Auswahl der wissenschaftlichen Literatur zum Thema „Picking route problem“

In order to improve the efficiency of automated warehouse, the order-picking task of the fixed shelve was researched and analysed. The picking mathematical model of automated warehouse was established and attributed to the classical traveling salesman problem (TSP) model. At the same time, using an improved genetic algorithms(improved GAs) solved the optimization problem. Firstly, the initial population of the algorithm was optimized, and then a 'reverse evolution operator' was introduced in the improved genetic algorithms because of the lack of local optimization ability of genetic algorithm. Results of experiments verify that the method can acquire satisfying the demands of the route picking and optimization of speed.

School transportation is one of the facilities provided by the school in the process of picking up students. In the process of picking up, taking the optimal route is needed to save costs and time. The purpose of this study is to develop a model School transportation routes using the Clustered Generalized Vehicle Routing Problem (CGVRP ) model and the Dijkstra algorithm to get a more optimal route from the route that has been used. Furthermore, comparing the real distance from the shuttle transportation routes with the distance from the results of this case study using the CGVRP model and the Dijkstra algorithm. From the research results obtained by using the CGVRP model and the Djikstra algorithm which is applied to the case study of shuttle students at Darus Sunnah SDIT We found that our result has shorter distance when compared to the distance of the usual route.. The total distance on the SDIT Darus Sunnah route is 2.746.416 meters and costs Rp. 2.214.288 in a month. While the distance from the proposed route obtained 2.333.616 meters for at Rp. 1.881.478 in a month. Therefore the difference in the distance from the real route and the proposed route is 357.288 meters with a difference in cost of Rp. 332.820, - in a month.

It researches the service efficient problem of logistics distribution center manual order picking, tour length and then. It builds picker route stochastic model under random storage S-type picking condition. It makes model approximation calculation with simulation result under order random arrival frequencies. It conducts well approximation between model and simulation results under the condition of permission error. It is the foundation for improving storage strategy. And it provides basic foundation for researching of picking time distribution function for manual order picking random service system.

Fast medicine dispensing system (FMDS) as a kind of medical logistic robot can dispense many drugs for one prescription at the same time. To guarantee the sustainability of drug dispensation, it is required that FMDS replenish drugs rapidly. The traditional order picking model (OPM) is difficult to meet the demand of prompt replenishment. To solve the problems of prolonged refilling route and inefficiency of drugs replenishment, a mixed refilling model based on multiple steps traveling salesman problem model (MTSPM) and vehicle routing problem model (VRPM) is proposed, and it is deployed in two circumstances of FMDS, including temporary replenishment mode (TRM) and concentrate replenishment mode (CRM). It not only meted the demand under different circumstances of drug replenishment but also shortened the refilling route significantly. First, the new pick sets were generated. Then, the orders of pick sets were optimized and the new paths were achieved. When the number of pickings is varied no more than 20, experiment results declared that the refilling route is the shortest by utilizing MTSPM when working under the TRM condition. Comparing MTSPM with OPM, the rate of refilling route length decreased up to 32.18%. Under the CRM condition, the refilling route is the shortest by utilizing VRPM. Comparing VRPM with OPM, the rate of refilling route length decreased up to 58.32%. Comparing VRPM with MTSPM, the rate of refilling route length has dropped more than 43.26%.

This paper analyzes characteristics of automated warehouse stocker picking operating process. Path optimization problem is considered as traveling salesman problem. The coordinates of picking points by calculating determine a stocker running route. The mathematical model of a path distance is built. And using the improved genetic algorithm solves the above problem. Finally, M-file program of stocker running path optimization is written and run in MATLAB. The simulation results that, in solving stocker path optimization problem, it can search for a shortest path by genetic algorithm. Thereby enhance the efficiency of automated warehouse system, increase greater benefits of the enterprise.

