Deterministic Framework for Robust Modular Design with Integrated-Demand Data Analysis

Supply chain under demand uncertainty has been a challenging problem due to increased competition and market volatility in modern markets. Flexibility in planning decisions makes modular manufacturing a promising way to address this problem. In this work, the problem of multiperiod process and supply chain network design is considered under demand uncertainty. A mixed integer two-stage stochastic programming problem is formulated with integer variables indicating the process design and continuous variables to represent the material flow in the supply chain. The problem is solved using a rolling horizon approach. Benders decomposition is used to reduce the computational complexity of the optimization problem. To promote riskaverse decisions, a downside risk measure is incorporated in the model. The results demonstrate the several advantages of modular designs in meeting product demands.A pareto-optimal curve for minimizing the objectives of expected cost and downside risk is obtained.

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“…Step 5: Use S to build an elastic net regression model. The capital cost estimator is shown by Equation (38).…”

Section: Uncertain Capital Cost Estimationmentioning

confidence: 99%

“…The kinetic mechanism was adopted from previous studies by Rooney and Biegler 37 and Goyal and Ierapetritou. 38 The equation describing the entire process is shown in Equation ( 39)…”

Section: Problem Definitionmentioning

confidence: 99%

Modular supply chain optimization considering demand uncertainty to manage risk

2021

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“…This is achieved by relaxing the lower bound of the chance constraints and forcing prod = 0, ∀ s , for that demand point k . Formulation (III) corresponds to a nonlinear multiobjective optimization problem, which is solved using the line-search approach proposed by Goyal and Ierapetritou, the basic steps of which are summarized in appendix A.…”

Section: Modular-based Design Optimization Frameworkmentioning

confidence: 99%

“…The basic idea of the proposed approach is to extend the boundaries of process design to incorporate demand analysis. In our previous work, we addressed this problem by assuming discrete demand points following a robust optimization framework . It was also illustrated that better designs could be obtained if the problem is addressed in a multiobjective fashion considering economic and feasibility objectives.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Framework for Modular Design Generation Incorporating Demand Uncertainty

Goyal

Ierapetritou

2005

Ind. Eng. Chem. Res.

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Recent developments in process design have focused on establishing optimization-based approaches to support decision making under uncertainty by creating customized designs under specified production requirements. Modular-based design has long been recognized as a preferred alternative because it results in more flexible solutions in a dynamically changing environment. This paper introduces an integrated data analysis, stochastic design optimization approach to generate a portfolio of standardized designs that span the random demand space. The main idea is to cluster the demand and generate a Pareto set of designs by modeling cost and robustness in a multiobjective framework. Two case studies are presented to illustrate the applicability of the proposed approach.

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“…The present survey identified 64 papers with economic objective functions used for the optimization of process flow sheets. The objective functions in 36 papers were expressed as the minimization of different types of cost, e.g., the total cost, the operating cost, the logistic and investment cost, etc. − A maximization of the profit or economic potential was observed in 17 papers, most often expressed as a difference between the incomes and the costs. − The latter usually comprised different types of operating costs and sometimes also the annualized capital cost. The net present worth (NPW) criterion appeared only in 7 papers. − Other interesting but less-common criteria were maximization of the cumulative cash flow, maximization of the monetary value added, the minimization of the investment and inventory opportunity costs reduced for the benefit to the stockholders, and real-options-based valuation to incorporate uncertainty .…”

Section: Introductionmentioning

confidence: 99%

Selection of the Economic Objective Function for the Optimization of Process Flow Sheets

Pintarič

Kravanja

2006

Ind. Eng. Chem. Res.

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This paper highlights the problem of selecting the most suitable economic optimization criteria for mathematical programming approaches to the synthesis, design, and optimization of chemical process flow sheets or their subsystems. Minimization of costs and maximization of profit are the most frequently used economic criteria in technical papers. However, there are many other financial measures which can lead to different optimal solutions if applied in the objective function. This paper describes the characteristics of the optimal solutions obtained with various optimization criteria like the total annual cost, the profit, the payback time, the equivalent annual cost, the net present worth, and the internal rate of return. It was concluded that the maximization of the net present worth (NPW) with a discount rate equal to the minimum acceptable rate of return (MARR) is probably the most appropriate method for the optimization of process flow sheets or their subsystems. Similar or equal solutions can be obtained by simpler criteria of minimum equivalent annual cost or maximum profit if the annual investment cost is calculated by using the MARR instead of the straight-line depreciation method. These criteria represent a thorough compromise between quantitative and qualitative measures, because they consider the absolute terms of future cash flows of investments equally important as profitability through the life cycle of the project. The uncertainty related to the value of the MARR was considered by the generation of Pareto optimal solutions for the NPW and by the stochastic analyses of two design example problems.

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Deterministic Framework for Robust Modular Design with Integrated-Demand Data Analysis

Cited by 9 publications

References 24 publications

Modular supply chain optimization considering demand uncertainty to manage risk

Modular supply chain optimization considering demand uncertainty to manage risk

Multiobjective Framework for Modular Design Generation Incorporating Demand Uncertainty

Selection of the Economic Objective Function for the Optimization of Process Flow Sheets

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