Jun-Hyung Ryu

A bilevel programming framework for enterprise-wide process networks under uncertainty

Abstract Enterprise-wide supply chain planning problems naturally exhibit a multi-level decision network structure, where for example, one level may correspond to a local plant control/scheduling/planning problem and another level to a corresponding plant-wide planning/network problem. Such a multi-level decision network structure can be mathematically represented by using multi-level programming principles. In this paper, we specifically address bilevel decision-making problems under uncertainty in the context of enterprise-wide supply chain optimization with one level corresponding to a plant planning problem, while the other to a distribution network problem. We first describe how such problems can be modelled as bilevel programming problems and then we present an effective solution strategy based on parametric programming techniques. An attractive feature of the proposed strategy is the fact that it transforms the bilevel problem into a family of single parametric optimization problems, which can be solved to global optimality. A numerical example is presented to illustrate the proposed framework.

A novel approach to scheduling of zero-wait batch processes under processing time variations

Zero-wait (ZW) is a special type of batch operation in which products are processed without being stored in order to produce a number of low volume high value-added chemical products. Because of its economic impact, there have been a number of studies on the scheduling of ZW processes. However, they are mainly focusing on formulating it into mathematical optimization problems assuming deterministic information. In reality, parameters in the ZW scheduling problem are subject to variation, which may make a fixed schedule suboptimal or even infeasible. Therefore, the scheduling problem has to be solved over and over again using the varying parameters. In order to overcome the inefficiency of such repeated computations, this paper introduces parametric programming technique for solving the ZW scheduling problem under uncertainty. The main advantage using the proposed technique is that a complete map of optimal schedules is obtained as a simple function of varying parameters. A new optimal schedule is thus obtained as a simple function evaluation instead of additional resource-expensive optimization computations. Computational experience with the proposed model and algorithm is presented in the form of two numerical examples.

Global optimization of bilevel programming problems via parametric programming

This paper presents a global optimization approach to a bilevel programming problem which refers to an optimization problem that is constrained by another problem. Using parametric programming techniques, the proposed approach transforms the bilevel problem into a family of single optimization problems, which can be solved to global optimality for linear-linear, linear-quadratic, quadratic-linear, and quadratic-quadratic bilevel models. Computational studies on several numerical examples are reported.

Generalized retrofit design of multiproduct batch plants

Abstract The problem of a generalized retrofit design of multiproduct batch plants is considered in this paper. Unlike previous research on this topic, which concerns only the ways to add a new unit which will be operated in-phase or out-of-phase with the existing units, we get a better result by altering the existing operating mode between existing units (or between new units). Disposing of the useless existing units and replacing outworn equipment with new are also considered. We present a new generalized superstructure with a concept of ‘group’, a collection of units operated in-phase, then formulate it as an MINLP problem with an implicit or explicit group expression. Five examples will verify the effectiveness of this method.

Solving scheduling problems under uncertainty using parametric programming

Abstract This work proposes a novel methodology to solve scheduling problems under uncertainty using parametric programming. Uncertainty, such as in processing times and equipment availabilities, is incorporated into scheduling models, which are then transformed into multi-parametric mixed integer linear programming (mp-MILP) problems. A solution procedure based upon recently proposed mp-MILP algorithms is then discussed. The key advantage of the proposed methodology is that the complete map of optimal schedules can be obtained as a function of varying parameters; rescheduling can thus be performed via simple function evaluations without any further optimization. Numerical examples are presented to illustrate the significance of the proposed methodology.

Developing a heuristics for glass cutting process optimization: A case of two-dimensional two-stage guillotine cutting with multiple stock sizes

This paper presents a heuristic algorithm for a two-dimensional two-stage guillotine cutting problem with multiple stock sizes by allowing the rotation of items by 90°. The proposed algorithm generates levels or strips where the first item or base item is selected according to the length of the strip and packs the next items beside the base item in the strip. For each type of item, strips are generated for packing each type of item in a base item. The best n orders in a yield of strips or the best n strips are selected for each type of item. The selected best n strips are packed in one type of bin. For the other types of bins, another best n strips are selected and packed in each type of bin. The best yield in all types of bins is then selected. This iteration is executed until the number of item demands in the overall demands is less than the number of item demands in the bin. Four numerical examples generated from actual industries are illustrated to highlight the applicability of the proposed algorithm with some comments.

Design and analysis of a diesel processing unit for a molten carbonate fuel cell for auxiliary power unit applications

Fuel cell-based auxiliary power units (APUs) are a promising technology for meeting global energy needs in an environmentally friendly way. This study uses diesel containing sulfur components such as dibenzothiophene (DBT) as a feed. The sulfur tolerance of molten carbonate fuel cell (MCFC) modules is not more than 0.5 ppm, as sulfur can poison the fuel cell and degrade the performance of the fuel cell module. The raw diesel feed in this study contains 10 ppm DBT, and its sulfur concentration should be reduced to 0.1 ppm. After desulfurization, the feed goes through several unit operations, including steam reforming, water-gas shift, and gas purification. Finally, hydrogen is fed to the fuel cell module, where it generates 500 kW of electrical energy. The entire process, with 52% and 89% fuel cell and overall system efficiencies, respectively, is rigorously simulated using Aspen HYSYS, and the results are input into a techno-economic analysis to calculate the minimum electricity selling price (MESP). The electricity cost for this MCFC-based APU was calculated as 1.57

Developing an integrated capacity planning framework for production processes and demand supply chains

/kWh. According to predictions, the cost reductions for fuel cell stacks will afford electricity selling prices of 1.51

A multi-level programming optimization approach to enterprise-wide supply chain planning

/kWh in 2020 and 1.495

Development an Optimization Model for Green Supply Chains: Integration of CO2 Disposal and Renewable Energy Supply

/kWh in 2030. Based on a sensitivity analysis, the diesel price and capital cost were found to have the strongest impact on the MESP.