Multirobot Task Allocation in e-Commerce Robotic Mobile Fulfillment Systems

The robotic mobile fulfillment system (RMFS) is a new automatic warehousing system, a type of green technology, and an emerging trend in the logistics industry. In this study, we take an RMFS as the research object and combine the connected issues of storage location assignment and path planning into one optimization problem from the perspective of collaborative optimization. A sustainable mathematical model for the collaborative optimization of storage location assignment and path planning (COSLAPP) is established, which considers the relationship between the location assignment of goods and rack storage and path planning in an RMFS. On this basis, we propose a location assignment strategy for goods clustering and rack turnover, which utilizes reservation tables, sets AGV operation rules to resolve AGV running conflicts, and improves the A-star(A*) algorithm based on the node load to find the shortest path by which the AGV handling the racks can complete the order picking. Ultimately, simulation studies were performed to ascertain the effectiveness of COSLAPP in the RMFS; the results show that the new approach can significantly improve order picking efficiency, reduce energy consumption, and lessen the operating costs of the warehouse of a distribution center.

Robotic Mobile Fulfillment Systems (RMFS) are crucial for increasing the efficiency of the logistics sector. One of the essential processes in such systems is efficiently allocating tasks to robots to reduce costs and speed up order fulfillment. In this context, we found a gap in exploring the possibility of using heterogeneous robot fleets in RMFS plus other real constraints, such as dynamic orders and multi-delivery stations. This work presents a space decomposition-driven heuristic based on Voronoi Tessellation concepts to efficiently allocate tasks to robots in an environment with several constraints. Experiments deal with a large simulated smart warehouse, with dynamic orders and thousands of tasks and robots. Results showed that our solution reduced route costs and time to fulfill orders up to 45% and 24%, respectively, compared to the state-of-the-art algorithm.

Multi-robot systems comprising of heterogeneous autonomous vehicles on land, air, water are being increasingly used to assist or replace humans in different hazardous missions. Two crucial aspects in such multi-robot systems are to: a) explore an initially unknown region of interest to discover tasks, and, b) allocate and share the discovered tasks between the robots in a coordinated manner using a multi-robot task allocation (MRTA) algorithm. In this paper, we describe results from our research on multi-robot terrain coverage and MRTA algorithms within an autonomous landmine detection scenario, done as part of the COMRADES project. Each robot is equipped with a different type of landmine detection sensor and different sensors, even of the same type, can have different degrees of accuracy. The landmine detection-related operations performed by each robot are abstracted as tasks and multiple robots are required to complete a single task. First, we describe a distributed and robust terrain coverage algorithm that employs Voronoi partitions to divide the area of interest among the robots and then uses a single-robot coverage algorithm to explore each partition for potential landmines. Then, we describe MRTA algorithms that use the location information of discovered potential landmines and employ either a greedy strategy, or, an opportunistic strategy to allocate tasks among the robots while attempting to minimize the time (energy) expended by the robots to perform the tasks. We report experimental results of our algorithms using accurately-simulated Corobot robots within the Webots simulator performing a multi-robot, landmine detection operation.

The Robotic Mobile Fulfillment System (RMFS) is a robotized material handling system where multiple robots transport movable shelves to and from stocking points and workstations in a warehouse. In this paper, we deal with the problem of finding collision-free routes for multiple robots to move from their starting points to destinations in a RMFS, which is defined as a generalization of the multi-agent path finding problem (MAPFP). In RMFSs, collisions among moving robots may occur due to the physical volume of robots and the inexact execution of planned routes by robots in terms of the planned timing of passing through points on the routes, which makes it difficult to directly apply the existing algorithms for MAPFP that assume the exact execution of planned routes by agents with no physical volume. Therefore, we defined the problem as a multi-agent path finding problem with the prespecified time spacing at points to address the possible sources of collisions among robots, and propose an integer optimization model for the problem. Since the proposed integer optimization model has a large number of decision variables and constraints, we propose heuristic and exact algorithms based on the Lagrangian relaxation technique and the conflict based search. Experimental results show that the proposed algorithms are effective in finding good quality solutions within a limited time even when the number of robots or the time spacing are large.

