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The current difficulties of distribution network working robots are mainly in the performance and operation mode. On the one hand, high-altitude power operation tasks require high load-carrying capacity and dexterity of the robot; on the other hand, the fully autonomous mode is uncontrollable and the teleoperation mode has a high failure rate. Therefore, this study aims to design a distribution network operation robot named Sky-Worker to solve the above two problems.

The heterogeneous arms of Sky-Worker are driven by hydraulics and electric motors to solve the contradiction between high load-carrying capacity and high flexibility. A human–robot collaborative shared control architecture is built to realize real-time human intervention during autonomous operation, and control weights are dynamically assigned based on energy optimization.

Simulations and tests show that Sky-Worker has good dexterity while having a high load capacity. Based on Sky-Worker, multiuser tests and practical application experiments show that the designed shared-control mode effectively improves the success rate and efficiency of operations compared with other current operation modes.

The designed heterogeneous dual-arm distribution robot aims to better serve distribution line operation tasks.

For the first time, the integration of hydraulic and motor drives into a distribution network operation robot has achieved better overall performance. A human–robot cooperative shared control framework is proposed for remote live-line working robots, which provides better operation results than other current operation modes.

This study aims to minimize the transportation-related cost in distribution while utilizing a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach. An increased cost in distribution is a major problem for many companies due to the absence of efficient planning methods to overcome operational challenges in distinct distribution networks. The problem addressed in this study is to minimize the transportation-related cost in distribution while using a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach which has not gained the adequate attention in the literature.

This study formulated the transportation problem as a vehicle routing problem with a heterogeneous fixed fleet and workload balancing, which is a combinatorial optimization problem of the NP-hard category. The model was solved using both the simulated annealing and a genetic algorithm (GA) adopting distinct local search operators. A greedy approach has been used in generating an initial solution for both algorithms. The paired t-test has been used in selecting the best algorithm. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet compositions of the heterogeneous fleet. Results were analyzed using analysis of variance (ANOVA) and Hsu’s MCB methods to identify the best scenario.

The solutions generated by both algorithms were subjected to the t-test, and the results revealed that the GA outperformed in solution quality in planning a heterogeneous fleet for distribution with load balancing. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet utilization with different compositions of the heterogeneous fleet. Results were analyzed using ANOVA and Hsu’s MCB method and found that removing the lowest capacities trucks enhances the average vehicle utilization with reduced travel distance.

The developed model has considered both planning of heterogeneous fleet and the requirement of work load balancing which are very common industry needs, however, have not been addressed adequately either individually or collectively in the literature. The adopted solution methodologies to solve the NP-hard distribution problem consist of metaheuristics, statistical analysis and scenario analysis are another significant contribution. The planning of distribution operations not only addresses operational-level decision, through a scenario analysis, but also strategic-level decision has also been considered.

The planning of distribution operations not only addresses operational-level decisions, but also strategic-level decisions conducting a scenario analysis.

People donate to charities with the aim of improving society. Yet, many charities fail to use donations efficiently or have ineffective interventions. The authors explore the strategic operational priorities and processes that enable charities to efficiently implement their interventions and have a positive impact on society.

The authors first review the literature on charities to gain a deeper understanding of the current state of knowledge on charity operations. The authors then employ the lens of paradox theory and perform a qualitative investigation of six case studies to explore various aspects of the operations of charities that are known for being cost-effective.

The authors reveal how the strategic operational decisions of charities, as well as the processes they implement, help them resolve the tensions arising from the cost-effectiveness paradox. The authors show that cost-effective charities make strategic operational decisions that help maintain two diverging priorities: prioritizing the status quo and prioritizing change in how they deliver value. Another set of strategic decisions helps balance these two diverging priorities. The authors then show how these charities create and then maintain cost-effective operations.

The authors address recent calls for research on non-profit organizations in the field of operations management. To authors’ knowledge, it is the first in-depth study of exemplary charity operations. The results can be used by charity executives as a benchmarking tool when they develop and implement their charitable interventions and by government agencies and potential donors when they select charities for their donations. Finally, the results should have implications for other organizations trying to have a positive societal impact.

