Insertion Heuristic Algorithm Research Articles

Q-learning guided algorithms for bi-criteria minimization of total flow time and makespan in no-wait permutation flowshops

Combining Deep Reinforcement Learning and meta-heuristic techniques represents a new research direction for enhancing the search capabilities of meta-heuristic methods in the context of production scheduling. Q-learning is a prominent reinforcement learning in which its utilization aims to direct the selection of actions, thus preventing the necessity for a random exploration in the iterative process of the metaheuristics. In this study, we provide Q-learning guided algorithms for the Bi-Criteria No-Wait Flowshop Scheduling Problem (NWFSP). The problem is treated as a bi-criteria combinatorial optimization problem where total flow time and makespan are optimized simultaneously. Firstly, a deterministic mixed-integer linear programming (MILP) model is provided. Then, Q-learning guided algorithms are developed: Bi-Criteria Iterated Greedy Algorithm with Q-Learning (BC-IGQL). Bi-Criteria Block Insertion Heuristic Algorithm with Q-Learning (BC-BIHQL). Moreover, the performance of the proposed Q-learning guided algorithms is compared over a collection of Bi-Criteria Genetic Local Search Algorithms (BC-GLS), Bi-Criteria Iterated Greedy Algorithm (BC-IG), Bi-Criteria Iterated Greedy Algorithm with a Local Search (BC-IGALL) and Bi-Criteria Variable Block Insertion Heuristic Algorithm (BC-VBIH). The complete computational experiment, performed on 480 problem instances of Vallada et al. (2015), which is known as the VRF benchmark set, indicates that the BC-BIHQL and the BC-IGQL algorithms outperform the BC-GLS, BC-IG, BC-IGALL, and BC-VBIH algorithms in comparative performance metrics. More specifically, the proposed BC-BIHQL and BC-IGQL algorithms can yield more non-dominated bi-criteria solutions with the most substantial competitiveness than the remaining algorithms. At the same time, both are competitive with each other on the benchmark problems. Moreover, the BC-IGQL algorithm dominates almost 97% and 99% of the solutions reached by the BC-IG, BC-IGALL, and BC-VBIH algorithms in small and large datasets. Similarly, The BC-BIHQL algorithm dominates almost 98% and 99% of the solutions reached by the BC-IG, BC-IGALL, and BC-VBIH algorithms in small and large datasets, respectively. This means that, among all the features that have been compared, the Q-learning-guided algorithms demonstrate the highest level of competitiveness. The outcomes of this study encourage us to discover many more bi-criteria NWFSPs to reveal the trade-off between other conflicting objectives, such as makespan & the number of early jobs, to overcome various industries' problems.

METODE CHEAPEST INSERTION HEURISTIC PADA SISTEM INFORMASI GEOGRAFIS TEMPAT WISATA DI KABUPATEN DELI SERDANG BERBASIS ANDROID

Geographic Information System (GIS) is a system created using information derived from processing a number of data, namely geographic data or data located on the earth's surface. Geographic Information Systems can be used in various fields, one of which is as a travel planning and tourist location. The lack of information conveyed on tourism in Deli Serdang Regency is one of the obstacles for people who want to travel. In this thesis, a Geographic Information System application for tourism in Deli Serdang Regency is developed which has detailed tourist information and the selection of the shortest travel route and alternative tours. To determine the shortest route and alternative travel, use the Cheapest Insertion Heuristic Algorithm. Then it is hoped that the community can be helped in planning tourist trips in Deli Serdang Regency. This application utilizes the google maps API as a virtual map provider which will later display tourist location points in Deli Serdang Regency. In addition, this application is also equipped with tourist list information along with tourist details that can be accessed by the public as a reference for traveling.

