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This paper aims to focus on time-constrained interactions involving industry and public actors, mainly universities, conducting research. This kind of interaction has become increasingly important to develop new pharmaceuticals, especially antibiotics. The proposed theoretical frame relies on industrial marketing and purchasing’s interactive perspective on inter-organizational relationships and especially the activities, resource, actors model, combined with key concepts on temporary organizing and project management. This study identifies the temporality and time constraints imposed by this project on public–private interactions, specific coordination tools used to create such temporality and time constraints and their consequences, including positive and negative effects for the interacting parties.

The study builds on a single in-depth qualitative case study of a major antibiotics R&D collaboration project called ENABLE.

For negative consequences, this model includes the need for constantly rebuilding trust due to fast turnover of actors, difficulties in combining resources as efficiently as possible, resource constraints, bottlenecks and neglect of some activities, such as publishing, which are normally pivotal for universities. Despite these problematic consequences of temporality, resources are rapidly made available and new competencies learned quickly. Another positive effect is the possibility to achieve complex adaptations of resources and activities even in short time frames. Importantly, projects can act as a springboard for the parties to continue collaboration and in the long term develop a continuous business relationship.

Based on the findings the authors develop a model of time-constrained inter-organizational interaction between public and private organizations.

Social sustainability is a concept frequently referred to in public debates concerning how to construct the governance of future societies. The interpretations of its meaning, however, are ambiguous, and practices often dubious. Confronting top-down technocratic governance structures, this paper aims to argue for for tripartite collaborations between residents, higher education institutions (HEIs) and local government, as an approach toward social sustainability that involves residents’ interests in local governance.

This study argues that a specific time-spatial method of analysis can benefit the co-creation of knowledge as it passes through the spectrum of resident–HEI–local government. It provides a way for resident perceptions to become structured knowledge that originates from the residents, effectively engendering a bottom-up governance structure.

This study shows how to include residents in policymaking and implementation processes as co-creators of knowledge, and thereby displays the possibility of examining knowledge and competence within municipal projects for social sustainability.

The model developed in this study can be used as a methodological instrument to analyze and expand resident participation in local social sustainability work. It thereby provides a toolbox for inclusive policymaking and strategies.

The interruption event will seriously affect the normal operation of urban rail transit lines,causing a large number of passengers to be stranded in the station and even making the train stranded in the interval between stations. This study aims to reduce the impact of interrupt events and improve service levels.

To address this issue, this paper considers the constraints of train operation safety, capacity and dynamic passenger flow demand. It proposes a method for adjusting small loops during interruption events and constructs a train operation adjustment model with the objective of minimizing the total passenger waiting time. This model enables the rapid development of train operation plans in interruption scenarios, coordinating train scheduling and line resources to minimize passenger travel time and mitigate the impact of interruptions. Regarding the proposed train operation adjustment model, an improved genetic algorithm (GA) is designed to solve it.

The model and algorithm are applied to a case study of interruption events on Beijing Subway Line 5. The results indicate that after solving the constructed model, the train departure intervals can be maintained between 1.5 min and 3 min. This ensures both the safety of train operations on the line and a good match with passengers’ travel demands, effectively reducing the total passenger waiting time and improving the service level of the urban rail transit system during interruptions. Compared to the GA algorithm, the algorithm proposed in this paper demonstrates faster convergence speed and better computational results.

This study explicitly outlines the adjustment method of using short-turn operation during operational interruptions, with train departure times and station stop times as decision variables. It takes into full consideration safety constraints on train operations, train capacity constraints and dynamic passenger demand. It has constructed a train schedule optimization model with the goal of minimizing the total waiting time for all passengers in the system.

Facing the diverse needs of large-scale customers, based on available railway service resources and service capabilities, this paper aims to research the design method of railway freight service portfolio, select optimal service solutions and provide customers with comprehensive and customized freight services.

Based on the characteristics of railway freight services throughout the entire process, the service system is decomposed into independent units of service functions, and a railway freight service combination model is constructed with the goal of minimizing response time, service cost and service time. A model solving algorithm based on adaptive genetic algorithm is proposed.

Using the computational model, an empirical analysis was conducted on the entire process freight service plan for starch sold from Xi'an to Chengdu as an example. The results showed that the proposed optimization model and algorithm can effectively guide the design of freight plans and provide technical support for real-time response to customers' diversified entire process freight service needs.

With the continuous optimization and upgrading of railway freight source structure, customer demands are becoming increasingly diverse and personalized. Studying and designing a reasonable railway freight service plan throughout the entire process is of great significance for timely response to customer needs, improving service efficiency and reducing design costs.

Failed deliveries (i.e. deliveries not accomplished due to the absence of customers) represent a critical issue in B2C (Business-to-consumer) e-commerce last-mile deliveries, implying high costs for e-commerce players and negatively affecting customer satisfaction. A promising option to reduce them would be scheduling deliveries based on the probability to find customers at home. This work proposes a solution based on presence data (gathered through Internet of Things [IoT] devices) to organise the delivery tours, which aims to both minimise the travelled distance and maximise the probability to find customers at home.

The adopted methodology is a multi-method approach, based on interviews with practitioners. A model is developed and applied to Milan (Italy) to compare the performance of the proposed innovative solution with traditional home deliveries (both in terms of cost and delivery success rate).

The proposed solution implies a significant reduction of missed deliveries if compared to the traditional operating mode. Accordingly, even if allocating the customers to time windows based on their availability profiles (APs) entails an increase in the total travel time, the average delivery cost per parcel decreases.

