Talel Ladhari

The integration of advanced technologies such as artificial intelligence, the Internet of Things, and location-tracking technologies has revolutionized our daily lives by enhancing the functionality of logistical services. However, these advancements have also placed significant pressure on the current supply networks and have impeded further growth within the logistics industry. This article presents an inventive logistic supply chain solution utilizing drone-based delivery to tackle the logistic supply chain issues. The proposed solution goal is to minimize the maximum amount of time required to finish all assigned delivery tasks. The goal was met by developing a set of sophisticated algorithms based on the combination of dispatching rule, randomization, and iterative methods to optimize the performance of the supply chain processes and effectively accomplished the aim of time savings. The deployed system enables the management of a larger volume of shipments and increases the efficiency of logistic supply chain management. The experimental results demonstrated that the proposed algorithms have the potential to decrease the maximum duration required to complete the delivery operations assigned to the drones in 1520 cases. The algorithm that exhibited the highest level of performance was the MID algorithm, which attained a staggering percentage of 87.7%, an average deviation from the optimal solution of 0.001, and an execution time of 0.0215 s. Moreover, the MID algorithm was determined to outperform the top two algorithms in the literature after comparing their outputs.

In this study, we address the minimisation of the consumed electric energy in a personal rapid transit transportation system (PRT). The PRT system can play a significant role in the supply chain, especially in the last mile delivery of goods in urban areas. Instead of transporting passengers and goods on different vehicles, the capacities offered by PRT system can be consolidated and utilised in a shared mode. Two compact formulations of polynomial size are developed for sequencing the vehicle trips. Since the existing model from the literature performed poorly, the first and the second models are developed by applying the lifting technique and Reformulation-Linearisation Technique, respectively. These techniques are well known for strengthening existing models whose linear relaxation is weak. In addition, we embed strengthened cuts within a constraint generation solution procedure. The performance of the proposed formulations is verified through computational experiments conducted on some instances from the literature. Furthermore, optimal solutions are provided for all the unsolved instances.

This paper reports a study of the flowshop scheduling problem with two machines where a set of tasks must be scheduled on a set of machines respecting the same fixed order. The objective was to design the best schedule optimizing the sum of completion times for the job criteria while respecting the release dates (i.e. start times) and the classical blocking constraint, whereby a job remains blocked on a machine as long as the next machine in execution is unavailable or the buffer cannot receive the job. We examine several metaheuristics based on simulated annealing, local search, discrete differential evolution, and genetic algorithms. The experimental results compare the performance of the proposed metaheuristics with each other.

This work focuses on the scheduling of a set of jobs with different release dates in a two-machine flowshop, where the blocking constraint is considered to minimize the sum of completion times. First, we provide the assignment and positional variable formulation for the problem under consideration and some mathematical models for particular cases of our problem. Subsequently, we derive four lower bounds based on linear relaxations. According to our formulations, we solve the studied problem exactly to provide optimal solutions. Computational experiments performed on large classes of instances show the effectiveness of the developed approaches.

We consider the problem of minimizing the sum of completion times in a two‐machine permutation flowshop subject to release dates. New procedures are proposed for effectively bounding the completion time of a given job that is processed at a given position. New assignment‐based lower bounds are derived as well as an enhanced mathematical programming formulation. Our computational analysis shows a consistent tightness of the proposed lower bounds and a high outperformance of the enhanced mathematical formulation with respect to the classical one.

This paper presents a new four-view-based framework to survey optimization approaches and the pioneering studies of optimization. The new four-view-based framework allows us to mainly understand optimization. Moreover, every proposed view explores a specific fundamental aspect of optimization. Therefore, classifying, organizing and comparing proposed works is much easier. The proposed four-view-based framework allow us to depict the lacks of the proposed frameworks and tools. Moreover, it allows us to raise many challenges to develop our new tool for combinatorial optimization problem with optimization property.

ABC analysis is an inventory management technique used to classify inventory items into three predefined and ordered categories A, B and C: Category A contains items with the highest impact for the company, while the category C contains items with the lowest impact. The aim of this classification technique is to keep related inventory costs under control. According to the ABC analysis, the classification of items into categories is based on their weighted scores. The score of each item is the result of an aggregation function that combines the item evaluations on the different criteria and the criteria weights. In ABC literature many classification models - issued from different fields such as Artificial Intelligence (AI), Mathematical Programming (MP) and Multi Criteria Decision Making (MCDM) - are proposed to infer the criteria weights. This paper proposes two classification models based on Genetic Algorithm (GA) to infer the criteria weights. The two classifications of items obtained by these models optimize respectively the two following objective functions: The Total Relevant Cost (TRC to be minimized) and the Inventory Turnover Ratio (ITR to be maximized). To estimate the performance of the proposed classification models, a benchmark data set of 47 items consumed in a Hospital Respiratory Therapy Unit (HRTU) is used.