Today, there is a great need for greener urban freight transportations due to their ever-increasing environmental impact. The planet’s climate has been significantly affected as the temperature is constantly rising and extreme weather events are occurring more and more often. Aiming to reduce the environmental impact of freight transportation in urban areas, an advanced vehicle routing and scheduling system for improving urban freight transportations, has been developed. This paper presents the functionality of the advanced system, while also analyzing its subsystems and demonstrating its use in a case study. The system is provided as an integrated cloud-based software to support the needs of logistics companies, in order to efficiently schedule their deliveries and perform the routing of their vehicles. The utilized multi-objective algorithm produces solutions that minimize either the distribution cost or the environmental emissions or a combination of these parameters. An application of the system is performed for validation purposes, concerning the comparison of the system’s results with corresponding real-life data provided by a medium-sized logistics company. The results of the testing reveal its significant contribution to the reduction of the environmental impact of the company’s distribution services.
The Vehicle Routing Problem with Time Windows (VRPTW) is an NP-Hard optimization problem which has been intensively studied by researchers due to its applications in real-life cases in the distribution and logistics sector. In this problem, customers define a time slot, within which they must be served by vehicles of a standard capacity. The aim is to define cost-effective routes, minimizing both the number of vehicles and the total traveled distance. When we seek to minimize both attributes at the same time, the problem is considered as multiobjective. Although numerous exact, heuristic and metaheuristic algorithms have been developed to solve the various vehicle routing problems, including the VRPTW, only a few of them face these problems as multiobjective. In the present paper, a Multiobjective Large Neighborhood Search (MOLNS) algorithm is developed to solve the VRPTW. The algorithm is implemented using the Python programming language, and it is evaluated in Solomon’s 56 benchmark instances with 100 customers, as well as in Gehring and Homberger’s benchmark instances with 1000 customers. The results obtained from the algorithm are compared to the best-published, in order to validate the algorithm’s efficiency and performance. The algorithm is proven to be efficient both in the quality of results, as it offers three new optimal solutions in Solomon’s dataset and produces near optimal results in most instances, and in terms of computational time, as, even in cases with up to 1000 customers, good quality results are obtained in less than 15 min. Having the potential to effectively solve real life distribution problems, the present paper also discusses a practical real-life application of this algorithm.
Nowadays, there are a plethora of business process modeling tools for use by researchers and practitioners. This paper presents the implementation of a methodological approach for the selection of such a tool in order to construct a process reference model. This reference model will support the development of a distribution system in the context of the supply chain in the phase of the system’s requirements definition, as well as in the final implementation of the system in real-life supply chain operations. The reference model is crucial for the easy and effective adoption of the system into companies’ processes. Therefore, the choice of the modeling tool can strongly support the design, development and implementation of the system. In this context, the application of a multi-criteria decision analysis (MCDA) was carried out in order to select the appropriate tool, utilizing a combination of the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) I method for the decision-making with the Analytic Hierarchy Process (AHP) method for the calculation of the weights of the evaluation criteria. The MCDA’s results showed that the Architecture of Integrated Information Systems (ARIS) Architect & Designer Tool was the one that better met the evaluation criteria. The sensitivity analysis that followed the MCDA reaffirmed its results by revealing that this tool had the highest consistency, despite the changes in the scenarios.
As countries interact more and more, technology gains a decisive role in facilitating today’s increased need for interconnection. At the same time, systems, becoming more advanced as technology progresses, feed each other and can produce highly complex and unpredictable results. However, with this ever-increasing need for interconnected operations, complex problems arise that need to be effectively tackled. This need extends far beyond the scientific and mechanical fields, covering every aspect of life. Systemic Thinking Philosophy and the System Dynamics methodology now seem to be more relevant than ever and their practical implementation in real-life industrial cases has started to become a trend. Companies that decide to implement such approaches can achieve significant improvements to the effectiveness of their operations and gain a competitive advantage. This research, influenced by the Systemic Thinking Philosophy, applies a System Dynamics approach in practice by improving the quality control process of a pharmaceutical company. The process is modeled, simulated, analyzed, and improvements are performed to achieve more effective and efficient operations. The results show that all these steps led to a successful identification and optimization of the critical factors, and a significant process improvement was achieved.
In today's challenging sector of logistics and transportation, companies, seek to adapt software which leads to efficient solutions at an acceptable cost. Conventional routing software is developed to solve vehicle routing problem and help managers and planners in decision making. Simultaneously, specific constraints and different VRP (Vehicle Routing Problem) variants are considered each time, such as the Capacitated, the Multi Depot and the Pickup and Delivery VRP. However, the last few years the need for more reliable deliveries and better customer services arose. In addition, reducing travel distance, travel cost and environmental impact are important factors encountered in urban freight transportation. Therefore, routing software needs to take into account multiple constraints. Such constraints are traffic congestion, speed limits, transportation regulations and restricted zones. These constraints affect mainly Time dependent VRP, VRP with Time Windows, Dynamic VRP and Green VRP. Data collection and processing are essential in routing software for solving these variants and offering the best solution. The methods for solving these problems, along with technological achievements, including cloud computing, can lead to efficient, easily adaptable routing software. Such software solutions can eventually render companies with complex transportation and logistics problems, competitive. The scope of this paper is to describe the concept and methodological approach for the development of such a routing and scheduling system, operating in a cloud environment. The definition of its requirements and the development of the system is the main purpose of an ongoing research project, being in its first stages of system's analysis and design.
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