Order Allocation and Purchasing Transportation Planning in the Garment Supply Chain: A Goal-Flexible Planning Approach

The garment supply chain is one of the most common supply chains in the world. In this supply chain, quality and cost are the most important factors that are strongly related to the selection of suppliers and the allocation of orders to them. Accordingly, the purpose of this paper is to integrate decisions for supplier selection, order allocation, and multi- source, multi-mode, multi-product shipping plans with consideration of discounts under uncertainty. For this purpose, a multi-objective mixed-integer mathematical model is presented, including the objectives of minimizing costs and products with delays and maximizing the total purchase value. In this mathematical model, the policy of purchasing materials and determining the number and type of transport equipment are specified. To solve this mathematical model, a goal-flexible programming approach with a utility function is presented. In the solution algorithm, a new possibility-flexible programming method has been developed to deal with the uncertainties in the model, which is based on the expected value method and chance constraint. Finally, using a numerical problem, the establishment of the above model in the garment supply chain is investigated. As indicated by the outcomes, the proposed model was touchy to certain boundaries, including blended leaders’ mentality, a boundary identified with fluffy imperatives, and the degree of certainty characterized by the chief for not exactly equivalent limitations

Economic order quantity model for growing items with incremental quantity discounts

Certain inventory items are living organisms, for example livestock, and are therefore capable of growing during the replenishment cycle. These items often serve as various saleable food items downstream in supply chains. The purpose of this paper is to develop a lot sizing model for growing items if the supplier of the items ofers incremental quantity discounts. A mathematical model is derived to determine the optimal inventory policy which minimises the total inventory cost in both the owned and rented facilities. A solution procedure for solving the model is developed and illustrated through a numerical example. Sensitivity analysis is performed to demonstrate the response of the order quantity and total costs to some key input parameters. Incremental quantity discounts result in reduced purchasing costs; however, ordering very large quantities has downsides as well. The biggest downsides include the increased holding costs, the risks of running out of storage capacity and item deterioration since the cycle time increases if larger quantities are purchased. Owing to the importance of growing items in the food supply chains, the model presented in this article can be used by procurement and inventory mangers when making purchasing decisions.https://link.springer.com/journal/40092/volumes-and-issuespm2020Industrial and Systems Engineerin

Coupling soft computing, simulation and optimization in supply chain applications : review and taxonomy

Supply chain networks are typical examples of complex systems. Thereby, making decisions in such systems remains a very hard issue. To assist decision makers in formulating the appropriate strategies, robust tools are needed. Pure optimization models are not appropriate for several reasons. First, an optimization model cannot capture the dynamic behavior of a complex system. Furthermore, most common practical problems are very constrained to be modeled as simple tractable models. To fill in the gap, hybrid optimization/simulation techniques have been applied to improve the decision-making process. In this paper we explore the near-full spectrum of optimization methods and simulation techniques. A review and taxonomy were performed to give an overview of the broad field of optimization/simulation approaches applied to solve supply chain problems. Since the possibilities of coupling them are numerous, we launch a discussion and analysis that aims at determining the appropriate framework for the studied problem depending on its characteristics. Our study may serve as a guide for researchers and practitioners to select the suitable technique to solve a problem and/or to identify the promising issues to be further explored

Production planning of biopharmaceutical manufacture.

Multiproduct manufacturing facilities running on a campaign basis are increasingly becoming the norm for biopharmaceuticals, owing to high risks of clinical failure, regulatory pressures and the increasing number of therapeutics in clinical evaluation. The need for such flexible plants and cost-effective manufacture pose significant challenges for planning and scheduling, which are compounded by long production lead times, intermediate product stability issues and the high cost - low volume nature of biopharmaceutical manufacture. Scheduling and planning decisions are often made in the presence of variable product titres, campaign durations, contamination rates and product demands. Hence this thesis applies mathematical programming techniques to the planning of biopharmaceutical manufacture in order to identify more optimal production plans under different manufacturing scenarios. A deterministic mixed integer linear programming (MILP) medium term planning model which explicitly accounts for upstream and downstream processing is presented. A multiscenario MILP model for the medium term planning of biopharmaceutical manufacture under uncertainty is presented and solved using an iterative solution procedure. An alternative stochastic formulation for the medium term planning of biomanufacture under uncertainty based on the principles of chance constrained programming is also presented. To help manage the risks of long term capacity planning in the biopharmaceutical industry, a goal programming extension is presented which accounts for multiple objectives including cost, risk and customer service level satisfaction. The model is applied to long term capacity analysis of a mix of contractors and owned biopharmaceutical manufacturing facilities. In the final sections of this thesis an example of a commercial application of this work is presented, followed by a discussion on related validation issues in the biopharmaceutical industry. The work in this thesis highlighted the benefits of applying mathematical programming techniques for production planning of biopharmaceutical manufacturing facilities, so as to enhance the biopharmaceutical industry's strategic and operational decision-making towards achieving more cost-effective manufacture

