Gintaras V. Reklaitis

A simulation based optimization approach to supply chain management under demand uncertainty

Cost effective supply chain management under various market, logistics and production uncertainties is a critical issue for companies in the chemical process industry. Uncertainties in the supply chain usually increase the variance of profits (or costs) to the company, increasing the likelihood of decreased profit. Demand uncertainty, in particular, is an important factor to be considered in the supply chain design and operations. To hedge against demand uncertainty, safety stock levels are commonly introduced in supply chain operations as well as in supply chain design. Although there exists a large body of literature on estimating safety stock levels based on traditional inventory theory, this literature does not provide an effective methodology that can address the complexity of real CPI supply chains and that can impact the current practice in their design, planning and scheduling. In this paper, we propose the use of deterministic planning and scheduling models which incorporate safety stock levels as a means of accommodating demand uncertainties in routine operation. The problem of determining the safety stock level to use to meet a desired level of customer satisfaction is addressed using a simulation based optimization approach. An industrial-scale case problem is presented to demonstrate the utility of the proposed approach.

Enterprise-wide modeling & optimization—An overview of emerging research challenges and opportunities

The process systems engineering (PSE) as well as the operations research and management science (ORMS) literature has hitherto focused on disparate processes and functions within the enterprise. These themes have included upstream R&D pipeline management, planning and scheduling in batch and continuous manufacturing systems and more recently supply chain optimization under uncertainty. In reality, the modern process enterprise functions as a cohesive entity involving several degrees of cross-functional co-ordination across enterprise planning and process functions. The complex organizational structures underlying horizontally and vertically integrated process enterprises challenge our understanding of cross-functional co-ordination and its business impact. This article looks at the impact of enterprise-wide cross-functional coordination on enterprise performance, sustainability and growth prospects. Cross-functional coordination is defined as the integration of strategic and tactical decision-making processes involving the control of financial and inventory flows (both internal and external) as well as resource deployments. Initially, we demonstrate the existence of cross-functional decision-making dependencies using an enterprise network model. Subsequently, we discuss interactions between enterprise planning decisions involving project financing, debt-equity balancing, R&D portfolio selection, risk hedging with real derivative instruments, supply chain asset creation and marketing contracts which influence decision-making at the activity/process level. Several case studies are included to re-enforce the point that planning and process decisions need to be integrated.

Risk and uncertainty in managing chemical manufacturing supply chains

Abstract A new metric is presented for evaluating supply chain design and planning projects in which there are significant elements of uncertainty and thus risk. The risk premium construct provides the basis for a rational balance between expected value of investment performance and variance. An effective polytope integration method for evaluation of expected values and variances of revenue is adopted which can account for the effects of demand uncertainties on revenue while recognizing the uncertainty in inventory over time. The combination of these elements with conventional deterministic mathematical programming models offers the promise of providing an effective approach to accommodating uncertainties and a rational basis for balancing risk. A small scale example is used to contrast the proposed approach with conventional stochastic programming-based methods. Another example shows the nature of the return and risk for a multiperiod production plan with stochastic effects on inventory. The computational complexities which are introduced by the risk premium construct are reviewed, and some directions for future research discussed.

Overview of Scheduling and Planning of Batch Process Operations

Scheduling of batch operations is an important area of batch process systems engineering which has been receiving increasing attention in the last decade, especially in its role within computer integrated process operations. In this paper, we review the basic issues which scheduling methodology seeks to address and outline some of the reasons for the growth of interest in this field. The components of the scheduling problem are described and the main threads of the available recent solution methodology are reviewed.

