Konstantinos Petridis

Optimal design of multi-echelon supply chain networks under normally distributed demand

This paper addresses the optimal design of a multiproduct, multi-echelon supply network under uncertainty of demand. The network consists of multiproduct production sites, warehouses and distribution centers and decisions about the selection of facilities and their capacity are taken. Furthermore, information about the flows of products transferred and the safety stock at each distribution center is derived. The lead time of an order to a customer is computed, using the probabilities of overstocking and understocking. All these decisions are incorporated into a single period mixed integer non-linear programming problem (MINLP) which minimizes cost. Linearization techniques for selected highly non-linear terms of the models are explored in order to reduce the computational effort for the solution of the model. Finally, a sensitivity analysis is performed by changing product demand parameters and assessing their effect on the supply chain structure.

Efficiency analysis of forestry journals: Suggestions for improving journals’ quality

In this paper we attempt to assess the impact of journals in the field of forestry, in terms of bibliometric data, by providing an evaluation of forestry journals based on data envelopment analysis (DEA). In addition, based on the results of the conducted analysis, we provide suggestions for improving the impact of the journals in terms of widely accepted measures of journal citation impact, such as the journal impact factor (IF) and the journal h-index. More specifically, by modifying certain inputs associated with the productivity of forestry journals, we have illustrated how this method could be utilized to raise their efficiency, which in terms of research impact can then be translated into an increase of their bibliometric indices, such as the h-index, IF or eigenfactor score.

Evaluating higher education teaching performance using combined analytic hierarchy process and data envelopment analysis

Evaluating higher education teaching performance is complex as it involves consideration of both objective and subjective criteria. The student evaluation of teaching (SET) is used to improve higher education quality. However, the traditional approaches to considering students’ responses to SET questionnaires for improving teaching quality have several shortcomings. This study proposes an integrated approach to higher education teaching evaluation that combines the analytical hierarchy process (AHP) and data envelopment analysis (DEA). The AHP allows consideration of the varying importance of each criterion of teaching performance, while DEA enables the comparison of tutors on teaching as perceived by students with a view to identifying the scope for improvement by each tutor. The proposed teaching evaluation method is illustrated using data from a higher education institution in Greece.

Developing and Validating a Multi-Criteria Model to Evaluate Mobile Service Quality

Even though electronic service (e-service) quality has been analyzed to a great extent, mobile service (m-service) quality still requires further investigation. The hierarchical and multi-criteria structure, which is adopted in this work, appears to be the most appropriate approach to define m-service quality. In the proposed theoretical framework, m-service quality is composed of three primary dimensions: (1) interaction, (2) environment, and (3) outcome quality. An overall view of m-service quality would propose interaction quality to include the sub-dimensions of expertise, problem solving, information, security/privacy, and customization/personalization, environment quality to comprise equipment, design, and context, and outcome quality to be composed of reliability, tangibles, and valence. In order to validate the proposed theoretical framework, each sub-dimension is further analyzed into a number of quality criteria by means of a number of experts. Following this method, the quality criteria are assessed through a survey conducted with a sample of mobile users. Using Principal Component Analysis (PCA) and Structural Equation Modeling (SEM), it is proved that the quality criteria were properly grouped into the sub-dimensions of the proposed theoretical framework. These findings entail that the sub-dimensions described in this paper are in fact the constituent parts of the m-service quality construct.

Measuring incineration plants’ performance using combined data envelopment analysis, goal programming and mixed integer linear programming

Incineration plants produce heat and power from waste, reduce waste disposal to landfills, and discharge harmful emissions and bottom ash. The objective of the incineration plant is to maximize desirable outputs (heat and power) and minimize undesirable outputs (emissions and bottom ash). Therefore, studying the overall impact of incineration plants in a region so as to maximize the benefits and minimize the environmental impact is significant. Majority of prior works focus on plant specific decision making issues including performance analysis. This study proposes a hybrid data envelopment analysis (DEA), goal programming (GP) and mixed integer linear programming (MILP) model to assess the performance of incineration plants, in a specific region, to enhance overall power production, consumption of waste and reduction of emissions. This model not only helps the plant operators to evaluate the effectiveness of incineration but also facilitates the policy makers to plan for overall waste management of the region through decision-making on adding and closing plants on the basis of their efficiency. Majority of prior studies on incineration plants emphasize on how to improve their performance on heat and power production and neglect the waste management aspects. Additionally, optimizing benefits and minimizing negative outputs through fixing targets in order to make decision on shutting down the suboptimal plants has not been modeled in prior research. This research combines both the aspects and addresses the overall performance enhancement of incineration plants within a region from both policy makers and plant operators’ perspectives. The proposed combined DEA, GP and MILP model enables to optimize incineration plants performance within a region by deriving efficiency of each plant and identifying plants to close down on the basis of their performance. The proposed model has been applied to a group of 22 incineration plants in the UK using secondary data in order to demonstrate the effectiveness of the model.

