Integrated Production and Distribution planning of perishable goods

Tese de doutoramento. Programa Doutoral em Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201

Optimizing lot sizing model for perishable bread products using genetic algorithm

This research addresses order planning challenges related to perishable products, using bread products as a case study. The problem is how to effi­ci­ently manage the various bread products ordered by diverse customers, which requires distributors to determine the optimal number of products to order from suppliers. This study aims to formulate the problem as a lot-sizing model, considering various factors, including customer demand, in­ven­tory constraints, ordering capacity, return rate, and defect rate, to achieve a near or optimal solution, Therefore determining the optimal order quantity to reduce the total ordering cost becomes a challenge in this study. However, most lot sizing problems are combinatorial and difficult to solve. Thus, this study uses the Genetic Algorithm (GA) as the main method to solve the lot sizing model and determine the optimal number of bread products to order. With GA, experiments have been conducted by combining the values of population, crossover, mutation, and generation parameters to maximize the feasibility value that represents the minimal total cost. The results obtained from the application of GA demonstrate its effectiveness in generating near or optimal solutions while also showing fast computational performance. By utilizing GA, distributors can effectively minimize wastage arising from expired or perishable products while simultaneously meeting customer demand more efficiently. As such, this research makes a significant contri­bution to the development of more effective and intelligent decision-making strategies in the domain of perishable products in bread distribution.Penelitian ini berfokus untuk mengatasi tantangan perencanaan pemesanan yang berkaitan dengan produk yang mudah rusak, dengan menggunakan produk roti sebagai studi kasus. Permasalahan yang dihadapi adalah bagaimana mengelola berbagai produk roti yang dipesan oleh pelanggan yang beragam secara efisien, yang mengharuskan distributor untuk menentukan jumlah produk yang optimal untuk dipesan dari pemasok. Untuk mencapai solusi yang optimal, penelitian ini bertujuan untuk memformulasikan masalah tersebut sebagai model lot-sizing, dengan mempertimbangkan berbagai faktor, termasuk permintaan pelanggan, kendala persediaan, kapasitas pemesanan, tingkat pengembalian, dan tingkat cacat. Oleh karena itu, menentukan jumlah pemesanan yang optimal untuk mengurangi total biaya pemesanan menjadi tantangan dalam penelitian ini. Namun, sebagian besar masalah lot sizing bersifat kombinatorial dan sulit untuk dipecahkan, oleh karena itu, penelitian ini menggunakan Genetic Algorithm (GA) sebagai metode utama untuk menyelesaikan model lot sizing dan menentukan jumlah produk roti yang optimal untuk dipesan. Dengan GA, telah dilakukan percobaan dengan mengkombinasikan nilai parameter populasi, crossover, mutasi, dan generasi untuk memaksimalkan nilai kelayakan yang merepresentasikan total biaya yang minimal. Hasil yang diperoleh dari penerapan GA menunjukkan keefektifannya dalam menghasilkan solusi yang optimal, selain itu juga menunjukkan kinerja komputasi yang cepat. Dengan menggunakan GA, distributor dapat secara efektif meminimalkan pemborosan yang timbul akibat produk yang kadaluarsa atau mudah rusak, sekaligus memenuhi permintaan pelanggan dengan lebih efisien. Dengan demikian, penelitian ini memberikan kontribusi yang signifikan terhadap pengembangan strategi pengambilan keputusan yang lebih efektif dan cerdas dalam domain produk yang mudah rusak dalam distribusi roti

Multi-level production planning with raw-material perishability and inventory bounds

