Многоцелевая модель смешанного целочисленного программирования для построения и оптимизации многоэшелонной сети постановок

Застосовується змішане лінійне цілочислове програмування до побудови багатоешелонної мережі поставок (SCN) за допомогою оптимізації перевезень і розподілу в SCN. Запропонована модель дозволяє враховувати багато задач SCN за допомогою розгляду загальних витрат на транспортування і місткості всіх ешелонів. У модель включено три різні цільові функції: перша – мінімізує повні вартості перевезень між усіма ешелонами; друга – мінімізує витрати від збереження і вартості замовлення в центрах розподілу (DCs), а остання цільова функція мінімізує зайву і невикористану потужність заводів і DCs.This paper applies a mixed integer linear programming to designing a multi echelon supply chain network (SCN) via optimizing commodity transportation and distribution of a SCN. Proposed model attempts to aim multi objectives of SCN by considering total transportation costs and capacities of all echelons. The model composed of three different objective functions. The first one is minimizing the total transportation costs between all echelons. Second one is minimizing of holding and ordering costs in distribution centers (DCs) and the last objective function is minimizing the unnecessary and unused capacity of plants and DCs.Применяется смешанное линейное целочисленное программирование к построению многоэшелонной сети поставок (SCN) посредством оптимизации перевозок и распределения в SCN. Предложенная модель позволяет учесть многие задачи SCN посредством рассмотрения общих затрат на транспортировку и емкостей всех эшелонов. В модель включены три различные целевые функции: первая – минимизирует полные стоимости перевозок между всеми эшелонами; вторая – минимизирует затраты от сохранения и стоимости заказа в центрах распределения (DCs), а последняя целевая функция минимизирует излишнюю и неиспользованную способность заводов и DCs

A Multi-Stage Supply Chain Network Optimization Using Genetic Algorithms

In today's global business market place, individual firms no longer compete as independent entities with unique brand names but as integral part of supply chain links. Key to success of any business is satisfying customer's demands on time which may result in cost reductions and increase in service level. In supply chain networks decisions are made with uncertainty about product's demands, costs, prices, lead times, quality in a competitive and collaborative environment. If poor decisions are made, they may lead to excess inventories that are costly or to insufficient inventory that cannot meet customer's demands. In this work we developed a bi-objective model that minimizes system wide costs of the supply chain and delays on delivery of products to distribution centers for a three echelon supply chain. Picking a set of Pareto front for multi-objective optimization problems require robust and efficient methods that can search an entire space. We used evolutionary algorithms to find the set of Pareto fronts which have proved to be effective in finding the entire set of Pareto fronts.Comment: 12 pages, 4 figure

