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

Multi-objective biopharma capacity planning under uncertainty using a flexible genetic algorithm approach

This paper presents a flexible genetic algorithm optimisation approach for multi-objective biopharmaceutical planning problems under uncertainty. The optimisation approach combines a continuous-time heuristic model of a biopharmaceutical manufacturing process, a variable-length multi-objective genetic algorithm, and Graphics Processing Unit (GPU)-accelerated Monte Carlo simulation. The proposed approach accounts for constraints and features such as rolling product sequence-dependent changeovers, multiple intermediate demand due dates, product QC/QA release times, and pressure to meet uncertain product demand on time. An industrially-relevant case study is used to illustrate the functionality of the approach. The case study focused on optimisation of conflicting objectives, production throughput, and product inventory levels, for a multi-product biopharmaceutical facility over a 3-year period with uncertain product demand. The advantages of the multi-objective GA with the embedded Monte Carlo simulation were demonstrated by comparison with a deterministic GA tested with Monte Carlo simulation post-optimisation

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

순서의존적 작업준비가 있는 생산계획 문제에 대한 정수 최적화 및 근사 동적 계획법 기반 해법

학위논문(박사) -- 서울대학교대학원 : 공과대학 산업공학과, 2022. 8. 이경식.Lot-sizing and scheduling problem, an integration of the two important decision making problems in the production planning phase of a supply chain, determines both the production amounts and sequences of multiple items within a given planning horizon to meet the time-varying demand with minimum cost. Along with the growing importance of coordinated decision making in the supply chain, this integrated problem has attracted increasing attention from both industrial and academic communities. However, despite vibrant research over the recent decades, the problem is still hard to be solved due to its inherent theoretical complexity as well as the evolving complexity of the real-world industrial environments and the corresponding manufacturing processes. Furthermore, when the setup activity occurs in a sequence-dependent manner, it is known that the problem becomes considerably more difficult. This dissertation aims to propose integer optimization and approximate dynamic programming approaches for solving the lot-sizing and scheduling problem with sequence-dependent setups. Firstly, to enhance the knowledge of the structure of the problem which is strongly NP-hard, we consider a single-period substructure of the problem. By analyzing the polyhedron defined by the substructure, we derive new families of facet-defining inequalities which are separable in polynomial time via solving maximum flow problems. Through the computational experiments, these inequalities are demonstrated to provide much tighter lower bounds than the existing ones. Then, using these results, we provide new integer optimization models which can incorporate various extensions of the lot-sizing and scheduling problem such as setup crossover and carryover naturally. The proposed models provide tighter linear programming relaxation bounds than standard models. This leads to the development of an efficient linear programming-based heuristic algorithm which provides a primal feasible solution quickly. Finally, we devise an approximate dynamic programming algorithm. The proposed algorithm incorporates the value function approximation approach which makes use of both the tight lower bound obtained from the linear programming relaxation and the upper bound acquired from the linear programming-based heuristic algorithm. The results of computational experiments indicate that the proposed algorithm has advantages over the existing approaches.