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This paper proposes an approach to solve the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) in the context of the Physical Internet (PI) supply chain. The main objective is to minimize the total distribution costs (transportation cost and holding cost) to supply retailers from PI hubs.

Mixed integer programming (MIP) is proposed to solve the problem in smaller instances. A random local search (RLS) algorithm and a simulated annealing (SA) metaheuristic are proposed to solve larger instances of the problem.

The results show that SA provides the best solution in terms of total distribution cost and provides a good result regarding holding cost and transportation cost compared to other heuristic methods. Moreover, in terms of total carbon emissions, the PI concept proposed a better solution than the classical supply chain.

The sustainability of the route construction applied to the PI is validated through carbon emissions.

This approach also relates to the main objectives of transportation in the PI context: reduce empty trips and share transportation resources between PI-hubs and retailers. The proposed approaches are then validated through a case study of agricultural products in Thailand.

This approach is also relevant with the reduction of driving hours on the road because of share transportation results and shorter distance than the classical route planning.

This paper addresses the VRPSPD problem in the PI context, which is based on sharing transportation and storage resources while considering sustainability.

Environmental issues have become an important concern in modern supply chain management. The structure of closed-loop supply chain (CLSC) networks, which considers both forward and reverse logistics, can greatly improve the utilization of materials and enhance the performance of the supply chain in coping with environmental impacts and cost control.

A biobjective mixed-integer programming model is developed to achieve the balance between environmental impact control and operational cost reduction. Various factors regarding the capacity level and the environmental level of facilities are incorporated in this study. The scenario-based method and the Epsilon method are employed to solve the stochastic programming model under uncertain demand.

The proposed stochastic mixed-integer programming (MIP) model is an effective way of formulating and solving the CLSC network design problem. The reliability and precision of the Epsilon method are verified based on the numerical experiments. Conversion efficiency calculation can achieve the trade-off between cost control and CO₂ emissions. Managers should pay more attention to activities about facility operation. These nodes might be the main factors of costs and environmental impacts in the CLSC network. Both costs and CO₂ emissions are influenced by return rate especially costs. Managers should be discreet in coping with cost control for CO₂ emissions barely affected by return rate. It is advisable to convert the double target into a single target by the idea of “Efficiency of CO₂ Emissions Control Reduction.” It can provide managers with a way to double-target conversion.

We proposed a biobjective optimization problem in the CLSC network considering environmental impact control and operational cost reduction. The scenario-based method and the Epsilon method are employed to solve the mixed-integer programming model under uncertain demand.

Despite widespread recognition of the importance of mindfulness in organizational science literature, little is known about how mindfulness motivates individuals to configure information processing and team member exchange relationships to increase creative process engagement. Drawing on motivated information processing theory, this study conceptualizes and empirically examines whether and how mindfulness motivates individuals toward creative process engagement.

The authors collected data through an online survey from 311 respondents working in the Research and Development (R&D) departments of organizations in multiple industries in Pakistan. For analytical purposes, the authors have applied the structural equation modeling technique.

This study advances a different view of individual mindfulness on the creative process engagement in the following ways. First, mindfulness enables individuals to self-regulate in specific situations and become effective in fostering creative process engagement. Second, this study extends research on relational information processing by linking it to mindfulness and creative process engagement. Relational information processing partially mediates the relationship between mindfulness and creative process engagement. Third, this study highlights that mindfulness motivates individuals to focus more on developing quality working relationships, but they seem less willing to participate in idea generation and problem-solving solutions.

The study findings provide implications for research on mindfulness, creativity and motivated information processing to enhance individuals’ creative process engagements. The authors also discuss the implications for executives on the relational and creative benefits of mindfulness.

Knowledge competencies and (R&D) activities are one of the most important sources of innovation and have been widely discussed in the literature. In comparison, the role of the competitive environment for the innovation activities of firms is still open to debate and has not been fully understood yet. Therefore, this paper intends to provide new evidence on the interaction between knowledge competencies and R&D activities of firms on the one side and their competitiveness in the market environment on the other. In particular, the moderating function of market competition is explored. In this respect, the analysis covers the main innovation types as well as both sectors, manufacturing and services.

