Energy and Route Optimization of Moving Devices

This thesis highlights our efforts in energy and route optimization of moving devices. We have focused on three categories of such devices; industrial robots in a multi-robot environment, generic vehicles in a vehicle routing problem (VRP) context, automatedguided vehicles (AGVs) in a large-scale flexible manufacturing system (FMS). In the first category, the aim is to develop a non-intrusive energy optimization technique, based on a given set of paths and sequences of operations, such that the original cycle time is not exceeded. We develop an optimization procedure based on a mathematical programming model that aims to minimize the energy consumption and peak power. Our technique has several advantages. It is non-intrusive, i.e. it requires limited changes in the robot program and can be implemented easily. Moreover,it is model-free, in the sense that no particular, and perhaps secret, parameter or dynamic model is required. Furthermore, the optimization can be done offline, within seconds using a generic solver. Through careful experiments, we have shown that it is possible to reduce energy and peak-power up to about 30% and 50% respectively. The second category of moving devices comprises of generic vehicles in a VRP context. We have developed a hybrid optimization approach that integrates a distributed algorithm based on a gossip protocol with a column generation (CG) algorithm, which manages to solve the tested problems faster than the CG algorithm alone. The algorithm is developed for a VRP variation including time windows (VRPTW), which is meant to model the task of scheduling and routing of caregivers in the context of home healthcare routing and scheduling problems (HHRSPs). Moreover,the developed algorithm can easily be parallelized to further increase its efficiency. The last category deals with AGVs. The choice of AGVs was not arbitrary; by design, we decided to transfer our knowledge of energy optimization and routing algorithms to a class of moving devices in which both techniques are of interest. Initially, we improve an existing method of conflict-free AGV scheduling and routing, such that the new algorithm can manage larger problems. A heuristic version of the algorithm manages to solve the problem instances in a reasonable amount of time. Later, we develop strategies to reduce the energy consumption. The study is carried out using an AGV system installed at Volvo Cars. The results are promising; (1)the algorithm reduces performance measures such as makespan up to 50%, while reducing the total travelled distance of the vehicles about 14%, leading to an energy saving of roughly 14%, compared to the results obtained from the original traffic controller. (2) It is possible to reduce the cruise velocities such that more energy is saved, up to 20%, while the new makespan remains better than the original one

On the Use of Equivalence Classes for Optimal and Suboptimal Bin Packing and Bin Covering

Bin packing and bin covering are important optimization problems in many industrial fields, such as packaging, recycling, and food processing. The problem concerns a set of items, each with its own value, that are to be sorted into bins in such a way that the total value of each bin, as measured by the sum of its item values, is not above (for packing) or below (for covering) a given target value. The optimization problem concerns minimizing, for bin packing, or maximizing, for bin covering, the number of bins. This is a combinatorial NP-hard problem, for which true optimal solutions can only be calculated in specific cases, such as when restricted to a small number of items. To get around this problem, many suboptimal approaches exist. This article describes the formulations of the bin packing and covering problems that allow finding the true optimum, for instance, counting hundreds of items using general-purpose MILP-solvers. Also presented are suboptimal solutions that come within less than 10% of the optimum while taking significantly less time to calculate, even ten to 100 times faster, depending on the required accuracy. Note to Practitioners - A typical case for bin covering is in food processing where food items are automatically sorted into trays of similar weight so that the overweight is minimized. Another application is in recycling, where items such as batteries should be put in crates of similar weight, so that the crates do not exceed a target weight due to later manual handling, but, at the same time, we want as few crates as possible. This is a bin packing problem. On an industrial scale, these tasks are fully automated. Though modern software tool\u27s efficiency to solve bin sorting problems has increased significantly in later years, the problems are inherently tough in the sense that the solution time grows exponentially with the number of items. This limits the problem sizes that can be solved to optimality within a reasonable time. Therefore, much research has focused on heuristic rules that give reasonable solving times while not giving the true optimal number of bins. However, in many cases, the true optimal solution is preferable, and sometimes even necessary, so this is an industrially interesting problem. This article describes an approach to solve the bin packing and covering problems to the true optimum that increases the limit of the number of items that can typically be handled. This is done by observing that items of the same value need not be distinguished. Instead, we can formulate packing/covering problems over item values rather than individual items and sort integer numbers of these values into bins, which allows us to solve to optimum for more than 500 items in a reasonable time. In addition, by redefining what we mean by the same value, we can consider more items to have the same value and achieve even better calculation efficiency

Using CP/SMT Solvers for Scheduling and Routing of AGVs

An improved method for solving conflict-free scheduling and routing of automated guided vehicles is proposed in this article, with promising results. This is achieved by reformulating the mathematical model of the problem, including several improvements and speedup strategies of an existing Benders decomposition method. A new heuristic is also presented that quickly yields high-quality solutions. Moreover, a real-large-scale industrial instance is solved using an open-source satisfiability module theories solver and a commercial constraint programming solver. According to the results, both of these general-purpose solvers can effectively solve the proposed models. Note to Practitioners - The problem of conflict-free routing and scheduling of automated guided vehicles (AGVs) in large-scale manufacturing systems has been an ever-present challenge for many AGV companies. Although these companies have developed rather efficient control policies and algorithms, retrofitting the existing heuristic to future\u27s denser, more complicated, and more demanding AGV layouts is not guaranteed to be easy. Furthermore, the installed system will not necessarily be as efficient as expected. Currently, it is common to use heuristics to allocate vehicles to orders and route them. There are also rules of thumbs to avoid collisions and deadlocks. However, with increasing demand for high-performance AGV solutions, it is of interest to employ optimization algorithms that handle the order allocation, scheduling, and routing in a more efficient way. In this article, we present an improved method to tackle this issue, with promising results. We have developed our work in collaboration with a Swedish AGV company, and we have investigated a real-large-scale industrial instance as our case study