This paper aims to present a novel lightweight distribution grid operating robot system with focus on lightweight and multi-functionality, aiming for autonomous and live-line maintenance operations.

A ground-up redesign of the dual-arm robotic system with 12-DoF is applied for substantial weight reduction; a dual-mode operating control framework is proposed, with vision-guided autonomous operation embedded with real-time manual teleoperation controlling both manipulators simultaneously; a quick-swap tooling system is developed to conduct multi-functional operation tasks. A prototype robotic system is constructed and validated in a series of operational experiments in an emulated environment both indoors and outdoors.

The overall weight of the system is successfully brought down to under 150 kg, making it suitable for the majority of vehicle-mounted aerial work platforms, and it can be flexibly and quickly deployed in population dense areas with narrow streets. The system equips with two dexterous robotic manipulators and up to six interchangeable tools, and a vision system for AI-based autonomous operations. A quick-change tooling system ensures the robot to change tools on-the-go without human intervention.

The resulting dual-arm robotic live-line operation system robotic system could be compact and lightweight enough to be deployed on a wide range of available aerial working platforms with high mobility and efficiency. The robot could both conduct routine operation tasks fully autonomously without human direct operation and be manually operated when required. The quick-swap tooling system enables lightweight and durable interchangeability of multiple end-effector tools, enabling future expansion of operating capabilities across different tasks and operating scenarios.

This empirical study aims to examine luxury fashion retailers' localised multiple channel distribution strategies in China.

Through case studies of 15 participating retailers, qualitative data were collected from 33 semi-structured interviews.

Strong impacts of internationalisation strategies, distribution strategies and channel length towards multiple channel retailing are revealed. Multi-channel retailing is widely employed by firms who have entered China and further developed their businesses through local partnerships and adopted a selective distribution strategy via relatively longer channels. Omni-channel retailing is only suitable for the few retailers using an exclusive distribution strategy through direct marketing and wholly owned customer relationship management. As a dynamic transformation from multi- to omni-channel retailing, cross-channel retailing is adopted by those who are withdrawing from local partnerships and shifting to wholly owned expansions and operations in host markets.

The results are potentially challenged by relatively small sample size.

Practitioners are suggested to adapt multiple channel retailing to their international expansion strategies, distribution strategies and channel length in the host markets.

This paper contributes to the literature in both multiple channel retailing and international retailing by offering insights into the motives, development patterns and suitability of multiple channel retailing in the international retail marketing context.

To assess the introduction and performance of light electric freight vehicles (LEFVs), more specifically cargo cycles in major 3PL organizations in at least two Nordic countries.

Case studies. Interviews. Company data on performance before as well as after the introduction. Study of differing business models as well as operational setups.

The results from the studied cases show that LEFVs can compete with conventional vans in last mile delivery operations of e-commerce parcels. We account for when this might be the case, during which circumstances and why.

Inherent limitations of the case study approach, specifically on generalization. Future research to include more public–private partnership and multi-actor approach for scalability.

Adding to knowledge on the public sector facilitation necessary to succeed with implementation and identifying cases in which LEFVs might offer efficiency gains over more traditional delivery vehicles.

One novelty is the access to detailed data from before the implementation of new vehicles and the data after the implementation. A fair comparison is made possible by the operational structure, area of delivery, number of customers, customer density, type of packages, and to some extent, the number of packages being quite similar. Additionally, we provide data showing how city hubs can allow cargo cycles to work synergistically with delivery vans. This is valuable information for organizations thinking of trying LEFVs in operations as well as municipalities/local authorities that are interested.

Nonfamily farms are responsible for a disproportionate amount of US agriculture production. The importance of these operations to the volume of agriculture production in the United States has led researchers and policymakers to understand nonfamily farms as large commercial operations. This paper examines whether the distinction between family and nonfamily helps explain the financial outcomes of farm operations and households.

We test for differences in financial outcomes of the household and operations of family and nonfamily farms using an Oaxaca-Blinder decomposition. We compare these results to a decomposition of other possible typologies.