The empirical analysis is based on a three years panel dataset of German manufacturing and service firms obtained from Mannheim Innovation Panel (MIP) and Community Innovation Surveys (CISs: 2011, 2013 and 2015). For the estimation, a binary instrumental variable treatment model with Heckman selection method is used. Also, it provides a suitable approach to estimating the binary variables in order to cope with endogeneity concerns.

The estimation results show that R&D activities and knowledge competencies are positively related to innovation activities of different types conditioned on firms' specific perception of their competitive environment, in terms of outdated products/services as well as strong competition from abroad. Most importantly, the results from the moderation estimation reveal that there is a significant difference between the manufacturing and service sector. Service firms engage more in internal R&D activities on generating product innovations while the manufacturing firms conduct more external R&D on specific types of innovation. Further, the authors find that strong competition from abroad positively and significantly reinforces the effect of knowledge competencies on innovation activities for more types in services than in manufacturing. In contrast, outdated products and services tend to decline the effect of knowledge competencies for some innovation types in both sectors. The authors also observe a positive and significant reinforcement effect on knowledge competencies. However, it is found more beneficial for service firms since they can employ more innovation strategies.

The focus of the study is mainly on the impact of firms' competitive environment on innovation activities in various types through its interaction with knowledge competencies and R&D activities, across manufacturing and service firms.

This paper focuses on multi-objective order allocation with product substitution for the vaccine supply chain under uncertainty.

The weighted-sum minimization approach is used to find a compromised solution between three objectives of minimizing inefficiently vaccinated people, postponed vaccinations, and purchasing costs. A mixed-integer formulation with substitution quantities is proposed, subject to capacity and demand constraints. The substitution ratios between vaccines are assumed to be exogenous. Besides, uncertainty in supplier reliability is formulated using optimistic, most likely, and pessimistic scenarios in the proposed optimization model.

Covid-19 vaccine supply chain process is studied for one government and three vaccine suppliers as an illustrative example. The results provide essential insights for the governments to have proper vaccine allocation and support governments to manage the Covid-19 pandemic.

This paper considers the minimization of postponement in vaccination plans and inefficient vaccination and purchasing costs for order allocation among different vaccine types. To the best of the authors’ knowledge, there is no study in the literature on order allocation of vaccine types with substitution. The analytical hierarchy process structure of the Covid-19 pandemic also contributes to the literature.

This research is designed to optimize the business process of a green tea dealer, who is a key supply chain partner of the Sri Lankan tea industry. The most appropriate trips for each vehicle in multiple trip routing systems are identified to minimize the total cost by considering the traveling distance.

The study has followed the concepts in vehicle routing problems and mixed-integer programming mathematical techniques. The model was coded with the Python programming language and was solved with the CPLEX Optimization solver version 12.10. In total, 20 data instances were used from the subjected green tea dealer for the validation of the model.

The result of the numerical experiment showed the ability to access supply over the full capacity of the available fleet. The model achieved optimal traveling distance for all the instances, with the capability of saving 17% of daily transpiration cost as an average.

This study contributes to the three index mixed-integer programing model formulation through in-depth analysis and combination of several extensions of vehicle routing problem.

This study contributes to the three index mixed-integer programming model formulation through in-depth analysis and combination of several extensions of the vehicle routing problem.

The proposed model provides a cost-effective optimal routing plan to the green tea dealer, which satisfies all the practical situations by following the multiple trip vehicle routing problems. Licensee green tea dealer is able to have an optimal fleet size, which is always less than the original fleet size. Elimination of a vehicle from the fleet has the capability of reducing the workforce. Hence, this provides managerial implication for the optimal fleet sizing and route designing.

Developing an optimization model for a tea dealer in Sri Lankan context is important, as this a complex real world case which has a significant importance in export economy of the country and which has not been analyzed or optimized through any previous research effort.

