Shop Floor Lot-sizing and Scheduling with a Two-stage Stochastic Programming Model Considering Uncertain Demand and Workforce Efficiency

Efficient and flexible production planning is necessary for the manufacturing industry to stay competitive in today’s global market. Shop floor lot-sizing and scheduling is one of the most challenging and rewarding subjects for the management. In this study, a two-stage stochastic programming model is proposed to solve a single-machine, multi-product shop floor lot-sizing and scheduling problem. Two sources of uncertainties are considered simultaneously: product demand from the market, and workforce efficiency, which is the major contribution of this study. The workforce efficiency affects the system productivity, and we propose different distributions to model its uncertainty with insufficient information.The model aims to determine optimal lot sizes and the production sequence that minimizes expected total system costs over the planning horizon, including setup, inventory, and production costs. A case study is performed on a supply chain producing brake equipment in the automotive industry. The numerical results illustrate the usefulness of the stochastic model under volatile environment, and the solution quality is analyzed

A practical assessment of risk-averse approaches in production lot-sizing problems

This paper presents an empirical assessment of four state-of-the-art risk-averse approaches to deal with the capacitated lot-sizing problem under stochastic demand. We analyse two mean-risk models based on the semideviation and on the conditional value-at-risk risk measures, and alternate first and second-order stochastic dominance approaches. The extensive computational experiments based on different instances characteristics and on a case-study suggest that CVaR exhibits a good trade-off between risk and performance, followed by the semideviation and first-order stochastic dominance approach. For all approaches, enforcing risk-aversion helps to reduce the cost-standard deviation substantially, which is usually accomplished via increasing production rates. Overall, we can say that very risk-averse decision-makers would be willing to pay an increased price to have a much less risky solution given by CVaR. In less risk-averse settings, though, semideviation and first-order stochastic dominance can be appealingalternatives to provide significantly more stable production planning costs with a marginal increase of the expected costs.Peer reviewe

Meta-heuristic & hyper-heuristic scheduling tools for biopharmaceutical production

The manufacturing of biopharmaceuticals requires substantial investments and necessitates long-term planning. Complicating the task of determining optimal production plans are large portfolios of products and facilities which limit the tractability of exact solution methods, and uncertainties & stochastic events which often render plans obsolete when reality deviates from the expectation. This thesis therefore describes decisional tools that are able to cope with these complexities. First, a capacity planning problem for a network of facilities and multiple products was tackled. Inspired by meta-heuristic approaches to job shop scheduling, a tailored construction heuristic that builds a production plan based on a sequence — optimised by a genetic algorithm—of product demands was proposed. Comparisons to a mathematical programming model demonstrated its competitiveness on certain scenarios and its applicability to a multi-objective problem. Next, a custom object-oriented model was introduced for a manufacturing scheduling system that utilised a failure-prone perfusion-based bioprocess. With this, process design decisions such as cell culture run time and process configuration, and single-product facility scheduling strategies were evaluated whilst incorporating simulations of stochastic failure events and uncertain demand. This model was then incorporated into a larger hyper-heuristic to determine optimal scheduling policies for a multi-product problem. Various policy representations are tested and a few policies are adapted from the literature to fit this specific problem. In addition, a novel policy utilising a look-ahead heuristic is proposed. The benefit of parameter tuning using evolutionary algorithms is demonstrated and shows that tuned policies perform much better than a policy that estimates parameters based on service level considerations. In addition, the disadvantages of relying on a fixed or rigid production sequence policy in the face of uncertainty is highlighted

The lot sizing problem: A tertiary study

This paper provides a survey of literature reviews in the area of lot sizing. Its intention is to show which streams of research emerged from Harris' seminal lot size model, and which major achievements have been accomplished in the respective areas. We first develop the methodology of this review and then descriptively analyze the sample. Subsequently, a content-related classification scheme for lot sizing models is developed, and the reviews contained in our sample are discussed in light of this classification scheme. Our analysis shows that various extensions of Harris' lot size model were developed over the years, such as lot sizing models that include multi-stage inventory systems, incentives, or productivity issues. The aims of our tertiary study are the following: firstly, it helps primary researchers to position their own work in the literature, to reproduce the development of different types of lot sizing problems, and to find starting points if they intend to work in a new research direction. Secondly, the study identifies several topics that offer opportunities for future secondary research