Optimization models for scheduling operations in robotic mobile fulfillment systems

Task allocation is one of the research focuses of multi-robot system. On the base of presenting the utility values matrix of robots relative to tasks and analyzing the characteristics of multi-robot task allocation, we build the multi-robot task allocation model based on robotic utility value. In order to prevent the basic particle swarm optimization (PSO) algorithm from converging on local optimum, this paper proposes a modified particle swarm optimization (MPSO) algorithm by introducing the linear decrease mechanism of inertia weight and the concept of adjustment operator and adjustment sequence. With the evolution of velocity in the MPSO algorithm, particle not only studies from the historical optimum individual of itself and population, but also studies from the other stochastic individuals with some probability. Finally, the MPSO algorithm is used to solve the task allocation problem of RoboCup 2D soccer robot system, the efficiency of this modified algorithm is proved through simulation results.

Robotic mobile fulfillment systems have received increased attention in high throughput supply chain solutions. These systems are complex and require understanding of the single components and their interaction. Components include picking, replenishing, stock keeping unit allocation and task allocation. This paper focuses on the component of stock keeping unit allocation which is an NP-Hard combinatorial optimization problem and belongs to the cutting & packing problems. The problem is defined via a mathematical model which is the first bin packing model for pods with compartments in RMFS. The resulting mixed integer programming model is then solved for small instances via Gurobi, a state-of-the-art solver for mathematical programming. The model is verified with a small-scale example. To obtain a solution for larger instances, we develop a simulated annealing approach and compare it to the exact model. Different operators are compared and evaluated on heterogeneous toy-examples of different size. Finally, a packing case with stock keeping units and pods derived from an industrial case is explored, which demonstrated the application of our packing algorithm to large instances in the industrial environment.

Amazon is a world leader in e-commerce and robotization. Its robotic mobile fulfillment systems (RMFS) aim to speed up product delivery by using robots to move shelves. In terms of speed, precision, and profitability, the RMFS solution seems to guarantee economic and commercial success. Nevertheless, the integration of robotization is accompanied by an increase in workload, daily tasks, and accident risks. The aim of the present study is to provide an in-depth analysis of Amazon RFMS employees' perceptions of current robotization. Through a netnographic study of the comments and opinions of Amazon employees working in robotized warehouses, the empirical study will gather argumentative thinking around the effect of digital transformation in RFMS in terms of employability, working conditions, and quality of work life. The results of the study show a consolidation of working conditions accompanied by stress and a risk of job loss. Certain risks and doubts are also present, notably around the possibility of work-related accidents and the man-machine interactions.

Joint optimization of storage assignment and order batching in robotic mobile fulfillment system with dynamic storage depth and surplus items

Many e-commerce warehouses use robotic mobile fulfillment system (RMFS), where humans collaborate with robots to pick the orders. The performance of such systems depends on the joint performance of robots and humans. The performance of the workers is affected by fatigue, or the energy that it takes them to pick the items. In this paper, we study the effect of scattered storage assignment, order batching, and pod selection to minimise the total picker energy expenditure and the total robot transport distance. We introduce a mixed-integer programming formulation (called JIOPP) and introduce the NSGAII-ILS algorithm to heuristically solve it for real-world instances. Extensive numerical experiments on real-world instances show that NSGAII-ILS is competitive compared to state-of-the-art algorithms and can find Pareto solution sets that are closer to the true Pareto frontier. We evaluate the effects of batch sizes, the number of pod layers, and different pod selection policies. The results show that batching orders can save more than 35% of the picker's energy expenditure and more than 70% of the robot's transportation distance. Using the ‘golden zone’ layers on the pod selecting the right pod for retrieval are important for striking a balance between worker fatigue and order picking efficiency.

