The inability of small companies to compete with large production systems has led to the sharing resources in cloud production systems among smaller companies to compensate for their production deficiencies. This study proposes a multi-objective, multi-level cloud production system by minimizing...

This study introduces a hybrid feature selection technique with a multi-objective algorithm incorporating Information Gain, Random Forest, and Relief F-based approach. We integrate the strengths of filter and wrapper methodologies to enhance the efficacy of addressing feature selection. The...

Metaheuristics are widely used to address complex optimisation problems where traditional exact methods are computationally infeasible or insufficiently flexible. With the rapid advancement of artificial intelligence, large language models, such as ChatGPT, Claude, Gemini, and LLaMA, have...

Background: Logistics and transport, core of many business processes, are continuously optimized to improve efficiency and market competitiveness. The paper describes a modular coordination of vehicle routing and bin packing problems that enables independent instances of the problems to be...

Employee turnover poses a significant challenge to organizations, leading to increased costs and disruptions in workforce stability. In this study, we propose a novel hybrid model for employee turnover prediction that integrates Genetic Algorithms (GA) for feature selection with LightGBM for...

This study evaluates the effectiveness of the Non-dominated Sorting Genetic Algorithm III (NSGA-III) in comparison to the traditional Mean-Variance optimization method for financial portfolio management. Leveraging a dataset of global financial assets, we applied both approaches to optimize...

Background: In today's business world, where competition lies between supply chains, customer expectations are changing dynamically. Effective order picking in warehouses has become a top concern given expectations for rapid delivery, a larger product range, and continuous support. Methods: In...

Uncertainty is critical in bulk terminals because it is inherent to many operations. In particular, the berth allocation problem (BAP) is greatly affected by the uncertain arrival times of the vessels. In this paper, we propose the first distributionally robust optimization (DRO) model for the...

As one of the strongest Othello agents, Edax employs an n-tuple network to evaluate the board, with points of interest represented as tuples. However, this network maintains a constant shape throughout the game, whereas the points of interest in Othello vary with respect to game’s progress....

Multi-objective optimization problems (MOPs) are common in real-world applications, including scheduling, vehicle routing, and engineering design. A key challenge in solving MOPs is balancing convergence and diversity, as these problems often involve conflicting objectives and complex...

This research focuses on modeling and solving a bi-objective hybrid flow shop scheduling problem, considering uncertain job-dependent setup times and worker constraints. The main objective of the mathematical model is to simultaneously minimize the Maximum Completion Time (Cmax) and minimize the...

Food insecurity remains a global challenge that demands efficient and equitable logistical solutions, especially in the distribution of food aid. This study addresses the Two-Echelon Vehicle Routing Problem (2E-VRP) in foodbank logistics by proposing a novel multi-objective optimization...

Purpose: Cloud manufacturing (CM) represents a new manufacturing paradigm that integrates distributed resources to provide on-demand services. The high consumer demand from various locations, coupled with the customizability and complexity of manufacturing, complicates task scheduling. In this...

Disasters are unforeseen occurrences requiring extensive transport deployment to support and relieve victims. Sometimes, this transportation is not feasible directly from some supply points to some destination points. Due to this tragedy, it is unclear precisely what is available at supply...

Economic uncertainty has been increasing, as evidenced by recent fluctuations in global markets and unpredictable economic indicators such as volatile demand, stock market fluctuations, and unpredictable interest rates. Economic profitability and working capital efficiency are pivotal indicators...

The Vehicle Routing Problem with Shipment Consolidation (VRPSC) is a novel variation of the vehicle routing problem that involves multiple commodities, multiple dimensions, a fleet with different types of vehicles, and the challenge of consolidating shipments during the route. Exact algorithms...

In this work, we extend the time-dependent vehicle routing problem with time windows on a road network by considering two types of vehicles, large and small, to serve customers. Motivated from city logistics applications, large vehicles are forbidden from the downtown area. Accordingly, goods...

Background: The Multi Depot Pickup and Delivery Problem with Time Windows and Heterogeneous Vehicle Fleets (MDPDPTWHV) is a strongly practically oriented routing problem with many real-world constraints. Due to its complexity, solution approaches with sufficiently good quality ideally contain...

Efficient route management is critical for optimizing maintenance activities in real estate management. This study delves into the intricate task of allocating maintenance duties among building surveyors, a pivotal concern for property management firms. The primary goal of route planning is to...