Application of Genetic Algorithms to Solve MTSP Problems with Priority (Case Study at the Jakarta Street Lighting Service)

Transportation is one thing that is very important and is the highest cost in the supply chain. One way to reduce these costs is to optimize vehicle routes. The Multiple Traveling Salesman Problem (MTSP) and Capacitated Vehicle Routing Problem (CVRP) are models that have been extensively researched to optimize vehicle routes. In its development based on actual events in the real world, some priorities must be visited first in optimizing vehicle routes. Several studies on MTSP and CVRP models have been conducted with exact solutions and algorithms. In a real case in the Jakarta City Street Lighting Section, the problem of determining the route in three shifts is a crucial problem that must be resolved to increase worker productivity to improve services. Services in MCB (Miniature Circuit Breaker) installation and maintenance activities for general street lights and priority is given to light points that require replacement. Because, in this case, the delivery capacity is not taken into account, the priority of the lights visited is random, and the number of street light points is enormous, in this study, we use the MTSP method with priority and solve by a genetic algorithm assisted by the nearest neighbor algorithm. From the resolution of this problem, it was found that the travel time reduction was 32 % for shift 1, 24 % for shift 2, and 23 % for shift 3. Of course, this time reduction will impact worker productivity so that MCB installation can be done faster for all lights and replace a dead lamp

Metadata of the chapter that will be visualized in SpringerLink Book Title Combinatorial Optimization and Applications Series Title Chapter Title Optimal Strategy for Walking in Streets with Minimum Number of Turns for a Simple Robot Optimal Strategy for

Abstract We consider the problem of walking a simple robot in an unknown street. The robot that cannot infer any geometric properties of the street traverses the environment to reach a target , starting from a point . The robot has a minimal sensing capability that can only report the discontinuities in the depth information (gaps), and location of the target point once it enters in its visibility region. Also, the robot can only move towards the gaps while moving along straight lines is cheap, but rotation is expensive for the robot. We maintain the location of some gaps in a tree data structure of constant size. The tree is dynamically updated during the movement. Using the data structure, we present an online strategy that generates a search path for the robot with optimal number of turns. Keywords (separated by '-') Computational geometry -Minimum link path -Simple robot -Street polygon -Unknown environment Abstract. We consider the problem of walking a simple robot in an unknown street. The robot that cannot infer any geometric properties of the street traverses the environment to reach a target t, starting from a point s. The robot has a minimal sensing capability that can only report the discontinuities in the depth information (gaps), and location of the target point once it enters in its visibility region. Also, the robot can only move towards the gaps while moving along straight lines is cheap, but rotation is expensive for the robot. We maintain the location of some gaps in a tree data structure of constant size. The tree is dynamically updated during the movement. Using the data structure, we present an online strategy that generates a search path for the robot with optimal number of turns

Combining an artificial intelligence algorithm and a novel vehicle for sustainable e-waste collection

Mobile collection of waste electrical and electronic equipment is a collection method that is convenient for residents and companies. New opportunities to use mobile apps and internet applications facilitate the ordering of waste pickups from households and preparation of a collection plan for a waste collection company. It improves the secondary raw materials collection in a circular economy approach after recycling waste equipment. This study presents a combined methodology for improving the efficiency of e-waste collection. An online ewaste collection supporting systemuses a Harmony Search algorithm for route optimization of waste collection vehicles. The results of the optimization are better compared to other artificial intelligence algorithms presented in the literature and the number of visited collection points is higher from1.2%–6.6% depending on the compared algorithm. To increase the efficiency ofwaste loading and packing, a novel collection vehicle body construction is presented. The design includes the convenient loading of waste from both sides of the vehicle and the rear side being equippedwith a hydraulic lift. The proposed vehiclemodel can be used for e-waste collection in placeswith limited parking spaces or where the parking time is limited, such as in densely populated city centers. The waste equipment packing efficiency increases and eliminates the necessity of including a container loading problem in the algorithm and allows increasing waste equipment number loaded in a collection vehicle