Many public figures, companies and associations are planning events in different cities and at the same time have active profiles on social media. The planning process requires processing a large amount of data and different parameters when choosing the best event venue. Social media captures a large number of fan actions per day. This paper describes the process of selecting the most appropriate cities to organize events, aided by data collected from social media. The problem is defined as a combinatorial optimization problem. A modified metaheuristic Bat algorithm was proposed, implemented, and described in detail to solve the problem. Although the original Bat algorithm is designed to solve continuous optimization problems, the implemented bat algorithm is adapted to solve the defined problem. The algorithm is compared to the exhaustive search method for smaller instances, and to the greedy and genetic algorithm for larger instances. The algorithm was tested on benchmark data on cities in 20 European countries, as well as on real data collected from pages on the social network Facebook. Bat algorithm has shown superior results compared to other techniques, both in time and in the quality of the solutions generated.

Distribution companies often store goods in large warehouses. Orders are collected and prepared for transport. Large-scale warehouses are often divided into sectors. Each worker collects a part of the order from the assigned sector. In that case, workers often pick small orders and the process is not optimal. Therefore, order batching is done, where one worker collects multiple orders at a time. In this paper, an innovative concept of orders batching in a warehouse with a 48-hour delivery based on a metaheuristic approach is described. The algorithm divides each order by sectors. An analysis of each part of the order is done and the possibility of batching based on the order content is checked. The order batching is based on the discrete Bat algorithm. The transport scheme and the order of loading goods into the truck are observed. In the order picking process, a number of standard constraints such as weight and item priorities are considered. The concept has been implemented and tested for 50 days of warehouse operation in one of the largest warehouses in Bosnia and Herzegovina. The algorithm is compared with the earlier approach of collecting orders in the warehouse, and significant progress has been observed in the number of kilometers traveled on a daily basis.

—The problem of transport optimization is of great importance for the successful operation of distribution companies. To successfully find routes, it is necessary to provide accurate input data on orders, customer location, vehicle fleet, depots, and delivery restrictions. Most of the input data can be provided through the order creation process or the use of various online services. One of the most important inputs is an estimate of the unloading time of the goods for each customer. The number of customers that the vehicle serves during the day directly depends on the time of unloading. This estimate depends on the number of items, weight and volume of orders, but also on the specifics of customers, such as the proximity of parking or crowds at the unloading location. Customers repeat over time, and unloading time can be calculated from GPS data history. The paper describes the innovative application of machine learning techniques and delivery history obtained through a GPS vehicle tracking system for a more accurate estimate of unloading time. The application of techniques gave quality results and significantly improved the accuracy of unloading time data by 83.27% compared to previously used methods. The proposed method has been implemented for some of the largest distribution companies in Bosnia and Herzegovina.

ABSTRACT Transportation management, as a part of the supply chain management, is a complex process that consists of planning and delivering goods to customers. The paper presents a complete multi-phase intelligent and adaptive transportation management system, which includes data collection, parameter tuning, and the heuristic algorithm based on the Tabu search for vehicle routing. The paper describes the procedure for collecting Global Positioning System (GPS) data and analyzing the compliance with the proposed routes based on the data collected. The described routing algorithm is powerful and supports many real-world limitations. An algorithm for the anomaly detection in the GPS data is presented as well as the usage of collected GPS data to improve the future results of the algorithm. The concept was implemented and tested on real data in some of the largest distribution companies in Bosnia and Herzegovina. The proposed approach resulted with more than satisfactory results in real-world application.

In all information systems it is very important to operate with correct information. Incorrect information can lead to many problems that can cause direct financial and reputation loss of the company. Data used by the system can be gathered by sensors, scripts or by hand. In all those cases, mistakes are possible. It is important to detect mistakes on time and stop them from propagating further into the system. In this paper, a novel multi-step anomaly detection algorithm based on the greatest common divisor and median value is described. The algorithm for anomaly detection in historical sales data is used as a part of the smart warehouse management system which is implemented in some of the largest distribution companies in Bosnia and Herzegovina. The algorithm showed significant results in anomaly detection on company orders and improved a number of processes in the operation of the smart warehouse management system. The algorithm described can also be used in other areas where the transaction data is collected, such as sales and banking,

It is well-known that determining the optimal number of guards which can cover the interior of a simple nonconvex polygon presents an NP-hard problem. The optimal guard placement can be described as a problem which seeks for the smallest number of guards required to cover every point in a complex environment. In this paper, we propose an exact twophase method as well as an approximate method for tackling the mentioned issue. The proposed exact approach in the first phase maps camera placement problem to the set covering problem, while in the second phase it uses famous state-of-the-art CPLEX solver to address set covering problem. The performance of our combined exact algorithm was compared to the performance of the approximate one. According to the results presented in the experimental analysis, it can be seen that the exact approach outperforms the approximate method for all instances.