A spatiotemporal Data Envelopment Analysis (S-T DEA) approach:the need to assess evolving units

One of the major challenges in measuring efficiency in terms of resources and outcomes is the assessment of the evolution of units over time. Although Data Envelopment Analysis (DEA) has been applied for time series datasets, DEA models, by construction, form the reference set for inefficient units (lambda values) based on their distance from the efficient frontier, that is, in a spatial manner. However, when dealing with temporal datasets, the proximity in time between units should also be taken into account, since it reflects the structural resemblance among time periods of a unit that evolves. In this paper, we propose a two-stage spatiotemporal DEA (S-T DEA) approach, which captures both the spatial and temporal dimension through a multi-objective programming model. In the first stage, DEA is solved iteratively extracting for each unit only previous DMUs as peers in its reference set. In the second stage, the lambda values derived from the first stage are fed to a Multiobjective Mixed Integer Linear Programming model, which filters peers in the reference set based on weights assigned to the spatial and temporal dimension. The approach is demonstrated on a real-world example drawn from software development.

P1 The potential for pharmacists to manage young patients attending emergency departments

Background There have been concerns about maintaining appropriate clinical staff levels in Emergency Departments in England.1 One possible solution to alleviating the workforce pressure is the extension of clinical activity performed by non-medical staff – including pharmacists.2 Aims and objectives To determine if Emergency Department attendees aged from 10–25 years (adolescents) could be clinically managed by community pharmacists or hospital pharmacist independent prescribers with or without further advanced clinical practice training. Method A prospective 49 site cross-sectional observational study of patients attending Emergency Departments (ED) in England, UK. Each site was requested to collect data for 400 admissions of all ages. Pharmacist independent prescribers (one for each site) were asked to identify patient attendance at their Emergency Department, record anonymised details of the cases – age, weight, presenting complain, clinical grouping (e.g. medicine, orthopaedics) and categorise each one into one of four possible categories: CP, Community Pharmacist, cases which could be managed by a community pharmacist outside an ED setting; IP – cases that could be managed in ED by a hospital pharmacist with independent prescriber status; IPT, Independent Prescriber Pharmacist with additional training – cases which could be managed in ED by a hospital pharmacist independent prescriber with additional clinical training; and MT, Medical Team only – cases that were unsuitable for the pharmacist to manage. An Impact Index was calculated for the two most frequent clinical groupings using the formula: Impact index (I)=proportion of the total workload of the clinical grouping (w) multiplied by the percentage ability of pharmacists to manage that clinical group (a). I=wa. The higher the Impact Index the greater potential for pharmacists to support the clinical workload related to that group. Results 2993 out of 18 613 (16%) attendees were young patients aged from 10 to 25 years of age (median 20 years, interquartile range 17–22 years) of which 1530 were female and 1463 were male. Of the 2993 patients, 6% of the cases were judge to be suitable for the community pharmacist (CP), 5% suitable for a hospital pharmacist independent prescriber (IP), 37% were deemed as suitable for a hospital independent prescriber with additional training (IPT) and the remaining 52% were only suitable for the medical team (MT). The most frequent clinical groups and Impact Index were general medicine=16.97 and orthopaedics=15.51. Conclusion Emergency Department attendees who were young patients were judged by independent prescriber pharmacists to be suitable for clinical management by community pharmacists outside a hospital setting in approximately 1 in 16 admissions, and by a hospital independent prescriber pharmacists in 1 in 20 cases. With further training, it was found that the total proportion of cases that could potentially be managed by a pharmacist (CP, IP or IPT) came to 48%. The greatest potential impact for pharmacist management occurs in general medicine and orthopaedics. References Paw RC. Emergency Department Staffing in England and Wales. Emer Med J 2008;24:420–23. Hughes E, et al. Future enhanced clinical role of pharmacists in Emergency Departments in England: Multi-site observational evaluation. Int J Clin Pharm 2017;39:960–968.

Investigating the factors that affect the time of maximum rejection rate of e-waste using survival analysis

Factors affecting positively or negatively e-waste rejection rates are examined.Economic, cultural, demographic factors are considered.Weibull parametric accelerated failure time model is applied.E-waste rejection rate is prolonged by economic disparity and cultural variables.Wealth causes a shorter time of rejection rate. This study aims at investigating the factors which influence positively or negatively electronic waste (e-waste) rejection rates. E-waste quantities have been calculated based on historical sales data worldwide and lifespan distribution. The methodology, which is adopted in this paper in order to estimate the effect that economic, cultural, and demographic factors have upon the time at which maximum e-waste rejection is achieved, is a Weibull parametric accelerated failure time model. Considering the event at which the maximum rejection of e-waste takes place as the dependent variable, it is assumed that it is a function of economic (GDP, GINI index, Internet users, exports/imports and prices), demographic (dependency ratio, gender, literacy, no of households), and cultural covariates (masculinity, uncertainty avoidance). The variables are fed to the model after transformation into two major constructs derived from Factor Analysis: the first construct is Wealth (exports, imports, and GDP) and the second is Economic Disparity (no of households, literacy, Internet users, and GINI). The results demonstrate that the time of maximum e-waste rejection rate is prolonged by economic disparity and cultural variables (uncertainty avoidance), while wealth causes a shorter time of rejection rate. The proposed methodology is of great value, as its application could provide useful information in order to develop policies for optimal management of e-waste quantities.