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The purpose of this paper is to generate optimised trajectories for an unmanned aerial vehicle (UAV) during a forest fire detection mission. It is assumed that the UAV flies 3D curvature-constrained Dubins manoeuvres and has a limited amount of battery energy that prevents it from covering the entire search area in a single trip.

In this paper, the search area is discretised into a grid of multiple targets, and each target assigned with a score that is proportional to the time elapsed since the last UAV visit. This problem, known as Dubins Airplane Orienteering Problem, consists of finding the number and order of targets to visit and the UAV heading at each target that maximises the total trip score without exceeding the UAV battery energy. The solution is found using the Randomised Variable Neighbourhood Search metaheuristic. All target scores are updated after each trajectory generation according to the elapsed time since the last UAV visit.

The proposed approach produced feasible results when generating optimised trajectories for a fire detection mission context where energy battery constraints are important.

The authors carry out the planning of UAV missions with limited amounts of onboard energy such as a real fire detection mission using a single electric propulsion and fixed-wing UAV.

This paper introduces an energy-based approach to the Dubins Airplane Orienteering Problem, which takes into account the UAV performance and energy budget when generating optimised trajectories.

The chapter examines a comprehensive review of cross-disciplinary literature in the domain of supply chain forecasting during research period 1991–2017, with the primary aim of exploring the growth of literature from operational to demand centric forecasting and decision making in service supply chain systems. A noted list of 15,000 articles from journals and search results are used from academic databases (viz. Science Direct, Web of Sciences). Out of various content analysis techniques (Seuring & Gold, 2012), latent sementic analysis (LSA) is used as a content analysis tool (Wei, Yang, & Lin, 2008; Kundu et al., 2015). The reason for adoption of LSA over existing bibliometric techniques is to use the combination of text analysis and mining method to formulate latent factors. LSA creates the scientific grounding to understand the trends. Using LSA, Understanding future research trends will assist researchers in the area of service supply chain forecasting. The study will be beneficial for practitioners of the strategic and operational aspects of service supply chain decision making. The chapter incorporates four sections. The first section describes the introduction to service supply chain management and research development in this domain. The second section describes usage of LSA for current study. The third section describes the finding and results. The fourth and final sections conclude the chapter with a brief discussion on research findings, its limitations, and the implications for future research. The outcomes of analysis presented in this chapter also provide opportunities for researchers/professionals to position their future service supply chain research and/or implementation strategies.

As the world is becoming more volatile and uncertain, organizations face much complexity in their daily operations. Further, there is a much ambiguity in business operations to achieve the effective utilization of resources. The work optimizes a novel constraint programming model approach of the utilization of shuttle services vehicle while considering cost savings, employee wellbeing and other real an Information Technology enabled service (ITES) industry constraints.

The present work considers a novel extension of the vehicle routing problem related to the shuttle service operation in an ITES industry in VUCA context. Additionally, the model considers the women safety aspects, which engages the company to provide a security guard for women employees in the night shift.

Numerical experiments were conducted on real instances data of ITES industrial partner. The results show that the vehicle utilization increased from 75% up to 96% while ensuring in parallel the wellbeing of employees and women safety during the night shift. Finally, the proposed model is converted to a decision support application allowing ITES partner to plan employees shuttle service operations efficiently.

Study has evaluated the shuttle services optimization for ITES industry using data from industrial which makes it a unique contribution to literature in shuttle operations. Further, the study used constraint programming to evaluate the vehicle utilization and security allocation, thereby introducing new parameter on security allocation in open VRP problem.

In repetitive projects, repetition offers more possibilities for activity scheduling at the sub-activity level. However, existing resource-constrained repetitive scheduling problem (RCRSP) models assume that there is only one sequence in performing the sub-activities of each activity, resulting in an inefficient resource allocation. This paper proposes a novel repetitive scheduling model for solving RCRSP with soft logic.

In this paper, a constraint programming model is developed to solve the RCRSP using soft logic, aiming at the possible relationship between parallel execution, orderly execution or partial parallel and partial orderly execution of different sub activities of the same activity in repetitive projects. The proposed model integrated crew assignment strategies and allowed continuous or fragmented execution.

When solving RCRSP, it is necessary to take soft logic into account. If managers only consider the fixed logic between sub-activities, they are likely to develop a delayed schedule. The practicality and effectiveness of the model were verified by a housing project based on eight different scenarios. The results showed that the constraint programming model outperformed its equivalent mathematical model in terms of solving speed and solution quality.

Available studies assume a fixed logic between sub-activities of the same activity in repetitive projects. However, there is no fixed construction sequence between sub-activities for some projects, e.g. hotel renovation projects. Therefore, this paper considers the soft logic relationship between sub-activities and investigates how to make the objective optimal without violating the resource availability constraint.

The idea that there is more to leadership than just teaching people what to do, that different styles suit different situations and personalities, that training can develop people's leadership and group membership skills, has received widespread acceptance amongst managers and professional trainers. There also seems to be agreement about the best way to proceed with developing these areas of interpersonal competence, for at the core of most training programmes is a series of class based exercises. These variously enable participants to experiment with their behaviour, to practise new skills and to receive feedback on their performance.

The purpose of this research is to develop a time-cost optimization model to schedule repetitive projects while considering limited resource availability.

The model is based on the constraint programming (CP) framework; it integrates multiple scheduling characteristics of repetitive activities such as continuous or fragmented execution, atypical activities and coexistence of different modes in an activity. To improve project performance while avoiding inefficient hiring and firing conditions, the strategy of bidirectional acceleration is presented and implemented, which requires keeping regular changes in the execution modes between successive subactivities in the same activity.