On the academic side, this work proposes and evaluates an innovative last-mile delivery (LMD) solution that exploits new AI (Artificial Intelligence)-based technological trends. On the managerial side, it proposes an efficient and effective novel option for scheduling last-mile deliveries based on the use of smart home devices, which has a significant impact in reducing costs and increasing the service level.

The research approach is based on the concept that a failure event is rarely random and is often generated by a chain of previous events connected by a sort of domino effect. Thus, the purpose of this study is the optimal selection of the components to predictively maintain on the basis of their failure probability, under budget and time constraints.

Assets maintenance is a major challenge for any process industry. Thanks to the development of Big Data Analytics techniques and tools, data produced by such systems can be analyzed in order to predict their behavior. Considering the asset as a social system composed of several interacting components, in this work, a framework is developed to identify the relationships between component failures and to avoid them through the predictive replacement of critical ones: such relationships are identified through the Association Rule Mining (ARM), while their interaction is studied through the Social Network Analysis (SNA).

A case example of a process industry is presented to explain and test the proposed model and to discuss its applicability. The proposed framework provides an approach to expand upon previous work in the areas of prediction of fault events and monitoring strategy of critical components.

The novel combined adoption of ARM and SNA is proposed to identify the hidden interaction among events and to define the nature of such interactions and communities of nodes in order to analyze local and global paths and define the most influential entities.

This research aimed to assess the leadership role of principals in the implementation of peace education in selected secondary schools in the Western Cape, South Africa.

This study employed qualitative research approach to assess the leadership role of principals in the implementation of peace education in selected secondary schools in the Western Cape, South Africa. Data were gathered from a small sample of six principals from six selected secondary schools which were engaged in the implementation of a peace education programme, and data were analysed using thematic content analyses.

Findings of the study suggest that principals possess a low level of understanding or awareness of their leadership role in the implementation of peace education. The study pointed out the constraints such as time constraints and learners' negative attitudes and social influences hinder the effective implementation of peace education in selected secondary schools.

First, the data were self-reported and therefore subject to social desirability bias; participants may have provided socially desirable responses rather than their true belief or experiences. Thus, participants may have overstated their role in and commitment to the peace education programme.

Studies that aim to explore alternative approaches to combat violence, such as peace education, are still limited in South Africa. Hence, this paper served to close that gap by contributing to the growing body of research on the leadership role of the principal in the implementation of peace education in the school and exploring barriers hampering its effective implementation.

This paper aims to study the implication of the stochastic gross-profit-per-day objective on the ship profitability and the ship capacity and speed.

The paper has used the mathematical model and the solution methodology given by El Noshokaty, 2013, 2014, 2017a, 2017b, and SOS, 2019.

The paper finds that if the ship owner follows the rate concept and the cargo demand forecast, he can improve the profitability of his company and be able to select the proper capacities and speeds for the ships used.

The findings are not only useful for the shipping or other cargo transport companies but also for businesses like gas reservoir development, car assembly lines in the industry, cooperative farming and crop harvesting in agriculture, port cargo handling in trade and road paving in construction.

The contribution of this paper lies in notifying the ship owners of the possible profitability improvement and the consequences of building ships of larger capacities and slower speeds.

The development of e-grocery allows people to purchase food online and benefit from home delivery service. Nevertheless, a high rate of failed deliveries due to the customer’s absence causes significant loss of logistics efficiency, especially for perishable food. The purpose of this paper is to propose an innovative approach to use customer-related data to optimize e-grocery home delivery. The approach estimates the absence probability of a customer by mining electricity consumption data, in order to improve the success rate of delivery and optimize transportation.

The methodological approach consists of two stages: a data mining stage that estimates absence probabilities, and an optimization stage to optimize transportation.

Computational experiments reveal that the proposed approach could reduce the total travel distance by 3-20 percent, and theoretically increase the success rate of first-round delivery approximately by18-26 percent.

The proposed approach combines two attractive research streams on data mining and transportation planning to provide a solution for e-commerce logistics.

This study gives an insight to e-grocery retailers and carriers on how to use customer-related data to improve home delivery effectiveness and efficiency.

The proposed approach can be used to reduce environmental footprint generated by freight distribution in a city, and to improve customers’ experience on online shopping.

Being an experimental study, this work demonstrates the effectiveness of data-driven innovative solutions to e-grocery home delivery problem. The paper also provides a methodological approach to this line of research.

The purpose of this paper is to investigate aspects related to difficulty to retain nursing assistants at nursing homes in Sweden related to perceived work environment characteristics.

To reveal aspects related to difficulty to retain nursing assistants, the paper uses the BIKVA model, sense of coherence and New Public Management (NPM). In total, three focus groups with nursing assistants at three nursing homes are interviewed with corresponding individual interviews with their senior managers and users. The purpose is to analyze the situation from the affected group of nursing assistants. The focus of this study is how nursing assistants discuss related to recruit and retain nursing assistants at nursing homes and elderly care and the response from senior management related to those aspects.

The main conclusions are that nursing assistants consider their job as meaningful, but limited latitude and direct involvement in managing their daily tasks in a continuous communication with management affect negatively. Furthermore and combined with wage levels, aspects related to scheduling, working hours, shift work, split shifts and understaffing generate a burdensome and stressful environment affecting the possibility to retain staff in a negative direction.

The research uses a new approach utilizing the BIKVA model, sense of coherence and NPM. The study shows that central in retaining nursing assistants at nursing homes relates to aspects such as wages, staffing, shift work and split shifts and continuous communication between nursing assistants and management.