The COVID-19 pandemic has forced countries to consider how to reach vulnerable communities with extended outreach services to improve vaccination uptake. The authors created an optimization model to align with decision-makers' objective to maximize immunization coverage within constrained budgets and deploy resources considering empirical data and endogenous demand.

A mixed integer program (MIP) determines the location of outreach sites and the resource deployment across health centers and outreach sites. The authors validated the model and evaluated the approach in consultation with UNICEF using a case study from The Gambia.

Results in The Gambia showed that by opening new outreach sites and optimizing resource allocation and scheduling, the Ministry of Health could increase immunization coverage from 91.0 to 97.1% under the same budget. Case study solutions informed managerial insights to drive gains in vaccine coverage even without the application of sophisticated tools.

The research extended resource constrained LMIC vaccine distribution modeling literature in two ways: first, endogenous calculation of demand as a function of distance to health facility location enabled the effective design of the vaccine network around convenience to the community and second, the model's resource bundle concept more accurately and flexibly represented complex requirements and costs for specific resources, which facilitated buy-in from stakeholders responsible for managing health budgets. The paper also demonstrated how to leverage empirical research and spatial analysis of publicly available demographic and geographic data to effectively represent important contextual factors.

This study aims to shed lights on the dynamics of involving and sharing knowledge with stakeholders in the process of new service development (NSD) over time.

The paper is based on a paradigmatic case focused on the development of the digital MBA program by the School of Management of Politecnico di Milano. Primary and secondary data have been largely collected and analyzed, involving multiple stakeholders of the development process.

This study describes how several stakeholders have been involved during the phase of the NSD process, showing two variables that ruled their involvement: the level of control exerted by the School on the stakeholders and the level of flexibility of the stakeholders.

This research offers insights to the understanding of the dynamics of involving and sharing knowledge with multiple-stakeholders in NSD. From a theoretical perspective, it contributes to stakeholder theory linking it with the service management literature, highlighting the role of cyclical fluctuations in the involvement activities.

This research offers insights to managers dealing with the development of new services, offering them a novel view on how various stakeholders may be involved over time, in different moment and in different ways, to properly enhance the development process thanks to their knowledge sharing.

This paper contributes to the service management literature emphasizing the role of multiple stakeholders while providing insights and suggestions to manage the complex relationships created by their involvement and their knowledge.

In this paper, we investigate the impact of remittances on control of corruption in Nigeria for a period of 1986–2016.

The study uses ARDL modeling framework, dynamic OLS estimation, variance decomposition and impulse response analysis to examine the relationship between the two variables.

The study finds that remittances significantly improve the control of corruption in Nigeria. We further examine the robustness test of the results using dynamic OLS estimation, variance decomposition and impulse response analysis. Our results remain significant and consistent to the earlier one reported in ARDL framework which supports the extant literature.

Our study suggests that international remittances can be used, through the cross-border transfer of norms and practices, to significantly impact the socioeconomic progresses of a country by reducing corruption.

The existing studies on the relationship between corruption and remittances document conflicting results. In addition, study on corruption - remittances nexus that specifically focuses on any African country is largely absent despite the fact that most of the countries in the region are recognized as highly corrupt. This paper provides insights on how remittances can be used as part of tool kits to control corruption in African nation.

The optimisation of forest fuels supply chain involves several entities actors, and particularities. To successfully manage these supply chains, efficient tools must be devised with the ability to deal with stakeholders dynamic interactions and to optimize the supply chain performance as a whole while being stable and robust, even in the presence of uncertainties. This work proposes a framework to coordinate different planning levels and event-based models to manage the forest-based supply chain. In particular, with the new methodology, the resilience and flexibility of the biomass supply chain is increased through a closed-loop system based on the system forecasts provided by a discrete-event model. The developed event-based predictive model will be described in detail, explaining its link with the remaining elements. The implemented models and their links within the proposed framework are presented in a case study in Finland and results are shown to illustrate the advantage of the proposed architecture.