Evolutionary algorithms for robot path planning, task allocation and collision avoidance in an automated warehouse

Thesis (PhD)--Stellenbosch University, 2022.ENGLISH ABSTRACT: Research with regard to path planning, task allocation and collision avoidance is important for improving the field of warehouse automation. The dissertation addresses the topic of routing warehouse picking and binning robots. The purpose of this dissertation is to develop a single objective and multi-objective algorithm framework that can sequence products to be picked or binned, allocate the products to robots and optimise the routing through the warehouse. The sequence of the picking and binning tasks ultimately determines the total time for picking and binning all of the parts. The objectives of the algorithm framework are to minimise the total time for travelling as well as the total time idling, given the number of robots available to perform the picking and binning functions. The algorithm framework incorporates collision avoidance since the aisle width does not allow two robots to pass each other. The routing problem sets the foundation for solving the sequencing and allocation problem. The best heuristic from the routing problem is used as the strategy for routing the robots in the sequencing and allocation problem. The routing heuristics used to test the framework in this dissertation include the return heuristic, the s-shape heuristic, the midpoint heuristic and the largest gap heuristic. The metaheuristic solution strategies for single objective part sequencing and allocating problem include the covariance matrix adaptation evolution strategy (CMA-ES) algorithm, the genetic algorithm (GA), the guaranteed convergence particle swarm optimisation (GCPSO) algorithm, and the self-adaptive differential evolution algorithm with neighbourhood search (SaNSDE). The evolutionary multi-objective algorithms considered in this dissertation are the non-dominated sorting genetic algorithm III (NSGA-III), the multi-objective evolutionary algorithm based on decomposition (MOEAD), the multiple objective particle swarm optimisation (MOPSO), and the multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES). Solving the robot routing problem showed that the return routing heuristic outperformed the s-shape, largest gap and midpoint heuristics with a significant margin. The return heuristic was thus used for solving the routing of robots in the part sequencing and allocation problem. The framework was able to create feasible real-world solutions for the part sequencing and allocation problem. The results from the single objective problem showed that the CMA-ES algorithm outperformed the other metaheuristics on the part sequencing and allocation problem. The second best performing metaheuristic was the SaNSDE. The GA was the third best metaheuristic and the worst performing metaheuristic was the GCPSO. The multi-objective framework was able to produce feasible trade-off solutions and MOPSO was shown to be the best EMO algorithm to use for accuracy. If a large spread and number of Pareto solutions are the most important concern, MOEAD should be used. The research contributions include the incorporation of collision avoidance in the robot routing problem when using single and multi-objective algorithms as solution strategies. This dissertation contributes to the research relating to the performance of metaheuristics and evolutionary multi-objective algorithms on routing, sequencing, and allocation problems. To the best of the author’s knowledge, this dissertation is the first where these four metaheuristics and evolutionary multi-objective algorithms have been tested for solving the robot picking and binning problem, given that all collisions must be avoided. It is also the first time that this specific variation of the part sequencing and allocation problem has been solved using metaheuristics and evolutionary multi-objective algorithms, taking into account that all collisions must be avoided.AFRIKAANSE OPSOMMING: Navorsing in verband met roete beplanning, part allokasie en botsing vermyding is belangrik vir die bevordering van die pakhuis automatisering veld. Die verhandeling handel oor die onderwerp van parte wat gestoor en gehaal moet word en die verkillende parte moet ook gealokeer word aan ’n spesifieke robot. Die doel van hierdie verhandeling is om ’n enkele doelwit en ’n multidoelwit algoritme raamwerk te ontwikkel wat parte in ’n volgorde rangskik en ook die parte aan ’n robot alokeer. Die roete wat die robot moet volg deur die pakhuis moet ook geoptimeer word om die minste tyd te verg. Die volgorde van die parte bepaal uiteindelik die totale tyd wat dit neem vir die robot om al die parte te stoor en te gaan haal. Die doelwitte van die algoritme raamwerk is om die totale reistyd en die totale ledige tyd te minimeer, gegewe die aantal beskikbare robotte in die sisteem om die stoor en gaan haal funksies uit te voer. Die algoritme raamwerk bevat botsingsvermyding, aangesien die gangbreedte van die pakhuis nie toelaat dat twee robotte mekaar kan verbygaan nie. Die roete probleem lˆe die grondslag vir die oplossing van die volgorde en allokerings probleem. Die beste heuristiek vir die roete probleem word verder gebruik in die volgorde en allokerings probleem. Die verskillende roete heuristieke wat in hierdie verhandeling oorweeg was, sluit in die terugkeer heuristiek, die s-vorm heuristiek, die middelpunt heuristiek en die grootste gaping heuristiek. Die metaheuristieke vir die volgorde en allokerings probleem sluit die volgende algoritmes in: die kovariansie matriks aanpassing evolusie algoritme (CMA-ES), die genetiese algoritme (GA), die gewaarborgde konvergerende deeltjie swermoptimerings (GCPSO) algoritme, en laastens die selfaanpassende differensi¨ele evolusie algoritme met die teenwoordigheid van buurtsoek (SaNSDE). Die evolusionêre multidoelwit algoritmes wat oorweeg was vir die volgorde en allokerings probleem sluit die volgende algoritmes in: die multidoelwit kovariansie matriks aanpassing evolusie algoritme (MO-CMA-ES), die nie-dominerende sortering genetiese algoritme III (NSGA-III), die multidoelwit evolusionˆere algoritme gebaseer op ontbinding (MOEAD) en laastens die multidoelwit deeltjie swermoptimering algoritme (MOPSO) Oplossings van die robot roete probleem het gewys dat die terugkeer heuristiek die s-vorm, grootste gaping en middelpunt heuristiek met ’n beduidende marge oortref het. Die terugkeer heuristiek is dus gebruik vir die oplossing van die roete beplanning van robotte in die volgorde en allokasie probleem. Die raamwerk was doeltreffend en die resultate het getoon, vir die enkel doelwit probleem, dat die CMA-ES algoritme beter gevaar het as die ander metaheuristieke vir die volgorde en allokasie probleem. Die SaNSDE was die naas beste presterende metaheuristiek. Die GA was die derde beste metaheuristiek, en die metaheuristiek wat die slegste gevaar het, was die GCPSO. Vir die multidoelwit probleem het die MOPSO die beste gevaar, as akkuraatheid die belangrikste doelwit is. As ’n grootter verskeidenheid die belangrikste is, is die MOEAD meer geskik om ’n oplossing te vind. Die navorsingsbydraes sluit in dat vermyding van botsings in ag geneem word in die robot roete probleem. Hierdie verhandeling dra by tot die navorsing oor die oplossing van roete beplanning, volgorde en allokasie probleme met metaheuristieke. Na die beste van die outeur se kennis is hierdie die eerste keer dat al vier metaheuristieke getoets was om die robot stoor-en-gaan haal probleem op te los, onder die kondisie dat alle botsings vermy moet word. Dit is ook die eerste keer dat hierdie spesifieke variant, enkel-en-multidoelwit probleem van die volgorde en allokasie van parte met behulp van metaheuristieke en multidoelwit evolusionˆere algoritmes opgelos was, met die inagneming dat alle botsings vermy moet word.Doctora

