29 research outputs found

    New solution approaches for optimization problems with combinatorial aspects in logistics management

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    This dissertation comprises five papers, which have been published in scientific journals between 2019 and 2022. The papers consider logistic optimization problems from three different subjects with a focus on intra-logistics. All considered optimization problems have strong combinatorial aspects. To solve the considered problems, various solution approaches including different decomposition techniques are employed. Paper 1 investigates the optimization of the layout and storage assignment in warehouses with U-shaped order picking zones. The paper considers two objectives, namely minimizing the order picker's walking distance and physical strain during order picking. To solve the problem, a semantic decomposition approach is proposed, which solves the problem in polynomial time. In a computational study, both considered objectives are found to be mostly complementary. Moreover, suggestions for advantageous layout designs and storage assignments are derived. Paper 2 considers the problem of how to stow bins on tow trains in order to minimize the handling personnel's physical strain for loading and unloading. The problem is shown to be NP-hard and decomposed semantically. Utilising the decomposition, the problem is solved exactly with dynamic programming and heuristically with a greedy randomized adaptive search procedure. A consecutive computational study shows that both procedures perform well. Beyond that, it investigates the influence of the tow train wagons' design on the considered objective. Paper 3 is concerned with the problem of scheduling jobs with time windows on unrelated parallel machines, which is a NP-hard optimization problem that has applications, i.a., in berth allocation and truck dock scheduling. The paper presents an exact logic-based Benders decomposition procedure and a heuristic solution approach based on a set partitioning formulation of the problem. Moreover, three distinct objectives, namely minimizing the makespan, the maximum flow time, and the maximum lateness are considered. Both procedures exhibit good performances in the concluding computational study. Paper 4 addresses the problem of order picker routing in a U-shaped order picking zone with the objective of minimizing the covered walking distance. The problem is proven to be NP-hard. An exact logic-based Benders decomposition procedure as well as a heuristic dynamic programming approach are developed and shown to perform well in computational tests. Beyond that, the paper discusses different storage assignment policies and compares them in a numeric study. Paper 5 studies scheduling electrically powered tow trains in in-plant production logistics. The problem is regarded as an Electric Vehicle Scheduling Problem, where tow trains must be assigned to timetabled service trips. Since the tow trains' range is limited, charging breaks need to be scheduled in-between trips, which require detours and time. The objective consists in minimizing the required fleet size. The problem is shown to be NP-hard. To solve the problem, Paper 5 proposes a branch-and-check approach that is applicable for various charging technologies, including battery swapping and plug-in charging with nonlinear charge increase. In a computational study, the approach's practical applicability is demonstrated. Moreover, influences of the batteries' maximum capacity and employed charging technology are investigated

    Desenvolvimento de uma metodologia baseada em um modelo exato para resolver o picker routing problem em um caso real

