A new Lagrangian decomposition approach applied to the integration of refinery planning and crude-oil scheduling

The aim of this paper is to introduce a methodology to solve a large-scale mixed-integer nonlinear program (MINLP) integrating the two main optimization problems appearing in the oil refining industry: refinery planning and crude-oil operations scheduling. The proposed approach consists of using Lagrangian decomposition to efficiently integrate both problems. The main advantage of this technique is to solve each problem separately. A new hybrid dual problem is introduced to update the Lagrange multipliers. It uses the classical concepts of cutting planes, subgradient, and boxstep. The proposed approach is compared to a basic sequential approach and to standard MINLP solvers. The results obtained on a case study and a larger refinery problem show that the new Lagrangian decomposition algorithm is more robust than the other approaches and produces better solutions in reasonable times.     

Integration of enterprise levels based on an ontological framework

Enterprises are highly complex systems in which one or more organizations share a definite mission, goals and objectives to offer a product or service. In this study, an ontological framework is built as a mechanism for exchanging information and knowledge models for multiple applications and effective integration between hierarchical levels. The potential of the general semantic framework that is developed is demonstrated using a case study concerning the enterprise supply chain network design-planning problem.     

Lagrangian decomposition approach to scheduling large-scale refinery operations

This work focuses on the scheduling of refinery operations from crude oil processing to the blending and dispatch of finished products. A new algorithm for Lagrangian decomposition (LD) is proposed and applied to realistic large scale refinery scheduling problem to evaluate its efficiency. A novel strategy is presented to formulate restricted relaxed sub-problems based on the solution of the Lagrangian relaxed sub-problems that take into consideration the continuous process characteristic of the refinery. This new algorithm, referred to as restricted Lagrangian decomposition algorithm, the best lower bound is obtained amongst the restricted-relaxed sub-problems and relaxed sub-problems in each iteration. The goal of the decomposition is to produce better solutions for those integrated scheduling problems that cannot be solved in reasonable computation times. The application of the proposed algorithm results in substantial reduction in CPU solution time, duality gap, and the total number of iterations compared to classical LD.     

Scheduling and control decision-making under an integrated information environment

The complexity of decision-making in process industries and the need of highly competitive organizations require new supporting tools to coordinate and optimize the information flow among decision levels. This work presents a framework for integrating the scheduling and control decision levels by means of an ontology, which allows and coordinates the information exchange among the different modeling paradigms/conventions currently used for the enterprise-wide optimization (EWO). The scheduling of two multiproduct batch plants with increasing complexity is presented for illustrating the proposed working procedure.     

A novel two-stage Lagrangian decomposition approach for refinery production scheduling with operational transitions in mode switching☆

To address large scale industrial processes, a novel Lagrangian scheme is proposed to decompose a refinery scheduling problem with operational transitions in mode switching into a production subproblem and a blending and delivery subproblem. To accelerate the convergence of Lagrange multipliers, some auxiliary constraints are added in the blending and delivery subproblem. A speed-up scheme is presented to increase the efficiency for solving the production subproblem. An initialization scheme of Lagrange multipliers and a heuristic algorithm to find feasible solutions are designed. Computational results on three cases with different lengths of time hori-zons and different numbers of orders show that the proposed Lagrangian scheme is effective and efficient.     

Integrated production planning and scheduling using a decomposition framework

To ensure the consistency between planning and scheduling decisions, the integrated planning and scheduling problem should be addressed. Following the natural hierarchy of decision making, integrated planning and scheduling problem can be formulated as bilevel optimization problem with a single planning problem (upper level) and multiple scheduling subproblems (lower level). Equivalence between the proposed bilevel model and a single level formulation is proved considering the special structure of the problem. However, the resulting model is still computationally intractable because of the integrality restrictions and large size of the model. Thus a decomposition based solution algorithm is proposed in this paper. In the proposed method, the production feasibility requirement is modeled through penalty terms on the objective function of the scheduling subproblems, which is further proportional to the amount of unreachable production targets. To address the nonconvexity of the production cost function of the scheduling subproblems, a convex polyhedral underestimation of the production cost function is developed to improve the solution accuracy. The proposed decomposition framework is illustrated through examples which prove the effectiveness of the method.     

Multi-bucket optimization for integrated planning and scheduling in the perishable dairy supply chain

This paper considers a dairy industry problem on integrated planning and scheduling of set yoghurt production. A mixed integer linear programming formulation is introduced to integrate tactical and operational decisions and a heuristic approach is proposed to decompose time buckets of the decisions. The decomposition heuristic improves computational efficiency by solving big bucket planning and small bucket scheduling problems. Further, mixed integer linear programming and constraint programming methodologies are combined with the algorithm to show their complementary strengths. Numerical studies using illustrative data with high demand granularity (i.e., a large number of small-sized customer orders) demonstrate that the proposed decomposition heuristic has consistent results minimizing the total cost (i.e., on average 8.75% gap with the best lower bound value found by MILP) and, the developed hybrid approach is capable of solving real sized instances within a reasonable amount of time (i.e., on average 92% faster than MILP in CPU time).     

