A group genetic algorithm for the fleet size and mix vehicle routing problem

In logistics management, the use of vehicles to distribute products from suppliers to customers is a major operational activity. Optimizing the routing of vehicles is crucial for providing cost-effective services to customers. This research addresses the fleet size and mix vehicle routing problem (FSMVRP), where the heterogeneous fleet and its size are to be determined. A group genetic algorithm (GGA) approach, with unique genetic operators, is designed and implemented on a number of existing benchmark problems. GGA demonstrates competitive performance in terms of cost and computation time when compared to other heuristics

Simulated metamorphosis - a novel optimizer

This paper presents a novel metaheuristic algorithm, simulated metamorphosis (SM), inspired by the biological concepts of metamorphosis evolution. The algorithm is motivated by the need for interactive, multi-objective, and fast optimization approaches to solving problems with fuzzy conflicting goals and constraints. The algorithm mimics the metamorphosis process, going through three phases: initialization, growth, and maturation. Initialization involves random but guided generation of a candidate solution. After initialization, the algorithm successively goes through two loops, that is, growth and maturation. Computational tests performed on benchmark problems in the literature show that, when compared to competing metaheuristic algorithms, SM is more efficient and effective, producing better solutions within reasonable computation times

Risk Modeling of the Supply Chain for Thai Cassava Chip Exports to China

This study aims to review supply chain risks and construct a model of supply chain performance for Thai cassava chip exports to China to test the relationship between supply chain risks and supply chain performance. The primary theoretical model was constructed from four main supply chain risk factors, product risks, demand-side risks, logistical and infrastructural risks, and political risks, and five main supply chain performance variables, dependability, speed, qualities, information and response, adapted from [1] and S.M. [2]. Questionnaires were distributed to 46 Thai cassava chip exporters and stakeholders and analyzed using the PLS-SEM algorithm. With confidence intervals of 95%, demand-side risks and political risks impact supply chain performance. The Thai government and Thai entrepreneurs can analyze the results of possible risk factors to develop a supply chain for the Thai cassava chip industry

Multi-objective Vehicle Routing Problem with Cost and Emission Functions

AbstractAmong the logistics activities, transportation, is presented as a major source of air pollution in Europe, generating harmful levels of air pollutants and is responsible for up to 24% of greenhouse gases (GHGs) emissions in the European Union. The growing environmental concern related to the economic activity has been transferred to the field of transport and logistics in recent decades. Therefore, environmental targets are to be added to economic targets in the decision-making, to find the right balance between these two dimensions. In real life, there are many situations and problems that are recognized as multi-objective problems. This type of problems containing multiple criteria to be met or must be taken into account. Often these criteria are in conflict with each other and there is no single solution that simultaneously satisfies everyone. Vehicle routing problems (VRP) are frequently used to model real cases, which are often established with the sole objective of minimizing the internal costs. However, in real life other factors could be taken into account, such as environmental issues. Moreover, in industry, a fleet of vehicles is rarely homogeneous. The need to be present in different segments of the market, forcing many companies to have vehicles that suit the type of goods transported. Similarly, to have vehicles of different load capacities enables a better adaptation to the customer demand. This paper proposes a multi-objective model based on Tchebycheff methods for VRP with a heterogeneous fleet, in which vehicles are characterized by different capacities, costs and emissions factors. Three objective functions are used to minimize the total internal costs, while minimizing the CO2 emissions and the emission of air pollutants such as NOx. Moreover, this study develops an algorithm based on C&W savings heuristic to solve the model when time windows are not considered. Finally, a real case application is analyzed to confirm the practicality of the model and the algorithm

A multi-criteria approach for nurse scheduling : fuzzy simulated metamorphosis algorithm approach

Motivated by the biological metamorphosis process and the need to solve multi-objective optimization problems with conflicting and fuzzy goals and constraints, this paper proposes a simulated metamorphosis algorithm, based on the concepts of biological evolution in insects, such as moths, butterflies, and beetles. By mimicking the hormone controlled evolution process the algorithm works on a single candidate solution, going through initialization, iterative growth loop, and finally maturation loop. The method is a practical way to optimizing multi-objective problems with fuzzy conflicting goals and constraints. The approach is applied to the nurse scheduling problem. Equipped with the facility to incorporate the user’s choices and wishes, the algorithm offers an interactive approach that can accommodate the decision maker’s expert intuition and experience, which is otherwise impossible with other optimization algorithms. By using hormonal guidance and unique operators, the algorithm works on a single candidate solution, and efficiently evolves it to a near-optimal solution. Computational experiments show that the algorithm is competitive

Solving Rich Vehicle Routing Problem Using Three Steps Heuristic

Vehicle Routing Problem (VRP) relates to the problem of providing optimum service with a fleet of vehicles to customers. It is a combinatorial optimization problem. The objective is usually to maximize the profit of the operation. However, for public transportation owned and operated by government, accessibility takes priority over profitability. Accessibility usually reduces profit, while increasing profit tends to reduce accessibility. In this research, we look at how accessibility can be increased without penalizing the profitability. This requires the determination of routes with minimum fuel consumption, maximum number of ports of call and maximum load factor satisfying a number of pre-determined constraints: hard and soft constraints. To solve this problem, we propose a heuristic algorithm. The results from this experiment show that the algorithm proposed has better performance compared to the partitioning set

A GRASPxELS with Depth First Search Split Procedure for the HVRP

Split procedures have been proved to be efficient within global framework optimization for routing problems by splitting giant tour into trips. This is done by generating optimal shortest path within an auxiliary graph built from the giant tour. An efficient application has been introduced for the first time by Lacomme et al. (2001) within a metaheuristic approach to solve the Capacitated Arc Routing Problem (CARP) and second for the Vehicle Routing Problem (VRP) by Prins (2004). In a further step, the Split procedure embedded in metaheuristics has been extended to address more complex routing problems thanks to a heuristic splitting of the giant tour using the generation of labels on the nodes of the auxiliary graph linked to resource management. Lately, Duhamel et al. (2010) defined a new Split family based on a depth first search approach during labels generation in graph. The efficiency of the new split method has been first evaluated in location routing problem with a GRASP metaheuristic. Duhamel et al. (2010) provided full numerical experiments on this topic

A GRASPxELS with Depth First Search Split Procedure for the HVRP

Split procedures have been proved to be efficient within global framework optimization for routing problems by splitting giant tour into trips. This is done by generating optimal shortest path within an auxiliary graph built from the giant tour. An efficient application has been introduced for the first time by Lacomme et al. (2001) within a metaheuristic approach to solve the Capacitated Arc Routing Problem (CARP) and second for the Vehicle Routing Problem (VRP) by Prins (2004). In a further step, the Split procedure embedded in metaheuristics has been extended to address more complex routing problems thanks to a heuristic splitting of the giant tour using the generation of labels on the nodes of the auxiliary graph linked to resource management. Lately, Duhamel et al. (2010) defined a new Split family based on a depth first search approach during labels generation in graph. The efficiency of the new split method has been first evaluated in location routing problem with a GRASP metaheuristic. Duhamel et al. (2010) provided full numerical experiments on this topic