Two case studies involving a real residential building construction project and a hotel refurbishing project are used to demonstrate the application of the proposed model based on four different scenarios. The results show that (1) the CP model has great advantages in terms of solving speed and solution quality than its equivalent mathematical model, (2) higher project performance can be obtained compared to using previously developed models and (3) the model can be easily replicated or even modified to enable multicrew implementation.

The original contribution of this research is presenting a novel CP-based repetitive scheduling optimization model to solve the multimode resource-constrained time-cost tradeoff problem of repetitive projects. The model has the capability of minimizing the project total cost that is composed of direct costs, indirect costs, early completion incentives and late completion penalties.

Project managers work to ensure successful project completion within the shortest period and at the lowest cost. One of the main tasks of a project manager in the planning phase is to generate the project time–cost curve, and furthermore, to determine the most appropriate schedule for the construction process. Numerous existing time–cost tradeoff analysis models have focused on solving a simple project representation without regarding for typical activity and project characteristics. This study aims to present a novel approach called “multiple-objective social group optimization” (MOSGO) for optimizing time–cost decisions in generalized construction projects.

In this paper, a novel MOGSO to mimic the time–cost tradeoff problem in generalized construction projects is proposed. The MOSGO has slightly modified the mechanism operation from the original algorithm to be a free-parameter algorithm and to enhance the exploring and exploiting balance in an optimization algorithm. The evidential reasoning technique is used to rank the global optimal obtained non-dominated solutions to help decision makers reach a single compromise solution.

Two case studies of real construction projects were investigated and the performance of MOSGO was compared to those of widely considered multiple-objective evolutionary algorithms. The comparison results indicated that the MOSGO approach is a powerful, efficient and effective tool in finding the time–cost curve. In addition, the multi-criteria decision-making approaches were applied to identify the best schedule for project implementation.

Accordingly, the first major practical contribution of the present research is that it provides a tool for handling real-world construction projects by considering all types of construction project. The second important implication of this study derives from research finding on the hybridization multiple-objective and multi-criteria techniques to help project managers in facilitating the time–cost tradeoff (TCT) problems easily. The third implication stems from the wide-range application of the proposed model TCT.

The model can be used in early stages of the construction process to help project managers in selecting an appropriate plan for whole project lifecycle.

The proposal model can be applied to multi-objective contexts in diversified fields. Moreover, the model is also a useful reference for future research.

This paper makes contributions to extant literature by: introducing a method for making TCT models applicable to actual projects by considering general activity precedence relations; developing a novel MOSGO algorithm to solving TCT problems in multi-objective context by a single simulation; and facilitating the TCT problems to project managers by using multi-criteria decision-making approaches.

The purpose of this paper is to consider three factors, namely, intra-week demand fluctuations, interrelationship between the number of robots and order scheduling and conflicting objectives (i.e. cost minimization and customer satisfaction maximization), to optimize the robot logistics system.

The number of robots and the sequence of delivery orders are first optimized using the heuristic algorithm NSGACoDEM, which is designed using genetic algorithm and composite difference evolution. The superiority of this method is then confirmed by a case study of a four-star grade hotel in South Korea and several comparative experiments.

Two performance metrics reveal the superior performance of the proposed approach compared to other baseline approaches. Results of comparative experiments found that the consideration of three influencing factors in the operation design of a robot logistic system can effectively balance cost and customer satisfaction over the course of a week in hotel operation and optimize robot scheduling flexibility.

The results of this study reveal that numerous factors (e.g. intra-week demand fluctuations) can optimize the performance efficiency of robots. The proposed algorithm can be used by hotels to overcome the influence of intra-week demand fluctuations on robot scheduling flexibility effectively and thereby enhance work efficiency.

The design of a novel algorithm in this study entails enhancing the current robot logistics system. This algorithm can successfully manage cost and customer satisfaction during off-seasons and peak seasons in the hotel industry while offering diversified schemes to various types of hotels.

Commentators in the library and information profession habitually voice concern about the perceived lack of a coherent and integrated set of ‘national information policies’ in the UK. These concerns are worthy but ultimately misplaced. The real problem is not our inability to come up with a nice neat package of the ‘right policies’, but our failure to think critically about the value systems that shape our perception of information policy problems in the first place. This article contributes to this new agenda by developing a concept mapping of the Field of information policy, based on term co‐occurrence data. This leads to a broader discussion about the values — rather than the specific laws and regulations — that underpin our conceptions of information policy.

In the context of virtual brand communities (VBCs), based on self-determination theory and organizational binary learning (OBL) theory, this study investigates the direct and indirect effects of various dimensions of consumer participation (CP) on firm innovation performance (FIP) and the mediating role of OBL. By introducing the perspective of knowledge absorptive capacity (KAC), this study examines the moderating effect of KAC on the relationship between OBL and TIP.

In this study, 751 samples are collected from Chinese firms, and stratified adjusted regression analysis is used to conduct empirical tests.

Information provision or co-creation directly affects FIP; the latter is more significant than the former, while social interaction does not directly affect FIP. Exploitative or exploratory learning plays a mediating role in the relationship between information provision or co-creation and FIP. In contrast, exploratory learning plays a mediating role in the relationship between social interaction and FIP. KAC has a positive moderating effect on the relationship between exploitative learning or exploratory learning and FIP. When the firm has a low KAC, the improvement of FIP through exploratory learning is slight.

In VBCs, this study tries to divide the dimensions of CP. It also clarifies the mediating effect of OBL on CP and FIP. In addition, it explores the moderating effect of KAC on OBL and FIP. The conclusions of this study provide theoretical support and practical inspiration for firms to use VBCs to improve FIP.