An improved MOEA/D algorithm for multi-objective multicast routing with network coding

Network coding enables higher network throughput, more balanced traffic, and securer data transmission. However, complicated mathematical operations incur when packets are combined at intermediate nodes, which, if not operated properly, lead to very high network resource consumption and unacceptable delay. Therefore, it is of vital importance to minimize various network resources and end-to-end delays while exploiting promising benefits of network coding. Multicast has been used in increasingly more applications, such as video conferencing and remote education. In this paper the multicast routing problem with network coding is formulated as a multi-objective optimization problem (MOP), where the total coding cost, the total link cost and the end-to-end delay are minimized simultaneously. We adapt the multi-objective evolutionary algorithm based on decomposition (MOEA/D) for this MOP by hybridizing it with a population-based incremental learning technique which makes use of the global and historical information collected to provide additional guidance to the evolutionary search. Three new schemes are devised to facilitate the performance improvement, including a probability-based initialization scheme, a problem-specific population updating rule, and a hybridized reproduction operator. Experimental results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art MOEAs regarding the solution quality and computational time

A study on flexible flow shop and job shop scheduling using meta-heuristic approaches

Scheduling aims at allocation of resources to perform a group of tasks over a period of time in such a manner that some performance goals such as flow time, tardiness, lateness, and makespan can be minimized. Today, manufacturers face the challenges in terms of shorter product life cycles, customized products and changing demand pattern of customers. Due to intense competition in the market place, effective scheduling has now become an important issue for the growth and survival of manufacturing firms. To sustain in the current competitive environment, it is essential for the manufacturing firms to improve the schedule based on simultaneous optimization of performance measures such as makespan, flow time and tardiness. Since all the scheduling criteria are important from business operation point of view, it is vital to optimize all the objectives simultaneously instead of a single objective. It is also essentially important for the manufacturing firms to improve the performance of production scheduling systems that can address internal uncertainties such as machine breakdown, tool failure and change in processing times. The schedules must meet the deadline committed to customers because failure to do so may result in a significant loss of goodwill. Often, it is necessary to reschedule an existing plan due to uncertainty event like machine breakdowns. The problem of finding robust schedules (schedule performance does not deteriorate in disruption situation) or flexible schedules (schedules expected to perform well after some degree of modification when uncertain condition is encountered) is of utmost importance for real world applications as they operate in dynamic environments

