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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 inte...

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.

The vehicle routing problem is one of the most complex problems in the field of combinatorial optimization. Creating optimal routes leads to timely delivery of orders to end customers, which increases the efficiency of the company and enables maximum earnings. The problem of vehicle routing with a series of real-world constraints is called the rich vehicle routing problem (RVRP). The paper presents an approach to solving RVRP, where the asymmetric routing problem with a heterogeneous vehicle fleet, time windows, customer-vehicle constraints and a number of others is observed. The approach solves the problem in two phases, by dividing customers into clusters using a discrete metaheuristic Bat algorithm, and by solving the routing problem for each obtained cluster. The proposed approach has been tested for 26 days of delivery from large warehouses in Bosnia and Herzegovina. Significant savings were achieved compared to previously implemented approaches. All created routes were feasible. The approach automatically creates routes, and gives results in a shorter time than previously used approaches. Time does not increase significantly with the increase in the number of customers, which is a great advantage of the proposed approach.

The Vehicle Routing Problem (VRP) is an important optimization problem, the solution of which brings great savings to the company. Finding the optimal solution is significantly hampered by the introduction of realistic constraints such as time windows, capacity, customer-vehicle restrictions, and more. The paper presents a two-phase approach to solving the problem of vehicle routing with the fulfillment of several realistic conditions. The approach consists of customer clustering based on the firefly algorithm and process to solve rich VRP based on the created clusters. The algorithm was implemented in the real world and tested in some of the largest distribution companies in Bosnia and Herzegovina. The algorithm showed quality results in relation to the previously used methods, and in relation to the manual division of customers by the distribution manager.

By successfully solving the problem of forecasting, the processes in the work of various companies are optimized and savings are achieved. In this process, the analysis of time series data is of particular importance. Since the creation of Facebook’s Prophet, and Amazon’s DeepAR+ and CNN-QR forecasting models, algorithms have attracted a great deal of attention. The paper presents the application and comparison of the above algorithms for sales forecasting in distribution companies. A detailed comparison of the performance of algorithms over real data with different lengths of sales history was made. The results show that Prophet gives better results for items with a longer history and frequent sales, while Amazon’s algorithms show superiority for items without a long history and items that are rarely sold.

The vehicle routing problem is one of the most difficult combinatorial optimization problems. Due to a large number of possibilities and practical limitations, there is no known algorithm that finds the optimal solution. Heuristics and metaheuristics have shown quality results in solving routing problems, however, for large instances of the problem these approaches have also shown weaknesses. The paper presents a two-phase algorithm to solve rich vehicle routing problems. The approach is based on customer clustering using a discrete Firefly algorithm, and solving an individual vehicle routing problem for each created cluster, with methods of sharing information and resources among clusters. Due to the smaller dimension, routing problems within a cluster can be solved more easily. The approach has been tested in practice in the creation of routes for large warehouses and has given quality results. Savings of between 15–20% were achieved during more than 30 days of testing. The obtained routes do not violate practical restrictions and can be used in practice. The increase in the number of customers does not significantly affect the complexity of the solution, which is a great advantage over standard methods.

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.

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.

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.

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

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.

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.

Many companies own a significant number of vehicles. To ensure the undisturbed company workflow, all vehicles have to be tracked. The standard way of vehicle tracking is via a GPS device. Sometimes, GPS devices are sending fallacious data to the server. That data can cause significant errors in daily reports or in the vehicle route preview. This paper describes an efficient technique for finding different types of anomalies in GPS data. The paper describes a connection between finding a QRS complex in ECG signal and anomalies in GPS data. The algorithm is implemented and used as a part of the GPS tracking system that is used by distribution companies in Bosnia and Herzegovina.

In real datasets often occur cases, where variable or multiple variables have unusual values. These cases are known as anomalies or outliers. For any analysis, it is essential to detect them, because they can bias the analysis. In this paper, a robust anomaly detection method is presented, and it is based on median, rather then on mean value. The method is explained, as well as its parameters and the way how they affect the results. The method is then implemented, and used on Internal Banking Payment Systems. Analysis is given and results are presented.

Two important problems distribution companies face on a daily basis are the routing and tracking of a vehicle fleet. The former is being overcome by solving the famous vehicle routing problem (VRP), a generalization of the traveling salesman problem (TSP), and the later analyses GPS data to get information of the moving vehicles. In this paper a system which uses GPS data to track the vehicles, analyze their routes and improve input data needed for the algorithm for the vehicle routing problem is described. In a real-world scenario, implementing an VRP algorithm is not enough. Algorithms which analyze GPS data ensure that the VRP algorithm takes correct input data and that the driven routes are those that the algorithm proposed.

Distribution companies use complex software systems called WMS (Warehouse Management System). The WMS is an important part of the company’s business and it can make processes simple to keep track of. Smart WMS optimizes processes to save resources and to create a more efficient working place. This paper describes the concept of a smart WMS that is implemented in one of the largest distribution companies in Bosnia and Herzegovina. The system uses artificial intelligence and optimization algorithms to improve working process. The paper describes the complete warehouse workflow that includes stock planning, initial product placement, transfer from stock to pick zone, order picking process, transport and tracking. The anomaly detection is used in some processes to improve the whole system. The main contribution of this paper is the presentation of an efficient and in the real world used smart WMS concept.

The warehouse layout directly affects the process of receiving and storing of goods, as well as the order picking process. A standard layout has its pallet places arranged in parallel shelves with aligned cross-aisles. However, a big number of warehouses use a shelf layout in which the complete warehouse cannot be represented as a sequence of parallel shelves. Effective calculation of distances between positions in the warehouse presents a significant step towards the effective order collection and distribution of goods. This paper describes a generic approach for calculating distances within a warehouse which does not necessarily have standard layout. It describes the application of the algorithm on different warehouse layouts in which the shelves can be split into smaller units so that inside of each unit the shelves are distributed in a standard way. Dynamic programming was used for the calculation of distances inside of those units. An analysis and testing of the algorithm were performed on two middle-sized warehouses with non-standard shelf layout. The algorithm was tested in the process of collecting orders, as well as the process of moving goods from the stock to the pick zone.

Building a successful warehouse management system encompasses solving many problems of different nature to reshape the general workflow and ensure improvements in terms of resource management. In order for such a system to be accepted and used by a logistics company, those solutions need to be presented through a simple, adaptable and most importantly, a feasible software solution. One of the aspects that needs to be covered while building a warehouse management system is the optimal product placement in the warehouse. If the products are strategically placed, all the other improvement strategies like stock to picking zone item transfer and item picking order become more efficient and easier to implement. In this paper, tactics and issues regarding the optimal product placement in a warehouse are analyzed in detail through a real-world case study. Solutions of this problem largely differ for new, empty warehouses and for already operative warehouses not able to carry out a stocktaking process. Approaches for both possible situations are proposed and tested out on two different warehouses in a medium-to-large logistics company.

Fluids, steam and gas carry out thermal energy transportation by long pipes as energy bearers. This transportation is associated with complex problems, where the special issue is optimization of pipe systems. This optimization should find optimum ratio between physical parameters of transportation, environment state and economical parameters. In this work, partial optimization of hydraulic parameters in pipeline systems is made in example of thermal energy for Tuzla city from Power Plant "Tuzla " BiH.