Performance Improvement Through Benchmarking for Small and Medium Manufacturers (SMM)

Die wichtigsten Kostenfaktoren innerhalb einer Lieferkette lassen sich drei Kategorien zuordnen: Produktions-, Transport-und Lagerkosten. Die Strukturen dieser operativen Kosten im Hinblick auf die Gesamtkosten variieren stark je nach Industriesektor. Produktionskosten stellen dennoch die höchste Kostenart in fast allen Branchen dar, weniger bedeutend folgen danach jeweils die Transport- und Lagerkosten. Die Optimierung einer dieser Kategorien ohne Rücksicht auf die anderen kann zur Erhöhung der Gesamtkosten sowie der allgemeinen Leistungsfähigkeit führen. Diese Dissertation befasst sich mit dem „production distribution problem“ wobei synchronisierte Strategien entwickelt werden können, um die Leistung der Supply Chain zu verbessern und gleichzeitig die Gesamtkosten zu minimieren. Dazu wurde eine Fallstudie aus der Realität untersucht, nämlich das Praxisbeispiel eines Herstellers von Waschmitteln. Zwei Hauptszenarien werden bewertet. Das erste Szenario ist der konventionelle Plan, wobei die Hersteller dominieren. Dies bedeutet, dass der Hersteller findet seinen eigenen optimalen Job-Scheduling-Plan, während die Distribution versucht mit Hilfe dessen ihren optimalen Plan zu finden. Dadurch erhöhen sich die Distributionskosten. Das zweite Szenario betrifft die Synchronisation der Produktions-, Lagerhaltungs- und Transportzeitpläne. Ein zu diesem Zweck entwickeltes Java-Programm und die Job-Scheduling-Software Simal wurden für die Modellierung der konventionellen und integrierten Szenarien verwendet. Beide Szenarien wurden verglichen und validiert. Die Fallstudie betrachtet mehrere Produkte sowie ein schwer zu planendes flowshop- System. Die Ergebnisse zeigen, dass die Gesamtkosten, einschließlich der Einrichtungs-, Lager- und Transportkosten, minimiert werden können, wenn das synchronisierte System angewendet wird.The main cost factors within a supply chain can be put into the categories of production, transportation, and inventory costs. The composition of these operational costs relative to total costs varies largely by industry. However, production cost is the largest of all in almost all the industries, followed by transportation and inventory costs. Optimizing one of these categories without consideration of the others may increase the total cost and reduce the overall performance. This dissertation deals with the production distribution problem of developing synchronized strategies to improve the supply chain performance and to minimize the total cost. A real case study is investigated. This real-life case study is a powder detergent plant located in Libya. There are two main scenarios evaluated. The first scenario is the conventional plan, where the manufacturer dominates. This means the manufacturer finds his own optimum job-scheduling plan, and the distributor tries to find the optimum plan according to it. This will increase the distribution cost. The second scenario involves synchronizing the production, inventory and transportation schedules. A Java program and SimAl (job-schedulingsoftware) were constructed for modelling conventional and integrated scenarios. The two scenarios were compared and validated. The case study considered multiple products and a flowshop system which is difficult to schedule. The results show that the total costs, including setup, inventory and transportation, can be minimized when the synchronized system is applied

Models and Algorithms for Inbound and Outbound Truck to Door Scheduling

Cross-docking is a logistic strategy that facilitates rapid movement of consolidated products between suppliers and retailers within a supply chain. It is also a warehousing strategy that aims at reducing or eliminating storage and order picking, two of which are known to be major costly operations of any typical warehouse. This strategy has been used in the retailing, manufacturing, and automotive industries. In a cross-dock, goods are unloaded from incoming trucks, consolidated according to their destinations, and then, loaded into outgoing trucks with little or no storage in between. In this thesis, we address an integrated cross-dock door assignment and truck scheduling problem in which the assignment and sequencing of incoming trucks to strip doors and outgoing trucks to stack doors is optimized to minimize the total time to process all trucks. We present a mixed integer programming formulation to model this problem and some valid inequalities to strengthen the formulation. We also present two metaheuristics to obtain high quality solutions in reasonable CPU times. These algorithms use a mix of composite dispatching rules, constructive heuristics, local search heuristics which are embedded into a greedy randomized adaptive search procedure (GRASP) and an iterated local search (ILS). Results of computational experiments are presented to assess the performance of the proposed algorithms, in comparison with a general purpose solver

