An application of EDA and GA to dynamic pricing.

E-commerce has transformed the way firms develop their pricing strategies, producing shift away from fixed pricing to dynamic pricing. In this paper, we use two different Estimation of distribution algorithms (EDAs), a Genetic Algorithm (GA) and a Simulated Annealing (SA) algorithm for solving two different dynamic pricing models. Promising results were obtained for an EDA confirming its suitability for resource management in the proposed model. Our analysis gives interesting insights into the application of population based optimization techniques for dynamic pricing

A genetic type-2 fuzzy logic based approach for the optimal allocation of mobile field engineers to their working areas

In utility based service industries with a large mobile workforce, there is a need to optimize the process of allocating engineers to tasks (i.e. fixing faults, installing new services, such as internet connections, gas or electricity etc.). Part of the process of optimizing the resource allocation to tasks involves finding the optimum area for an engineer to operate within, which we term as work area optimization. Work area optimization in large businesses can have a noticeable impact on business costs, revenues and customer satisfaction. However when attempting to optimize the workforce in real world scenarios, mostly single objective optimization algorithms are used while employing crisp logic. Nevertheless, there are many objectives that need to be satisfied and hence multi-objective based optimization will be more suitable. Even where multi-objective optimization is employed, the involved systems fail to recognize that these real world problems are full of uncertainties. Type-2 fuzzy logic systems can handle the high level of uncertainties associated with the dynamic and changing environments, such as those presented with real world scheduling problems. This paper presents a novel multi-objective genetic type-2 Fuzzy Logic based System for the optimal allocation of mobile workforces to their working areas. The method has been applied in a real world service industry workforce environment. The results show strong improvements when the proposed multi-objective type-2 fuzzy genetic based optimization system was applied to the work area optimization problem as compared to the heuristic or type-1 single objective optimization of the work area. Such optimization improvements of the working areas will result in improving the utilization of the workforce

Predicting service levels using neural networks.

In this paper we present a method to predict service levels in utility companies, giving them advanced visibility of expected service outcomes and helping them to ensure adherence to service level agreements made to their clients. Service level adherence is one of the key targets during the service chain planning process in service industries, such as telecoms or utility companies. These specify a time limit for successful completion of a certain percentage of tasks on that service level agreement. With the increasing use of automation within the planning process, the requirement for a method to evaluate the current plan decisions effects on service level outcomes has surfaced. We build neural network models to predict using the current state of the capacity plan, investigating the accuracy when predicting both daily and weekly service level outcomes. It is shown that the models produce a high accuracy, particularly in the weekly view. This provides a solution that can be used to both improve the current planning process and also as an evaluator in an automated planning process

FieldPlan: tactical field force planning in BT

In a highly competitive market, BT1 faces tough challenges as a service provider for telecommunication solutions. A proactive approach to the management of its resources is absolutely mandatory for its success. In this paper, an AI-based planning system for the management of parts of BT’s field force is presented. FieldPlan provides resource managers with full visibility of supply and demand, offers extensive what-if analysis capabilities and thus supports an effective decision making process.IFIP International Conference on Artificial Intelligence in Theory and Practice - Industrial Applications of AIRed de Universidades con Carreras en Informática (RedUNCI

Developing a catalogue of explainability methods to support expert and non-expert users.

Organisations face growing legal requirements and ethical responsibilities to ensure that decisions made by their intelligent systems are explainable. However, provisioning of an explanation is often application dependent, causing an extended design phase and delayed deployment. In this paper we present an explainability framework formed of a catalogue of explanation methods, allowing integration to a range of projects within a telecommunications organisation. These methods are split into low-level explanations, high-level explanations and co-created explanations. We motivate and evaluate this framework using the specific case-study of explaining the conclusions of field engineering experts to non-technical planning staff. Feedback from an iterative co-creation process and a qualitative evaluation is indicative that this is a valuable development tool for use in future company projects

Explainability through transparency and user control: a case-based recommender for engineering workers.

