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

Stochastic Programming for Selection Variables in Cluster Analysis

Cluster analysis is one of the most important techniques in the exploratory data analysis; it is goal to discover a natural grouping in a set of observations without knowledge of any class labels.  Variable selection has been very important for a lot of research in several areas of application. The study suggested a stochastic programming approach which selects the most important variables in clustering a set of data. The study evaluates the performance of the stochastic programming suggested approach for selection variables in cluster analysis used numerical example. The suggested stochastic programming approach selects the most important variable in cluster analysis simultaneously and the results are satisfied

Weighted Goal Programming and Penalty Functions: Whole-farm Planning Approach Under Risk

The paper presents multiple criteria approach to deal with risk in farmer’s decisions. Decision making process is organised in a framework of spreadsheet tool. It is supported by deterministic and stochastic mathematical programming techniques applying optimisation concept. Decision making process is conceptually divided into seven autonomous modules that are mutually linked up. Beside the common maximisation of expected income through linear programming it enables also reconstruction of current production practice. Income risk modelling is based on portfolio theory resting on expected value, variance (E,V) paradigm. Modules dealing with risk are therefore supported with quadratic and constrained quadratic programming. Non-parametric approach is utilised to estimate decision maker’s risk attitude. It is measured with coefficient of risk aversion, needed to maximise certainty equivalent for analysed farms. Multiple criteria paradigm is based on goal programming approach. In contribution focus is put on benefits and possible drawbacks of supporting weighted goal programming with penalty functions. Application of the tool is illustrated with three dairy farm cases. Obtained results confirm advantage of utilizing penalty function system. Beside greater positiveness it proves as useful approach for fine tuning of the model enabling imitation of farmer’s behaviour, which is due to his/her conservative nature not perfect or rational. Results confirm hypothesis that single criteria decision making, based on maximisation of expected income, might be biased and does not necessary lead to the best - achievable option for analysed farm.goal programming, risk modelling, risk aversion, production planning, Risk and Uncertainty,

A Stochastic Dynamic Programming Approach To Balancing Wind Intermittency With Hydropower

Hydropower is a rapid response energy source and thus a perfect complement to the intermittency of wind power. However, the effect wind energy has on conventional hydropower systems can be felt, especially if the system is subject to several other environmental and non-power use constraints. The goal of this paper is to develop a general method for optimizing short-term hydropower operations of a realistic multireservoir hydropower system in a deregulated market setting when there is a stochastic wind input. The approach used is a modification of stochastic dynamic programming (SDP). The methodology is applied to a representation of multiple projects in the Federal Columbia River Power System, which is currently being dispatched by the Bonneville Power Administration. Currently, studies on hydropower operations optimization with wind have involved linear programming orstochastic programming, which are based on linearity of the objective function and constraints. SDP, by contrast, is a stochastic optimization method that does not require assumptions of linearity of the objective function or the constraints. The true adaptive and stochastic nonlinear formulation of the objective function can be applied to multiple timesteps, and is efficient for many timesteps compared to stochastic programming

Rural Investment and the Cost of Income Uncertainty

This paper studies optimal investment decision in agriculture under diminishing income expectations. The goal is to study the cost of income uncertainty and its implications to the efficiency of investment subsidies. Investment decision is modelled as a Markov decision process, extended to account for risk. Applying a stochastic programming approach, the cost of imperfect information is evaluated as the difference between the profitability of investment under stable income and under uncertain income. Computational experiments demonstrate that the cost of imperfect information can be high, deteriorating the efficiency of investment subsidies. Also, examples suggest that the optimal timing of the investment can be sensitive to risk.

Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain

In this paper a multi-period multi-product multi-objective aggregate production planning (APP) model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP) approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method

Dynamic Surgery Assignment of Multiple Operating Rooms With Planned Surgeon Arrival Times

International audienceThis paper addresses the dynamic assignment of a given set of surgeries to multiple identical operating rooms (ORs). Surgeries have random durations and planned surgeon arrival times. Surgeries are assigned dynamically to ORs at surgery completion events. The goal is to minimize the total expected cost incurred by surgeon waiting, OR idling, and OR overtime. We first formulate the problem as a multi-stage stochastic programming model. An efficient algorithm is then proposed by combining a two-stage stochastic programming approximation and some look-ahead strategies. A perfect information-based lower bound of the optimal expected cost is given to evaluate the optimality gap of the dynamic assignment strategy. Numerical results show that the dynamic scheduling and optimization with the proposed approach significantly improve the performance of static scheduling and First Come First Serve (FCFS) strategy

Interactive Two-Stage Stochastic fuzzy Rough Programming for Water Resources Management

This paper deals with a fuzzy programming approach for treating an interactive two-stage stochastic rough-interval water resource management. The approach has been developed by incorporating an interactive fuzzy resolution method within a rough two-stage stochastic programming framework. The approach can not only tackle dual rough intervals presented as an inexact boundary intervals that exist in the objective function and the left- and right-hand sides of the constraints that are associated with different levels of economic penalties when the promised policy targets are violated. The results indicate that a set of solutions under different feasibility degrees has been generated for planning the water resources allocation. They can help the decision makers to conduct in depth analysis of tradeoffs between economic efficiency and constraint-violation risk, as well as enable them to identify, in an interactive way, a desired compromise between satisfaction degree of the goal and feasibility of the constraints. A management example in terms of rough-intervals water resources allocation has been treated for the sake of applicability of the proposed approach

A learning-based algorithm to quickly compute good primal solutions for Stochastic Integer Programs

We propose a novel approach using supervised learning to obtain near-optimal primal solutions for two-stage stochastic integer programming (2SIP) problems with constraints in the first and second stages. The goal of the algorithm is to predict a "representative scenario" (RS) for the problem such that, deterministically solving the 2SIP with the random realization equal to the RS, gives a near-optimal solution to the original 2SIP. Predicting an RS, instead of directly predicting a solution ensures first-stage feasibility of the solution. If the problem is known to have complete recourse, second-stage feasibility is also guaranteed. For computational testing, we learn to find an RS for a two-stage stochastic facility location problem with integer variables and linear constraints in both stages and consistently provide near-optimal solutions. Our computing times are very competitive with those of general-purpose integer programming solvers to achieve a similar solution quality