考虑需求不确定性的化工生产计划与调度集成

生产计划与调度是化工供应链优化中两个重要的决策问题。为了提高生产决策的效率,不仅要对计划与调度进行集成,而且要考虑不确定性的影响。对于多周期生产计划与调度问题,首先在每个生产周期内,分别建立计划与调度的确定性模型,通过产量关联对二者进行集成。然后考虑需求不确定性,使用有限数量的场景表达决策变量,建立二阶段随机规划模型。最后运用滚动时域求解策略,使计划与调度结果在迭代过程中达到一致。实例结果表明,在考虑需求不确定性时,与传统方法相比,随机规划方法可以降低总费用,结合计划与调度的分层集成策略,实现了生产操作性和经济性的综合优化。     

Supply chain optimisation for the process industries: Advances and opportunities

Supply chain management and optimisation is a critical aspect of modern enterprises and a flourishing research area. This paper presents a critical review of methodologies for enhancing the decision-making for process industry supply chains towards the development of optimal infrastructures (assets and network) and planning. The presence of uncertainty within supply chains is discussed as an important issue for efficient capacity utilisation and robust infrastructure decisions. The incorporation of business/financial and sustainability aspects is also considered and future challenges are identified.     

Integration of production planning and scheduling: Overview, challenges and opportunities

We review the integration of medium-term production planning and short-term scheduling. We begin with an overview of supply chain management and the associated planning problems. Next, we formally define the production planning problem and explain why integration with scheduling leads to better solutions. We present the major modeling approaches for the integration of scheduling and planning decisions, and discuss the major solution strategies. We close with an account of the challenges and opportunities in this area.     

Supply chain redesign through optimal asset management and capital budgeting

Manufacturing and inventory facilities along with the materials present in them are the physical assets of a company. Companies strive to maximize their shareholder values by managing these assets through a variety of business decisions. In this work, we present a new mixed-integer linear programming model for asset management and capital budgeting, which can aid the decision-making process for supply chain redesign. The decisions include facility location, relocation, investment, disinvestment, technology upgrade, production–allocation, distribution, supply contracts, capital generation, etc. To the best of our knowledge, this model is the first to address disinvestment, technology upgrade, material supply contracts, and loans and bonds for capital generation, while including strategic asset management and tactical planning, capacity planning, financial/regulatory factors, and production–distribution. We illustrate the impact of ignoring disinvestment and/or relocation decisions as a 14% decrease in profit for an example case study.     

Mathematical programming and game theory optimization-based tool for supply chain planning in cooperative/competitive environments

This work proposes to improve the tactical decision-making of a supply chain (SC) under an uncertain competition scenario through the use of different optimization criteria, which allows to manage not only the specific objectives of the SC of interest, but also the way how its clients address their selection between different potential suppliers, identifying best market share for the SC of interest and the strategy to attain it. The resulting multi-objective optimization problem has been solved using the ?-constraint method in order to approximate the Pareto space of non-dominated solutions while a framework based on game theory is used as a reactive decision making support tool to deal with the uncertainty of the competitive scenario. The use of the proposed system is illustrated through its application to a multi-product, multi-echelon supply chain case study, which is intended to cooperate or to compete with another SC of similar characteristics.     

The multi-echelon vehicle routing problem with cross docking in supply chain management

Multi-echelon distribution networks are quite common in supply chain and logistics. Deliveries of multiple items from factories to customers are managed by routing and consolidating shipments in warehouses carrying on long-term inventories. On the other hand, cross-docking is a logistics technique that differs from warehousing because products are no longer stored at intermediate depots. Instead, cross-dock facilities consolidate incoming shipments based on customer demands and immediately deliver them to their destinations. Hybrid strategies combining direct shipping, warehousing and cross-docking are usually applied in real-world distribution systems. This work deals with the operational management of hybrid multi-echelon multi-item distribution networks. The goal of the N-echelon vehicle routing problem with cross-docking in supply chain management (the VRPCD-SCM problem) consists of satisfying customer demands at minimum total transportation cost. A monolithic optimization framework for the VRPCD-SCM based on a mixed-integer linear mathematical formulation is presented. Computational results for several problem instances are reported.     