Integrated management of chemical processes in a competitive environment

El objetivo general de esta Tesis es mejorar el proceso de la toma de decisiones en la gestión de cadenas de suministro, tomando en cuenta principalmente dos diferencias: ser competitivo considerando las decisiones propias de la cadena de suministro, y ser competitivo dentro de un entorno global. La estructura de ésta tesis se divide en 4 partes principales: La Parte I consiste en una introducción general de los temas cubiertos en esta Tesis (Capítulo 1). Una revisión de la literatura, que nos permite identificar las problemáticas asociadas al proceso de toma de decisiones (Capítulo 2). El Capítulo 3 presenta una introducción de las técnicas y métodos de optimización utilizados para resolver los problemas propuestos en esta Tesis. La Parte II se enfoca en la integración de los niveles de decisión, buscando mejorar la toma de decisiones de la propia cadena de suministro. El Capítulo 4 presenta una formulación matemática que integra las decisiones de síntesis de procesos y las decisiones operacionales. Además, este capítulo presenta un modelo integrado para la toma de decisiones operacionales incluyendo las características del control de procesos. El Capítulo 5 muestra la integración de las decisiones del nivel táctico y el operacional, dicha propuesta está basada en el conocimiento adquirido capturando la información relacionada al nivel operacional. Una vez obtenida esta información se incluye en la toma de decisiones a nivel táctico. Finalmente en el capítulo 6 se desarrolla un modelo simplificado para integrar múltiples cadenas de suministro. El modelo propuesto incluye la información detallada de las entidades presentes en una cadena de suministro (suministradores, plantas de producción, distribuidores y mercados) introduciéndola en un modelo matemático para su coordinación. La Parte III propone la integración explicita de múltiples cadenas de suministro que tienen que enfrentar numerosas situaciones propias de un mercado global. Asimismo, esta parte presenta una nueva herramienta de optimización basada en el uso integrado de métodos de programación matemática y conceptos relacionados a la Teoría de Juegos. En el Capítulo 7 analiza múltiples cadenas de suministro que cooperan o compiten por la demanda global del mercado. El Capítulo 8 incluye una comparación entre el problema resuelto en el Capítulo anterior y un modelo estocástico, los resultados obtenidos nos permiten situar el comportamiento de los competidores como fuente exógena de la incertidumbre típicamente asociada la demanda del mercado. Además, los resultados de ambos Capítulos muestran una mejora sustancial en el coste total de las cadenas de suministro asociada al hecho de cooperar para atender de forma conjunta la demanda disponible. Es por esto, que el Capítulo 9 presenta una nueva herramienta de negociación, basada en la resolución del mismo problema (Capítulo 7) bajo un análisis multiobjetivo. Finalmente, la parte IV presenta las conclusiones finales y una descripción general del trabajo futuro.This Thesis aims to enhance the decision making process in the SCM, remarking the difference between optimizing the SC to be competitive by its own, and to be competitive in a global market in cooperative and competitive environments. The structure of this work has been divided in four main parts: Part I: consists in a general introduction of the main topics covered in this manuscript (Chapter I); a review of the State of the Art that allows us to identify new open issues in the PSE (Chapter 2). Finally, Chapter 3 introduces the main optimization techniques and methods used in this contribution. Part II focuses on the integration of decision making levels in order to improve the decision making of a single SC: Chapter 4 presents a novel formulation to integrate synthesis and scheduling decision making models, additionally, this chapter also shows an integrated operational and control decision making model for distributed generations systems (EGS). Chapter 5 shows the integration of tactical and operational decision making levels. In this chapter a knowledge based approach has been developed capturing the information related to the operational decision making level. Then, this information has been included in the tactical decision making model. In Chapter 6 a simplified approach for integrated SCs is developed, the detailed information of the typical production‐distribution SC echelons has been introduced in a coordinated SC model. Part III proposes the explicit integration of several SC’s decision making in order to face several real market situations. As well, a novel formulation is developed using an MILP model and Game Theory (GT) as a decision making tool. Chapter 7 includes the tactical and operational analysis of several SC’s cooperating or competing for the global market demand. Moreover, Chapter 8 includes a comparison, based on the previous results (MILP‐GT optimization tool) and a two stage stochastic optimization model. Results from both Chapters show how cooperating for the global demand represent an improvement of the overall total cost. Consequently, Chapter 9 presents a bargaining tool obtained by the Multiobjective (MO) resolution of the model presented in Chapter 7. Finally, final conclusions and further work have been provided in Part IV.Postprint (published version

Production distribution planning in a multiechelon supply chain using carbon policies: A review and reflections

Sustainability of a supply chain has gained more attention from economists, environmentalists, consumers, manufacturers, government and the academia. In this paper, the literature survey has been performed on production allocation problem in a multi-echelon supply chain with carbon policies. With web-based search engines such as Scopus and Web of Science several resources such as journals, conference proceedings and books are selected and reviewed. It is observed from the literature that the mentioned problem traces the progression of carbon policies in a supply chain over the past 22 years to provide substantiation for Green Supply Chain. The research papers are then analyzed and categorized to construct the useful foundation of previous studies. Moreover, the importance of this problem in recent years needs has been highlighted by mentioning the gaps in the literature. Further, at the end of the paper, several future work directions in this area also suggested.(undefined)info:eu-repo/semantics/publishedVersio

Applying nonlinear MODM model to supply chain management with quantity discount policy under complex fuzzy environment