Perbandingan Algoritma Cheapest Insertion Heuristic Dan Nearest Neighbor Dalam Menyelesaikan Traveling Salesman Problem

The main problem of the Traveling Salesman Problem is that a salesman travels to several places to go with a known distance and then returns to his original place by using the shortest route from his journey, and all the places the salesman goes to are only allowed once. This research focuses on the problem of distributing goods at PT. The Medan Nugraha Ekakurir (JNE) route with the destination delivery address in the Medan area. The Cheapest Insertion Heuristic Algorithm is an algorithm used to form tours (travels) by gradually building the shortest path route with minimal weight, by adding new points one at a time. One. The Nearest Neighbor Algorithm is a simple and fast algorithm to build a feasible initial tour length from TSP where the technique takes the shortest distance from the initial position regardless of other distances. This study resulted in the conclusion that the application of the cheapest insertion heuristic and nearest neighbor algorithms in terms of finding the distance to the problem of shipping goods at PT. The Medan Nugraha Ekakurir (JNE) route starts with finding the distance between addresses with the help of google maps, then continues with the help of the WinQSB software. Based on the research results obtained using the cheapest insertion heuristic and nearest neighbor algorithms, it is obtained that the search for the shortest route distance for shipping goods at PT. The smaller Medan Nugraha Ekakurir (JNE) route is generated by the nearest neighbor algorithm. This shows that the nearest neighbor algorithm is more effective in terms of finding the traveling distance on the Traveling Salesman Problem problem of shipping goods at PT. Medan's Nugraha Ekakurir (JNE) Line.

OPTIMASI RUTE PENDISTRIBUSIAN BBM MENGGUNAKAN ALGORITMA TABU SEARCH DAN CHEAPEST INSERTION HEURISTIC


 
 
 
 Distribution is an activity carried out on various types of transportation to carry out product marketing to various customers through planning by each company. In distribution, there needs to be systematic planning so as to minimize delays, one of which is the mileage factor. The purpose of this research is to optimize the optimal fuel distribution route at PT. Pertamina (Persero) Gorontalo VII Marketing Unit uses the heuristic method using the Tabu Search and Cheapest Insertion Heuristic algorithms and compares which algorithm is better which can be applied in determining the optimal fuel distribution route. The results showed that the Tabu Search algorithm was better at determining the optimal fuel distribution route with a total distance of 815.2 km, while the Cheapest Insertion Heuristic algorithm produced a total distance of 1039.7 km.
 
 
 

Implementation of Cheapest Insertion Heuristic Algorithm in Determining Shortest Delivery Route

The buying and selling transaction system that many people use today is the online buying and selling system. In this system, there is a process of goods delivery by one branch of a freight forwarder in Indonesia, namely SiCepat Express Baleendah. In the process of shipping goods, a delivery route with the shortest path is needed in order to minimize of the goods delivery process. The problem of the route of goods delivery can be solved by one of the algorithms in the Traveling Salesman Problem (TSP), namely the Cheapest Insertion Heuristic (CIH) Algorithm. This study aims to determine the shortest route and distance for goods delivery using the CIH Algorithm by the Asymmetric TSP CIH application, as well as knowing its efficiency compared to the route that used by SiCepat Express Baleendah. The result shows that the use of the CIH Algorithm is proven to produce a more efficient delivery route than the route created by SiCepat Express Baleendah. Based on the goods delivery route from SiCepat Express Baleendah, the result of the total distance is 18.55 km. On the other hand, based on the CIH Algorithm, the delivery route obtained result is 13.45 km. The efficiency of using the CIH Algorithm is 27.48% better than the result from SiCepat Express Baleendah route.

SISTEM REKOMENDASI TEMPAT WISATA MENGGUNAKAN ALGORITMA CHEAPEST INSERTION HEURISTIC DAN NAÏVE BAYES

Bali is one of the best tourism areas in Indonesia, as evidenced in 2016 Bali received a number of awards on the TripAdvisor Travelers Choice Award in global and Asian scope. However, the Corona virus outbreak from 2019, caused the tourism sector in Bali to decline, thus a solution is needed to restore the tourism sector in Bali, where one solution is to increase cultural tourism to the maximum, as the main attraction of tourist destinations in Bali. Bali. So the author proposes a tourism recommendation system, which aims to recommend tourist attractions that are suitable for tourists, which in this recommendation system is also recommended cultural tourism destinations that are directly recommended by the community, and there is also a mapping of tourist attractions as part of a tourist recommendation system, mapping of tourist attractions public and cultural attractions. In this tourism recommendation system, using the Naïve Bayes Algorithm to recommend general tourist destinations based on the personal motivation of tourists, which is based on the attributes of age, gender, natural interest, artificial interest, cultural interest of tourists, using 200 training data consisting of 14 classes of tourist attractions. . In addition, this tourist recommendation system is equipped with recommendations for routing tourist attractions using the Cheapest Insertion Heuristic Algorithm, to arrange a list of tourist attractions.
 Keywords: Recommendation System, Naïve Bayes Algorithm, Cheapest Insertion Heuristic Algorithm, Personal Motivation, Place Mapping.

