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The purpose of this paper is to propose an optimum form of incentive contracts with multiple outcomes and multiple agents.

Utility theory and principal-agent theory provide the underlying basis for this paper. A sample of 60 practitioners from public organizations and private companies participated in an exercise to validate the proposed model.

The paper shows that, in outcome sharing contracts, the contributions of agents toward outcomes are positively related, while agent effort costs, outcome uncertainty, outcome correlation and agent level of risk aversion are negatively related. The paper further demonstrates that outcome sharing is positively associated with the level of effort selected by the agents.

Outcome sharing models might be used in construction contracts to encourage the agent to act in the interests of the principal. However, few studies have looked at contracts with multiple outcomes and multiple agents. This paper contributes to the current practice of contract management through simplifying the complex nature of multiple incentive contracts and providing theoretical guidance for multi arrangements.

The purpose of this paper is to investigate the impact of different agency practice on agency fees, business efficiency, and housing market liquidity.

The paper studies the effect of sole and multiple agency practices on estate agent efficiency, housing market liquidity, and commission fee levels. The analysis uses the survey data from 2000 to 2006 to investigate the different agency practices across England and Wales and their effect on estate agency business efficiency, housing market liquidity, selling price, and fee levels.

The empirical analysis confirms that agency practice has a locality bias, that is, some regions are more likely to adopt sole agency practice than other regions. The estate agents with a sole agency practice charge a lower agency fee, help clients to achieve better selling price and are more efficient; whereas multiple agency practice facilitates liquidity in the housing market, but experiences higher fall‐through rate.

The research focuses on estate agent rather than consumers due to the limitation of the data based on a research project concerning transaction costs designed prior to this analysis.

There is little other research that investigates the residential estate agency practice and its impact on housing market in the past three decades in England and Wales. The findings are a useful guide for practitioners to better understand the issues associated with different agency practices and should enhance business efficiency and performance.

The purpose of this paper is to propose a model for a target searching problem in a two-dimensional region with time constraints. The proposed model facilitates the search operation by minimizing the mission time and fuel usage, and the search operation is performed by a set of agents divided into a number of groups.

The authors have applied optimization techniques, Cartesian product, inclusion–exclusion principle, cooperative strategy, Shapley value, fuzzy Shapley function and Choquet integral to model the problem.

The proposed technique optimizes the placement of base stations that minimizes the sortie length of the agents. The results show that the cooperative strategy outperforms the non-cooperative strategy. The Shapley values quantify the rewards of each group based on their contributions to the search operation, whereas the fuzzy Shapley values determine the rewards of each group based on their contributions and level of cooperation in the search operation.

The proposed model can be applied to model many real-time problems such as patrolling in international borders, urban areas, forests and managing rescue operations after natural calamities, etc. Therefore, defence organizations, police departments and other operation management sectors will be benefitted by applying the proposed approach.

To the best of the authors’ knowledge, determining the optimal locations of base stations in a region is not explored in the existing works on target searching problems with fuel constraints. The proposed approach to cooperatively search the targets in a region is new. Introducing the Shapley function and fuzzy Shapley function is a novel idea to quantify the rewards of each group based on their contributions and level of cooperation in the search operation. This paper addresses these unexplored areas.

Electronic information retrieval is becoming a necessity for most businesses, including medical practice. Agent technology promises to provide a facile means for intelligently searching the Internet. However, any agent or other electronic information placed onto the Internet poses a security risk. Encryption and firewalls are traditional methods for increasing Internet information security, but are these techniques sufficient to protect adequately electronic information carried by Internet agents? Need to know and partial information strategies are discussed as methods for improving information security for Internet agents.

This paper aims to develop a scheduler for multiple picking agents in a warehouse that takes into account distance and loading queue delay minimization within the context of minimizing makespan (i.e. picking time).

