Hybrid Genetic Algorithm for Multi-Period Vehicle Routing Problem with Mixed Pickup and Delivery with Time Window, Heterogeneous Fleet, Duration Time and Rest Area

Most logistics industries are improving their technology and innovation in competitive markets in order to serve the various needs of customers more efficiently. However, logistics management costs are one of the factors that entrepreneurs inevitably need to reduce, so that goods and services are distributed to a number of customers in different locations effectively and efficiently. In this research, we consider the multi-period vehicle routing problem with mixed pickup and delivery with time windows, heterogeneous fleet, duration time and rest area (MVRPMPDDR). In the special case that occurs in this research, it is the rest area for resting the vehicle after working long hours of the day during transportation over multiple periods, for which with confidence no research has studied previously. We present a mixed integer linear programming model to give an optimal solution, and a meta-heuristic approach using a hybrid genetic algorithm with variable neighborhood search algorithm (GAVNS) has been developed to solve large-sized problems. The objective is to maximize profits obtained from revenue after deducting fuel cost, the cost of using a vehicle, driver wage cost, penalty cost and overtime cost. We prepared two algorithms, including a genetic algorithm (GA) and variable neighborhood search algorithm (VNS), to compare the performance of our proposed algorithm. The VNS is specially applied instead of the mutation operator in GA, because it can reduce duplicate solutions of the algorithms that increase the difficulty and are time-consuming. The numerical results show the hybrid genetic algorithm with variable neighborhood search algorithm outperforms all other proposed algorithms. This demonstrates that the proposed meta-heuristic is efficient, with reasonable computational time, and is useful not only for increasing profits, but also for efficient management of the outbound transportation logistics system

A hybrid particle swarm optimization for the generalized assignment problem with time window

This study focuses on the inbound logistics of the sugarcane industry, which has three main procedures consisting of cultivation, harvest and transportation. Generally, small-scale growers cannot manage all of the procedures effectively, because of their lack of bargaining power and inadequate equipment. For this reason a resource-sharing policy, such as harvester and truck sharing, is used by factories to reduce the cost of the sugarcane harvest, and increase harvester and truck utilization. To solve the generalized assignment problem (GAP) with time window, thus minimizing the total cost from the assignment of the third-party logistics providers to service small-scale growers under capacity and time limitations, a mathematical model has been developed for small-sized problems. For large-scale problems, particle swarm optimization (PSO) is applied and improved by the hybridization of PSO with k-cyclic moves algorithm (PSOK). The results demonstrate that the proposed metaheuristics can solve the problem efficiently since the results are equal to, or close to, the optimal solutions in which the averaged performances of PSO and PSOK are 99.61% and 99.64%, respectively and the averaged relative improvement is 0.1519%

A hybrid particle swarm optimization for the generalized assignment problem with time window

This study focuses on the inbound logistics of the sugarcane industry, which has three main procedures consisting of cultivation, harvest and transportation. Generally, small-scale growers cannot manage all of the procedures effectively, because of their lack of bargaining power and inadequate equipment. For this reason a resource-sharing policy, such as harvester and truck sharing, is used by factories to reduce the cost of the sugarcane harvest, and increase harvester and truck utilization. To solve the generalized assignment problem (GAP) with time window, thus minimizing the total cost from the assignment of the third-party logistics providers to service small-scale growers under capacity and time limitations, a mathematical model has been developed for small-sized problems. For large-scale problems, particle swarm optimization (PSO) is applied and improved by the hybridization of PSO with k-cyclic moves algorithm (PSOK). The results demonstrate that the proposed metaheuristics can solve the problem efficiently since the results are equal to, or close to, the optimal solutions in which the averaged performances of PSO and PSOK are 99.61% and 99.64%, respectively and the averaged relative improvement is 0.1519%

Metaheuristics in Business Model Development for Local Tourism Sustainability Enhancement

This study focused on analyzing planning and scheduling services in the tourism industry. Because dealing with these issues necessitates consideration of several important factors and stakeholders in the tourism business, it is challenging to operate resources efficiently. The purpose of this research is to propose a novel approach that allows maximizing the profits of tourism-related service sectors while considering many real-life constraints, such as sequence-dependent travel time, tourist time windows, points of interest, and specific destination constraints. We test our mathematical model for solving first small-scale problems and then metaheuristics proposed for finding a solution for real-life size problems. Moreover, sensitivity analysis was used to analyze the case study’s worthiness when the total cost and the revenue factor were changed. A real case study from Thailand’s Khon Kaen and Kanchanaburi provinces were used to verify the proposed models. The results indicate that the proposed models can be applied to investment decisions and strategy development. Furthermore, the outputs of the proposed models (i.e., the mathematical and metaheuristics models) can be employed to enhance the sustainability of other supply chains

Metaheuristics in Business Model Development for Local Tourism Sustainability Enhancement

This study focused on analyzing planning and scheduling services in the tourism industry. Because dealing with these issues necessitates consideration of several important factors and stakeholders in the tourism business, it is challenging to operate resources efficiently. The purpose of this research is to propose a novel approach that allows maximizing the profits of tourism-related service sectors while considering many real-life constraints, such as sequence-dependent travel time, tourist time windows, points of interest, and specific destination constraints. We test our mathematical model for solving first small-scale problems and then metaheuristics proposed for finding a solution for real-life size problems. Moreover, sensitivity analysis was used to analyze the case study’s worthiness when the total cost and the revenue factor were changed. A real case study from Thailand’s Khon Kaen and Kanchanaburi provinces were used to verify the proposed models. The results indicate that the proposed models can be applied to investment decisions and strategy development. Furthermore, the outputs of the proposed models (i.e., the mathematical and metaheuristics models) can be employed to enhance the sustainability of other supply chains