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This paper aims to focus on a medical goods distribution problem and pharmacological waste collection by plug-in hybrid vehicles with some real-world restrictions. In this research, considering alternative energy sources and simultaneous pickup and delivery led to a decrease in greenhouse gas emissions and distribution costs, respectively.

Here, this problem has been modeled as mixed-integer linear programming with the traveling and energy consumption costs objective function. The GAMS was used for model-solving in small-size instances. Because the problem in this research is an NP-hard problem and solving real-size problems in a reasonable time is impossible, in this study, the artificial bee colony algorithm is used.

Then, the algorithm results are compared with a simulated annealing algorithm that recently was proposed in the literature. Finally, the results obtained from the exact solution and metaheuristic algorithms are compared, analyzed and reported. The results showed that the artificial bee colony algorithm has a good performance.

In this paper, medical goods distribution with pharmacological waste collection is studied. The paper was focused on plug-in hybrid vehicles with simultaneous pickup and delivery. The problem was modeled with environmental criteria. The traveling and energy consumption costs are considered as an objective function.

Replication is a primary self-correction device in science. In this paper, we have two aims: to examine how and when the results of replications are used in management and organization research and to use the results of this examination to offer guidelines for improving the self-correction process.

Study 1 analyzes co-citation patterns for 135 original-replication pairs to assess the direct impact of replications, specifically examining how often and when a replication study is co-cited with its original. In Study 2, a similar design is employed to measure the indirect impact of replications by assessing how often and when a meta-analysis that includes a replication of the original study is co-cited with the original study.

Study 1 reveals, among other things, that a huge majority (92%) of sources that cite the original study fail to co-cite a replication study, thus calling into question the impact of replications in our field. Study 2 shows that the indirect impact of replications through meta-analyses is likewise minimal. However, our analyses also show that replications published in the same journal that carried the original study and authored by teams including the authors of the original study are more likely to be co-cited, and that articles in higher-ranking journals are more likely to co-cite replications.

We use our results to formulate recommendations that would streamline the self-correction process in management research at the author-, reviewer- and journal-level. Our recommendations would create incentives to make replication attempts more common, while also increasing the likelihood that these attempts are targeted at the most relevant original studies.

In response to the challenge of reduced efficiency or failure of robot motion planning algorithms when faced with end-effector constraints, this study aims to propose a new constraint method to improve the performance of the sampling-based planner.

In this work, a constraint method (TC method) based on the idea of cross-sampling is proposed. This method uses the tangent space in the workspace to approximate the constrained manifold pattern and projects the entire sampling process into the workspace for constraint correction. This method avoids the need for extensive computational work involving multiple iterations of the Jacobi inverse matrix in the configuration space and retains the sampling properties of the sampling-based algorithm.

Simulation results demonstrate that the performance of the planner when using the TC method under the end-effector constraint surpasses that of other methods. Physical experiments further confirm that the TC-Planner does not cause excessive constraint errors that might lead to task failure. Moreover, field tests conducted on robots underscore the effectiveness of the TC-Planner, and its excellent performance, thereby advancing the autonomy of robots in power-line connection tasks.

This paper proposes a new constraint method combined with the rapid-exploring random trees algorithm to generate collision-free trajectories that satisfy the constraints for a high-dimensional robotic system under end-effector constraints. In a series of simulation and experimental tests, the planner using the TC method under end-effector constraints efficiently performs. Tests on a power distribution live-line operation robot also show that the TC method can greatly aid the robot in completing operation tasks with end-effector constraints. This helps robots to perform tasks with complex end-effector constraints such as grinding and welding more efficiently and autonomously.

In demand-driven markets, customer value, sometimes called perceived use value or consumer surplus, is defined by the customer rather than the firm. The value a firm can appropriate, its profits, is driven by the customer’s willingness to pay for the value they receive, adjusted by costs. This paper introduces a conceptual framework that helps understand value creation and appropriation in demand-driven markets and shows how to influence them through strategic decision-making.

