water volume is a serious concern in the age of rapid urbanization of big cities like Guwahati.

None of the studies have been done previously to analyze the decline in the volume of Deepor Beel lake. Therefore, this study will provide useful insights in the water resource management and the conservation of Deepor Beel lake.