This thesis focuses on studying one of the most important and fundamental links in supply chain management: production planning. A considerably common assumptions in most of the production planning research literature is that the intermediate items involved in the production process have unlimited lifespans, meaning they can be stored and used indefinitely. In real life applications, whether referring to physical exhaustion, loss of functionality, or obsolescence, most items deteriorate over time and cannot be stored infinitely without enforcing specific constraints on a set of crucial production planning decisions. This is specially the case for multi-level production structures. In the thesis, we first introduce the fundamental characteristics in production planning modeling and discuss some of the common elements and assumptions used to model complex production planning problems. We also present an overview of the production planning research evolution. Our attention is then focused on the most relevant modeling approaches for perishability in production planning available in the research literature. We present lot-sizing problems that incorporate raw-material perishability and analyze how these considerations enforce specific constraints on a set of fundamental decisions. Three variants of the two-level lot-sizing problem are studied: with fixed raw-material shelf-life, with raw-material functionality deterioration, and with functionality and volume deterioration. We propose mixed-integer programming formulations for each of these variants and perform computational experiments with sensitivity analyses, showing the added value of explicitly incorporating perishability considerations into production planning problems. Using a Silver-Meal-based rolling-horizon algorithm, we develop a sequential approach to solve the studied problems and compare the results with our proposed formulations. We then shift our attention to study the multi-item, multi-level lot-sizing problem with raw-material perishability and batch ordering, inspired by an application in advanced composite manufacturing processes. We proposed a mixed-integer programming formulation for the problem and perform computational experiments with sensitivity analyses, demonstrating its potentials for practical applications in planning composite production. Finally, we address the study of production planning involving inventory bounds. This characteristic is shown to be related to the perishable raw-material considerations and constitutes another fundamental aspect of this family of problems. We study the multi-item uncapacitated lot-sizing problem with inventory bounds, presenting a new mixed-integer programming formulation for the case of non-speculative (Wagner-Whitin) cost structure using a special set of variables to determine the production intervals for each item. We then reformulate the problem using a variable-splitting technique that allows for a Dantzig-Wolfe decomposition. The Dantzig-Wolfe principle exploits the structure of the problem by decomposing it into two sub-problems: one relating to the production decisions per item and another that relates to the inventory decisions per period. We propose a Column Generation algorithm for solving the Dantzig-Wolfe reformulation. Computational experiments are performed to evaluate the proposed formulations and algorithms on a set of benchmark instances. This research presents important contributions on a variety of fields related to production planning that had only been partially studied in the literature. It also opens important research paths for the integration of different types of raw-material perishability in multi-level product structures processes, with the study of finished product inventory bounds

Modeling Industrial Lot Sizing Problems: A Review

In this paper we give an overview of recent developments in the field of modeling single-level dynamic lot sizing problems. The focus of this paper is on the modeling various industrial extensions and not on the solution approaches. The timeliness of such a review stems from the growing industry need to solve more realistic and comprehensive production planning problems. First, several different basic lot sizing problems are defined. Many extensions of these problems have been proposed and the research basically expands in two opposite directions. The first line of research focuses on modeling the operational aspects in more detail. The discussion is organized around five aspects: the set ups, the characteristics of the production process, the inventory, demand side and rolling horizon. The second direction is towards more tactical and strategic models in which the lot sizing problem is a core substructure, such as integrated production-distribution planning or supplier selection. Recent advances in both directions are discussed. Finally, we give some concluding remarks and point out interesting areas for future research

Efficient inventory control for imperfect quality items

In this paper, we present a general EOQ model for items that are subject to inspection for imperfect quality. Each lot that is delivered to the sorting facility undertakes a 100 per cent screening and the percentage of defective items per lot reduces according to a learning curve. The generality of the model is viewed as important both from an academic and practitioner perspective. The mathematical formulation considers arbitrary functions of time that allow the decision maker to assess the consequences of a diverse range of strategies by employing a single inventory model. A rigorous methodology is utilised to show that the solution is a unique and global optimal and a general step-by-step solution procedure is presented for continuous intra-cycle periodic review applications. The value of the temperature history and flow time through the supply chain is also used to determine an efficient policy. Furthermore, coordination mechanisms that may affect the supplier and the retailer are explored to improve inventory control at both echelons. The paper provides illustrative examples that demonstrate the application of the theoretical model in different settings and lead to the generation of interesting managerial insights

Grocery omnichannel perishable inventories: performance measures and influencing factors

Purpose- Perishable inventory management for the grocery sector has become more challenging with extended omnichannel activities and emerging consumer expectations. This paper aims to identify and formalize key performance measures of omnichannel perishable inventory management (OCPI) and explore the influence of operational and market-related factors on these measures. Design/methodology/approach- The inductive approach of this research synthesizes three performance measures (product waste, lost sales and freshness) and four influencing factors (channel effect, demand variability, product perishability and shelf life visibility) for OCPI, through industry investigation, expert interviews and a systematic literature review. Treating OCPI as a complex adaptive system and considering its transaction costs, this paper formalizes the OCPI performance measures and their influencing factors in two statements and four propositions, which are then tested through numerical analysis with simulation. Findings- Product waste, lost sales and freshness are identified as distinctive OCPI performance measures, which are influenced by product perishability, shelf life visibility, demand variability and channel effects. The OCPI sensitivity to those influencing factors is diverse, whereas those factors are found to moderate each other's effects. Practical implications- To manage perishables more effectively, with less waste and lost sales for the business and fresher products for the consumer, omnichannel firms need to consider store and online channel requirements and strive to reduce demand variability, extend product shelf life and facilitate item-level shelf life visibility. While flexible logistics capacity and dynamic pricing can mitigate demand variability, the product shelf life extension needs modifications in product design, production, or storage conditions. OCPI executives can also increase the product shelf life visibility through advanced stock monitoring/tracking technologies (e.g. smart tags or more comprehensive barcodes), particularly for the online channel which demands fresher products. Originality/value- This paper provides a novel theoretical view on perishables in omnichannel systems. It specifies the OCPI performance, beyond typical inventory policies for cost minimization, while discussing its sensitivity to operations and market factors