Shared bicycle users with commuting purposes generally need to take a traditional public transit and then use the shared bicycle to complete the first/last mile transport. While shared bicycle provides convenient travel for travelers, it also brings a series of problems such as disorderly parking and road occupancy. Therefore, exploring the problem of travel mode shift between shared bicycle and public transit is of significance for improving the traffic environment and increasing the sharing rate of public transit. This paper introduces the flex-route transit system and quantitatively analyzes the rationality and feasibility of using flex-route transit to pick up and drop off shared bicycle users with commuting demand from the temporal perspectives. A flex-route transit route design model is established with the objective of minimizing the sum of vehicle driving time cost and passenger time cost, and the time cost models of the shared bicycle commuting system and the flex-route transit system are constructed, compared, and analyzed to explore the feasibility of flex-route transit picking up or dropping off shared bicycle users under different conditions. Through the subsequent sensitivity analysis, the influence of passenger demand density, fixed station spacing, and travel preference attributed to the two systems are analyzed separately. The results demonstrate that the flex-route transit can efficiently complete the picking up or dropping off for shared bicycle users under certain conditions.

In picking manipulator location system, it is the key problem that the positions of obi-object and picking manipulator are exactly determined in complex environment. Based on multi-sensor information fusion method, a data fusion system of multi-sensor integrating laser-sensor for absolute location with ultrasonic-sensor for inspection impediment was presented. Firstly, data collection and fusion were implemented employing a two- level distribution system. Secondly, the method of data collection and fusion in virtual environment was discussed, and the result data could drive picking manipulator 3D model to dynamically move in real-time using event and route mechanisms provided by virtual environment, which could simulate the process of picking manipulator being accurately located. Finally, a location simulation system was developed by VC++ and EON SDK.

In this study, we develop a model for routing cross-docking centers considering time windows and pricing routs. In this model picking and delivery in several times is permitted and each knot can be serviced by more than one vehicle. Every truck can transport one or more product, in other words, we consider compatibility between product and vehicle. This model includes two goals: reducing the total cost and reducing the cost of carrying goods (freight fare). The total cost includes the cost required to traverse between the points, the cost of traversing the routes between the central cross-docking center and the first points after moving, and the cost to traverse the routes between the last points in each route and the depots that must be minimized. In general, the purpose of the model is to obtain the number of cross-docking center, the number of vehicles and the best route in the distribution network. We present a nonlinear programming model for this problem. We have solved the proposed model by GAMS. As the dimensions of the problem increase, the implementation time of the program increases progressively. So, in order to solve the model in medium and large scales, we proposed a genetic meta-heuristic algorithm. The results of examining different issues by the meta-heuristic approach show the very high efficiency of the developed algorithms in terms of the solution time and the answer of the problem.

Demand robust counterpart-open capacitated vehicle routing problem with time windows and deadline (DRC-OCVRPtw,d) model formed and explained in this paper, is the model used to find the minimum distance and the time needed for vehicles to transport garbage in Sukarami Sub-District, Palembang that consists of the time it takes for the vehicle to pass through the route. Time needed to transport garbage to the vehicle is called time windows. Combination of the thoses times is called deadline. The farther 
 the distance passed by vehicle and the more garbage transported, the longer the deadline is needed. This DRC-OCVRPtw,d model is completed by LINGO 13.0 to obtain the optimal route and time deadline for Sukarami Sub-District. The model shows that the improved model of open vehicle routing problem involving the robustness, time windows and deadline can achieve the optimal routes that enable driver to save operational time in picking up the garbage compared to similar problem not involving no-time windows and deadline stated in previous research.

The path planning problem of logistics robots is mainly subjected to the time cost of the operation of the mathematical model. To save the time of refilling process in the fast medicine dispensing system (FMDS), the optimization procedure is divided into two steps in this study. First, a new mathematical model called the multiple steps traveling salesman problem model (MTSPM) is proposed to optimize the replenishment quantity of each picking and establish picking sets. Second, an improved ant colony optimization (IACO) algorithm is employed, considering the effects of velocity, acceleration, and deceleration in the refilling route during the development of the new model. Simulation results and operational results demonstrated that MTSPM-IACO was better than both the order picking model (OPM) and MTSPM-ACO in terms of saving refilling time. Compared to the OPM, the optimization of the refilling time of MTSPM-IACO was more than 1.73% in simulation and 15.26% in operation. Compared to MTSPM-ACO, the optimization of the refilling time of MTSPM-IACO was more than 0.13% in simulation and 1.67% in operation.