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    Orientador: Prof. Dr. Cassius Tadeu ScarpinDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Sociais Aplicadas, Programa de Pós-Graduação em Gestão de Organizações, Liderança e Decisão. Defesa : Curitiba, 14/10/2022Inclui referênciasResumo: Neste trabalho apresenta-se uma aplicação real de um modelo exato para o Problema de Roteamento de Separadores de Pedidos, também conhecido com Picker Routing Problem (PRP), em uma Rede varejista do setor supermercadista. O estudo de caso feito na pesquisa foi no Centro de Distribuição desta rede supermercadista. O PRP consiste em determinar a menor rota a ser percorrida por um separador em um Centro de Distribuição (CD) de forma a coletar manualmente todos os produtos contidos em um determinado pedido. Tem-se como objetivo a aplicação de um modelo de Programação Linear Inteira Mista (PLIM), encontrado na literatura, e a comparação dos resultados obtidos com o atual método utilizado na empresa, a heurística SShape. Para isso, dados reais de pedidos de um determinado período foram coletados e algumas suposições relativas ao tamanho do problema e ao leiaute do CD foram feitas para gerar os 65 cenários de testes estabelecidos. Para atingir o objetivo almejado, foi necessário elaborar um algoritmo em três etapas, em linguagem de programação C#. A primeira etapa é o tratamento de dados e ajuste do leiaute para a elaboração do modelo Matemático. Com uso do solver GUROBI para a resolução dos testes, realizou-se a segunda etapa. A terceira etapa consistiu na aplicação da heurística S-Shape para possibilitar a comparação entre os métodos. As comparações entre o modelo aplicado e a heurística da empresa foram avaliadas em termos de economias (em metros) do trajeto gerado e tempo de resolução. Em 81,54% dos testes, o modelo obteve melhores resultados, gerando rotas com distâncias menores. Os outros 18,46% ambos os métodos retornaram o mesmo resultado. A melhoria média geral ficou em 8,41%. O modelo com parâmetro alterado resolveu 87,69% dos testes em até 30 minutos, considerado como tempo aceitável em termos práticos operacionais. Para os 12,31% dos testes resolvidos acima de 30 minutos, uma manipulação nos dados para contornar essa situação foi sugerida. Dessa forma, foi considerada como vantajosa a aplicação do modelo para o problema real de roteamento de pickers.Abstract: This work presents a real application of an exact model for the Picker Routing Problem (PRP), in a retail chain in the supermarket sector. The case study done in the research was in the Distribution Center of this supermarket chain. The PRP consists of determining the shortest route to be taken by a picker in a Distribution Center (DC) in order to manually collect all the products contained in a given order. The objective is to apply a Mixed Integer Linear Programming (MILP) model, found in the literature, and to compare the results obtained with the current method used in the company, the SShape heuristic. For this, actual order data for a given period was collected and some assumptions regarding the size of the problem and the CD layout were made to generate the 65 established test scenarios. To achieve the desired goal, it was necessary to develop an algorithm in three steps, in C # programming language. The first step is the data treatment and adjustment of the layout for the elaboration of the Mathematical model. Using the GUROBI solver to solve the tests, the second step was performed. The third step consisted of applying the S-Shape heuristic to make it possible to compare the methods. The comparisons between the applied model and the company's heuristic were evaluated in terms of savings (in meters) of the generated route and resolution time. In 81.54% of the tests, the model obtained better results, generating routes with shorter distances. The other 18.46% both methods returned the same result. The overall average improvement was 8.41%. The model with an altered parameter solved 87.69% of the tests within 30 minutes, considered an acceptable timeframe in operational practical terms. For the 12.31% of the tests resolved over 30 minutes, a manipulation of the data to get around this situation was suggested. Thus, it was considered advantageous to apply the model to the real problem of picker routing

    Holistic, data-driven, service and supply chain optimisation: linked optimisation.

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    The intensity of competition and technological advancements in the business environment has made companies collaborate and cooperate together as a means of survival. This creates a chain of companies and business components with unified business objectives. However, managing the decision-making process (like scheduling, ordering, delivering and allocating) at the various business components and maintaining a holistic objective is a huge business challenge, as these operations are complex and dynamic. This is because the overall chain of business processes is widely distributed across all the supply chain participants; therefore, no individual collaborator has a complete overview of the processes. Increasingly, such decisions are automated and are strongly supported by optimisation algorithms - manufacturing optimisation, B2B ordering, financial trading, transportation scheduling and allocation. However, most of these algorithms do not incorporate the complexity associated with interacting decision-making systems like supply chains. It is well-known that decisions made at one point in supply chains can have significant consequences that ripple through linked production and transportation systems. Recently, global shocks to supply chains (COVID-19, climate change, blockage of the Suez Canal) have demonstrated the importance of these interdependencies, and the need to create supply chains that are more resilient and have significantly reduced impact on the environment. Such interacting decision-making systems need to be considered through an optimisation process. However, the interactions between such decision-making systems are not modelled. We therefore believe that modelling such interactions is an opportunity to provide computational extensions to current optimisation paradigms. This research study aims to develop a general framework for formulating and solving holistic, data-driven optimisation problems in service and supply chains. This research achieved this aim and contributes to scholarship by firstly considering the complexities of supply chain problems from a linked problem perspective. This leads to developing a formalism for characterising linked optimisation problems as a model for supply chains. Secondly, the research adopts a method for creating a linked optimisation problem benchmark by linking existing classical benchmark sets. This involves using a mix of classical optimisation problems, typically relating to supply chain decision problems, to describe different modes of linkages in linked optimisation problems. Thirdly, several techniques for linking supply chain fragmented data have been proposed in the literature to identify data relationships. Therefore, this thesis explores some of these techniques and combines them in specific ways to improve the data discovery process. Lastly, many state-of-the-art algorithms have been explored in the literature and these algorithms have been used to tackle problems relating to supply chain problems. This research therefore investigates the resilient state-of-the-art optimisation algorithms presented in the literature, and then designs suitable algorithmic approaches inspired by the existing algorithms and the nature of problem linkages to address different problem linkages in supply chains. Considering research findings and future perspectives, the study demonstrates the suitability of algorithms to different linked structures involving two sub-problems, which suggests further investigations on issues like the suitability of algorithms on more complex structures, benchmark methodologies, holistic goals and evaluation, processmining, game theory and dependency analysis