This paper uses an axiomatic approach combined with an extended analytical formulation of the jobs-to-be-done framework to contextualise demand-driven markets. It mathematically derives implications for managerial decision-making concerning selecting customer segments, optimising customer value creation and maximising firm value appropriation in a competitive environment.

Rooting strategic decision-making in the jobs-to-be-done framework allows distinguishing between what customers want to achieve (goal), what product attributes need to be satisfied (opportunity space/constraints) and what value creation criteria related to features are important (utility function). This paper shows that starting from a job-to-be-done, the problem of identifying which customer segments to serve, what product to offer and what price to charge, can be formulated as an optimisation problem that simultaneously (rather than sequentially) solves for the three decision variables, customer segments, product features and price, by maximising the value that a firm can appropriate, subject to maximising customer value creation and constrained by the competitive environment.

Applying the derived results to simultaneously deciding which customer segments to target, what product features to offer and what price to charge, given a set of competing products, allows managers to increase their chances of winning the competitive game.

This paper shows that starting from a job-to-be-done and simultaneously focusing on customers, product features, price and competitors enhances firm profitability. Strategic decision-making is formulated as an optimisation problem based on an axiomatic approach contextualising demand-driven markets.

This study aims to examine how the effect of gender on entrepreneurial growth aspirations is moderated differently by individual resources (human and financial capital) compared to those within the social environment (availability of entrepreneurial knowledge and role models).

A multilevel estimator is used to investigate the determinants of growth aspirations of owners-managers of nascent start-ups. The Global Entrepreneurship Monitor database is employed, covering the period 2007–2019, with 99,000 useable cases drawn from 95 countries.

The results suggest that individual financial resources and human capital have positive effects on entrepreneurial growth aspirations; yet these effects are weaker for female entrepreneurs relative to males. In contrast, the impact of the availability of entrepreneurial social knowledge and role models on their growth aspirations is more positive than for male entrepreneurs.

This study offers a novel insight into entrepreneurial growth ambition, as it utilises a global perspective to scrutinise whether individual and social resources contribute differently to male versus female growth-aspirations, employing a multilevel approach. It also integrates insights from the resource-based view and from the relevant business literature on entrepreneurs’ gender to develop theoretical explanations.

Research for the case study was conducted using a combination of semi-structured interviews and secondary data sources.

This case traces the international expansion of Sadowsky Guitars’ bass guitar product line. Roger Sadowsky is one of the most respected instrument makers in the world and gained early acclaim for his outstanding repair and restoration work on guitars and basses. Some of his early clients included Prince, Will Lee (The Tonight Show), Tom Hamilton of Aerosmith, Jason Newsted of Metallica, Eddie Van Halen and Marcus Miller. Roger’s reputation and the demand for his instruments led to some customers having to wait for more than a year to obtain the chance to purchase a Sadowsky instrument, while others were unable to do so due to financial constraints. In 2003, Roger made the decision to form Sadowsky Japan to begin the contract manufacturing of more affordable Sadowsky instruments in Tokyo, Japan. As the company grew in size, Roger realized he was becoming more focused on running a business than building instruments. Furthermore, his Japanese partners were only interested in serving the Japanese market. This required him to handle the sales and distribution in the remaining parts of the world. In December of 2019, he announced a new, exclusive licensing agreement and distribution partnership between Sadowsky Guitars and Warwick GmbH & Co Music Equipment KG. The new agreement allowed Roger to continue running the Sadowsky NYC Custom Shop while Warwick would take over building and distributing the Metro instruments and a less-expensive, Chinese-built version of the MetroExpress instruments.

This case is appropriate for undergraduate and graduate-level courses related to marketing and consumer behavior. The case walks students through a real-life scenario when the founder of a well-known musical brand sought to expand internationally as a way to meet growing market demand. Students are asked to consider the advantages and disadvantages of the five key international market entry strategies: exporting, licensing, contract manufacturing, joint ventures and investment (equity/acquisition).