Vehicle routing problem as the generalization of the Travelling Salesman Problem (TSP) is one of the most studied optimization problems. Industry itself pays special attention to this problem, since transportation is one of the most crucial segments in supplying goods. This paper presents an innovative cluster-based approach for the successful solving of real-world vehicle routing problems that can involve extremely complex VRP problems with many customers needing to be served. The validation of the entire approach was based on the real data of a distribution company, with transport savings being in a range of 10-20 %. At the same time, the transportation routes are completely feasible, satisfying all the realistic constraints and conditions.

To tackle a specific class of engineering problems, in this paper, we propose an effectively integrated bat algorithm with simulated annealing for solving constrained optimization problems. Our proposed method (I-BASA) involves simulated annealing, Gaussian distribution, and a new mutation operator into the simple Bat algorithm to accelerate the search performance as well as to additionally improve the diversification of the whole space. The proposed method performs balancing between the grave exploitation of the Bat algorithm and global exploration of the Simulated annealing. The standard engineering benchmark problems from the literature were considered in the competition between our integrated method and the latest swarm intelligence algorithms in the area of design optimization. The simulations results show that I-BASA produces high-quality solutions as well as a low number of function evaluations.

This paper presents a data visualization method in 3D space that includes actual positions, volumes and space relations of the chunks of data that are being visualized. Data that is being visualized is real-time information provided by the smart warehouse management system about packages distributed on pallet places within a warehouse. Three different visualizations are shown: qualitative, quantitative and cumulative. The method is graded for the time needed to determine the location of all pallet places that fulfill searched criteria and getting the exact value of searched information for each pallet place. Challenges in presenting this data and interacting with resulting visualizations are discussed. It is concluded that showing actual positions of chunks of data greatly increases the speed of acquiring searched values and positions at the same time for outliers but has issues with clusters and multiple types of queried data.

One of the frequently occurring tasks during the development of a warehouse management system is the implementation of a routing algorithm of some kind. Whether it is for guiding workers during order picking, routing delivery vehicles or for routing company representatives, this task has proven to be challenging in the technical as well as the social sense. In other words, the task is heavily dependent on various company-specific constraints and it directly dictates the way employees should do their job. This paper describes a strategic approach to the development and gradual integration process of such algorithms which makes sure that all constraints are satisfied and, more importantly, ensures that route suggestions are viewed by the employees as a helpful tool rather than a threat to their job. Described through a real-world case study in a medium-to-large warehouse, the routing efficiency is almost doubled in comparison to the previous approach and critical factors are analysed and discussed throughout different stages of the process.

One of the frequently occurring tasks during the development of warehouse management systems is the implementation of routing algorithms of some kind. Whether it is for routing workers during order picking, delivery vehicles or company representatives, this task has proven to be challenging in the technical as well as the social sense. In other words, the task is heavily dependent on various general and company-specific constraints and it directly dictates the way employees should do their job. This paper describes a strategic approach to the development and gradual integration of such algorithms which makes sure that all constraints are satisfied and, more importantly, ensures that route suggestions are viewed by the employees as a helpful tool rather than a threat to their job. In the first part of this paper, the approach is described and evaluated on a warehouse representative routing problem through a real-world case study in a medium-to-large warehouse. In the second part, the same approach is adapted to a delivery vehicle routing problem for a smaller retailer company. In both cases, routing efficiency almost doubled in comparison to previous approaches used by the companies. The most important factors of the implementation and integration stages as well as the impact of the changes on employee satisfaction are aggregated, analysed in detail, and discussed throughout different stages of development.

Many users need social media platforms to improve business. The usage of those platforms is usually focused on the marketing and customer targeting. Platforms like Facebook, Instagram or YouTube give their users a large number of reports and analytic tools. Public figures and organizations have a large number of followers who generate a significant number of activities. This paper focuses on the use of Facebook's geography analytic in the process of events planning. The problem is formulated as a combinatorial optimization problem. Data from social media platforms are used as an input to nature-inspired optimization algorithm. A public data set has been created with cities from 20 European countries. An adjusted genetic algorithm (AGA) is proposed. The greedy approach and AGA are compared on real data from several Facebook pages and on the created public dataset. The genetic algorithm shows better results and it gives the same solution as an exhaustive search for smaller instances.

The planning of concert tours can be a challenging process which requires a large amount of data to be analyzed. The greatest profit cannot be obtained only by maximizing the expected number of visitors. However, most of the organizers mainly focus on that part of planning. To achieve the maximum profit possible, organizers must include other data in their analysis. Social media play a powerful role in music industry. Most of the mentioned data can be found online on social media like Facebook, YouTube or Instagram. Such data can be found in analytic sections of fan or event pages. In this paper, algorithms for tour planning have been introduced by using above mentioned data. Proposed algorithms are based on heuristic methods such as simulated annealing and genetic algorithm. A clustering based method is also implemented. Aforementioned algorithms were tested on real-world instances from Facebook fan page analytics and use number of fans and distance between cities.