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

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Optimal Inventory Control and Distribution Network Design of Multi-Echelon Supply Chains

Optimale Bestandskontrolle und Gestaltung von Vertriebsnetzen mehrstufiger Supply Chains Aufgrund von Global Sourcing, Outsourcing der Produktion und Versorgung weltweiter Kunden innerhalb eines komplexen Vertriebsnetzes, in welchem mehrere Anlagen durch verschiedene Aktivitäten miteinander vernetzt sind, haben die meisten Unternehmen heutzutage immer komplexere Supply Chain-Netzwerke in einer immer unbeständiger werdenden Geschäftsumgebung. Mehr beteiligte Unternehmen in der Wertschöpfungskette bedeuten mehr Knoten und Verbindungen im Netzwerk. Folglich bringt die Globalisierung Komplexität und neue Herausforderungen, obwohl Unternehmen immer stärker von globalen Supply Chains profitieren. In einer solchen Geschäftsumgebung müssen sich die Akteure innerhalb der Supply Chain (SC) auf die effiziente Verwaltung und Koordination des Materialflusses im mehrstufigen System fokussieren, um diesen Herausforderungen handhaben zu können. In vielen Fällen beinhaltet die Supply Chain eines Unternehmens unterschiedliche Entscheidungen auf verschiedenen Planungsebenen, wie der Anlagenstandort, die Bestände und die Verkehrsmittel. Jede dieser Entscheidungen spielt eine bedeutende Rolle hinsichtlich der Gesamtleistung und das Verhältnis zwischen ihnen kann nicht ignoriert werden. Allerdings wurden diese Entscheidungen meist einzeln untersucht. In den letzten Jahren haben zahlreiche Studien die Bedeutung der Integration von beteiligten Entscheidungen in Supply Chains hervorgehoben. In diesem Zusammenhang sollten Entscheidungen über Anlagenstandort, Bestand und Verkehrsmittel gemeinsam in einem Optimierungsproblem des Vertriebsnetzes betrachtet werden, um genauere Ergebnisse für das Gesamtsystem zu erzeugen. Darüber hinaus ist ein effektives Management des Materialflusses über die gesamte Lieferkette hinweg, aufgrund der dynamischen Umgebung mit mehreren Zielen, ein schwieriges Problem. Die Lösungsansätze, die in der Vergangenheit verwendet wurden, um Probleme mehrstufiger Supply Chains zu lösen, basierten auf herkömmlichen Verfahren unter der Verwendung von analytischen Techniken. Diese sind jedoch nicht ausreichend, um die Dynamiken in Lieferketten zu bewältigen, aufgrund ihrer Unfähigkeit, mit den komplexen Interaktionen zwischen den Akteuren der Supply Chain umzugehen und das stochastische Verhalten zu repräsentieren, das in vielen Problemen der realen Welt besteht. Die Simulationsmodellierung ist in letzter Zeit zu einem wichtigen Instrument geworden, da ein analytisches Modell nicht in der Lage ist, ein System abzubilden, das sowohl der Variabilität als auch der Komplexität unterliegt. Allerdings erfordern Simulationen umfangreiche Laufzeiten, um möglichst viele Lösungen zu bewerten und die optimale Lösung für ein definiertes Problem zu finden. Um mit dieser Schwierigkeit umzugehen, muss das Simulationsmodell in Optimierungsalgorithmen integriert werden. In Erwiderung auf die oben genannten Herausforderungen, ist eines der Hauptziele dieser Arbeit, ein Modell und ein Lösungsverfahren für die optimale Gestaltung von Vertriebsnetzwerken integrierter Supply Chains vorzuschlagen, das die Beziehung zwischen den Entscheidungen der verschiedenen Planungsebenen berücksichtigt. Die Problemstellung wird mithilfe von Zielfunktionen formuliert, um die Kundenabdeckung zu maximieren, den maximalen Abstand von den Anlagenstandorten zu den Bedarfspunkten zu minimieren oder die Gesamtkosten zu minimieren. Um die optimale Anzahl, Kapazität und Lage der Anlagen zu bestimmen, kommen der Nondominated Sorting Genetic Algorithm II (NSGA-II) und der Quantum-based Particle Swarm Optimization Algorithm (QPSO) zum Einsatz, um dieses Optimierungsproblem im Spannungsfeld verschiedener Ziele zu lösen. Aufgrund der Komplexität mehrstufiger Systeme und der zugrunde liegenden Unsicherheiten, wurde die Optimierung von Beständen über die gesamte Lieferkette hinweg zur wesentlichen Herausforderung, um die Kosten zu reduzieren und die Serviceanforderungen zu erfüllen. In diesem Zusammenhang ist das andere Ziel dieser Arbeit die Darstellung eines simulationsbasierten Optimierungs-Frameworks, in dem die Simulation, basierend auf der objektorientierten Programmierung, entwickelt wird und die Optimierung metaheuristische Techniken mit unterschiedlichen Kriterien, wie NSGA-II und MOPOSO, verwendet. Insbesondere das geplante Framework regt einen großen Nutzen an, sowohl für das Bestandsoptimierungsproblem in mehrstufigen Supply Chains, als auch für andere Logistikprobleme.Today, most companies have more complex supply chain networks in a more volatile business environment due to global sourcing, outsourcing of production and serving customers all over the world with a complex distribution network that has several facilities linked by various activities. More companies involved within the value chain, means more nodes and links in the network. Therefore, globalization brings complexities and new challenges as enterprises increasingly benefit from global supply chains. In such a business environment, Supply Chain (SC) members must focus on the efficient management and coordination of material flow in the multi-echelon system to handle with these challenges. In many cases, the supply chain of a company includes various decisions at different planning levels, such as facility location, inventory and transportation. Each of these decisions plays a significant role in the overall performance and the relationship between them cannot be ignored. However, these decisions have been mostly studied individually. In recent years, numerous studies have emphasized the importance of integrating the decisions involved in supply chains. In this context, facility location, inventory and transportation decisions should be jointly considered in an optimization problem of distribution network design to produce more accurate results for the whole system. Furthermore, effective management of material flow across a supply chain is a difficult problem due to the dynamic environment with multiple objectives. In the past, the majority of the solution approaches used to solve multi-echelon supply chain problems were based on conventional methods using analytical techniques. However, they are insufficient to cope with the SC dynamics because of the inability to handle to the complex interactions between the SC members and to represent stochastic behaviors existing in many real world problems. Simulation modeling has recently become a major tool since an analytical model is unable to formulate a system that is subject to both variability and complexity. However, simulations require extensive runtime to evaluate many feasible solutions and to find the optimal one for a defined problem. To deal with this problem, simulation model needs to be integrated in optimization algorithms. In response to the aforementioned challenges, one of the primary objectives of this thesis is to propose a model and solution method for the optimal distribution network design of an integrated supply chain that takes into account the relationship between decisions at the different levels of planning horizon. The problem is formulated with objective functions to maximize the customer coverage or minimize the maximal distance from the facilities to the demand points and minimize the total cost. In order to find optimal number, capacity and location of facilities, the Nondominated Sorting Genetic Algorithm II (NSGA-II) and Quantum-based Particle Swarm Optimization Algorithm (QPSO) are employed for solving this multiobjective optimization problem. Due to the complexities of multi-echelon system and the underlying uncertainty, optimizing inventories across the supply chain has become other major challenge to reduce the cost and to meet service requirements. In this context, the other aim of this thesis is to present a simulation-based optimization framework, in which the simulation is developed based on the object-oriented programming and the optimization utilizes multi-objective metaheuristic techniques, such as the well-known NSGA-II and MOPSO. In particular, the proposed framework suggests a great utility for the inventory optimization problem in multi-echelon supply chains, as well as for other logistics-related problems