The paper uses tabu search to solve the scheduling problem in a more global sense. Each search iteration is enhanced by a custom local search (LS) procedure that hastens convergence by driving a given schedule configuration quickly to a local minimum. In particular, basic operators transfer demand among agents to balance load and minimize makespan. The new load distribution is further improved by considering a vehicle‐routing problem on the picking assignments of the agents with relocated demands. Loading queue delays that may arise from the reassignments are systematically minimized using a fast scheduling heuristic.

The proposed tabu scheduler greatly improves over a widely practiced scheduling procedure for the given problem. Variants of the tabu scheduler produce solutions that are roughly of the same quality but exhibit considerable differences in computational time.

The proposed methodology is applicable only to the static scheduling problem where all inputs are known beforehand. Furthermore, of the possible delays during picking, only loading queues are explicitly addressed (although this is justifiable, given that these delays are dominant in the problem).

The proposed approach can significantly increase through‐put and productivity in picking systems that utilize multiple intelligent agents (human pickers included), e.g. in warehouses/distribution centers.

The paper addresses a practical scheduling problem with a high degree of complexity, i.e. scheduler explicitly deals with delays while trying to minimize makespan (generally, delays are ignored in the literature to simplify things). In the tabu implementation, an LS procedure is introduced in the metaheuristic loop that enhances the search process by minimizing non‐productive time of picking agents (travel time and delays).

The purpose of this paper is to propose a model for a two-agent multi-target-searching scenario in a two-dimensional region, where some places of the region have limited resource capacity in terms of the number of agents that can simultaneously pass through those places and few places of the region are unreachable that expand with time. The proposed cooperative search model and Petri net model facilitate the search operation considering the constraints mentioned in the paper. The Petri net model graphically illustrates different scenarios and helps the agents to validate the strategies.

In this paper, the authors have applied an optimization approach to determine the optimal locations of base stations, a cooperative search model, inclusion–exclusion principle, Cartesian product to optimize the search operation and a Petri net model to validate the search technique.

The proposed approach finds the optimal locations of the base stations in the region. The proposed cooperative search model allows various constraints such as resource capacity, time-dependent unreachable places/obstacles, fuel capacities of the agents, two types of targets assigned to two agents and limited sortie lengths. On the other hand, a Petri net model graphically represents whether collisions/deadlocks between the two agents are possible or not for a particular combination of paths as well as effect of time-dependent unreachable places for different combination of paths are also illustrated.

The problem addressed in this paper is similar to various real-time problems such as rescue operations during/after flood, landslide, earthquake, accident, patrolling in urban areas, international borders, forests, etc. Thus, the proposed model can benefit various organizations and departments such as rescue operation authorities, defense organizations, police departments, etc.

To the best of the authors’ knowledge, the problem addressed in this paper has not been completely explored, and the proposed cooperative search model to conduct the search operation considering the above-mentioned constraints is new. To the best of the authors’ knowledge, no paper has modeled time-dependent unreachable places with the help of Petri net.

The purpose of this work is to bridge FL and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. FL is an emerging collaborative machine learning technique that trains a model across multiple devices or servers holding private data samples without exchanging their data. The locally trained results are aggregated by a centralized server in a privacy-preserving way. However, there is an assumption where the centralized server is trustworthy, which is impractical. Fortunately, blockchain technology has opened a new era of data exchange among trustless strangers because of its decentralized architecture and cryptography-supported techniques.

In this study, the author proposes a novel design of a smart agent inspired by the smart contract concept. Specifically, based on the proposed smart agent, a fully decentralized, privacy-preserving and fair deep learning blockchain-FL framework is designed, where the agent network is consistent with the blockchain network and each smart agent is a participant in the FL task. During the whole training process, both the data and the model are not at the risk of leakage.

A demonstration of the proposed architecture is designed to train a neural network. Finally, the implementation of the proposed architecture is conducted in the Ethereum development, showing the effectiveness and applicability of the design.