An Integrated Model for Lot Sizing with Supplier Selection Considering Quantity Discounts, Expiry Dates, and Budget Availability

In this paper, a dynamic multi-product multi-period lot sizing with supplier selection problem (DLSSP) with quantity discount, expiry dates, and budget availability is presented. Demand of products for each period are independent and known. The cost consists of ordering, purchasing, transportation, expiry, holding, and interest charge. The objective is to find the optimal order quantity of all items in each period to minimize inventory cost. A mixed integer nonlinear model programming (MINLP) is first developed to model the problem. Since model is hard to solve using exact method, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) is applied, in which design parameters are set using Taguchi method. Computational results demonstrate the applicability of the proposed model and comparing the results show efficiency of both algorithms as well. The results show that, while both algorithms have statistically similar performances, GA is the better algorithm in all problems

Computational methods and parallel strategies in dynamic decision making

Cada uno de estos objetivos han sido tratados en un capítulo independiente de esta tesis. En el segundo capítulo, un modelo de programación estocástica es presentado para un problema práctico de planificación de producción de un producto perecedero en un horizonte de tiempo finito. Una política estática es estudiada para el modelo. Tal política ha demostrado ser óptima asumiendo una estrategia de incertidumbre estática, que es considerada para instancias con un tiempo de espera largo. El tercer capítulo trata el uso de computación paralela para los algoritmos desarrollados en el capítulo previo. Dos implementaciones fueron desarrolladas para plataformas heterogéneas: una versión multi-GPU usando CUDA y una versión multinúcleo usando Pthreads y MPI. Para la primera implementación la simulación de Monte Carlo (la tarea más costosa) es paralelizada. La versión multinúcleo mostró una buena escalabilidad, una vez tratada la carga no balanceada entre los procesadores. El cuarto capítulo trata la efectividad de heurísticas para un problemas de tamaño de lote de productos perecederos similar. La clásica heurística de Silver es extendida para productos perecederos y se presentan variantes del procedimiento: una analítica y una basada en simulación. Los resultados de la heurística son comparados con las soluciones óptimas dadas por un modelo SDP generado para el problema, mostrando que los costes de las heurísticas son se presentan, de media, un 5% sobre el coste óptimo para la estrategia basada en simulación y un 6% para la aproximación analítica. En el quinto capítulo, se presenta un modelo MILP para seleccionar la flota de embarcaciones óptima para el mantenimiento de un parque eólico marino. El modelo se presenta como un problema de dos niveles, seleccionando la flota optima en el primer nivel y optimizando la programación de las operaciones, usando dicha flota, en el segundo. Dado que el modelo es determinístico, como otros en la literatura que aspiran a resolver problemas con un horizonte temporal largo usando periodos cortos, el sexto capítulo trata la cuestión de cómo la anticipación de los eventos estocásticos como los fallos en las turbinas o las condiciones meteorológicas afectan la decisión de la flota de embarcaciones óptima. Este capítulo presenta una heurística que ilustra este efecto.Esta tesis analiza aplicaciones de toma de decisiones dinámica para un conjunto de problemas. Pueden diferenciarse dos líneas principales. La primera trata problemas de gestión de la cadena de suministro para productos perecederos, mientras que la segunda estudia el diseño de flotas de embarcaciones para realizar labores de mantenimiento en parques eólicos marinos. Los modelos de inventario para productos perecederos estudiados en esta tesis consideran un único producto, única localización de suministro y una planificación de producción sobre un horizonte de tiempo finito. El problema de toma de decisiones para programar las operaciones de mantenimiento en parques eólicos marinos es tratado como un problema de cadena de suministro: la instalación requiere programar operaciones de mantenimiento y atender los fallos en turbinas durante el horizonte planificado. Una flota de embarcaciones tiene que ser seleccionada para realizar estas operaciones. Para este conjunto de problemas, las decisiones no son solo dinámicas, sino que además se realizan bajo incertidumbre. Los principales objetivos de esta tesis son los siguientes: (1) estudiar que políticas de pedido son las más apropiadas para los problemas de tamaño de lote? ¿En qué casos una política de pedido da una solución óptima?; (2) analizar el efecto del uso de computación paralela para mejorar el rendimiento de los algoritmos derivados para diseñar políticas para problemas de tamaño de lote de productos perecederos; (3) explorar como de efectivas pueden ser las heurísticas para problemas de toma de decisiones dinámica sobre tamaño de lote de productos perecederos; (4) elaborar un modelo MILP para seleccionar una flota de embarcaciones para realizar las operaciones de mantenimiento en parques eólicos marinos; y (5), diseñar una heurística para programar las operaciones de mantenimiento en parques eólicos marinos considerando fallos en turbinas e incertidumbre meteorológica