In this research, the focus is on addressing the capacitated vehicle routing problem (CVRP), a prominent logistical challenge within transportation management. The discrete salp swarm algorithm (DSSA) has been introdu...
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In this research, the focus is on addressing the capacitated vehicle routing problem (CVRP), a prominent logistical challenge within transportation management. The discrete salp swarm algorithm (DSSA) has been introduced as an innovative adaptation of the traditional salpswarmalgorithm (SSA), specifically redesigned to cater to discrete characteristics inherent in problems like the CVRP. This algorithm integrates the core principles of SSA with mutation and crossover techniques, enhancing its applicability for discrete problems. The paper contributes in two main areas: firstly, the development of DSSA, tailored to address the unique requirements of VRP scenarios and ensuring a balanced approach between exploration and exploitation in discrete optimization. Secondly, the effectiveness of DSSA is demonstrated through practical applications, including an 8-customer routing task and a real-world case study involving cement delivery in Vietnam. In these scenarios, DSSA consistently demonstrates superior performance over other meta-heuristic strategies, marking a noteworthy advancement in the domain of optimization and providing a potential tool for complex routing challenges in logistics and distribution systems.
In the salpswarmalgorithm (SSA), the update mechanism is inspired by the unique chain movement of the salpswarm. Numerous versions of SSA were already put forward to deal with various optimization problems, but the...
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In the salpswarmalgorithm (SSA), the update mechanism is inspired by the unique chain movement of the salpswarm. Numerous versions of SSA were already put forward to deal with various optimization problems, but there are very few discrete versions among them. d-opt is improved based on the 2-opt algorithm: a decreasing factor d is introduced to control the range of neighborhood search;TPALS are modified by Problem Aware Local Search (PALS) based on the characteristics of Travelling Salesman Problem (TSP);The second leader mechanism increases the randomness of the algorithm and avoids falling into the local optimal solution to a certain extent. We also select six classical crossover operators to experiment and select Subtour Exchange Crossover (SEC) and the above three mechanisms to integrate them into the SSA algorithm framework to form discrete salp swarm algorithm (DSSA). In addition, DSSA was tested on 23 known TSP instances to verify its performance. Comparative simulation studies with other advanced algorithms are conducted and from the results, it is observed that DSSA satisfactorily solves TSP.
The emphasis on this paper is about implementing an algorithm for the vehicle routing problem with time windows (VRPTW), which is a key class of vehicle routing problem. Due to its NP-hard feature, it is quite difficu...
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The emphasis on this paper is about implementing an algorithm for the vehicle routing problem with time windows (VRPTW), which is a key class of vehicle routing problem. Due to its NP-hard feature, it is quite difficult to solve it within an acceptable calculation time. As a result, many metaheuristics have been developed to find suboptimal solutions to VRPTW. To settle the VRPTW, a discrete salp swarm algorithm (DSSA) is put forward. To prove the property of this algorithm in solving solutions, the algorithm is applied to the Solomon benchmark. Compared with existing methods, DSSA provides relatively better results in total distance.
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