In this study we have conducted research on how to optimize inventory management within logistics. The focus in this study is to examine the picking rounds, the reason for this is because it is the most time consuming and expensive part within a warehouse. Is it possible to minimize the handling time to create efficient picking rounds? As a part of the research project AI has been investigated as well. If it is possible with help of AI, create a streamlining of current warehouse logistics. The purpose of this report is to investigate how to minimize the distance in picking rounds for efficient warehouse management. To be able to fulfil the purpose of the report research questions where conducted. The methodology that was chosen at first was traditional data collection. With the help of other studies conducted in this area we started to collect information. To be able to compare this information to the chosen company Care of Carl a case study was performed. A case study on the current situation at Care of Carl, and what the current optimization is based on. With the help of these two methods a result emerged. The result that was conducted by this study is that placement and categorization of products as well as route planning has a significant role when streamlining the picking process and minimizing the picking process. To store items in a warehouse the most suitable option is to use a free item placement, or storage out of sale frequency. But important to acknowledge is that it requires support systems to make this storage possible. When categorizing articles, it is crucial to combine this with a suiting picking method. In the case study, combining ABC categorization with zone picking was a possible solution. In general, it might be a good idea to invest in AI to use the picking position principle. With AI it is possible to analyse more complex data such as customer patterns and if this implementation succeeds it can lead to great advantages within a warehouse and the picking processes. The traveling distance constitutes most of the total picking time, it is important to have a route method that works with how you have chosen to place the items. This study shows that the optimal routing method is the one to use. This study showed that there are a lot of different strategies and methods on the current market. According to the case study Care of Carl can make big savings by changing strategies and methods. The reason why is because they have been reactive when investing in IT support systems. But in general, if a company wants to meet the current increasing requirements according to the globalization and the continuous changes within logistics operations, AI is the next step. The methods that are currently used are not sufficient, with the help of AI there is room for improvements within product allocation and route planning.<br>I denna studie har det undersökts hur man kan optimera lagerhanteringen inom logistik. Fokus har varit att undersöka plockrundorna, då det är den mest tidskrävande och kostsamma delen inom ett lager. Är det möjligt att minimera hanteringstiden och därmed effektivisera plockrundorna? Studien har även varit en del av ett forskningsprojekt där man har undersökt om det med hjälp av AI är möjligt att skapa en effektivisering av lagerhantering. Syftet med denna rapport är att undersöka hur man minimerar avståndet i plockrundorna för att effektivisera lagerhanteringen. För att kunna uppfylla syftet med rapporten utformades det forskningsfrågor kopplat till syftet. Traditionell datainsamling var den metod som användes för att komma i gång med studien. Den teoretiska referensramen som skapades i denna rapport var utifrån andra studier som genomförts inom detta område, men även utifrån att kunna besvara de forskningsfrågor som skapats. Det genomfördes även en fallstudie på företaget Care of Carl, med en nulägesbeskrivning samt en förklaring gällande hur deras nuvarande optimering tagits fram. För att kunna besvara syftet med rapporten och forskningsfrågorna jämfördes den teoretiska referensramen med den fallstudien som genomförts i samband med denna studie. Resultatet som framkom under studien var att placering och kategorisering av produkter såväl som ruttplanering har en avgörande roll gällande effektivisering av plockprocessen i ett lager. Gällande inlagringsmetod är det lämpligast att använda sig av flytande artikelplacering alternativt lagring utifrån försäljningsfrekvens. Vad som är viktigt att nämna är att båda metoder kräver ett stödsystem för att kunna implementeras. Gällande kategorisering av artiklar är det viktigt att kombinera detta med en passande plockmetod. I fallstudien var en möjlig lösning att kombinera ABC-kategorisering med zonplockning. Generellt sätt är AI en framtida värd investering då man kan använda sig av plockpositionsprincipen. AI möjliggör analysering av mer komplexa data som kundmönster och om denna implementering lyckas kan det leda till stora fördelar inom ett lager och för plockprocessen. Det är även viktigt att ha en ruttmetod som fungerar ihop med den placeringsmetod man använt sig av, då gångtiden och gångavståndet är det som utgör det mesta av den totala plocktiden. Denna studie visar att den optimala ruttmetoden är den som bör användas, och detta kräver en investering i ett stödsystem. Denna studie visade att det för tillfället finns många olika strategier och metoder på marknaden idag. Enligt fallstudien kan Care of Carl göra stora besparingar bara genom att ändra sina strategier och metoder. Orsaken är att de har varit reaktiva vid investeringav IT-stödsystem. Generellt sätt, om ett företag vill uppfylla de ökande kraven som finns till följd av globaliseringen och de kontinuerliga förändringarna inom logistikverksamheten, är AI nästa steg att ta. Metoderna som för närvarande används är inte tillräckliga och med hjälp av AI finns det möjlighet för förbättringar inom produktallokering och ruttplanering.