Optimisation of takeaway delivery routes considering the mutual satisfactions of merchants and customers

To solve a series of problems (including high cost and delivery delay) during takeaway delivery, a model for the vehicle routing problem (VRP) during goods pickup and delivery is developed by considering constraints such as the capacity of delivery vehicles, delivery mileage and time window. The model is constructed by transforming the satisfactions of merchants and customers into a penalty function and aiming to minimise the total delivery cost. As for the drawbacks in a conventional genetic algorithm (CGA), such as a low convergence rate and locally optimum solutions, an improved GA (IGA) is designed by separately using the insertion heuristic algorithm to construct initial solutions and introducing the forward continuous crossover and differential mutation strategies. On the one hand, the numerical analysis and test of weights indicate that the model can reduce the delivery cost of enterprises offering takeaway service and improve satisfactions of merchants and customers. It verifies that reasonably considering satisfactions of merchants and customers during vehicle routing is conducive to cost-reduction and increased efficiency of enterprises. On the other hand, a simulation is conducted to compare and analyse various algorithms based on two different scales of test examples, which validates the proposed algorithm as effective. The study provides a theoretical basis and decision reference for enterprises offering takeaway service to improve delivery efficiency and competitiveness.

Study of variable neighborhood descent and tabu search algorithm in VRPSDP

The application of graph theory can be used to solve distribution optimization problems, especially the Vehicle Routing Problems (VRP). One variant of VRP is the Vehicle Routing Problem with Simultaneous Delivery and Pickup (VRPSDP), which has particular constraints, namely requests, and returns, which are done simultaneously. The VRPSDP problems can be solved by a variety of algorithms, such as the tabu search algorithm and the Variable Neighborhood Descent (VND) algorithm. The basic principle of the VND algorithm is to determine the initial solution and repair the solution using the neighborhood operator. In this article, the initial solution is determined by the insertion heuristic algorithm and makes improvements using six neighborhood operators. The optimum condition of the VND algorithm is reached if there is the most optimal solution for all operators, and the solution has converged to a minimum value. The application of the VND algorithm on VRPSDP was made using Borland Delphi 7 software. The VND algorithm application program was tested using standardized data sets with 100 and 200 customers, then compared multiple routes, solutions, and gaps (%). For many customers, 100 and 200 on the two types of data sets, there are many more VND algorithm routes than the tabu search algorithm. The results of the distance solution for the four data sets show that the tabu search algorithm is smaller than the VND algorithm. The type c data set has a smaller gap compared to the type r data set for both 100 and 200 customers to test tabu search algorithms and VND algorithms.

Heuristic Approach For Robust Counterpart Open Capacitated Vehicle Routing Problem With Time Windows

Garbage is one of the environmental problems. The process of transporting garbage sometimes occurs delays such as congestion and engine failure. Robust optimization model called a robust counterpart open capacitated vehicle routing problem (RCOCVRP) with time windows was formulated to get over this delays. This model has formulated with the limitation of vehicle capacity and time windows with an uncertainty of waste volume and travel time. The RCOCVRP model with time windows is solved by a heuristic approach. The heuristic approach used to solve the RCOCVRP model with time windows uses the nearest neighbor and the cheapest insertion heuristic algorithm. The RCOCVRP with time windows model is implemented on the problem of transporting waste in Sako sub-district. The solutions of these two heuristic approaches are compared and analyzed. The RCOCVRP model with time windows to optimize the route problems of waste transport vehicles that is solved using the cheapest insertion heuristics algorithm is more effective than the nearest neighbor method.