Decision of Multimodal Transportation Scheme Based on Swarm Intelligence

In this paper, some basic concepts of multimodal transportation and swarm intelligence were described and reviewed and analyzed related literatures of multimodal transportation scheme decision and swarm intelligence methods application areas. Then, this paper established a multimodal transportation scheme decision optimization mathematical model based on transportation costs, transportation time, and transportation risks, explained relevant parameters and the constraints of the model in detail, and used the weight coefficient to transform the multiobjective optimization problems into a single objective optimization transportation scheme decision problem. Then, this paper is proposed by combining particle swarm optimization algorithm and ant colony algorithm (PSACO) to solve the combinatorial optimization problem of multimodal transportation scheme decision for the first time; this algorithm effectively combines the advantages of particle swarm optimization algorithm and ant colony algorithm. The solution shows that the PSACO algorithm has two algorithms’ advantages and makes up their own problems; PSACO algorithm is better than ant colony algorithm in time efficiency and its accuracy is better than that of the particle swarm optimization algorithm, which is proved to be an effective heuristic algorithm to solve the problem about multimodal transportation scheme decision, and it can provide economical, reasonable, and safe transportation plan reference for the transportation decision makers

Inter-firm collaboration in transportation

Dans la littérature académique et professionnelle relative au transport de marchandise, il y a longtemps que les méthodes de planification avancées ont été identifiées comme un moyen de dégager des économies grâce à une efficacité accrue des opérations de transport. Plus récemment, la collaboration interentreprises dans la planification du transport a été étudiée comme une source de gain supplémentaire en efficacité et, par conséquent, une opportunité pour dégager de nouvelles économies pour les collaborateurs. Cependant, la mise en œuvre d'une collaboration interentreprises en transports soulève un certain nombre d’enjeux. Cette thèse aborde trois thèmes centraux de la collaboration interentreprises et démontre les contributions via des études de cas dans l’industrie forestière et du meuble. Premièrement, les moyens technologiques pour soutenir une collaboration en planification du transport sont étudiés. Un système d’aide à la décision supportant la collaboration en transport forestier est présenté. Deuxièmement, le partage entre les collaborateurs du coût commun en transport est étudié. Une méthode de répartition du coût de transport tenant compte de l'impact - l’augmentation du coût de transport - des exigences inégales entre des collaborateurs est proposée. Troisièmement, la création de groupes collaboratifs - des coalitions - dans un ensemble de collaborateurs potentiel est étudiée. Un modèle réseau pour la formation d’une coalition selon les intérêts d’un sous-ensemble de collaborateurs adoptant ou pas un comportement opportuniste est détaillé. De plus, pour soutenir l'étude des thèmes précédents, la thèse comprend deux revues de la littérature. Premièrement, une revue sur les méthodes de planification et les systèmes d’aide à la décision en transport forestier est présenté. Deuxièmement, à travers la proposition d'un cadre pour créer et gérer une collaboration en transport et, plus généralement en logistique, une revue de travaux sur le transport et la logistique collaborative est offerte.In the academic and professional literature on freight transportation, computer-based planning methods have a long time ago been identified as a means to achieve cost reduction through enhanced transportation operations efficiency. More recently, inter-firm collaboration in transportation planning has been investigated as a means to provide further gains in efficiency and, in turn, to achieve additional cost reduction for the collaborators. However, implementation of inter-firm collaboration in transportation raises a number of issues. This thesis addresses three central themes in inter-firm collaboration and exemplifies the contributions in case studies involving collaboration in furniture and forest transportation. First, technological means to enable collaboration in transportation planning are studied. Embedding a computer-based planning method for truck routing, a decision support system enabling collaborative transportation is presented. Second, sharing the common transportation cost among collaborators is studied. A cost allocation method taking into account the impact – an increase of the transportation cost – of uneven requirements among collaborators is proposed. Third, building collaborating groups (i.e. coalitions) among a set of potential collaborators is studied. A network model for coalition formation by a subset of self-interested collaborators adopting or not an opportunistic behaviour is detailed. Moreover, to support the study of the aforementioned themes, the thesis includes two literature reviews. First, a survey on planning methods and decision support systems for vehicle routing problem in forest transportation is presented. Second, through the proposition of a framework for building and managing collaboration in transportation and, more generally in logistics, a survey of works on collaborative transportation and logistics is given

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

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

Order Picking Problem: A Model for the Joint Optimisation of Order Batching, Batch Assignment Sequencing, and Picking Routing

Background: Order picking is a critical activity in end-product warehouses, particularly using the picker-to-part system, entail substantial manual labor, representing approximately 60% of warehouse work. Methods: This study develops a new linear model to perform batching, which allows for defining, assigning, and sequencing batches and determining the best routing strategy. Its goal is to minimise the completion time and the weighted sum of tardiness and earliness of orders. We developed a second linear model without the constraints related to the picking routing to reduce complexity. This model searches for the best routing using the closest neighbour approach. As both models were too complex to test, the earliest due date constructive heuristic algorithm was developed. To improve the solution, we implemented various algorithms, from multi-start with random ordering to more complex like iterated local search. Results: The proposed models were tested on a real case study where the picking time was reduced by 57% compared to single-order strategy. Conclusions: The results showed that the iterated local search multiple perturbation algorithms could successfully identify the minimum solution and significantly improve the solution initially obtained with the heuristic earliest due date algorithm