A Descriptive and Normative Analysis of Marketing Budgeting

Marketing budgeting is one of the most important aspects of management and of highly relevance for business success. Due to rising competitive pressure and a considerable increase in marketing investments the importance of this subject has additionally grown in the last years. For this reason, marketing budgeting receives a huge amount of attention by research and practitioners alike. Accordingly, it is stated in the CMO Council Report of 2007: „The number-one challenge for most chief marketing officers is to quantify, measure, and improve the value of marketing investments and resource allocations“. The Marketing Science Instituteset this issue as top research priority for the time period 2010-2012: „How should firms determine the absolute level of marketing spending and how should spending be allocated at the strategic level - that is, across products, customer groups, and geographies?” The academic literature has been dealing with questions regarding the marketing budget process for a long time and therefore this issue has been discussed and analyzed in multiple ways. The focus of this literature has been on the allocation of budgets as previous research has shown that profit improvement from better allocation is much higher than from improving the overall budget. To give an overview of the existing literature we may distinguish between two main research streams: (1) the descriptive and (2) the normative analysis of marketing budgeting. Descriptive literature discusses the status quo of the marketing budgeting process in companies, i.e. it identifies how marketing budgets are actually determined and allocated by managers. Two types of descriptive studies have emerged in the literature. The first type covers a broad range of manager surveys about budgeting behavior. They indicate that budget decisions are mainly based on the application of some simple budgeting rules, such as the “Percentage of Sales” or “Competitive Parity” method, which are easy to apply and therefore be preferred by manager. But these studies ignore for the high complexity of the budgeting process and are exposed to several biases of survey studies. Therefore insights on the budgeting process based on survey results are quite limited. The second type of descriptive studies try to explain budgeting behavior by estimating the impact of relevant factors on the observable size and allocation of the marketing budget to identify determinants of budget setting. But as all of these studies apply highly different approaches in model design results across studies about the impact of determinants on budgeting are characterized by high heterogeneity. So in summary, literature may only provide a fuzzy and fragmented picture on how manager determine their marketing budget. Normative literature discusses how the marketing budget should be determined. A large body of work assists practitioners by developing diverse approaches for allocation optimization, covering several aspects of resource allocation. All of these solutions offer important general insights into the budgeting problem but generally are not implemented in the marketing practice as they cover only some aspects of the budget allocation problem and/or give suggestions on budget allocation which are not understood and therefore are not accepted by manager. For this reason, researcher developed several heuristics or decision calculus models which address the problem that optimization models cannot be well implemented in companies and offer easy to understand and close to optimum solutions for the complex allocation problem. But while all of the heuristics are focused on short-term profit maximization and thus ignore for dynamic effects which are highly important for budget allocation, the decision calculus models may only give imprecise implications for budget allocation. This explains why the application of scientific models for resource allocation by practitioners is quite rare. The objective of this dissertation is to offer a comprehensive analysis of marketing budgeting. Therefore this work contributes to descriptive as well as normative research by addressing two main research gaps which exist in the literature. In terms of descriptive analysis the existing literature provides only a fragmented picture about influential factors in the budgeting process. In terms of normative literature no method has been developed which address the complexity of the budget allocation task for a multi-country, multi product-firm as well as the need of practitioners for simple allocation rules. The first two papers of this dissertation address the descriptive analysis issues by (1) reviewing and structuring the fragmented literature of marketing budgeting behavior, and (2) developing an innovative approach to analyze empirically the application of budgeting methods in pharmaceutical companies. The last two papers of this dissertation address the normative analysis issues by (3) introducing and implementing an innovative solution to the dynamic marketing allocation budget problem for multi-product, multi-country firms, and (4) analyzing and comparing the performance of different allocation rules by simulation analysis. In summary, the dissertation’s focus is to understand how marketing budgets are set by practitioners, and how the allocation decision process can be improved