ALGORITMA VARIABLE NEIGHBORHOOD DESCENT (VND) PADA VEHICLE ROUTING PROBLEM WITH SIMULTANEOUS DELIVERY AND PICKUP (VRPSDP) DAN IMPLEMENTASINYA

Vehicle Routing Problem with Simultaneous Delivery and Pickup (VRPSDP) is a variant of Vehicle Routing Problem (VRP). VRPSDP has special constraints, namely requests and returns are carried out simultaneously. In this article we will use the Variable Neighborhood Descent (VND) algorithm to solve VRPSDP problems. There are two steps taken to use the VND algorithm on VRPSDP, namely finding an initial solution with the Insertion Heuristic algorithm and improving the position of the customer by using the neighborhood operator on the VND algorithm. The implementation of the VND algorithm on VRPSDP has been successfully made using the Borland Delphi 7.0 programming language. Inputs contained in the program are point position, distance between points, customer requests and returns and vehicle capacity. The output contained in the program in the form of routes that have been completed using an algorithm and output in the form of images of the final solution that has been obtained. Based on the results obtained, an example with 6 customers produces 3 routes with a total distance of 266 km, while an example with 10 customers produces 4 routes with a total distance of 100 km.

An augmented Tabu search algorithm for the green inventory-routing problem with time windows

Abstract Transportation allocates a significant proportion of Gross Domestic Product (GDP) to each country, and it is one of the largest consumers of petroleum products. On the other hand, many efforts have been made recently to reduce Greenhouse Gas (GHG) emissions by vehicles through redesigning and planning transportation processes. This paper proposes a novel Mixed-Integer Linear Programming (MILP) mathematical model for Green Inventory-Routing Problem with Time Windows (GIRP-TW) using a piecewise linearization method. The objective is to minimize the total cost including fuel consumption cost, driver cost, inventory cost and usage cost of vehicles taking into account factors such as the volume of vehicle load, vehicle speed and road slope. To solve the problem, three meta-heuristic algorithms are designed including the original and augmented Tabu Search (TS) algorithms and Differential Evolution (DE) algorithm. In these algorithms, three heuristic methods of improved Clarke-Wright algorithm, improved Push-Forward Insertion Heuristic (PFIH) algorithm and heuristic speed optimization algorithm are also applied to deal with the routing structure of the problem. The performance of the proposed solution techniques is analyzed using some well-known test problems and algorithms in the literature. Furthermore, a statistical test is conducted to efficiently provide the required comparisons for large-sized problems. The obtained results demonstrate that the augmented TS algorithm is the best method to yield high-quality solutions. Finally, a sensitivity analysis is performed to investigate the variability of the objective function.

Komparasi Algoritma Cheapest Insertion Heuristic (CIH) Dan Greedy Dalam Optimasi Rute Pendistribusian Barang

This study was aimed to compare algorithms that can effectively provide better solutions related to the problem of determining the shortest route in the distribution of goods. This research was a qualitative research. The object of research was the route of shipping goods of a business that is engaged in printing and convection. The algorithms compared in this study were Cheapest Insertion Heuristic (CIH) and Greedy algorithms. Both algorithms have advantages and disadvantages in finding the shortest route. From the results of the analysis using these two algorithms, the Cheapest Insertion Heuristic (CIH) and Greedy algorithm can provide almost the same optimization results. The difference was only the selection of the journey. The strength of the Greedy algorithm was that the calculation steps are simpler than the Cheapest Insertion Heuristic (CIH) algorithm. While the disadvantage of the Greedy algorithm was that it is inappropriate to find the shortest route with a relatively large number of places visited. The advantage of the Cheapest Insertion Heuristic (CIH) algorithm was that this algorithm is still stable, used for the relatively large number of places visited. While the lack of Cheapest Insertion Heuristic (CIH) algorithm was a complicated principle of calculation and was relatively longer than the Greedy algorithm.

