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This paper aims to optimize the interactions of businesses located within industrial clusters (ICs) by using a supply-demand hub in ICs (SDHIC) as a conjoint provider of logistics and depository facilities for small- and medium-sized enterprises (SMEs) as producers, where all of these interactions are under supervision of a third-party logistics provider (3PL).

To evaluate the values of SDHIC, three mathematical models are proposed, optimally solved via GAMS and then compared. Also, a “linear relaxation-based heuristic” procedure is proposed to yield a feasible initial solution within a significant shorter computational time. To illustrate the values of SDHIC, comprehensive calculations over a case study and generated sets of instances are conducted, including several sensitivity analysis.

The experimental results demonstrate the efficiency of SDHIC for SMEs via combining batches and integrating the holding space of inventories, while the outcomes of the case study are aligned with those obtained from random sample examples, which confirms the trueness of used parameters and reveals the applicability of using SDHIC in real world. Finally, several interesting managerial implications for practitioners are extracted and presented.

Some of the managerial and practical implications are optimizing interactions of businesses involved in a supply chain of an IC containing some customers, suppliers and manufacturers and rectifying the present noteworthy gaps pertaining to the previously published research via using real assumptions and merging upstream and downstream of the supply chain through centralizing on storage of raw materials (supply echelon) and finished products (demand echelon) at the same place simultaneously to challenge a classic concept in which supply and demand echelons were being separately planned regarding their inventory management and logistics activities and showing the positive consequences of such challenge, showing the performance improvement of the proposed model compared to the classic model, by increasing the storing cost of raw materials and finished products, considering some disadvantages of using SDHIC and showing the usefulness of SDHIC in total, presenting some applied findings according to the obtained results of sensitivity analysis.

The key contributions of this paper to the literature are suggesting a new applied mathematical methodology to the supply chain (SC) of ICs by means of a conjoint provider of warehousing activities called SDHIC, comparing the new proposed model with the two classic ones and showing the proposed model’s dominancy, showing the helpful outcomes of collaborating 3PL with the SMEs in a cluster, proposing a “linear relaxation-based heuristic” procedure to yield a feasible initial solution within a significant shorter computational time and minimizing total supply chain costs of such IC by optimum application of facilities, lands and labor.

In this paper, a multi-objective multi-echelon supply-distribution model is proposed to optimize interactions of entities located within an Industrial Cluster (IC) including small- and medium-sized enterprises (SMEs), using a third-party logistics provider (3PL)-managed supply-demand hub in industrial cluster (SDHIC) as a specific public provider of warehousing and logistics services.

The three considered objectives are minimizing the total logistics costs, maximizing the rate of demand satisfaction and maximizing the quality of delivery. Because some parameters such as “demand of customers” are naturally fuzzy because of incompleteness and/or inaccessibility of the needed information, the triangular fuzzy number is applied for all fuzzy parameters to handle this difficulty. The proposed model is primarily changed into a correspondent supplementary crisp model. To solve such a model, a revised multi-choice goal programming (RMCGP) approach is then used with the purpose of finding a compromise solution.

Experimental results demonstrate that all enterprises involved in such a supply chain benefit with several advantages using SDHIC by consolidating shipments and merging the storage space of inventories. The applicability of the presented model is shown by conducting these experiments over an applied industrial case study.

The main contributions of this research are proposing a practical mathematical approach to the supply chain of ICs using a specific public warehouse managed by a 3PL, called SDHIC, bridging the existing gaps with respect to the already published researches in this area by applying real-world assumptions such as uncertainty; optimizing the interactions of involved entities in the supply chain of an IC, comprising suppliers, SMEs as manufacturers and customers; minimizing the total incurred logistics costs to such a system through optimum usage of lands, facilities, labors, etc. and boosting the satisfaction of customers through maximizing the service level criteria, illustrating the positive consequences of cooperation of 3PL with the SMEs/manufacturers in an IC, showing the applicability of the adopted approach by implementing it on an applied industrial instance.

The manufacturing strategies and practices in a number of newly industrialising countries (NICs) are studied. The IMSS data for four Latin American industrialising countries, referred to as Group 1, are analysed, comparing these with two benchmarks, which represent more advanced manufacturing situations in well established industrialised countries. Comparing results indicated similarities for Group 1 with the two benchmarks, on simultaneously aiming at all their competitive goals. The study also proved a mismatch in their emphasis on the goals and the level of the objectives achieved in this regard. Links for this mismatch were searched for in various areas including human resource and technological aspects. Results also highlighted serious shortcomings for Group 1 in process technology criteria as compared with the two benchmarks. Comparison results with regard to human resources as well as planning and control aspects are also discussed in the paper. Based on this study, it has been concluded that environmental features have to be thoroughly analysed before any manufacturing strategy is developed for the firms in NICs. Further, continual interactions between manufacturing strategies for these firms with their environmental features have to be included in their strategy formulation process.