Ontological informatics infrastructure for pharmaceutical product development and manufacturing

Informatics infrastructure plays a crucial role in supporting different decision making activities related to pharmaceutical product development, pilot plant and commercial scale manufacturing by streamlining information gathering, data integration, model development and managing all these for easy and timely access and reuse. The foundation of such an infrastructure is the explicitly and formally modeled information. This foundation enables knowledge in different forms, and best manufacturing practices, to be modeled and captured into tools to support the product lifecycle management. This paper discusses the development of ontologies, Semantic Web infrastructure and Web related technologies that make such an infrastructure development possible. While many of the issues addressed in this paper are applicable to a wide spectrum of molecular-based products, we focus our work on the development of pharmaceutical informatics to support Active Pharmaceutical Ingredient (API) as well as drug product development as case studies to illustrate the various aspects of this infrastructure.

A simulation—optimization framework for addressing combinatorial and stochastic aspects of an R&D pipeline management problem

Abstract The R&D pipeline management problem has far-reaching economic implications for new-product-development driven industries, such as pharmaceutical, biotechnology, and agrochemical industries. Effective decision-making is required with respect to portfolio selection and project task scheduling in the face of significant uncertainty and an ever-constrained resource pool. In this paper, the here-and-now stochastic optimization problem inherent to the management of an R&D pipeline is described in its most general form. Subsequently, a computing architecture, Sim—Opt, is presented that combines mathematical programming and discrete event system simulation to assess the uncertainty and control the risk present in the pipeline. The concept of timelines, that studies multiple unique realizations of the controlled evolution of the discrete-event pipeline system, is introduced. Three different implementations of the decision-making module in Sim—Opt have been described and studied through an example case study.

Simulation-based optimization with surrogate models—Application to supply chain management

Simulation is widely used in the decision-making processes associated with supply chain management. In this paper, we present an extension of the simulation-based optimization framework which has been previously proposed for analyzing supply chains. The extension consists of the iterative construction of a surrogate model based on systematically accumulated simulation results to capture the causal relation between the key decision variables and supply chain performance. The decision variables can then be optimized using the surrogate model in place of individual simulation runs to economize on the overall computational effort. Several techniques are embedded in the framework to achieve the targeted objective: least square support vector machine (LSSVM), Bayesian evidence framework, and design and analysis of computer experiment (DACE). The extended framework is illustrated using two small examples and then applied to optimize the inventory levels in a three-stage supply chain. The results show that the framework identifies good solutions efficiently, can accommodate chance constraints and scales up well.

Mathematical programming formulation for scheduling of batch operations based on nonuniform time discretization

A flexible formulation for handling a wide range of short-term scheduling problems arising in multi-product/multipurpose batch chemical plants is presented. Time is directly used to model events arising in the schedule and thus use of binary variables over periods during which no changes in system state occur is avoided. Batch processes involving a variety of operational complexities can be readily represented. The scheduling problem is formulated as a mixed integer nonlinear program (MINLP). The resulting model can be simplified via exact linearization to yield a mixed integer bilinear program (MIBLP) in which the only nonlinearity arises in the objective function as a product of continuous variables. A preliminary computational comparison is made against a uniform time discretization formulation.

The design of multiproduct batch plants under uncertainty with staged expansion

Abstract Flexibility is of particular relevance for contemporary multiproduct batch operations. This paper presents a conceptual formulation for solving the multiproduct batch design problem under uncertainty. The constraints are separated into hard and soft categories and feasibility criteria are developed for both types. Penalty terms are included in the objective functions to reflect the expected profit loss due to unfilled orders, thereby associating a cost with violation of the soft constraints. Short- and long-term variations are allowed, with staged expansion introduced to accommodate the long-term uncertainties. The solution approach is illustrated using an application involving a step change in the demand levels.

Challenges and opportunities in enterprise-wide optimization in the pharmaceutical industry

Abstract Enterprise-wide decision support applications have received increased attention in the chemical process industry in the last decade. In this paper applications, which have real or potential relevance to the pharmaceutical industry, are reviewed. Specific attention is given to the three key phases in the life cycle of an innovative drug product, namely, product development pipeline management, capacity planning and supply chain management. The status of published research in these domains is reviewed, some gaps in the literature are identified and opportunities for further research effort by the process systems engineering community suggested.