공급망의 생산 계획 단계에서의 주요한 두 가지 단기 의사결정 문제인 Lot-sizing 문제와 Scheduling 문제가 통합된 문제인 Lot-sizing and scheduling problem (LSP)은 계획대상기간 동안 주어진 복수의 제품에 대한 수요를 최소의 비용으로 만족시키기 위한 단위 기간 별 최적의 생산량 및 생산 순서를 결정한다. 공급망 내의 다양한 요소에 대한 통합적 의사 결정의 중요성이 커짐에 따라 LSP에 대한 관심 역시 산업계와 학계 모두에서 지속적으로 증가하였다. 그러나 최근 수십 년에 걸친 활발한 연구에도 불구하고, 문제 자체가 내포하는 이론적 복잡성 및 실제 산업 환경과 제조 공정의 고도화/복잡화 등으로 인해 LSP를 해결하는 것은 여전히 어려운 문제로 남아있다. 특히 순서의존적 작업준비가 있는 경우 문제가 더욱 어려워진다는 것이 잘 알려져 있다. 본 논문에서는 순서의존적 작업준비가 있는 LSP를 해결하기 위한 정수 최적화 및 근사 동적 계획법 기반의 해법을 제안한다. 먼저, 이론적으로 강성 NP-hard에 속한다는 사실이 잘 알려진 LSP의 근본 구조에 대한 이해를 높이기 위하여 단일 기간만을 고려하는 부분구조에 대해 다룬다. 단일 기간 부분구조에 의해 정의되는 다면체에 대한 이론적 분석을 통해 새로운 유효 부등식 군을 도출하고 해당 유효 부등식들이 극대면(facet)을 정의할 조건에 대해 밝힌다. 또한, 도출된 유효 부등식들이 다항시간 내에 분리 가능함을 보이고, 최대흐름문제를 활용한 다항시간 분리 알고리듬을 고안한다. 실험 결과를 통해 제안한 유효 부등식들이 모형의 선형계획 하한강도를 높이는 데 큰 영향을 줌을 확인한다. 또한 해당 부등식들이 모두 추가된 모형과 이론적으로 동일한 하한을 제공하는 확장 수리모형(extended formulation)을 도출한다. 이를 활용하여, 실제 산업에서 발생하는 LSP에서 종종 고려하는 주요한 추가 요소들을 다룰 수 있는 새로운 수리 모형을 제안하며 해당 모형이 기존의 모형에 비해 더욱 강한 선형계획 하한을 제공함을 보인다. 이 모형을 바탕으로 빠른 시간 내에 가능해를 찾을 수 있는 선형계획 기반 휴리스틱 알고리듬을 개발한다. 마지막으로 해당 문제에 대한 근사 동적 계획법 알고리듬을 제안한다. 제안하는 알고리듬은 가치함수 근사 기법을 활용하며 특정 상태의 가치를 근사하기 위해 해당 상태에서의 근사함수의 상한 및 하한을 활용한다. 이 때, 좋은 상한 및 하한값을 구하기 위해 제안된 모형의 선형계획 완화문제와 선형계획 기반 휴리스틱 알고리듬을 사용한다. 실험 결과를 통해 제안한 알고리듬이 기존의 방법들과 비교하여 우수한 성능을 보임을 확인한다.Abstract i Contents iii List of Tables vii List of Figures ix Chapter 1 Introduction 1 1.1 Backgrounds 1 1.2 Integrated Lot-sizing and Scheduling Problem 6 1.3 Literature Review 9 1.3.1 Optimization Models for LSP 9 1.3.2 Recent Works on LSP 14 1.4 Research Objectives and Contributions 16 1.5 Outline of the Dissertation 19 Chapter 2 Polyhedral Study on Single-period Substructure of Lot-sizing and Scheduling Problem with Sequence-dependent Setups 21 2.1 Introduction 22 2.2 Literature Review 27 2.3 Single-period Substructure 30 2.3.1 Assumptions 31 2.3.2 Basic Polyhedral Properties 32 2.4 New Valid Inequalities 37 2.4.1 S-STAR Inequality 37 2.4.2 Separation of S-STAR Inequality 42 2.4.3 U-STAR Inequality 47 2.4.4 Separation of U-STAR Inequality 49 2.4.5 General Representation of the Inequalities 52 2.5 Extended Formulations 55 2.5.1 Single-commodity Flow Formulations 55 2.5.2 Multi-commodity Flow Formulations 58 2.5.3 Time-ow Formulations 59 2.6 Computational Experiments 63 2.6.1 Experiment Settings 63 2.6.2 Experiment Results on Single-period Instances 65 2.6.3 Experiment Results on Multi-period Instances 69 2.7 Summary 73 Chapter 3 New Optimization Models for Lot-sizing and Scheduling Problem with Sequence-dependent Setups, Crossover, and Carryover 75 3.1 Introduction 76 3.2 Literature Review 78 3.3 Integer Optimization Models 80 3.3.1 Standard Model (ST) 82 3.3.2 Time-ow Model (TF) 84 3.3.3 Comparison of (ST) and (TF) 89 3.3.4 Facility Location Reformulation 101 3.4 LP-based Naive Fixing Heuristic Algorithm 104 3.5 Computational Experiments 108 3.5.1 Test Instances 108 3.5.2 LP Bound 109 3.5.3 Computational Performance with MIP Solver 111 3.5.4 Performance of LPNF Algorithm 113 3.6 Summary 115 Chapter 4 Approximate Dynamic Programming Algorithm for Lot-sizing and Scheduling Problem with Sequence-dependent Setups 117 4.1 Introduction 118 4.1.1 Markov Decision Process 118 4.1.2 Approximate Dynamic Programming Algorithms 121 4.2 Markov Decision Process Reformulation 124 4.3 Approximate Dynamic Programming Algorithm 127 4.4 Computational Experiments 131 4.4.1 Comparison with (TF-FL) Model 131 4.4.2 Comparison with Big Bucket Model 134 4.5 Summary 138 Chapter 5 Conclusion 139 5.1 Summary and Contributions 139 5.2 Future Research Directions 141 Bibliography 145 Appendix A Pattern-based Formulation in Chapter 2 159 Appendix B Detailed Test Results in Chapter 2 163 Appendix C Detailed Test Results in Chapter 3 169 국문초록 173박