The purpose of this paper is to investigate the issues pertinent to sourcing internationally from low‐cost countries (LCCs) and to understand which issues are associated most strongly with a firm's logistics performance.

Comparative examination of supply sources in developed countries and LCCs is conducted using a paired‐sample setting. Data acquired by a mail survey of US‐based manufacturing firms are analyzed using a canonical correlation analysis (CCA). CCA helps to reveal the structure of relationships within and between a set of sourcing issue variables and a set of the logistics performance variables investigated.

Results indicate that the issues that should be priorities for improvement are: supplier production capability; business culture and practices; and communication infrastructure.

The survey data and analysis focused on US manufacturing firms importing from LCCs. However, there is increasing activity of US retailers importing from LCCs, suggesting a need for a follow‐on study which considers the retailer perspective.

Results not only validate the issues to be considered in sourcing from LCCs, but also provide a direction for logistics managers in allocating an organization's scarce resources to the issues of highest potential for improving logistics performance.

The fast emerging role of LCCs as supply sources of US firms and the cost advantages of sourcing from LCCs are widely recognized. However, challenges associated with sourcing internationally from LCCs and the extent to which they are related to a firm's logistics performance have received limited attention in existing logistics research. This study contributes valuable insights into this area of international sourcing and logistics management.

In recent years, resource‐based theory has emerged as one of the most promising theoretical frameworks in the field of management. In this paper, the authors aim to explore the application of a resource‐based view when implementing organizational change in Chinese organizations. The problems associated with change are explored from the perspective of human resources (HR).

Structured interviews were conducted with top or middle managers in 160 companies in several large cities in the northern part of China: Beijing, Tianjin, Jinan and Zibo.

From the perspective of HR, the main problems faced when implementing change in Chinese organizations include the following: bureaucratic regulations and strict orders remain the core features of the process of implementing changes in Chinese companies. Meanwhile, the intrinsic values and emotions of employees were neglected and coercion and manipulation was frequently used as a strategy to overcome resistance to change. Moreover, Chinese managers are found to lack the skill to involve employees in the change process.

The study provides some insights into the human side of the change management process in China. Based on the research results the authors have identified the main problems associated with HR and recommend that the human resource management function facilitate the success of organizational changes.

This paper aims to study the impact of existence and lack of discount on the relationships between one manufacturer and one retailer under the cooperative and the non-cooperative games and the members’ profits are compared.

In the first approach, the manufacturer’s price function is constant, and in the second approach, this price function is a decreasing function with respect to lot size. These approaches are modeled through three games structure, including two Stackelberg games and one cooperative game.

Some numerical instances comprising sensitivity analysis are provided, and then the members’ profits in different scenarios are compared. This paper reveals that in the presented models, whether the members are inclined to change their profits.

This paper presents a tool of decision-making for the type of relationships of members in two different circumstances, and an approach is also presented to maximize the members’ profit.

In this paper, the relationships between one manufacturer and one retailer are studied under six different circumstances, where pricing, cooperative advertising and inventory cost are considered simultaneously. Also, a different model is presented to make a balance in individual profits and gain more profit for each member compared to the cooperative and non-cooperative game.

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.

The purpose of this paper is to present a new method and model for constructing a new decision-making paradigm of Medicare, which can not only satisfy the needs of the sick people but also reduce the possibility of people slipping back to poverty due to diseases under the policy of Targeted Poverty Alleviation (TPA) of China.

This paper uses the traditional supply chain theory to analyze the Medicare of impoverished people with the policy of TPA of China and transforms it into a multi-layer supply chain optimization decision-making problem. First, a nonlinear integer programming model for poor people’s Medicare decision with opportunity constraints is constructed. To facilitate the solution of the optimal decision scheme, the abovementioned model is transformed into a linear integer programming model with opportunity constraints by using the Newsvendor model for reference. Meanwhile, the scope of the inventory model is discussed, for it can be combined with the construction of the medical insurance system better. Second, the theoretical model is applied to the practical problem. Finally, based on the results of the theoretical model applying the practical problem, we give further improvement and modification of the theoretical model applies it to the actual situation further.

