Information Management in the Logistics and Distribution Sector Using Metaheuristic Techniques

Introduction

The demand for businesses to find new ways to develop value and provide it for their consumers is rising ever more strongly in today's intensely dynamic global marketplace (Yavas & Ozkan-Ozen, 2020, Sgarbossa, et al. 2020, & Karaman, et al. 2020). The automotive sector starts to compete for demand on its goods on a global market. Service dimensions, quality, and cost havecontributed significantly to the need to build more effective logistic structures than those used historically (Orjuela, et al. 2021 & Hu, et al. 2020). Moreover, logistics has evolved from a company to the business stage over the last two decades. Effective distribution control in the business and supply has been increasingly accepted. Active distribution strategy across business and supply chain has continually been recognized as a great contribution to the mission of cost savings and service development (Khalaf, et al. 2002, Ali & Mahmood, 2020, & Tayal, et al. 2020). In terms of logistics management, success must be accomplished throughout the supply chain, from the vendor to the customer, by the convergence of operations, collaboration, communication and knowledge exchange (Manogaran & Alazab, 2020). Modern organizations need innovative decision-making processes (DSS) focused on strong statistical models and solution methods and developments in information and communication technology in reaction to the challenge of integration (Su, et al. 2021).

Quantitative models and computer-based decision-making techniques play a significant role in today's corporate climate. In the fast-growing field of logistics administration, this is particularly so. Such computerized logistics systems may even have a huge effect on organizations on the decision-making process (Sankayya, et al. 2021 & Gupta, et al. 2021). There is an increasing desire from both industry and academia to use logistic management and logistics DSS to solve the challenges and problems presented by changes in the field.There have been several well-known optimization algorithms, and it is evident that most did not have the desired influence on the decisions on logistics problems architecture and optimization (Lv et al. 2020). In addition, specific strategies rely heavily on issues and require high expertise. The implementation of decision-making processes leads to difficulties that clash with quick performance in a rapidly changing environment. Any of the more common commercial packages currently use heuristic methods or thumb rules.

Metaheuristic optimization algorithm integrated logistical management methods can handle a complicated optimization problem that cannot be solved in the conventional optimization approach (Al-Turjman, 2020 & Jan et al. 2020). In these last decades, extensive studies have been carried out in heuristic techniques, which suggest new, effective techniques, including various metaheuristic methods for addressing difficult issues (Sennan et al. 2020). For that cause, a sophisticated logistics decision support system is required, which helps companies react rapidly to new obstacles and logistics management problems. However, developments are needed in the field of metaheuristics, which can efficiently react to complex problems (Thaseen et al. 2020). Metaheuristics allow users to settle on the system for certain parameters and settings, and then modelling techniques to evaluate system actions in the event of uncertainty can be introduced. In this sense, metaheuristics play a key role. They can quickly achieve a very good solution that can be modified and built effectively to cope with very difficult logistics issues. Due to the knowledge sharing, high scalability, low cost and efficiency of a single processor computer, distributed computing model have received much attention in recent years. The most prominent distributed computing model has arisen in the current scenario from cloud computing (Gao et al. 2020 & Goodarzian et al. 2020).