A Variable Block Insertion Heuristic for Solving Permutation Flow Shop Scheduling Problem with Makespan Criterion

In this paper, we propose a variable block insertion heuristic (VBIH) algorithm to solve the permutation flow shop scheduling problem (PFSP). The VBIH algorithm removes a block of jobs from the current solution. It applies an insertion local search to the partial solution. Then, it inserts the block into all possible positions in the partial solution sequentially. It chooses the best one amongst those solutions from block insertion moves. Finally, again an insertion local search is applied to the complete solution. If the new solution obtained is better than the current solution, it replaces the current solution with the new one. As long as it improves, it retains the same block size. Otherwise, the block size is incremented by one and a simulated annealing-based acceptance criterion is employed to accept the new solution in order to escape from local minima. This process is repeated until the block size reaches its maximum size. To verify the computational results, mixed integer programming (MIP) and constraint programming (CP) models are developed and solved using very recent small VRF benchmark suite. Optimal solutions are found for 108 out of 240 instances. Extensive computational results on the VRF large benchmark suite show that the proposed algorithm outperforms two variants of the iterated greedy algorithm. 236 out of 240 instances of large VRF benchmark suite are further improved for the first time in this paper. Ultimately, we run Taillard’s benchmark suite and compare the algorithms. In addition to the above, three instances of Taillard’s benchmark suite are also further improved for the first time in this paper since 1993.

Multi-UAV Cooperative Patrol Task Planning Novel Method Based on Improved PFIH Algorithm

This paper investigates the cooperative patrol task planning problems for multiple unmanned aerial vehicles by considering the patrol task requirements. A new cooperative patrol task planning approach is proposed to complete the patrol task. Firstly, the cooperative patrol task planning model for multiple unmanned aerial vehicles is established. Then, the corresponding complex constraint condition processing method with two steps is designed; that is, obtaining the initial result via Floyd algorithm firstly and then generating the optimal solution by adopting the improved Push Forward Insertion Heuristic (IPFIH) algorithm. It is proved that the proposed algorithm can obtain feasible solutions that meet all constraints and maximize the patrol payoff under the limited resources conditions. Finally, a numerical simulation is provided to demonstrate the effectiveness of the proposed cooperative patrol task planning approach.

PENENTUAN RUTE KENDARAAN PADA SISTEM DISTRIBUSI LOGISTIK PASCA BENCANA (STUDI KASUS)

The success indicators of disaster mitigation can be seen from the disaster logistics system. Effective and efficient distribution network can make a good disaster logistics system. The problem that related to the design of this network is the vehicle routing problem. The objective is determined optimal route of relief distribution from warehouse to victims with minimum time duration. The problem is solved by branch and bound, insertion heuristic, and local search algorithms. The results obtained by branch and bound and local search algorithm are optimal global. Time duration of vehicle using these algoritm is 1.0562 hours. However, computation time using branch and bound algorithm is very long until 22 hours while local search algorithm only takes 60 seconds. The insertion heuristic algorithm also produces a good solution. Time duration of vehicle using this algoritm is 1,1030 hours. This solution is local optimal, but the computation time is very short, only 0.001 seconds.

OPTIMASI RUTE SALES COVERAGE MENGGUNAKAN ALGORITMA CHEAPEST INSERTION HEURISTIC DAN LAYANAN GOOGLE MAPS API

Traveling Salesman Problem (TSP) is one of the distribution issues which is long discussed in the optimization review, where the review problem is how a salesman visits the entire travel route within the coverage area and returns to the starting point of departure with the rule that no destination should be visited more than once. PT. Panjunan is a company engaged in the products distribution of various principles. Currently the managing process of customer location data at the company is still not maximal due to the absence of calculations in every daily visit routes so it impact on the effectiveness of time. In addition, the absence of visualization of real customer data on the field leads to the error distribution and marketing of goods which is uneven, not on target and impact on the decline in sales value. One of the techniques used to accelerate the search solutions of TSP problems is to use an insertion algorithm, or also called the Cheapest Insertion Heuristic (CIH) algorithm. The discussion of this research is to study and apply CIH algorithm in determining the optimization of daily sales traffic route with the aim of this research is to make daily trip route optimization program by applying Cheapest Insertion Heuristics algorithm and provide real customer data visualization on original map in the field as analysis material for operational team and management by utilizing Google Maps API service. System development method used in this research using waterfall model with implementation tools is laravel framework and mySQL as DBMS.