Smart operation of transformers for sustainable electric vehicles integration and model predictive control for energy monitoring and management

The energy transmission and distribution systems existing today are stillsignificantly dependent on transformers,despite beingmore efficient and sustainable than those of decadesago. However, a large numberof power transformers alongwith other infrastructures have been in service for decades and are considered to be in their final ageing stage. Anymalfunction in the transformerscouldaffect the reliability of the entire electric network and alsohave greateconomic impact on the system.Concernsregardingurban air pollution, climate change, and the dependence on unstable and expensive supplies of fossil fuels have lead policy makers and researchers to explore alternatives to conventional fossil-fuelled internal combustion engine vehicles. One such alternative is the introduction of electric vehicles. A broad implementation of such mean of transportation could signify a drastic reduction in greenhouse gases emissions and could consequently form a compelling argument for the global efforts of meeting the emission reduction targets. In this thesis the topic of a high penetration of electric vehicles and their possible integration in insular networksis discussed. Subsequently, smart grid solutions with enabling technologies such as energy management systems and smart meters promote the vision of smart households, which also allows for active demand side in the residential sector.However, shifting loads simultaneously to lower price periods is likely to put extra stress on distribution system assets such as distribution transformers. Especially, additional new types of loads/appliances such as electric vehicles can introduce even more uncertaintyon the operation of these assets, which is an issue that needs special attention. Additionally, in order to improve the energy consumption efficiencyin a household, home energy management systems are alsoaddressed. A considerable number ofmethodologies developed are tested in severalcasestudies in order to answer the risen questions.Os sistemas de transmissão e distribuição de energia existentes hoje em dia sãosignificativamente dependentes dos transformadores, pese embora sejammais eficientes e sustentáveis do que os das décadas passadas. No entanto, uma grande parte dos transformadores ao nível dadistribuição, juntamente com outras infraestruturassubjacentes, estão em serviço há décadas e encontram-se nafasefinal do ciclo devida. Qualquer defeito no funcionamento dos transformadorespode afetara fiabilidadede toda a redeelétrica, para além de terum grande impactoeconómico no sistema.Os efeitos nefastos associadosàpoluição do arem centro urbanos, asmudançasclimáticasea dependência de fontes de energiafósseis têm levado os decisores políticos e os investigadores aexplorar alternativas para os veículos convencionais de combustão interna. Uma alternativa é a introdução de veículos elétricos. Umaampla implementação de tal meio de transporte poderia significar uma redução drástica dos gases de efeito de estufa e poderiareforçar os esforços globais para ocumprimento das metas de redução de emissõesde poluentes na atmosfera.Nesta tese é abordado o tema da elevada penetração dos veículos elétricose a sua eventual integração numarede elétricainsular. Posteriormente, são abordadas soluções de redeselétricasinteligentes com tecnologias específicas, tais como sistemas de gestão de energia e contadores inteligentes que promovamo paradigmadas casas inteligentes, que também permitem a gestão da procura ativano sector residencial.No entanto, deslastrando significativamente as cargaspara beneficiar de preçosmais reduzidosé suscetíveldecolocarconstrangimentosadicionaissobre os sistemas de distribuição, especialmentesobre ostransformadores.Osnovos tipos de cargas tais como os veículos elétricospodem introduzir ainda mais incertezassobre a operação desses ativos, sendo uma questão que suscitaespecial importância. Além disso, com ointuitode melhorar a eficiência do consumo de energia numa habitação, a gestão inteligente daenergia é um assunto que também éabordadonesta tese. Uma pletora de metodologias é desenvolvida e testadaemvários casos de estudos, a fim de responder às questões anteriormente levantadas

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice