M. HEMMAK Allaoua

Prof

Directory of teachers

Department

Informatics Department

Research Interests

optimisation COMBINATORIAL OPTIMIZATION METAHEURISTICS SCHEDULING QUANTUM COMUTING COMPUTATION THEORY

Contact Info

University of M'Sila, Algeria

Email : allaoua.hemmak@univ-msila.dz

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Recent Publications

2024-03-04

Optimal adjusting of simulated annealing parameters

Introduction/purpose: Simulated annealing is a powerful technique widely used in optimization problems. One critical aspect of using simulated annealing effectively is a proper and optimal adjustment of its parameters. This paper presents a novel approach to efficiently adjust the parameters of simulated annealing to enhance its performance and convergence speed. Methods: Since the simulated algorithm is inspired by the cooling Metropolis process, the basic idea is to simulate and analyze this process using a mathematical model. The proposed work tends to properly imitate the Metropolis cooling process in the algorithmic field. By intelligently adjusting the temperature schedule, temperature reduction and cooling rate, the algorithm optimizes the balance between exploration and exploitation, leading to improved convergence and higher-quality solutions. Results: To evaluate the effectiveness of this approach, it was applied first on a chosen sample function to be minimized, and then on some usual known optimization functions. The results demonstrate that our approach, called Optimal Adjusting of Simulated Annealing parameters (OASA), achieves superior performance compared to traditional static parameter settings and other existing approaches, showing how to well adjust the parameters of the simulated annealing algorithm to improve its efficiency in terms of solution quality and processing time. Conclusion: Adjusting the algorithm parameters could have a significant contribution in the optimization field even for other metaheuristics.
Citation

M. HEMMAK Allaoua, (2024-03-04), "Optimal adjusting of simulated annealing parameters", [national] Military Technical Courier/Vojnotehnički glasnik , University of Defense Belgrad

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2023-07-01

Exact Algorithm for Batch Scheduling on Unrelated Machine

In this paper, we propose a new linear algorithm to tackle a specific class of unrelated machine scheduling
problem, considered as an important real-life situation, which we called Batch Scheduling on Unrelated Machine (BSUM),
where we have to schedule a batch of identical and non-preemptive jobs on unrelated parallel machines. The objective is to
minimize the makespan (Cmax) of the whole schedule. For this, a mathematical formulation is made and a lower bound is
computed based on the potential properties of the problem in order to reduce the search space size and thus accelerate the
algorithm. Another property is also deducted to design our algorithm that solves this problem. The latter is considered as a
particular case of RmCmax family problems known as strongly NP-hard, therefore, a polynomial reduction should realize a
significant efficiency to treat them. As we will show, Batch BSUM is omnipresent in several kind of applications as
manufacturing, transportation, logistic and routing. It is of major importance in several company activities. The problem
complexity and the optimality of the algorithm are reported, proven and discussed.
Citation

M. HEMMAK Allaoua, (2023-07-01), "Exact Algorithm for Batch Scheduling on Unrelated Machine", [national] The International Arab Journal of Information Technology (IAJIT) , Zarqa University, Jordan

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2023

Smart platform for Blood Management in Healthcare using AI/ML Approach

The blood management system confronts a challenge with blood transfusions and their distribution regardless of the efforts of the World Health Organization and other global health organizations: inadequate supply, excessive demand, and a shortage of accessible blood. Due to its ability to raise labor efficiency and service quality via systematic management, artificial intelligence is currently necessary to enhance blood supply operations. The objective of this work is to provide an AI/ML platform that facilitates the use of data to assist health professionals in making the most effective management choices that are consistent with methods for minimizing waste and costs. By more accurately anticipating blood demand. As production models, we are using both time series and machine learning methods as prediction models. The optimal performance model for the provided case study was determined by comparing the performance outcomes of each method. In this work, autoregressive Moving Average models, autoregressive Integrated Moving Average models, and seasonal ARIMA models are applied. In addition, we used four native algorithms for machine learning: Artificial Neural Networks, Linear Regression, and Support Vector Regression. The results demonstrate that both types of forecasting models can significantly enhance the management of the blood supply.
Citation

M. HEMMAK Allaoua, Benaoumeur Senouci, , (2023), "Smart platform for Blood Management in Healthcare using AI/ML Approach", [international] International Conference on Artificial Intelligence in Information and Communication (ICAIIC) , Bali, Indonesia

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Smart Platform for Data Blood Bank Management: Forecasting Demand in Blood Supply Chain Using Machine Learning

Despite the efforts of the World Health Organization, blood transfusions and delivery are still the crucial challenges in blood supply chain management, especially when there is a high demand and not enough blood inventory. Consequently, reducing uncertainty in blood demand, waste, and shortages has become a primary goal. In this paper, we propose a smart platform-oriented approach that will create a robust blood demand and supply chain able to achieve the goals of reducing uncertainty in blood demand by forecasting blood collection/demand, and reducing blood wastage and shortage by balancing blood collection and distribution based on an effective blood inventory management. We use machine learning and time series forecasting models to develop an AI/ML decision support system. It is an effective tool with three main modules that directly and indirectly impact all phases of the blood supply chain: (i) the blood demand forecasting module is designed to forecast blood demand; (ii) blood donor classification helps predict daily unbooked donors thereby enhancing the ability to control the volume of blood collected based on the results of blood demand forecasting; and (iii) scheduling blood donation appointments according to the expected number and type of blood donations, thus improving the quantity of blood by reducing the number of canceled appointments, and indirectly improving the quality and quantity of blood supply by decreasing the number of unqualified donors, thereby reducing the amount of invalid blood after and before preparation. As a result of the system’s improvements, blood shortages and waste can be reduced. The proposed solution provides robust and accurate predictions and identifies important clinical predictors for blood demand forecasting. Compared with the past year’s historical data, our integrated proposed system increased collected blood volume by 11%, decreased inventory wastage by 20%, and had a low incidence of shortages.
Citation