The case works well in the classroom, even if people are unfamiliar with the musical instrument retail industry. Participants are most likely aware of some of the artists and musicians mentioned in the case. Some may also be or know musicians. The instructor should be able to quickly engage participants in a lively discussion about Roger Sadowsky’s vision for his instruments and the opportunities and challenges of expanding product offerings and increasing market share.

Teaching notes are available for educators only.

The proposed model aims to consider the flying hours as a criterion to initiate maintenance operation. Based on this condition, aircraft must be checked before flying hours threshold is met. After receiving maintenance service, the model ignores previous flying hours and the aircraft can keep on flying until the threshold value is reached again. Moreover, the model considers aircraft age and efficiency to assign them to flights.

The aircraft maintenance routing problem (AMRP), as one of the most important problems in the aviation industry, determines the optimal route for each aircraft along with meeting maintenance requirements. This paper presents a bi-objective mixed-integer programming model for AMRP in which several criteria such as aircraft efficiency and ferrying flights are considered.

As the solution approaches, epsilon-constraint method and a non-dominated sorting genetic algorithm (NSGA-II), including a new initializing algorithm, are used. To verify the efficiency of NSGA-II, 31 test problems in different scales are solved using NSGA-II and GAMS. The results show that the optimality gap in NSGA-II is less than 0.06%. Finally, the model was solved based on real data of American Eagle Airlines extracted from Kaggle datasets.

The authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.

The purpose of this systematic literature review is to catalog, explore and disseminate knowledge developed related to the affordances, constraints and collaborative practices of e-mentoring in order to offer recommendations for mentoring programs.

After identifying studies utilizing inclusion and exclusion criteria and a variety of relevant databases, chosen studies were organized into three categories based on e-mentoring practices.

Results reflect chat-room-based e-mentoring and e-mentoring with a video component both to have various affordances and collaborative features. These forms of e-mentoring can potentially be relevant to new teachers’ needs, may promote community and are disconnected from mentees’ school systems. A third form of e-mentoring, virtual reality e-mentoring, however, has the potential for accessibility issues and lack of relevancy to the mentoring process.

Chat-room-based e-mentoring and e-mentoring with a video component may benefit new teachers who do not have access to in-person mentors. However, these forms of e-mentoring may necessitate a component that ensures mentors and mentees alike remain consistently engaged in the e-mentoring process.

This study aims to address the inherent uncertainties within closed-loop supply chain (CLSC) networks through the application of a multi-objective approach, specifically focusing on the optimization of integrated production and transportation processes. The primary purpose is to enhance decision-making in supply chain management by formulating a robust multi-objective model.

In dealing with uncertainty, this study uses Pythagorean fuzzy numbers (PFNs) to effectively represent and quantify uncertainties associated with various parameters within the CLSC network. The proposed model is solved using Pythagorean hesitant fuzzy programming, presenting a comprehensive and innovative methodology designed explicitly for handling uncertainties inherent in CLSC contexts.

The research findings highlight the effectiveness and reliability of the proposed framework for addressing uncertainties within CLSC networks. Through a comparative analysis with other established approaches, the model demonstrates its robustness, showcasing its potential to make informed and resilient decisions in supply chain management.

This study successfully addressed uncertainty in CLSC networks, providing logistics managers with a robust decision-making framework. Emphasizing the importance of PFNs and Pythagorean hesitant fuzzy programming, the research offered practical insights for optimizing transportation routes and resource allocation. Future research could explore dynamic factors in CLSCs, integrate real-time data and leverage emerging technologies for more agile and sustainable supply chain management.

This research contributes significantly to the field by introducing a novel and comprehensive methodology for managing uncertainty in CLSC networks. The adoption of PFNs and Pythagorean hesitant fuzzy programming offers an original and valuable approach to addressing uncertainties, providing practitioners and decision-makers with insights to make informed and resilient decisions in supply chain management.