OPTIMASI RUTE SALES COVERAGE MENGGUNAKAN ALGORITMA CHEAPEST INSERTION HEURISTIC DAN LAYANAN GOOGLE MAPS API

Traveling Salesman Problem (TSP) is one of the distribution issues which is long discussed in the optimization review, where the review problem is how a salesman visits the entire travel route within the coverage area and returns to the starting point of departure with the rule that no destination should be visited more than once. PT. Panjunan is a company engaged in the products distribution of various principles. Currently the managing process of customer location data at the company is still not maximal due to the absence of calculations in every daily visit routes so it impact on the effectiveness of time. In addition, the absence of visualization of real customer data on the field leads to the error distribution and marketing of goods which is uneven, not on target and impact on the decline in sales value. One of the techniques used to accelerate the search solutions of TSP problems is to use an insertion algorithm, or also called the Cheapest Insertion Heuristic (CIH) algorithm. The discussion of this research is to study and apply CIH algorithm in determining the optimization of daily sales traffic route with the aim of this research is to make daily trip route optimization program by applying Cheapest Insertion Heuristics algorithm and provide real customer data visualization on original map in the field as analysis material for operational team and management by utilizing Google Maps API service. System development method used in this research using waterfall model with implementation tools is laravel framework and mySQL as DBMS.

Routing problems in agricultural cooperatives: a model for optimization of transport vehicle logistics

AbstractThis study considers the problem of optimizing the routes of vehicles used in an agricultural cooperative that distributes animal feed to its customers. Different peculiarities distinguish our problem from the general Vehicle Routing Problem because there exists a type of time window constraint, truck capacity is limited, trucks are compartmentalized, there are access restrictions for trucks to some farms and our problem has the main objective of maximizing the amount of feed distributed every day and an auxiliary criterion of minimizing the transport costs. We present a mathematical programming formulation of the model that represents the logistics management problem. As solving the exact model is computationally expensive, we opt for a hybrid heuristic approach that first uses an insertion heuristic algorithm to obtain an initial solution and then improvement processes are performed using the so-called simulated annealing metaheuristic. We have built a graphical interface that allows easy use of the system and, in particular, efficient post-optimality analysis. Moreover, this interface can easily interface with other management tools. The utility of our model is shown with real and simulated data sets.

Implementasi Algoritma Cheapest Insertion Heuristic (CIH) dalam Penyelesaian Travelling Salesman Problem (TSP)

Traveling salesman problem (TSP) is the problem of a salesman to visit the city of each city connected to each other and there is the weight of travel between the cities so as to form a complete weighted graph. Departing from a certain initial city, a salesman had to visit (n-1) another city exactly once and return on the initial city of departure. The purpose of TSP is to find the route of all cities with minimum total weight.Many algorithms have been found to solve the TSP, one of which is the Cheapest Insertion Heuristic (CIH) algorithm in the process of inserting weighted steps obtained from the equation c (i, k, j) = d (i, k) + d (k, j) - d (i, j). This algorithm provides different travel routes depending on the order of insertion of cities on the subtour in question.In this final project, the writer took the problem of distribution route of mineral water of al-ma'some 240 ml cup type, with vehicle capacity to meet 1200 carton and have different customer / agent demand that is the distance of depot and agent far from each other, distribution costs.

Penentuan Rute Kendaraan Heterogen Menggunakan Algoritma Insertion Heuristic

Distribution process at PT XYZ is identical to one of the variants of Vehicle Routing Problem (VRP) which there are several vehicles with different capacities and operation cost, called as Heterogeneous Fleet Vehicle Routing Problem (HFVRP). There are two kinds of vehicle that have been used, namely, vehicle As and vehicle Bs. Vehicle As has the higher operation cost than vehicle Bs. Currently, the company implements policies where vehicle Bs is used if all vehicle As have been used. This policy may lead the distribution cost is so expensive and its equal with the operation cost of vehicle As that also high. Routing aims to combine the use of available vehicles to generate minimal distribution costs in servicing the customers. This research uses mathematical model according to the description of the distribution system in the company. The model is verified and tested using the optimization software. Then the constraints of the verifiable mathematical model are used as the basis for developing Insertion Heuristic (IH) algorithm. Comparison of model solutions with developed algorithm solutions showed a difference of 11.6%. This implies that the algorithm generated solution is quite good as one of the heuristic approaches that result in optimal local solutions. Implementation of IH algorithm provides cost savings of 5.97% towards the actual distribution system in the company.