The Siting Of Multi-User Inland Intermodal Container Terminals In Transport Networks

Almost without exception, cargo movements by sea have their origins and destinations in the hinterlands and efficient land transport systems are required to support the transport of these cargo to and from the port. Furthermore, not all goods produced are exported or all goods consumed are imported. Those produced and consumed domestically also require efficient transport to move them from their production areas to areas of consumption. The use of trucks for these transport tasks and their disproportionate contribution to urban congestion and harmful emissions has led governments, transport and port authorities and other policy-makers to seek for more efficient and sustainable means of transport. A promising solution to these problems lies in the implementation of intermodal container terminals (IMTs) that interface with both road and rail or possibly inland waterway networks to promote the use of intermodal transport. This raises two important linked questions; where should IMTs be located and what will be their likely usage by individual shippers, each having a choice of whether or not to use the intermodal option. The multi-shipper feature of the problem and the existence of competing alternative modes means the demand for IMTs are outcome of many individual mode choice decisions and the prevailing cargo production and distribution patterns in the study area. This thesis introduces a novel framework underpinned by the principle of entropy maximisation to link mode choice decisions and variable cargo production and distribution problems with facility location problems. The overall model allows both decisions on facility location and usage to be driven by shipper preferences, following from the random utility interpretation of the discrete choice model. Several important properties of the proposed model are presented as propositions including the demonstration of the link between entropy maximisation and welfare maximisation. Exact and heuristic algorithms have been also developed to solve the overall problem. The computational efficiency, solution quality and properties of the heuristic algorithm are presented along with extensive numerical examples. Finally, the implementation of the model, illustration of key model features and use in practice are demonstrated through a case study

The design of effective and robust supply chain networks

Tableau d’honneur de la Faculté des études supérieures et postdoctorales, 2009-2010Pour faire face aux risques associés aux aléas des opérations normales et aux périls qui menacent les ressources d'un réseau logistique, une méthodologie générique pour le design de réseaux logistiques efficaces et robustes en univers incertain est développée dans cette thèse. Cette méthodologie a pour objectif de proposer une structure de réseau qui assure, de façon durable, la création de valeur pour l'entreprise pour faire face aux aléas et se prémunir contre les risques de ruptures catastrophiques. La méthodologie s'appuie sur le cadre de prise de décision distribué de Schneeweiss et l'approche de modélisation mathématique qui y est associée intègre des éléments de programmation stochastique, d'analyse de risque et de programmation robuste. Trois types d'événements sont définis pour caractériser l'environnement des réseaux logistiques: des événements aléatoires (ex. la demande, les coûts et les taux de changes), des événements hasardeux (ex. les grèves, les discontinuités d'approvisionnement des fournisseurs et les catastrophes naturelles) et des événements profondément incertains (ex. les actes de sabotage, les attentats et les instabilités politiques). La méthodologie considère que l'environnement futur de l'entreprise est anticipé à l'aide de scénarios, générés partiellement par une méthode Monte-Carlo. Cette méthode fait partie de l'approche de solution et permet de générer des replications d'échantillons de petites tailles et de grands échantillons. Elle aide aussi à tenir compte de l'attitude au risque du décideur. L'approche générique de solution du modèle s'appuie sur ces échantillons de scénarios pour générer des designs alternatifs et sur une approche multicritère pour l'évaluation de ces designs. Afin de valider les concepts méthodologiques introduits dans cette thèse, le problème hiérarchique de localisation d'entrepôts et de transport est modélisé comme un programme stochastique avec recours. Premièrement, un modèle incluant une demande aléatoire est utilisé pour valider en partie la modélisation mathématique du problème et étudier, à travers plusieurs anticipations approximatives, la solvabilité du modèle de design. Une approche de solution heuristique est proposée pour ce modèle afin de résoudre des problèmes de taille réelle. Deuxièmement, un modèle incluant les aléas et les périls est utilisé pour valider l'analyse de risque, les stratégies de resilience et l'approche de solution générique. Plusieurs construits mathématiques sont ajoutés au modèle de base afin de refléter différentes stratégies de resilience et proposer un modèle de décision sous risque incluant l'attitude du décideur face aux événements extrêmes. Les nombreuses expérimentations effectuées, avec les données d'un cas réaliste, nous ont permis de tester les concepts proposés dans cette thèse et d'élaborer une méthode de réduction de complexité pour le modèle générique de design sans compromettre la qualité des solutions associées. Les résultats obtenus par ces expérimentations ont pu confirmer la supériorité des designs obtenus en appliquant la méthodologie proposée en termes d'efficacité et de robustesse par rapport à des solutions produites par des approches déterministes ou des modèles simplifiés proposés dans la littérature