Three-scale integrated optimization model of furnace simulation,cyclic scheduling,and supply chain of ethylene plants

In order to explore the potential of profit margin improvement, a novel three-scale integrated optimization model of furnace simulation, cyclic scheduling, and supply chain of ethylene plants is proposed and evaluated. A decoupling strategy is proposed for the solution of the three-scale model, which uses our previously proposed reactor scale model for operation optimization and then transfers the obtained results as a parameter table in the joint MILP optimization of plant-supply chain scale for cyclic scheduling. This optimization framework simplifies the fundamental mixed-integer nonlinear programming (MINLP) into several sub-models, and improves the interpretability and extendibility. In the evaluation of an industrial case, a profit increase at a percentage of 3.25% is attained in optimization compared to the practical operations. Further sensitivity analysis is carried out for strategy evolving study when price policy, supply chain, and production requirement parameters are varied. These results could provide useful suggestions for petrochemical enterprises on thermal cracking production.     

Multiscale strategic planning model for the design of integrated ethanol and gasoline supply chain

The design and planning of an integrated ethanol and gasoline supply chain is addressed, and is composed of harvesting and production sites for ethanol, petroleum refineries, distribution centers where blending takes place, and the retail gas stations where blends of gasoline and ethanol are sold. We postulate a superstructure that combines all the components of the supply chain and different means of transportation, and model this multiscale design problem as a multiperiod MILP model. In order to identify regions where investments are needed and the optimal configuration of the network, a strategic planning model is considered in which gasoline stations are aggregated in different regions. A detailed formulation is considered where regions are disaggregated into gas stations to determine the retrofit projects for the selection of blending pumps over their expected life. Also, the application of these MILP models with two large‐scale problems are illustrated. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4655–4672, 2013     

An optimization framework for the integration of water management and shale gas supply chain design

This study presents the mathematical formulation and implementation of a comprehensive optimization framework for the assessment of shale gas resources. The framework simultaneously integrates water management and the design and planning of the shale gas supply chain, from the shale formation to final product demand centers and from fresh water supply for hydraulic fracturing to water injection and/or disposal. The framework also addresses some issues regarding wastewater quality, i.e., total dissolved solids (TDS) concentration, as well as spatial and temporal variations in gas composition, features that typically arise in exploiting shale formations. In addition, the proposed framework also considers the integration of different modeling, simulation and optimization tools that are commonly used in the energy sector to evaluate the technical and economic viability of new energy sources. Finally, the capabilities of the proposed framework are illustrated through two case studies (A and B) involving 5 well-pads operating with constant and variable gas composition, respectively. The effects of the modeling of variable TDS concentration in the produced wastewater is also addressed in case study B.     

Mid-term supply chain planning under demand uncertainty: customer demand satisfaction and inventory management

This paper utilizes the framework of mid-term, multisite supply chain planning under demand uncertainty to safeguard against inventory depletion at the production sites and excessive shortage at the customer. A chance constraint programming approach in conjunction with a two-stage stochastic programming methodology is utilized for capturing the trade-off between customer demand satisfaction (CDS) and production costs. In the proposed model, the production decisions are made before demand realization while the supply chain decisions are delayed. The challenge associated with obtaining the second stage recourse function is resolved by first obtaining a closed-form solution of the inner optimization problem using linear programming duality followed by expectation evaluation by analytical integration. In addition, analytical expressions for the mean and standard deviation of the inventory are derived and used for setting the appropriate CDS levels in the supply chain. A three-site example supply chain is studied within the proposed framework for providing quantitative guidelines for setting customer satisfaction levels and uncovering effective inventory management options. Results indicate that significant improvement in guaranteed service levels can be obtained for a small increase in the total cost.     

Optimal planning and campaign scheduling of biopharmaceutical processes using a continuous-time formulation

This work addresses the optimal planning and campaign scheduling of biopharmaceutical manufacturing processes, considering multiple operational characteristics, such as the campaign schedule of batch and/or continuous process steps, multiple intermediate deliveries, sequence dependent changeovers operations, product storage restricted to shelf-life limitations, and the track-control of the production/campaign lots due to regulatory policies. A new mixed integer linear programing (MILP) model, based on a Resource Task Network (RTN) continuous time single-grid formulation, is developed to comprise the integration of all these features. The performance of the model features is discussed with the resolution of a set of industrial problems with different data sets and process layouts, demonstrating the wide application of the proposed formulation. It is also performed a comparison with a related literature model, showing the advantages of the continuous-time approach and the generality of our model for the optimal production management of biopharmaceutical processes.     

过程工业的集成供应链管理研究

分析了过程工业的特点和供应链管理在过程工业的应用现状,提出过程工业应通过采用集成供应链管理获得效益,即结合过程工业自身的特点,根据过程系统的功能层次模型和流结构模型,建立过程系统在投资决策、市场运营、管理控制3个不同运行层次上的集成供应链模型,并采用适当的优化策略求解,以求得企业在全生命周期内最大的综合效益。最后介绍了面向智能体的过程工业集成供应链建模方法以及求解策略。