Optimization of process in textile industry: models and solution methods

As decisões operacionais de produção em uma indústria de fiação são planejadas na prática determinando soluções dos sub-problemas de dimensionamento e sequenciamento de lotes e da mistura de fardos de algodão. As tarefas são: definir o tamanho, a sequência, o tempo e alocação de cada lote de produção e quais fardos de algodão devem ser utilizados na produção. Por si só, os sub-problemas representam grandes desafios no planejamento da produção. Entretanto, para melhor representar o ambiente produtivo e alcançar custos de produção mais baixos, indústrias de processo, como as de fiação, procuram integrar mais e mais seus sub-problemas de planejamento. O objetivo dessa tese é apresentar modelos matemáticos e métodos de solução para auxiliar a tomada de decisão no nível operacional do planejamento da produção. Três formulações matemáticas para o dimensionamento e sequenciamento de lotes em um sistema de dois estágios com produção sincronizada são propostas. Um novo método baseado em programação matemática e metaheurísticas e também desenvolvida para a solucão desse sub-problema. Além disso, a integração das decisões relativas a matéria-prima (fardos de algodão) ao dimensionamento e sequenciamento de lotes é analisada. As novas formulações propostas representam de forma mais realista o problema de dimensionamento e sequenciamento de lotes da indústria de fiação e de indústrias de processo com ambiente produtivo similares. O método de solução encontra boas soluções para o problema e supera outros méodos similares presentes em softwares comerciais. Além disso, o método é geral o suficiente para a solução de outros problemas de otimização. O problema integrado de dimensionamento e sequenciamento de lotes e mistura comprovou que restrições relativas à qualidade dos fios influenciam os custos e viabilidade do planejamento da produção. O planejamento integrado dessas óperações trata o sistema considerando restrições que se relacionam, definindo planos de produção mais realistasIn the practice of a spinning industry, the operational decisions of the production planning are determined by the hierarchical solution of the lot-sizing and scheduling problem and the blending problem of the cotton bales. The tasks are: to define the size, sequence, timing and allocation of each production lot and to select which cotton bales are used for production. Each of these problems represents a large challenge in planning the production. However, in order to better represent the production environment and to reach lower production costs, process industries (as the spinning industry) are integrating more and more of the production sub-problems into the planning. The aim of this thesis is to propose novel mathematical models and solution methods to assist the decision maker to plan the production at the operational level. Three formulations for the synchronized two-stage lot sizing and scheduling are proposed. A new method based on mathematical programming and metaheuristics is also developed to solve this sub-problem. In addition, the integration of the lot sizing and scheduling with decisions related to the raw materials (cotton bales) is analyzed. The novel models represent a more realistic lot sizing and scheduling for the spinning industry and process industries of similar production environment. The solution method finds good solutions to the mentioned problem and outperforms other state-of-the-art methods incorporated in commercial softwares. Moreover, the method is general enough to solve other optimization problems. The integrated lot-sizing, scheduling and blending prove that constraints related to the yarn quality influence the costs and the feasibility of the production planning. The integrated planning of these operations approaches the system considering the constraint relationship and defines more realistic production plan

Cost Factor Focused Scheduling and Sequencing: A Neoteric Literature Review

The hastily emergent concern from researchers in the application of scheduling and sequencing has urged the necessity for analysis of the latest research growth to construct a new outline. This paper focuses on the literature on cost minimization as a primary aim in scheduling problems represented with less significance as a whole in the past literature reviews. The purpose of this paper is to have an intensive study to clarify the development of cost-based scheduling and sequencing (CSS) by reviewing the work published over several parameters for improving the understanding in this field. Various parameters, such as scheduling models, algorithms, industries, journals, publishers, publication year, authors, countries, constraints, objectives, uncertainties, computational time, and programming languages and optimization software packages are considered. In this research, the literature review of CSS is done for thirteen years (2010-2022). Although CSS research originated in manufacturing, it has been observed that CSS research publications also addressed case studies based on health, transportation, railway, airport, steel, textile, education, ship, petrochemical, inspection, and construction projects. A detailed evaluation of the literature is followed by significant information found in the study, literature analysis, gaps identification, constraints of work done, and opportunities in future research for the researchers and experts from the industries in CSS

Strategic Workforce Planning and sales force : a demographic approach to productivity

Sales force Return on Investment (ROI) valuation with marketing mix frameworks is nowadays common. Sizing discussions then generally follow based on market data and business assumptions. Yet, according to our knowledge, little has been done to embed sales force demographic data (age/experience, tenure, gender etc...) as well as dynamics (especially turnover) in order to investigate the impact of the salesforce characteristics on sales. This paper illustrates such an attempt. It shows that sales force ROI valuation can benefit from a correction on turnover and that optimizinga sales rep hiring policy can unleash additional ROI points. The results are yet heavily dependent in the data structure of the study and their generalization would have to be investigated