The author aims to investigate the feasibility and practicality of linking the three areas together, namely, multi-agent system, FL and blockchain. A blockchain-FL framework, which is based on a smart agent system, has been proposed. The author has made several contributions to the state-of-the-art. First of all, a concrete design of a smart agent model is proposed, inspired by the smart contract concept in blockchain. The smart agent is autonomous and is able to disseminate, verify the information and execute the supported protocols. Based on the proposed smart agent model, a new architecture composed by these agents is formed, which is a blockchain network. Then, a fully decentralized, privacy-preserving and smart agent blockchain-FL framework has been proposed, where a smart agent acts as both a peer in a blockchain network and a participant in a FL task at the same time. Finally, a demonstration to train an artificial neural network is implemented to prove the effectiveness of the proposed framework.

The purpose of this paper is to investigate the impact of incentive contracts in multi-partner project teams (MPPTs) on the agents’ effort expenditure and project performance, analyze how the agents allocate their efforts between production and cooperation and offer suggestions for project managers on how to design incentive contracts.

The paper proposes a model of MPPT in which agents are inequity-averse and their effort expenditures are exogenously bounded. An extensive numerical example is presented in online Appendix 2 to illustrate the theoretical results.

The paper suggests that if the potential benefit of the agents’ cooperation in MPPT is high or if both agents exhibit inequity aversion and the efforts’ marginal costs are low, then group-based incentive contracts outperform individual-based incentive contracts. It also shows that the impact of the incentive contract on the agents’ effort expenditure and project team performance is correlated with several critical project attributes.

Fulfilling a need to study the design of incentive structures in MPPTs, the paper complements the existing literature in three ways. First, in contrast to single-partner project teams, it considers projects with multiple partners where cooperation between them enhances the project outcome. Second, rather than focusing on individual production problems, it considers multi-task projects with constrained efforts that must be allocated between production and cooperation. Third, it analyzes the effects of changes in the project attributes, incentive intensities and information transparency on the effectiveness of the contract.

Smart spaces are open complex computing systems, consisting of a large variety of cooperative smart things. Central to building smart spaces is the support for sophisticated coordination among diverse smart things collaborating to accomplish specified tasks. Multi‐agent systems are often used as the software infrastructures to address the coordination issue in smart spaces. However, since agents in smart spaces are dynamic, resource‐bounded and have complicated service dependencies, current approaches to coordination in multi‐agent systems encounter new challenges when applied in smart spaces. The purpose of this paper is to address these issues.

The paper presents Baton, a service management system to explicitly resolve the particular issues stemming from smart spaces when coordinating agents. Baton is designed as a complement to coordination approaches in multi‐agent systems with a focus on mechanisms for service discovery, composition, request arbitration and dependency maintenance. Baton is now deployed in our own smart spaces to achieve better agent coordination.

The effectiveness and efficiency of Baton is validated by its practical use in the designed scenario and some evaluation experiments.

An attempt at performing dynamic service composition in Baton is made by using semantic information in future work.

Baton, a service management system to explicitly resolve the particular issues stemming from smart spaces when coordinating agents is presented.

The paper aims to explain how agency theory can be used to inform our understanding of the dynamics surrounding supply chain behaviours and relationships.

A structured review of the literature using a three‐stage refinement process is used. The articles were sourced through online databases and keyword classifications, such as “agency theory”, “principal‐agent relationships” and “supply chain management”. The search initially identified over 86 articles. After further screening these were reduced to 19 for final assessment and comparison.

Despite agency theory's prevailing descriptive and predictive qualities there is scarcity in its application to the SCM discipline. The authors posit that agency theory provides valuable insights for relationship engineering within supply chains where social, political, legal and behavioural dynamics dominate.

It is a critical task for managers to understand and mitigate abnormal behaviours across the supply chain. Agency theory serves this need by providing them with a useful tool to respond to transaction cost dilemmas through contractual and non‐contractual remedies.

This is one of the first studies that examines the current state of agency theory application in the SCM literature and suggests potential avenues for future research.