Robotic handling systems are increasingly applied in distribution centers. They require little space, provide flexibility in managing varying demand requirements, and are able to work 24/7. This makes them particularly fit for e-commerce operations. This paper reviews new categories of automated and robotic handling systems, such as shuttle-based storage and retrieval systems, shuttle-based compact storage systems, and robotic mobile fulfillment systems. For each system, we categorize the literature in three groups: system analysis, design optimization, and operations planning and control. Our focus is to identify the research issue and operations research modeling methodology adopted to analyze the problem. We find that many new robotic systems and applications have hardly been studied in academic literature, despite their increasing use in practice. Because of unique system features (such as autonomous control, flexible layout, networked and dynamic operation), new models and methods are needed to address the design and operational control challenges for such systems, in particular, for the integration of subsystems. Integrated robotic warehouse systems will form the next category of warehouses. All vital warehouse design, planning, and control logic, such as methods to design layout, storage and order-picking system selection, storage slotting, order batching, picker routing, and picker to order assignment, will have to be revisited for new robotized warehouses.

With the development of the social economy and the improvement of the consumption concept, a new business model combining offline and online has been promoted. The warehousing system is one of the important links of commodity production and circulation, which involves storage, sorting, and distribution. It has a significant impact on the operation cost and the efficiency of the whole logistics system. The progress of robot technology, the Internet of things, and artificial intelligence technology promotes the automation and intelligence of storage systems. The Robotic Mobile Fulfillment Systems (RMFS), which takes the automatic guided vehicles (AGVs) as the way of handling and picking, greatly improves the space utilization, operation efficiency, and flexibility of the system. This paper studies the RMFS with fixed shelves and establishes the performance evaluation model of the picking system considering the AGVs congestion by establishing the queuing network. The effectiveness of the model is verified by simulation, and the optimization of system parameter configuration is further discussed according to the experimental data.

Task allocation is an important aspect of many multi-robot systems. The features and complexity of multi-robot task allocation (MRTA) problems are dictated by the requirements of the particular domain under consideration. These problems can range from those involving instantaneous distribution of simple, independent tasks among members of a homogenous team, to those requiring the time-extended scheduling of complex interrelated multi-step tasks for members of a heterogenous team related by several constraints. The existing widely used taxonomy for task allocation in multi-robot systems was designed for problems with independent tasks and does not deal with problems with interrelated utilities and constraints. While that taxonomy was a ground-breaking contribution to the MRTA literature, a survey of recent work in MRTA reveals that it is no longer a sufficient taxonomy, due to the increasing importance of interrelated utilities and constraints in realistic MRTA problems under consideration. Thus, in this paper, we present a new, comprehensive taxonomy, iTax, that explicitly takes into consideration the issues of interrelated utilities and constraints. Our taxonomy maps categories of MRTA problems to existing mathematical models from combinatorial optimization and operations research, and hence draws important parallels between robotics and these fields.

Edge intelligence and agnostic robotic paradigm in resource synchronisation and sharing in flexible robotic and facility control system

With the advent of Industry 4.0, the seamless integration of industrial systems and unmanned technologies has significantly accelerated the development of smart industries. However, the research on task allocation for railway maintenance robots remains limited, particularly with respect to optimizing costs and efficiency within smart railway systems. To address this gap, the present study explores multi-robot task allocation for automated orbital bolt maintenance, aiming to enhance operational efficiency by minimizing both makespan and total travel distance for all robots. To achieve this, an improved hybrid algorithm combining NSGA-II and MOPSO is proposed. Initially, a dynamic task planning method, tailored to the specific conditions of railway operations, is developed. This method uses the coordinates of track bolts to extract environmental features, enabling the dynamic partitioning of task areas. Subsequently, a multi-elite archive strategy is introduced, along with an adaptive mechanism for adjusting crossover and mutation probabilities. This ensures the preservation and maintenance of multiple solutions across various Pareto fronts, effectively mitigating the premature convergence commonly observed in traditional NSGA-II algorithms. Moreover, the integration of the MOPSO algorithm strikes a balance between local and global search capabilities, thereby enhancing both optimization efficiency and solution quality. Finally, a series of experiments, conducted with varying task sizes and robot quantities during the railway maintenance window, validate the effectiveness and improved performance of the proposed algorithm in addressing the multi-robot task allocation problem.