We present evidence that nonfamily farms are a heterogeneous group with a majority of small operations that are dominated by a small number of large operations. We discover that differences associated with the family-nonfamily distinction are largely explained by observable farm and operator characteristics that arise mechanically from the definition. However, we find suggestive evidence that family-nonfamily classification captures differences in economic behavior that lead to higher profitability measures to nonfamily farms. We find little evidence of any inherent structural differences between family and nonfamily farms that helps explain financial outcomes related to leverage or household finances.

We conclude that including nonfamily farms in official statistics of farm households may provide a more comprehensive overview of the farm sector, as our results suggest that family and nonfamily farms do not have innate differences that help explain many of their financial outcomes.

We incorporate previously unused data on nonfamily farm households and test the difference in mean financial outcomes between family and nonfamily farms.

In the reduction of food waste and the provision of food to the hungry, food banks play critical roles. However, as they are generally run by charitable organisations that are chronically short of human and other resources, their inbound logistics efforts commonly experience difficulties in two key areas: 1) how to organise stocks of donated food, and 2) how to assess the donated items quality and fitness for purpose. To address both these problems, the authors aimed to develop a novel artificial intelligence (AI)-based approach to food quality and warehousing management in food banks.

For diagnosing the quality of donated food items, the authors designed a convolutional neural network (CNN); and to ascertain how best to arrange such items within food banks' available space, reinforcement learning was used.

Testing of the proposed innovative CNN demonstrated its ability to provide consistent, accurate assessments of the quality of five species of donated fruit. The reinforcement-learning approach, as well as being capable of devising effective storage schemes for donated food, required fewer computational resources that some other approaches that have been proposed.

Viewed through the lens of expectation-confirmation theory, which the authors found useful as a framework for research of this kind, the proposed AI-based inbound-logistics techniques exceeded normal expectations and achieved positive disconfirmation.

As well as enabling machines to learn how inbound logistics are handed by human operators, this pioneering study showed that such machines could achieve excellent performance: i.e., that the consistency provided by AI operations could in future dramatically enhance such logistics' quality, in the specific case of food banks.

This paper’s AI-based inbound-logistics approach differs considerably from others, and was found able to effectively manage both food-quality assessments and food-storage decisions more rapidly than its counterparts.

This paper investigates how agile capabilities in humanitarian settings work in combination, and its effects on performance outcome. The study was conducted in the frame of reference of response operations during cyclones and floods, which is considered most complex and with the most widespread impact.

A survey-based method was used to collect empirical data on response operations from 131 field officers who were involved in disaster response during cyclones or floods. A partial least square based structural equation model was used to study the path model of interaction of agile capabilities, and their effect on performance outcomes.

The results show that integration of agile capabilities is important for enhancing effectiveness of humanitarian response. The results indicated a serial mediation effect involving visibility, responsiveness and flexibility capability on the effectiveness of emergency response.

This research has implications for response units of humanitarian organisations. This includes capacity building for key agile capabilities, integration, supply chain re-configuration and differential positioning of response phase as against preparedness and recovery phases.

This study is unique for the chosen humanitarian setting, which is considered most difficult. The authors demonstrate from empirical evidence the interaction effects of agile capabilities during response phase for cyclones and floods, and their impact. The research insights will help practitioners to configure and position supply chains for better effectiveness during response operations, which have markedly different objectives vis-à-vis other phases or types of humanitarian settings.

This study aims to investigate and classify the hospital logistics key performance indicators (KPIs) using the context of public hospitals in remote areas.

The public hospitals in northern Thailand were selected for the case study. The questionnaire was developed and used to collect data from hospital logistics experts. Then, the analytic hierarchy process (AHP) method was used to evaluate the hospital logistics KPIs in each dimension.

This research found that the procurement management dimension is ranked highest. Information and technology management is the last rank in the hospital logistics KPIs used for public hospitals in remote areas.

The public hospitals located in northern Thailand were selected for the case study. Fuzzy multi-criteria decision-making methods can be used to reduce the vagueness of the values.

The results from this study can be a guideline for hospitals to improve the efficiency of their logistics operations.

The decision-makers in the hospital can use these results to improve the hospital’s logistics performance in the future, which could help increase the service level and the safety of the patients.

The hospital logistics KPIs were revised, and the crucial KPIs were prioritized for improving the hospital logistics using the AHP method.