Ethical Stochastic Objectives Programming Approach for Portfolio Selection

The paper develops an ethical multiple stochastic objectives approach to address the ethical portfolio selection problem in the stochastic environment under the Shari’ah compliant framework. Two random objectives considered in this paper which are maximizing portfolio return and maximizing social welfare of portfolio. The risk of portfolio is measured by covariance matrix of total return. The ethical stochastic objectives program approach is based on goal programming approach, a chance constrained approach and Shari’ah compliant framework. The model is applied on 60 stocks including conventional and Islamic securities in GCC. The results show that, portfolios with higher proportion of ethical Islamic securities in the portfolio and with higher expected loss the higher is the portfolio performance in terms of Sharpe measure. Keywords: Shari’ah compliant, Ethical investment, Goal programming, Multiple objectives, Stochastic Multiple objectives programming, Chance constrained approach, Sharpe index as portfolio performance measure

A multi-objective particle swarm optimization algorithm for business sustainability analysis of small and medium sized enterprises

Sustainability is the major issue of small and medium sized enterprises (SMEs) all across the globe. Although SMEs contribute to GDP of any country their negative contribution to environment is also significant. Prior studies on SMEs’ sustainability mainly classified into three categories—the correlation between environmental and social practices with economic performance, sustainable supply chain performance measurement, and empirical research on sustainability practices. There is no study that objectively derives the sustainable structure of SMEs through optimal combination of sustainability practices (inputs) and performance (outputs). Therefore, the main objective of this paper is to generate optimal structure of sustainable SMEs by combining neural network and particle swarm algorithm while considering Multi-Objective framework. The study uses data from 54 SMEs of Normandy in France and 30 SMEs of Midlands in the UK. The data was gathered through questionnaire survey. As we do not have the explicit expression of our objective functions, we train a neural network on our databases in order to enable the generation of value of the different objectives for any profile. We design and run a multi-objective version of particle swarm optimization (MPSO) to generate efficient companies’ structures. The weighted sum method is then used for different weights. The comparison of observed data and the results of the PSO analysis facilitates to derive improvement measures for each individual SME

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

A compromise solution for the multiobjective stochastic linear programming under partial uncertainty

This paper solves the multiobjective stochastic linear program with partially known probability. We address the case where the probability distribution is defined by crisp inequalities. We propose a chance constrained approach and a compromise programming approach to transform the multiobjective stochastic linear program with linear partial information on probability distribution into its equivalent uniobjective problem. The resulting program is then solved using the modified L-shaped method. We illustrate our results by an example.Multiobjective stochastic programming Compromise programming Chance constrained approach Modified L-shaped method

Strategic investments in R&D and efficiency in the presence of free riders

We consider an industry composed of two types of firms, namely, innovators that invest in process research and development (R&D), and surfers that do not but benefit from knowledge spillover.We verify if the conclusions reached in the seminal paper by d’Aspremont and Jacquemin hold in this setting.We obtain that cooperation among innovators still lead to higher R&D and output levels than when they do not cooperate.Our main result is that the presence of surfers in an industry can be welfare improving under some conditions

A Multi-objective based AMOSA approach to the dynamic location of bloodmobiles problem

International audienceOne of the most vital resources for the human being is blood, its collection requires the establishment of a strategy allowing to maximize the collected quantity with a minimum cost. Several constraints should consider the blood and its components as perishable products which affects the storage and transfer activities. Our paper defines the optimal structure of bloodmobiles network over several periods of time. Based on the P-median facility location problem, we suppose that the number of located bloodmobiles is fixed in advance for all periods. Furthermore, located bloodmobiles in a given period are moved to new locations in the next period in order to have new donors (relocation). A non-linear mixed-integer programming mathematical model is proposed to define the best possible locations for bloodmobiles, for each period and analyzes the trade-off between economic costs and total collected quantity. We propose a multi-objective approach based on an Archived Multi-objective Simulated Annealing Approach (AMOSA). We apply this approach to the Tlemcen city bloodmobile network by proposing several locations as potential locations to make it easier for donors and maximize the quantity of collected blood