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A multiple objective stochastic programming model for working capital management

TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 2018年第Jun.期131卷 141-146页

作者： Masri, Hatem Abdulla, Yomna Univ Bahrain Coll Business Adm POB 32038 Sakhir Bahrain

The level of working capital is one of the main financial decisions affecting a firm's value, shareholder wealth, competitiveness, liquidity, and profitability. An aggressive (conservative) working capital policy has a positive (negative) impact on profitability but a negative (positive) impact on liquidity. In this paper, we propose a multiple objective stochastic programming model to select an efficient working capital strategy that takes into consideration not only the conflicting impact of working capital policy on the two firm objectives of liquidity and profitability but also the interrelationships and stochastic aspects of the components of working capital. The proposed model was used to help a start-up retailer to determine its optimal working capital.

关键词： Working capital stochastic goal programming multiple objective stochastic programming Regression Retailing industry

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Portfolio selection problem: a review of deterministic and stochastic multiple objective programming models

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ANNALS OF OPERATIONS RESEARCH 2018年第1-2期267卷 335-352页

作者： Masmoudi, Meryem Ben Abdelaziz, Fouad Univ Bahrain Coll Business Adm POB 32038 Sakhir Bahrain NEOMA Business Sch Rouen CampusBlvd Andre Siegfried F-76130 Mont St Aignan France

The literature on portfolio selection mostly concentrates on computational analysis rather than on modelling efforts. In response, this paper provides a comprehensive literature review of multiple objective deterministic and stochastic programming models for the portfolio selection problem. First, we summarize different concepts related to portfolio selection theory, including pricing models and portfolio risk measures. Second, we report the mathematical models that are generally used to solve deterministic and stochastic multiple objective programming problems. Finally, we present how these models can be used to solve the portfolio selection problem.

关键词： Portfolio selection multiple objective programming multiple objective stochastic programming

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A Bi-objective stochastic programming model for the household waste collection and transportation problem: case of the city of Sousse

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OPERATIONAL RESEARCH 2021年第3期21卷 1613-1639页

作者： Jammeli, Haifa Argoubi, Majdi Masri, Hatem Inst Higher Studies Tunis 43 Rue Platon Le Kram 2089 Tunisia Univ Sousse Rue Abdlaaziz El Behi Sousse Tunisia Univ Bahrain POB 32038 Sakhir Bahrain

This paper's aim is to develop a model for the household waste collection and transportation problem in the city of Sousse, one of the largest cities in Tunisia. Several vehicles with a finite capacity are located at the depot. The vehicles must collect the waste accumulated in all bins. The waste is then delivered to a transfer center, before vehicles return to the depot. The proposed model determines the routes of the vehicles and the number of bins to be assigned to each potential location, while minimizing the collection costs and the environmental impact. The problem can be considered as a bi-objective optimization problem, as cost minimization will be ensured by the optimal assignment of the determined minimum number of bins. We also consider the stochastic aspect of population size, which is supposed to follow a normal distribution. Our model is then a stochastic bi-objective programming model. A solution is obtained with reasonable computational effort using a hierarchical approach consisting of two stages as "cluster-first route-second". In the first stage, a set of n locations of bins is assigned into k disjoint clusters using the K-means clustering algorithm. In the second stage, a certainty equivalent program to the bi-objective stochastic program is proposed, based on a chance-constrained, recourse and a goal programming approach. The model is tested and implemented using real data from the municipality of Sousse. The study shows that our model leads to lower environmental impact and an almost 38% reduction in the economic costs.

关键词： Household waste management multiple objective stochastic programming Vehicle-routing problem and assignment problem

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A multiple objective stochastic portfolio selection problem with random Beta

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2014年第6期21卷 919-933页

作者： Ben Abdelaziz, Fouad Masmoudi, Meryem Rouen Business Sch F-76130 Mont St Aignan France Univ Tunis Inst Super Gest LARODEC Le Bardo 2000 Tunisia

When selecting a portfolio, we need to consider, in general, the portfolio return and portfolio risk. Many risk measures have been used in portfolio selection problems as the Beta risk measure, introduced by the capital asset pricing model. Most of the existing research papers suppose that security's Beta has a deterministic value. Recently, many researchers argued that in selecting the optimal portfolio, securities' Beta should be considered as an uncertain parameter. In this paper, we set up fundamentals to model the portfolio's Beta as a random variable and propose a multiple objective stochastic portfolio selection model with random Beta. To solve the proposed model, we apply a stochastic goal programming approach. A numerical example from the US stock exchange market is reported.

关键词： portfolio selection multiple objective stochastic programming stochastic goal programming Beta risk measures

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A Bi-objective Covering Location Problem Case of ambulance location in the Beirut area, Lebanon

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MANAGEMENT DECISION 2019年第2期57卷 432-444页

作者： El Itani, Bilal Ben Abdelaziz, Fouad Masri, Hatem NEOMA Business Sch Dept Informat Syst Mont St Aignan France Univ Bahrain Coll Business Adm Dept Management & Mkt Sakhir Bahrain

Purpose Ambulance response time is an important factor in saving lives and is highly linked with the ambulance location problem. The Maximum Expected Covering Location Problem (MEXCLP), introduced by Daskin (1983), is one of the most used ambulance location models that maximize the probability of stratifying demands for emergency medical service (EMS) centers. Due to huge increase in the operational costs of EMS centers, ambulance location models must consider the cost of coverage and the opportunity to use other companies' private ambulances to answer emergency calls. The paper aims to discuss these issues. Design/methodology/approach In this paper, the authors propose to extend the MEXCLP to a bi-objective optimization problem where the cost of satisfying emergency calls is minimized. Findings The proposed model is tested using data retrieved from the Lebanese Red Cross (LRC) in Beirut capital of Lebanon. The reported findings show significant enhancements in the results where the LRC can fully satisfy the perceived demands from all areas in Beirut within 9 min with an affordable cost. Originality/value The model is a first attempt to reduce operational costs of EMS centers while constraining the response time to satisfy emergency calls at an acceptable rate.

关键词： Ambulance location Epsilon-constraint approach MEXCLP multiple objective stochastic programming

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