    Planning and Scheduling Optimization

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    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

    Managing complex assembly lines : solving assembly line balancing and feeding problems

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    Sequencing and Routing in a Large Warehouse with High Degree of Product Rotation

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    The paper deals with a sequencing and routing problem originated by a real-world application context. The problem consists in defining the best sequence of locations to visit within a warehouse for the storage and/or retrieval of a given set of items during a specified time horizon, where the storage/retrieval location of an item is given. Picking and put away of items are simultaneously addressed, by also considering some specific requirements given by the layout design and operating policies which are typical in the kind of warehouses under study. Specifically, the considered sequencing policy prescribes that storage locations must be replenished or emptied one at a time by following a specified order of precedence. Moreover, two fleet of vehicles are used to perform retrieving and storing operations, whose routing is restricted to disjoint areas of the warehouse. We model the problem as a constrained multicommodity flow problem on a space-time network, and we propose a Mixed-Integer Linear Programming formulation, whose primary goal is to minimize the time traveled by the vehicles during the time horizon. Since large-size realistic instances are hardly solvable within the time limit commonly imposed in the considered application context, a matheuristic approach based on a time horizon decomposition is proposed. Finally, we provide an extensive experimental analysis aiming at identifying suitable parameter settings for the proposed approach, and testing the matheuristic on particularly hard realistic scenarios. The computational experiments show the efficacy and the efficiency of the proposed approach

    Online fulfillment: f-warehouse order consolidation and bops store picking problems

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    Fulfillment of online retail orders is a critical challenge for retailers since the legacy infrastructure and control methods are ill suited for online retail. The primary performance goal of online fulfillment is speed or fast fulfillment, requiring received orders to be shipped or ready for pickup within a few hours. Several novel numerical problems characterize fast fulfillment operations and this research solves two such problems. Order fulfillment warehouses (F-Warehouses) are a critical component of the physical internet behind online retail supply chains. Two key distinguishing features of an F-Warehouse are (i) Explosive Storage Policy – A unique item can be stored simultaneously in multiple bin locations dispersed through the warehouse, and (ii) Commingled Bins – A bin can stock several different items simultaneously. The inventory dispersion profile of an item is therefore temporal and non-repetitive. The order arrival process is continuous, and each order consists of one or more items. From the set of pending orders, efficient picking lists of 10-15 items are generated. A picklist of items is collected in a tote, which is then transported to a packaging station, where items belonging to the same order are consolidated into a shipment package. There are multiple such stations. This research formulates and solves the order consolidation problem. At any time, a batch of totes are to be processed through several available order packaging stations. Tote assignment to a station will determine whether an order will be shipped in a single package or multiple packages. Reduced shipping costs are a key operational goal of an online retailer, and the number of packages is a determining factor. The decision variable is which station a tote should be assigned to, and the performance objective is to minimize the number of packages and balance the packaging station workload. This research first formulates the order consolidation problem as a mixed integer programming model, and then develops two fast heuristics (#1 and #2) plus two clustering algorithm derived solutions. For small problems, the heuristic #2 is on average within 4.1% of the optimal solution. For larger problems heuristic #2 outperforms all other algorithms. Performance behavior of heuristic #2 is further studied as a function of several characteristics. S-Strategy fulfillment is a store-based solution for fulfilling online customer orders. The S-Strategy is driven by two key motivations, first, retailers have a network of stores where the inventory is already dispersed, and second, the expectation is that forward positioned inventory could be faster and more economical than a warehouse based F-Strategy. Orders are picked from store inventory and then the customer picks up from the store (BOPS). A BOPS store has two distinguishing features (i) In addition to shelf stock, the layout includes a space constrained back stock of selected items, and (ii) a set of dedicated pickers who are scheduled to fulfill orders. This research solves two BOFS related problems: (i) Back stock strategy: Assignment of items located in the back stock and (ii) Picker scheduling: Effect of numbers of picker and work hours. A continuous flow of incoming orders is assumed for both problems and the objective is fulfillment time and labor cost minimization. For the back-stock problem an assignment rule based on order frequency, forward location and order basket correlations achieves a 17.6% improvement over a no back-stock store, while a rule based only on order frequency achieves a 12.4 % improvement. Additional experiments across a range of order baskets are reported