Multi-objective optimisation using the Bees Algorithm

In the real world, there are many problems requiring the best solution to satisfy numerous objectives and therefore a need for suitable Multi-Objective Optimisation methods. Various Multi-Objective solvers have been developed recently. The classical method is easily implemented but requires repetitive program runs and does not generate a true "Pareto" optimal set. Intelligent methods are increasingly employed, especially population-based optimisation methods to generate the Pareto front in a single run. The Bees Algorithm is a newly developed population-based optimisation algorithm which has been verified in many fields. However, it is limited to solving single optimisation problems. To apply the Bees Algorithm to a Multi- Objective Optimisation Problem, either the problem is converted to single objective optimisation or the Bees Algorithm modified to function as a Multi- Objective solver. To make a problem into a single objective one, the weighted sum method is employed. However, due to failings of this classical method, a new approach is developed to generate a true Pareto front by a single run. This work also introduces an enhanced Bees Algorithm. A new dynamic selection procedure improves the Bees Algorithm by reducing the number of parameters and new neighbourhood search methods are adopted to optimise the Pareto front. The enhanced algorithm has been tested on Multi-Objective benchmark functions and the classical Environmental/Economic power Dispatch Problem (EEDP). The results obtained compare well with those produced by other population- based algorithms. Due to recent trends in renewable energy systems, it is necessary to have a new model of the EEDP. Therefore, the EEDP was amended in conjunction with the Bees Algorithm to identify the best design in terms of energy performance and carbon emission reduction by adopting zero and low carbon technologies. This computer-based tool supports the decision making process in the design of a Low-Carbon City.EThOS - Electronic Theses Online ServiceGBUnited Kingdo