The Arithmetic Optimization Algorithm

Abstract This work proposes a new meta-heuristic method called Arithmetic Optimization Algorithm (AOA) that utilizes the distribution behavior of the main arithmetic operators in mathematics including (Multiplication ( ), Division (), Subtraction (), and Addition ()). AOA is mathematically modeled and implemented to perform the optimization processes in a wide range of search spaces. The performance of AOA is checked on twenty-nine benchmark functions and several real-world engineering design problems to showcase its applicability. The analysis of performance, convergence behaviors, and the computational complexity of the proposed AOA have been evaluated by different scenarios. Experimental results show that the AOA provides very promising results in solving challenging optimization problems compared with eleven other well-known optimization algorithms. Source codes of AOA are publicly available at and

A “Soft” Approach to Analysing Mobile Financial Services Sociotechnical Systems

Advances in mobile computing have presented a huge opportunity to provide Mobile Financial Services (MFS) to half of the world’s population who currently do not have access to financial services. However, cybersecurity concerns in the mobile computing ecosystem have slowed down the adoption of MFS. The adoption of MFS is further hampered by the lack of a clear understanding of the interaction between the complex infrastructures and human factors that exist in the ecosystem for Mobile Financial Services Socio-Technical Systems (MFSSTS). This paper presents the work in progress of investigating the problem of MFSSTS. It discusses the preliminary results and understanding obtained from using Human Factor approaches to build and analyse the model for MFSSTS

Combined quay crane assignment and quay crane scheduling with crane inter-vessel movement and non-interference constraints

Integrated models of the quay crane assignment problem (QCAP) and the quay crane scheduling problem (QCSP) exist. However, they have shortcomings in that some do not allow movement of quay cranes between vessels, others do not take into account precedence relationships between tasks, and yet others do not avoid interference between quay cranes. Here, an integrated and comprehensive optimization model that combines the two distinct QCAP and QCSP problems which deals with the issues raised is put forward. The model is of the mixed-integer programming type with the objective being to minimize the difference between tardiness cost and earliness income based on finishing time and requested departure time for a vessel. Because of the extent of the model and the potential for even small problems to lead to large instances, exact methods can be prohibitive in computational time. For this reason an adapted genetic algorithm (GA) is implemented to cope with this computational burden. Experimental results obtained with branch-and-cut as implemented in CPLEX and GA for small to large-scale problem instances are presented. The paper also includes a review of the relevant literature

An evolutionary approach to a combined mixed integer programming model of seaside operations as arise in container ports

This paper puts forward an integrated optimisation model that combines three distinct problems, namely berth allocation, quay crane assignment, and quay crane scheduling that arise in container ports. Each one of these problems is difficult to solve in its own right. However, solving them individually leads almost surely to sub-optimal solutions. Hence, it is desirable to solve them in a combined form. The model is of the mixed-integer programming type with the objective being to minimize the tardiness of vessels and reduce the cost of berthing. Experimental results show that relatively small instances of the proposed model can be solved exactly using CPLEX. Large scale instances, however, can only be solved in reasonable times using heuristics. Here, an implementation of the genetic algorithm is considered. The effectiveness of this implementation is tested against CPLEX on small to medium size instances of the combined model. Larger size instances were also solved with the genetic algorithm, showing that this approach is capable of finding the optimal or near optimal solutions in realistic times

Eco-efficiency measurement and material balance principle:an application in power plants Malmquist Luenberger Index

Incorporating Material Balance Principle (MBP) in industrial and agricultural performance measurement systems with pollutant factors has been on the rise in recent years. Many conventional methods of performance measurement have proven incompatible with the material flow conditions. This study will address the issue of eco-efficiency measurement adjusted for pollution, taking into account materials flow conditions and the MBP requirements, in order to provide ‘real’ measures of performance that can serve as guides when making policies. We develop a new approach by integrating slacks-based measure to enhance the Malmquist Luenberger Index by a material balance condition that reflects the conservation of matter. This model is compared with a similar model, which incorporates MBP using the trade-off approach to measure productivity and eco-efficiency trends of power plants. Results reveal similar findings for both models substantiating robustness and applicability of the proposed model in this paper

Enabling sustainable energy futures: Factors influencing green supply chain collaboration

In order to explore the relationship between sustainability strategies and future energy needs and demands (hence energy futures), supply chains (SCs) need to continue to reduce their CO2 emissions through developing their green credentials and improving overall performance; noting that the assimilation of such environmental aspects into production, SCs and logistics are considered as complex processes. Knowledge management (KM) has long been seen as an enabler to support intensive collaboration efforts – on which green initiatives are largely based. The supply chain management (SCM) and KM areas have largely focused on improving organisational performance. While the latter research has yielded successful outcomes in many different sectors, there is still a scarcity of research studies focusing on identifying influential factors that highlight those aspects which may enable green supply chain collaboration (GrSCC) to occur, thus leading to sustainable energy futures and carbon-efficient production. To increase inter-organisational synchronisation, organisations often call for SC partners to implement common business processes and sources of knowledge. This paper therefore aims to contribute to the research domain by examining the role of KM in facilitating GrSCC. Through the identification of key factors extrapolated from the normative literature, a model for implementing GrSCC using a futures-based perspective is proposed. This paper inductively demonstrates the relationship between identified GrSCC factors through the application of a fuzzy cognitive mapping (FCM) technique. Findings from this research support a futures-based perspective that enhances understanding and refines forward-looking strategies for GrSCC. Through the exploration of two GrSCC scenarios using the given technique, this paper reports a granular perspective of positive and negative causal factors that support enabling energy futures that enhance green supply credentials

Mixed-integer second-order cone programming model for bus route clustering problem

202304 bckwAccepted ManuscriptOthersNational Natural Science Foundation of ChinaPublishe