    Order picking optimisation on a unidirectional cyclical picking line

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    Thesis (PhD)--Stellenbosch University, 2020.ENGLISH SUMMARY : The order picking system in a company's distribution centre is the biggest contributor to the operational cost within the DC. Optimisation should thus aim at running this activity as effciently as possible. The order picking process consists of three main activities, namely walking to the stock, picking stock in fullment of a customer order and handling the picked stock for further processing. While the total amount of work for the picking and handling activities remain constant, the minimisation of walking distance becomes the main objective when minimising the total picking effort. The minimisation of walking distance can be translated into a reduced overall picking time which can lead to a decrease in the total cost of operating the picking system. The main objective of this dissertation is to optimise the order picking system on a unidirectional cyclical picking line. Order batching is introduced to the picking system, since it is an effective methodology that minimises walking distance in operations research literature. Order batching has been introduced to the standard single block parallel-aisle warehouse layout, but not to the specic layout of a unidirectional cyclical picking line. Additionally, the unidirectional cyclical picking line can offer two conguration options that change the physical set up and thereby inffuence the way in which pickers walk during the order picking process. Order batching is introduced to the unidirectional cyclical picking line through picking location based order-to-route closeness metrics. These metrics are further extended by taking the characteristics of the layout into account. The distribution centre of a prominent South African retailer provides real life test instances. Introducing the layout specic stops non-identical spans metric in combination with the greedy smallest entry heuristic results in a reduction of 48:3% in walking distance. Order batching increases the pick density which may lead to higher levels in picker congestion. In a discrete event simulation, the reduction of the overall picking time through a decrease in walking distance is thus conrmed. On tested sample picking waves, the overall picking time can be reduced by up to 21% per wave. A good number of pickers in the picking system is dependent on the pick density. The pick density, amongst other explanatory variables, can also be used to predict the reduction in picking time. The effects of different structural options of the unidirectional cyclical picking line, namely the U- and Z-conguration, are investigated. This results in four decision tiers that have to be addressed while optimising the order picking system. The rst decision tier assigns stock to picking lines, the second arranges stock around a picking line, the third chooses the conguration and the last sequences the orders to be picked. Order batching is added as an additional layer. An increase in pick density benets the reduction of walking distance throughout the decision tiers and supports the choice of the U-conguration after evaluating different test instances. The total completion time of a picking wave can thus be reduced by up to 28% when compared to benchmark instances. The dissertation is concluded by suggesting further research directions.AFRIKAANSE OPSOMMING : Die opmaak van bestellings op 'n uitsoeklyn in 'n onderneming se distribusiesentrum is die grootste bydraer tot die bedryfskoste van 'n distribusiesentrum. Dit is dus belangrik om hierdie aktiwiteit so doeltreffend moontlik te maak. Die proses om bestellings op te maak bestaan uit drie hoofaktiwiteite, naamlik stap na die voorraad, uitsoek (kies en bymekaarsit) van die voorraad vir 'n bestelling en die pak van die gekose voorraad in kartonne vir verdere verwerking en verspreiding. Omdat die totale hoeveelheid werk vir die uitsoek- en hanteringsaktiwiteite konstant bly, word die vermindering van loopafstand die hoofdoelwit om die totale koste van hierdie proses te minimeer. Die minimering van loopafstand lei tot 'n vermindering in totale tyd om bestellings op te maak, wat op sy beurt weer lei tot 'n afname in die totale koste van die stelsel om bestellings op te maak. Die hoofdoel van hierdie proefskrif is om die stelsel vir die uitsoek van bestellings op 'n eenrigting sikliese uitsoeklyn te optimeer. Metodes vir die samevoeging of groepering (Eng.: batching) van bestellings (om gelyktydig opgemaak te word) word ontwikkel vir hierdie uitsoekstelsel aangesien operasionelenavorsingsliteratuur aantoon dat groepering van bestellings 'n effektiewe metode is om loopafstand te verminder. Groepering van bestellings is reeds gedoen vir die standaard blokuitleg van distribusiesentra, maar nie vir hierdie spesieke uitleg van 'n eenrigting sikliese uitsoeklyn nie. Daarbenewens het die eenrigting sikliese uitsoeklyn twee kongurasie-opsies wat die siese opstelling verander en sodoende die manier beinvloed waarop werkers tydens die uitsoekproses loop. Die groepering van bestellings word ontwikkel vir 'n eenrigting sikliese uitsoeklyn deur middel van 'n plek-gebaseerde maatstaf wat die nabyheid van bestellings se roetes meet. Hierdie maatstaf word verder uitgebrei deur die eienskappe van die uitleg in ag te neem. Regte voorbeelde van die probleem uit 'n distribusiesentrum van 'n prominente Suid-Afrikaanse kleinhandelaar word gebruik vir toetsing. Die ontwikkeling en implementering van 'n uitlegspesieke stop-nie identiese-strek-maatstaf in kombinasie met die gulsige kleinste-invoegingsheuristiek lei tot 'n vermindering van 48:3% in stapafstand. Die groepering van bestellings verhoog die digtheid van plekke waar werkers stop vir voorraad, wat kan lei tot ho er vlakke van kongestie vir werkers. 'n Diskrete-gebeurtenis-simulasie bevestig dat 'n afname in loopafstand ook 'n vermindering van die totale voltooiingstyd tot gevolg het. Met behulp van werklike historiese data kon die totale tyd vir die uitsoek van bestellings met tot 21% per golf verminder word. 'n Goeie aantal werkers in die uitsoekstelsel is afhanklik van die uitsoekdigtheid. Die uitsoekdigtheid en andere verklarende veranderlikes, kan ook gebruik word om die vermindering in totale tyd om bestellings op te maak, te voorspel. Die invloed van verskillende strukturele opsies van die eenrigting sikliese uitsoeklyn, naamlik die U- en Z-kongurasie, word ook ondersoek. Dit het tot gevolg dat vier besluitnemingsvlakke aangespreek moet word om die uitsoekstelsel te optimeer. Die eerste besluitnemingsvlak ken voorraad aan die uitsoeklyne toe, die tweede rangskik voorraad binne die uitsoeklyn, die derde kies die kongurasie van die lyn en die laaste kies die volgorde waarin die bestellings uitgesoek word. Groepering van bestellings word bygevoeg as 'n addisionele vlak. 'n Toename in werksdigtheid bevoordeel die vermindering van loopafstand deur die besluitvlakke en bevoordeel die U-kongurasie na evaluering van verskillende toetsdata. Die totale voltooiingstyd van 'n uitsoekgolf kan dus verminder word met tot 28% in vergelyking met eweknie voorbeelde. Die studie word afgesluit deur verdere navorsingsmoontlikhede voor te stel.Doctora