Efficient product allocation strategy to enable network-wide risk mitigation

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 65-66).Amgen Inc. currently manufactures, formulates and fills substantially all of their global drug product units in a single primary facility ("Site 1A"). Concerned about the inherent risks posed by the geographic concentration of these activities, Amgen has decided to acquire a new international Risk Mitigation Site ("RMS"), expand existing bulk manufacturing infrastructure at Site 1A, and construct a new formulation and filling facility colocated with Site 1A ("Site IB"). Bringing both sites online in the near future will create a novel operational challenge for Amgen, as it will present a broad range of formulation/fill production allocation decisions that did not previously exist. If per-unit costs (production, logistics, etc.) were considered to be typically higher at either RMS or Site 1A/B, an unconstrained optimization model might suggest filling/finishing all product at whichever site has the lowest average cost. However, we assume that RMS should be able to ramp up to full capacity within 3 months of an adverse occurrence at Site 1A. This translates to a minimum product flow constraint through RMS, irrespective of per unit costs, that will keep the facility sufficiently staffed to prepare for a fast ramp-up. Furthermore, helping Amgen mitigate the risks of geographic concentration, RMS may typically produce only a portion of global demand for any product. Given this situation, this thesis develops a product allocation strategy that will: 1) minimize the financial cost of filling various quantities of drug product at the new facility, yet 2) maintain at RMS the expertise required begin manufacturing all drugs in a short period of time. A mixed-integer linear program ("MILP") was developed to capture variable costs of the formulation & fill process for each drug product ("DP") and market combination. The objective of this model is to minimize total supply chain costs subject to meeting market demand and maintaining a sufficient amount of product flow through the RMS facility. The analysis assumes that the decision to develop fill capacity at both RMS and Site lB is complete and that both facilities will be licensed to fill all products that currently run through Site 1A (i.e. capital investment decisions will not be analyzed in this study). The outcome of this study is a product allocation strategy that minimizes network costs as well as a tool that will enable Amgen to solve for minimal network costs under additional future scenarios.by Roy J. Lehman, III.S.M.M.B.A

Evaluating the Potential of Continuous Processes for Monoclonal Antibodies: Economic, Environmental and Operational Feasibility

The next generation of monoclonal antibody (mAb) therapies are under increasing pressure from healthcare providers to offer cost effective treatments in the face of intensified competition from rival manufacturers and the looming loss of patent exclusivity for a number of blockbusters. To remain completive in such a challenging environment companies are looking to reduce R&D and manufacturing costs by improving their manufacturing platform processes whilst maintaining flexibility and product quality. As a result companies are now exploring whether they should choose conventional batch technologies or invest in novel continuous technologies, which may lead to lower production costs. This thesis explores the creation of a dynamic tool as part of a decision-support framework that is capable of simulating and optimising continuous monoclonal antibody manufacturing strategies to assist decision-making in this challenging environment. The decision-support framework is able to tackle the complex problem domain found in biopharmaceutical manufacturing, through holistic technology evaluations employing deterministic discrete-event simulation, Monte Carlo simulation and multi-attribute decision-making techniques. The hierarchal nature of the framework (including a unique sixth hierarchal layer; sub-batches) made it possible to simulate multiple continuous manufacturing scenarios on a number of levels of detail, ranging from high-level process performance metrics to low-level ancillary task estimates. The framework is therefore capable of capturing the impact of future titres, multiple scales of operation and key decisional drivers on manufacturing strategies linking multiple continuous unit operations (perfusion cell culture & semi-continuous chromatography). The work in this thesis demonstrates that the framework is a powerful test bed for assessing the potential of novel continuous technologies and manufacturing strategies, via integrated techno-economic evaluations that take proof-of-concept experimental evaluations to complete life-cycle performance evaluations

Cost Evaluation and Portfolio Management Optimization for Biopharmaceutical Product Development

The pharmaceutical industry is suffering from declining R&D productivity and yet biopharmaceutical firms have been attracting increasing venture capital investment. Effective R&D portfolio management can deliver above average returns under increasing costs of drug development and the high risk of clinical trial failure. This points to the need for advanced decisional tools that facilitate decision-making in R&D portfolio management by efficiently identifying optimal solutions while accounting for resource constraints such as budgets and uncertainties such as attrition rates. This thesis presents the development of such tools and their application to typical industrial portfolio management scenarios. A drug development lifecycle cost model was designed to simulate the clinical and non-clinical activities in the drug development process from the pre-clinical stage through to market approval. The model was formulated using activity-based object-oriented programming that allows the activity-specific information to be collected and summarized. The model provides the decision-maker with the ability to forecast future cash flows and their distribution across clinical trial, manufacturing, and process development activities. The evaluation model was applied to case studies to analyse the non-clinical budgets needed at each phase of development for process development and manufacturing to ensure a market success each year. These cost benchmarking case studies focused on distinct product categories, namely pharmaceutical, biopharmaceutical, and cell therapy products, under different attrition rates. A stochastic optimization tool was built that extended the drug development lifecycle cost evaluation model and linked it to combinatorial optimization algorithms to support biopharmaceutical portfolio management decision-making. The tool made use of the Monte Carlo simulation technique to capture the impact of uncertainties inherent in the drug development process. Dynamic simulation mechanisms were designed to model the progression of activities and allocation of resources. A bespoke multi-objective evolutionary algorithm was developed to locate optimal portfolio management solutions from a large decision space of possible permutations. The functionality of the tool was demonstrated using case studies with various budget and capacity constraints. Analysis of the optimization results highlighted the cash flow breakdowns across both activity categories and development stages. This work contributed to the effort of providing quantitative support to portfolio management decision-making and illustrated the benefits of combining cost evaluation with portfolio optimization to enhance process understanding and achieve better performance

Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems

Livro de atas do XVI Congresso da Associação Portuguesa de Investigação Operacional

Fundação para a Ciência e Tecnologia - FC