In several societies of emerging economies, an increasingly large community collects and buys bouquets for celebration purposes linked to the scalability and adjustment of components. Many schools use floral design customization to improve aspects of sociability in schools with problems of structured violence. So, second-hand cargoes of separate parts can build bouquets at a low cost. An order-picking algorithm can be used as the primary guideline because providers often implement a route system depending on the needs for the assembly of the floral design considering the different components that are required and using a model associated with the relevance of a match according to the customization of the new bouquets. In many cases, it has been challenging to develop an adequate transfer of the components; thus, the same routes have been used for a long time, and new provisions on traffic behavior and new routes are not considered. In this research is proposed a new algorithm based on the behavior of birds, called PSO to improve and solve the problem of this order picking model.

Mobile ateliers, also called pop-up stores, sell their products away from their warehouse. Therefore, it causes them to go back to it once a product in their mobile store runs out since the customer is waiting for them at the store. The need to spend as little time searching for the product at the warehouse is of the utmost importance. To solve this problem, the authors have decided to attack it as an order picking problem. With the use of the elephant search algorithm, they aim to optimize the time it takes to retrieve the product needed to form the warehouse by giving the sales representative the optimum picking order route, so he can go in and out of the warehouse in as few steps as possible.

These days the human factor is key to improving order picking processes. For example, in a line of processes to replace different parts of equipment or devices, it is necessary to find the best route in each case to minimize the time consumed. One of the tools used to search for solutions are the metaheuristic methods that use probability to find the best solution in the required time. One of them is the “bat algorithm” that has had outstanding results since its appearance in 2010. This metaheuristic is based on how bats hunt, supported by their system of echolocation. It is one of the most recent metaheuristics and has proven to be more effective than other similar optimization algorithms in different processes. However, the algorithm was initially designed for addressing continuous problems. In this chapter, the authors present an adaptation of bat algorithms to solve a discrete problem: order picking.

An increasingly large community—mainly from emerging economies—collects and buys “click toys” (i.e., scalable and adjustable toys composed by many pieces); this type of toy has been used for educational purposes; low-cost toys can be obtained from second-hand cargoes of separate parts, but it requires solving an order-picking problem where the primary guideline is that pieces could fit together. This is a common concern of logistical applications with limited resources. In general, providers of this type of service implement a handmade routing system in which a route is selected based on the pieces and customization required. Using this system, it is difficult to generate effective routes to pick-up pieces; for example, in many situations, the same routes are used for a long time, and new provisions on traffic behavior or new routes available to ensure the correct supply of each piece are not considered. In this chapter, the authors propose an amalgam model using case-based reasoning and an algorithm based on the behavior of birds to solve the problem.

A company that transports goods to supply customers usually needs to plan the routes that the fleet must follow, since transportation means a high percentage of the value added to goods. The vehicles must be loaded with the products located in the warehouse to fulfill the orders requested by customers. Many enterprises use order batching and picking systems to improve the warehouse management. This chapter describes a novel algorithm based on the methodology of variable neighborhood search for the variable cost and size bin packing problem. This problem considers that the sizes and costs of the bins can be different and there is no correlation between them, which is a sensible assumption for the order batching process in many applications. A series of computational experiments were conducted with a set of hard instances to validate this approach. The results show that the proposed algorithm outperforms the state-of-the-art algorithm, and a statistical hypothesis test is used to support the obtained conclusions.