Enterprise-wide optimization (EWO) is a new emerging area that lies at the interface of chemical engineering and operations research, and has become a major goal in the process industries due to the increasing pressures for remaining competitive in the global marketplace. EWO involves optimizing the operations of supply, manufacturing and distribution activities of a company to reduce costs and inventories. A major focus in EWO is the optimal operation of manufacturing facilities, which often requires the use of nonlinear process models. Major operational items include planning, scheduling, realtime optimization and inventory control. One of the key features of EWO is integration of the information and the decision-making among the various functions that comprise the supply chain of the company. This can be achieved with modern IT tools, which together with the internet, have promoted e-commerce. However, as will be discussed, to fully realize the potential of transactional IT tools, the development of sophisticated deterministic and stochastic linear/nonlinear optimization models and algorithms (analytical IT tools) is needed to explore and analyze alternatives of the supply chain to yield overall optimum economic performance, as well as high levels of customer satisfaction. An additional challenge is the integrated and coordinated decision-making across the various functions in a company (purchasing, manufacturing, distribution, sales), across various geographically distributed organizations (vendors, facilities and markets), and across various levels of decision-making (strategic, tactical and operational). © 2005 American Institute of Chemical Engineers AIChE J, 51: 1846 –1857, 2005

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Enterprise‐wide optimization: A new frontier in process systems engineering

This manuscript introduces ANTIGONE, Algorithms for coNTinuous/Integer Global Optimization of Nonlinear Equations, a general mixed-integer nonlinear global optimization framework. ANTIGONE is the evolution of the Global Mixed-Integer Quadratic Optimizer, GloMIQO, to general nonconvex terms. The purpose of this paper is to show how the extensible structure of ANTIGONE realizes our previously-proposed mixed-integer quadratically-constrained quadratic program and mixed-integer signomial optimization computational frameworks. To demonstrate the capacity of ANTIGONE, this paper presents computational results on a test suite of $$2{,}571$$ 2 , 571 problems from standard libraries and the open literature; we compare ANTIGONE to other state-of-the-art global optimization solvers.

ANTIGONE: Algorithms for coNTinuous / Integer Global Optimization of Nonlinear Equations

Process industry supply chains : Advances and challenges

Planning and scheduling models for refinery operations

https://www.ou.edu/class/che-design/pub-papers/Multiobjective%20supply%20chain%20design%20under%20uncertainty(Guillen%20et%20al)-05.pdf

Multiobjective supply chain design under uncertainty

This paper addresses the problem of inventory management of a refinery that imports several types of crude oil which are delivered by different vessels. This problem involves optimal operation of crude oil unloading, its transfer from storage tanks to charging tanks, and the charging schedule for each crude oil distillation unit. A mixed-integer optimization model is developed which relies on time discretization. The problem involves bilinear equations due to mixing operations. However, the linearity in the form of a mixed-integer linear program (MILP) is maintained by replacing bilinear terms with individual component flows. The LP-based branch and bound method is applied to solve the model, and several techniques, such as priority branching and bounding, and special ordered sets are implemented to reduce the computation time. This formulation and solution method was applied to an industrial-size problem involving 3 vessels, 6 storage tanks, 4 charging tanks, and 3 crude oil distillation units over 15 time intervals. The MILP model contained 105 binary variables, 991 continuous variables, and 2154 constraints and was effectively solved with the proposed solution approach.

Mixed-Integer Linear Programming Model for Refinery Short-Term Scheduling of Crude Oil Unloading with Inventory Management

A multiperiod optimization model is proposed for addressing the supply chain optimization in continuous flexible process networks. The main feature of this study is that detailed operational decisions are considered over a short time horizon ranging from 1 week to 1 month. For given flexible process networks where dedicated and flexible processes coexist, we take into account the supply chain for sales, intermittent deliveries, production shortfalls, delivery delays, inventory profiles, and job changeovers. The proposed optimization model requires efficient solution strategies to reduce the computational expense. We describe a bilevel decomposition algorithm that involves a relaxed problem (RP) and a subproblem (SP) for the original supply chain problem. Decisions for purchasing raw materials are made in the RP in which the changeover constraints are relaxed, yielding an upper bound to the profit. In the SP, fixing the delivery predicted in RP, the supply chain optimization is performed with job changeove...

Sunwon Park

Papers

Mixed-Integer Linear Programming Model for Refinery Short-Term Scheduling of Crude Oil Unloading with Inventory Management

Supply chain optimization in continuous flexible process networks