M. HEMMAK Allaoua, Benaoumeur Senouci, , (2023), "Smart Platform for Data Blood Bank Management: Forecasting Demand in Blood Supply Chain Using Machine Learning", [national] Information , MDPI

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2022

Data Blood Management for Algerian Healthcare System : Smart Platform Oriented Approach

Information and computer technology are gaining popularity in blood banks because of their potential to enhance labor productivity and service quality. This article focuses on the significance of web-based technologies in blood bank information management in order to improve the efficacy of this vital, particularly challenging activity in developing nations. Despite the efforts of the World Health Organization and others, blood transfusions and their delivery pose a problem in developing countries: insufficient supply, high demand, and inadequate availability. The research revealed that blood banking institutions in developing countries lacked coordination, as each blood bank maintained its own records that were not shared with other banks. The objective of this paper is to propose a solution that: (1) facilitates coordination between blood banks, health institutions, and donors by interacting with a central database. (2) Providing a solution to one of the most important issues of blood bank information systems, namely, how to effectively use the growing amount of data and information to aid in decisionmaking. (3) Proposes a decision support system for blood supply management that enables the incorporation of human knowledge into an automated system to improve the efficacy of blood bank management; the objective is to assist health administrators in making the best management decisions that are in line with the best strategies.
Citation

M. HEMMAK Allaoua, Benaoumeur Senouci, , (2022), "Data Blood Management for Algerian Healthcare System : Smart Platform Oriented Approach", [international] 5th International Conference on Embedded Systems in Telecommunications and Instrumentation , Annaba, Algeria

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2017

New Properties for Solving the Single-Machine Scheduling Problem with Early/Tardy Jobs

: This paper presents a mathematically enhanced genetic algorithm (MEGA) using the mathematical
properties of the single-machine scheduling of multiple jobs with a common due date. The objective of the
problem is to minimize the sum of earliness and tardiness penalty costs in order to encourage the completion
time of each job as close as possible to the common due date. The importance of the problem is derived from
its NP-hardness and its ideal modeling of just-in-time concept. This philosophy becomes very significant in
modern manufacturing and service systems, where policy makers emphasize that a job should be completed
as close as possible to its due date. That is to avoid inventory costs and loss of customer’s goodwill. Five mathematical properties are identified and integrated into a genetic algorithm search process to avoid premature
convergence, reduce computational effort, and produce high-quality solutions. The computational results
demonstrate the significant impact of the introduced properties on the efficiency and effectiveness of MEGA
and its competitiveness to state-of-the-art approaches.
Citation

M. HEMMAK Allaoua, (2017), "New Properties for Solving the Single-Machine Scheduling Problem with Early/Tardy Jobs", [national] JOURNAL OF INTELLIGENT SYSTEMS , DE GRUYTER

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2015-01-01

Combination of Genetic Algorithm with Dynamic Programming for Solving TSP

This paper presents a combination of Genetic Algorithm (GA) with Dynamic Programming (DP) to solve the well-known Travelling Salesman Problem (TSP). In this work, DP is integrated as a GA operator with a certain probability. In specific, at a given GA generation, the individuals are subdivided into a number of equal segments of genes, and the shortest path on each segment is obtained by applying a DP algorithm. Since the computational complexity of the DP is O (k22k), it becomes of O(1) when k is small. Experimental analyses are conducted to investigate the impact and trade-offs among DP probability, segment size and time processing on the solution quality and computational effort. In addition, we will implement a basic GA approach to compare results and show the contribution of combination of combination approach. Experimental results on benchmark instances showed that the combined GA-DP algorithm reduces significantly the computational effort, produces a clearly improved solution quality and avoids early premature convergence of GA.
Citation

M. HEMMAK Allaoua, (2015-01-01), "Combination of Genetic Algorithm with Dynamic Programming for Solving TSP", [national] Int. J. Advance Soft Compu. Appl , Int. J. Advance Soft Compu. Appl

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2010

Variable Parameters Lengths Genetic Algorithm for Minimizing Earliness-Tardiness Penalties of Single Machine Scheduling With a Common Due Date

Modern manufacturing philosophy of just-in-time emphasizes that a job should be completed as close as possible to its due date to avoid inventory cost and loss of customers goodwill. In this paper, the single machine scheduling problem with a common due date, where the objective is to minimize the total earliness and tardiness penalties in the schedule of jobs, is considered. A new genetic algorithm inspired by the philosophy of dynamic programming, where the chromosome and the population lengths are varied from one iteration to another, is proposed.
Citation

M. HEMMAK Allaoua, ibrahim hoceine osman, , (2010), "Variable Parameters Lengths Genetic Algorithm for Minimizing Earliness-Tardiness Penalties of Single Machine Scheduling With a Common Due Date", [national] Electronic Notes in Discrete Mathematics , elsevier

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