This paper presents a theoretical model about determine the optimal the inventory, under the framework of traditional supply chain decision-making, for it can be combined with the construction of the medical insurance system better. The theoretical model is applied to the practical problem of the fight against poverty in XX County, China. By using the actual data and MATLAB, optimal decision scheme is obtained.

There are two aspects of value. On the one hand, this paper provides a new way to construct a Medicare system of impoverished people with TPA of China. On the other hand, this paper tries making a new way to handle the storage of medicines and related medical devices at basic standard clinics decision-making problems based on above mentioned Medicare system.

Effectively predicting research and development project portfolio benefit (R&D PPB) could assist organizations in monitoring the execution of research and development project portfolio (R&D PP). However, due to the uncertainty and complexity of R&D PPB, current research remains lacking a valid R&D PPB prediction tool. Therefore, an R&D PPB prediction model is proposed via a backpropagation neural network (BPNN).

The R&D PPB prediction model is constructed via a refined immune genetic algorithm coupling backpropagation neural network (RIGA-BPNN). Firstly, considering the characteristics of R&D PP, benefit evaluation criteria are identified. Secondly, the benefit criteria values are derived as input variables to the model via trapezoidal fuzzy numbers, and then the R&D PPB value is determined as the output variable through the CRITIC method. Thirdly, a refined immune genetic algorithm (RIGA) is designed to optimize BPNN by enhancing polyfitness, crossover and mutation probabilities. Lastly, the R&D PPB prediction model is constructed via the RIGA-BPNN, followed by training and testing.

The accuracy of the R&D PPB prediction model stands at 99.26%. In addition, the comparative experiment results indicate that the proposed model surpasses BPNN and the immune genetic algorithm coupling backpropagation neural network (IGA-BPNN) in both convergence speed and accuracy, showcasing superior performance in R&D PPB prediction. This study enriches the R&D PPB predicting methodology by providing managers with an effective benefits management tool.

The research implications of this study encompass three aspects. First, this study provides a profound insight into R&D PPB prediction and enriches the research in PP fields. Secondly, during the construction of the R&D PPB prediction model, the utilization of the composite system synergy model for quantifying synergy contributes to a comprehensive understanding of intricate interactions among benefits. Lastly, in this research, a RIGA is proposed for optimizing the BPNN to efficiently predict R&D PPB.

This study carries threefold implications for the practice of R&D PPM. To begin with, the approach proposed serves as an effective tool for managers to predict R&D PPB. Then, the model excels in efficiency and flexibility. Furthermore, the proposed model could be used to tackle additional challenges in R&D PPM, such as gauging the potential risk level of R&D PP.

Effective predicting of R&D PPB enables organizations to allocate their limited resources more strategically, ensuring optimal use of capital, manpower and time. By accurately predicting benefit, an organization can prioritize high-potential initiatives, thereby improving innovation efficiency and reducing the risk of failed investments. This approach not only strengthens market competitiveness but also positions organizations to adapt more effectively to changing market conditions, fostering long-term growth and sustainability in a competitive business environment.

Incorporating the characteristics of R&D PP and quantifying the synergy between benefits, this study facilitates a more insightful R&D PPB prediction. Additionally, improvements to the polyfitness, crossover and mutation probabilities of IGA are made, and the aforementioned RIGA is applied to optimize the BPNN. It significantly enhances the prediction accuracy and convergence speed of the neural network, improving the effectiveness of the R&D PPB prediction model.

The cluster supply chain is widely used in the professional towns in China, and improves the competitiveness of small and medium enterprises through integrating the supply chain with the industrial cluster. The paper aims to discuss this issue.

This paper studies a cluster supply chain under vendor managed inventory (VMI) system, which includes vendors, third-party logistics (TPL) enterprises and retail enterprises, and aims to study the replenishment decisions and coordination contracts in the supply chain. The economic order quantity model is applied to analyze the influence of marginal transportation cost factor under two replenishment modes – direct delivery and milk-run delivery, in order to find out the optimal replenishment decisions corresponding to different marginal transportation cost factors. And then, the revenue sharing contract is used to identify the change of profits of enterprises in the supply chain before and after the coordination contract.

It is concluded that the marginal transportation cost factor is an important factor influencing the replenishment decision especially in milk-run delivery, and the introduction of the revenue sharing contract can improve the revenue in the supply chain.

This is the first study that explores the relationship between a single transport cost and a single transport batch of cluster supply chain in centralized VMI & TPL system. The conclusions of the study have certain theoretical significance for the decision making and coordination of cluster supply chain.