Within the service providing industries, field engineers can struggle to access tasks which are suited to their individual skills and experience. There is potential for a recommender system to improve access to information while being on site. However the smooth adoption of such a system is superseded by a challenge for exposing the human understandable proof of the machine reasoning.With that in mind, this paper introduces an explainable recommender system to facilitate transparent retrieval of task information for field engineers in the context of service delivery. The presented software adheres to the five goals of an explainable intelligent system and incorporates elements of both Case-Based Reasoning and heuristic techniques to develop a recommendation ranking of tasks. In addition we evaluate methods of building justifiable representations for similarity-based return on a classification task developed from engineers' notes. Our conclusion highlights the trade-off between performance and explainability

Variable Neighbourhood Search: A Case Study for a Highly-Constrained Workforce Scheduling Problem

This paper describes a Variable Neighbourhood Search (VNS) combined with simulated annealing to tackle a highly constrained workforce scheduling problem at British Telecommunications plc (BT). A refined greedy algorithm is firstly designed to create an initial solution which meets all hard constraints and satisfies some of the soft constraints. The VNS is then used to swap out less promising combinations, continually moving towards a more optimal solution until meeting finishing requirements. The results are promising when compared to the stand- alone greedy algorithm. We believe there is scope for this to be extended in several ways, i.e. into a more complex variation of VNS to further improve results, to be applied to further data sets and workforce scheduling problem scenarios, and to have input parameters to the algorithm selectively optimized to discover what kind of improvements in efficiency and fitness are possible. There is also scope for this to be used in similar combinatorial optimization problems

Development of Non-Proprietary Ultra-High Performance Concrete Mixtures

The development of non-proprietary Ultra-High Performance Concrete (UHPC) is one way to reduce the initial cost of construction. However, workability is a major issue for which such mixtures are not practical in field conditions. Ultra-high performance cannot be achieved in field conditions if the concrete is not placed, finished, and compacted properly during placement. In this research, six UHPC mixtures were developed (three with steel fibers and three without fibers) using materials which are readily available on the local marketplace with water-to-cementitious materials ratios ranging between 0.17 to 0.30. The workability was determined using standard ASTM flow table apparatus, and specimens were prepared to determine compressive strength, splitting tensile strength, and permeable porosity. Flow table test exhibited flow values greater than 250 mm. Such high workability of the mixtures was achieved by optimizing the silica fume content and water reducing admixture dosage. These mixtures exhibited compressive strengths greater than 120 MPa and splitting tensile strengths greater than 5.10 MPa in both ambient and elevated curing temperatures. Results indicated that UHPC can be produced with a water-to-cementitious materials ratio as high as 0.30. Steel fibers helped to increase splitting tensile strength due to fiber-matrix interactions. Very low permeable porosity (1.7-16.7%) was observed which indicates superior durability due to the significant reduction of ingress of deleterious ions

Charity can still begin at home:Examining the drivers and boundary conditions of Africa-to-Africa outward foreign direct investment (OFDI)

Recent studies on ‘Africa Rising’ and ‘Africa-to-Africa Internationalization’ have propelled conversations on how African Small and Medium-Sized Enterprises (SMEs) can continue to internationalize within African countries. From the tenets of the institutional theory and the dynamic capabilities perspectives, this study proposes and tests a framework of how and when dysfunctional competition drives SMEs' outward foreign direct investments within African countries. Analysis of a survey data from 196 Ghanaian SMEs operating across the African continent indicates that cross-border open innovation mediates the relationship between dysfunctional competition and SMEs' intra- Africa OFDI activities. Further analysis revealed that SMEs' strategic agility plays a double-edged sword moderating role in enhancing the effects of dysfunctional competitions and cross-border open innovation on intra-Africa OFDI. These findings have significant implications for the international business and finance literature as well as the management and growth of African SMEs

Facility location problem and permutation flow shop scheduling problem: a linked optimisation problem.

There is a growing literature spanning several research communities that studies multiple optimisation problems whose solutions interact, thereby leading researchers to consider suitable approaches to joint solution. Real-world problems, like supply chain, are systems characterised by such interactions. A decision made at one point of the supply chain could have significant consequence that ripples through linked production and transportation systems. Such interactions would require complex algorithmic designs. This paper, therefore, investigates the linkages between a facility location and permutation flow shop scheduling problems of a distributed manufacturing system with identical factory (FLPPFSP). We formulate a novel mathematical model from a linked optimisation perspective with objectives of minimising facility cost and makespan. We present three algorithmic approaches in tackling FLPPFSP; Non-dominated Sorting Genetic Algorithm for Linked Problem (NSGALP), Multi-Criteria Ranking Genetic Algorithm for Linked Problem (MCRGALP), and Sequential approach. To understand FLPPFSP linkages, we conduct a pre-assessment by randomly generating 10000 solution pairs on all combined problem instances and compute their respective correlation coefficients. Finally, we conduct experiments to compare results obtained by the selected algorithmic methods on 620 combined problem instances. Empirical results demonstrate that NSGALP outperforms the other two methods based on relative hypervolume, hypervolume and epsilon metrics