Energy Optimization of Large-Scale AGV Systems

We propose an efficient optimization method, which addresses several performance criteria such as makespan, maximum lateness, and the sum of tardiness for an automated guided vehicle (AGV) system, together with its energy consumption. We show that the most important factors in energy consumption of AGVs are their cruise velocities and traveled distances. We also demonstrate that optimizing the productivity-related performance criteria also reduces energy consumption through less traveled distance. It also allows for the reduction of the cruise velocity that leads to more energy savings. Our experiments demonstrate that the optimization method outperforms the existing traffic controller with respect to the performance criteria and reduces energy consumption. The proposed method can reduce the energy consumption by around 38%, while the values of makespan, lateness, and tardiness remain better than those obtained from the existing traffic controller. An important advantage of this paper is that the evaluations are based oncollected data from a real large-scale manufacturing plant

Parallelization of a gossip algorithm for vehicle routing problems

A large body of research on the vehicle routing problem and its variants focuses on developing efficient solution procedures. Yet, not so many research articles have addressed parallelism in their proposed algorithms. Parallelized optimization algorithms can yield better solution quality in less amount of time. The main contribution of this paper is parallelization of a distributed algorithm based on the gossip protocol for vehicle routing problems VRP. The proposed algorithm can be applied to different variants of VRPs. While the resulting speed-ups are promising, the required effort for implementation of the parallelism is minimal, which makes the algorithm even more appealing

From trapezoid to polynomial: Next-generation energy-efficient robot trajectories

This paper evaluates some techniques for generating robot trajectories including state of the art energy-efficient trajectories, as well as more traditional trapezoidal velocity profiles used in today\u27s robot traditional trajectory planning. In our recent publications we have demonstrated that it is possible to convert the existing velocity profiles of KUKA robots to energy-efficient ones, with impressive results in reducing energy consumption and peak-power. In this paper we give more in-depth insight as to how the proposed velocity profiles compare with their trapezoidal or polynomial counter-parts

Productivity/energy optimisation of trajectories and coordination for cyclic multi-robot systems

The coordination of cyclic multi-robot systems is a critical issue to avoid collisions but also to obtain the shortest cycle-time. This paper presents a novel methodology for trajectory and coordination optimisation of cyclic multi-robot systems. Both velocity tuning and time delays are used to coordinate the robots that operate in close proximity and avoid collisions. The novel element is the non-linear programming optimisation model that directly co-adjusts the multi-robot coordination during the trajectory optimisation, which allows optimising these as one problem. The methodology is demonstrated for productivity/smoothness optimisation, and for energy efficiency optimisation. An experimental validation is done for a real-world case study that considers the multi-robot material handling system of a multi-stage tandem press line. The results show that the productivity optimisation with the methodology is competitive compared to previous research and that substantial improvements can be achieved, e.g. up to 50% smoother trajectories and 14% reduction in energy consumption for the same productivity. This paper addresses the current lack of systematic methodologies for generating optimal coordinated trajectories for cyclic multi-robot systems to improve the productivity, smoothness, and energy efficiency

Energy and Peak-power Optimization of Time-bounded Robot Trajectories

This paper, as an outcome of the EU project AREUS, heralds an optimization procedure that reduces up to 30% of energy consumption and up to 60% in peak-power for the trajectories that were tested on a real industrial robot. We have evaluated a number of cost functions and tested our algorithm for a variety of scenarios such as varying cycle times, payloads, and single/two-robot cases. The significance of our work is not only in the impressive savings, simplicity of implementation and preserving path and cycle time, but also in the effort made to carry out the optimization and experiments in as realistic conditions as possible, and the guidelines we provide to achieve this

Modeling and Optimization of Hybrid Systems for the Tweeting Factory

In this paper a predicate transition model for discrete event systems is generalized to include continuous dynamics, and the result is a modular hybrid predicate transition model. Based on this model, a hybrid Petri net including explicit differential equations and shared variables is also proposed. It is then shown how this hybrid Petri net model can be optimized based on a simple and robust nonlinear programming formulation. The procedure only assumes that desired sampled paths for a number of interacting moving devices are given, while originally equidistant time instances are adjusted to minimize a given criterion. This optimization of hybrid systems is also applied to a real robot station with interacting devices, which results in about 30\% reduction in energy consumption. Moreover, a flexible online and event-based information architecture called the Tweeting Factory is proposed. Simple messages (tweets) from all kinds of equipment are combined into high-level knowledge, and it is demonstrated how this information architecture can be used to support optimization of robot stations