    Designing new models and algorithms to improve order picking operations

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    Order picking has been identified as a crucial factor for the competitiveness of a supply chain because inadequate order picking performance causes customer dissatisfaction and high costs. This dissertation aims at designing new models and algorithms to improve order picking operations and to support managerial decisions on facing current challenges in order picking. First, we study the standard order batching problem (OBP) to optimize the batching of customer orders with the objective of minimizing the total length of order picking tours. We present a mathematical model formulation of the problem and develop a hybrid solution approach of an adaptive large neighborhood search and a tabu search method. In numerical studies, we conduct an extensive comparison of our method to all previously published OBP methods that used standard benchmark sets to investigate their performance. Our hybrid outperforms all comparison methods with respect to average solution quality and runtime. Compared to the state-of-the-art, the hybrid shows the clearest advantages on the larger instances of the existing benchmark sets, which assume a larger number of customer orders and larger capacities of the picking device. Finally, our method is able to solve newly generated large-scale instances with up to 600 customer orders and six items per customer order with reasonable runtimes and convincing scaling behavior and robustness. Next, we address a problem based on a practical case, which is inspired by a warehouse of a German manufacturer of household products. In this warehouse, heavy items are not allowed to be placed on top of light items during picking to prevent damage to the light items. Currently, the case company determines the sequence for retrieving the items from their storage locations by applying a simple S-shape strategy that neglects this precedence constraint. As a result, order pickers place the collected items next to each other in plastic boxes and sort the items respecting the precedence constraint at the end of the order picking process. To avoid this sorting, we propose a picker routing strategy that incorporates the precedence constraint by picking heavy items before light items, and we develop an exact solution method to evaluate the strategy. We assess the performance of our strategy on a dataset provided to us by the manufacturer. We compare our strategy to the strategy used in the warehouse of the case company, and to an exact picker routing approach that does not consider the given precedence constraint. The results clearly demonstrate the convincing performance of our strategy even if we compare our strategy to the exact solution method that neglects the precedence constraint. Last, we investigate a new order picking problem, in which human order pickers of the traditional picker-to-parts setup are supported by automated guided vehicles (AGVs). We introduce two mathematical model formulations of the problem, and we develop a heuristic to solve the NP-hard problem. In numerical studies, we assess the solution quality of the heuristic in comparison to optimal solutions. The results demonstrate the ability of the heuristic in finding high-quality solutions within a negligible computation time. We conduct several computational experiments to investigate the effect of different numbers of AGVs and different traveling and walking speed ratios between AGVs and order pickers on the average total tardiness. The results of our experiments indicate that by adding (or removing) AGVs or by increasing (or decreasing) the AGV speed to adapt to different workloads, a large number of customer orders can be completed until the respective due date
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