Purpose: The aim of this paper is to deal with the supply chain management (SCM) with quantity discount policy under the complex fuzzy environment, which is characterized as the bi-fuzzy variables. By taking into account the strategy and the process of decision making, a bi-fuzzy nonlinear multiple objective decision making (MODM) model is presented to solve the proposed problem. Design/methodology/approach: The bi-fuzzy variables in the MODM model are transformed into the trapezoidal fuzzy variables by the DMs's degree of optimism ?1 and ?2, which are de-fuzzified by the expected value index subsequently. For solving the complex nonlinear model, a multi-objective adaptive particle swarm optimization algorithm (MO-APSO) is designed as the solution method. Findings: The proposed model and algorithm are applied to a typical example of SCM problem to illustrate the effectiveness. Based on the sensitivity analysis of the results, the bi-fuzzy nonlinear MODM SCM model is proved to be sensitive to the possibility level ?1. Practical implications: The study focuses on the SCM under complex fuzzy environment in SCM, which has a great practical significance. Therefore, the bi-fuzzy MODM model and MO-APSO can be further applied in SCM problem with quantity discount policy. Originality/value: The bi-fuzzy variable is employed in the nonlinear MODM model of SCM to characterize the hybrid uncertain environment, and this work is original. In addition, the hybrid crisp approach is proposed to transferred to model to an equivalent crisp one by the DMs's degree of optimism and the expected value index. Since the MODM model consider the bi-fuzzy environment and quantity discount policy, so this paper has a great practical significance.Peer Reviewe

Multi-objective optimization of a transportation network of a HMSC

In this paper, we investigated a transportation network of a tri-layer Halal meat supply chain (HMSC) in which Halal meat transportation process was monitored by a Radio Frequency Identification (RFID) communication system to ensure safety and integrity of Halal meats. This monitoring system is subject to an extra cost in investment that needs to be taken into account. Thus, a multi-objective linear programming model (MOLPM) was developed aiming to minimize the total cost in transportation and number of transportation vehicles and maximize the service level in product quantity as requested by abattoirs and retailers. The facility location-allocation problem in farms, abattoirs and retailers needs also to be addressed in relevance to the quantity flow of products from farms to abattoirs and from abattoirs to retailers. The utility function method was employed to obtain Pareto-optimal solutions and the global criterion method was used for searching the most suitable Pareto solution by minimizing the distance to its ideal objective value. The research work shows that the developed model can be useful for supply chains design through a case study based on numerical results

A multi-objective mixed integer programming model for multi echelon supply chain network design and optimization

This paper applies a mixed integer linear programming to designing a multi echelon supply chain network (SCN) via optimizing commodity transportation and distribution of a SCN. Proposed model attempts to aim multi objectives of SCN by considering total transportation costs and capacities of all echelons. The model composed of three different objective functions. The first one is minimizing the total transportation costs between all echelons. Second one is minimizing of holding and ordering costs in distribution centers (DCs) and the last objective function is minimizing the unnecessary and unused capacity of plants and DCs.Застосовується змішане лінійне цілочислове програмування до побудови багатоешелонної мережі поставок (SCN) за допомогою оптимізації перевезень і розподілу в SCN. Запропонована модель дозволяє враховувати багато задач SCN за допомогою розгляду загальних витрат на транспортування і місткості всіх ешелонів. У модель включено три різні цільові функції: перша — мінімізує повні вартості перевезень між усіма ешелонами; друга — мінімізує витрати від збереження і вартості замовлення в центрах розподілу (DCs), а остання цільова функція мінімізує зайву і невикористану потужність заводів і DCs.Применяется смешанное линейное целочисленное программирование к построению многоэшелонной сети поставок (SCN) посредством оптимизации перевозок и распределения в SCN. Предложенная модель позволяет учесть многие задачи SCN посредством рассмотрения общих затрат на транспортировку и емкостей всех эшелонов. В модель включены три различные целевые функции: первая — минимизирует полные стоимости перевозок между всеми эшелонами; вторая — минимизирует затраты от сохранения и стоимости заказа в центрах распределения (DCs), а последняя целевая функция минимизирует излишнюю и неиспользованную способность заводов и DCs