As a result of rapid developments in production technologies in recent years, flexible job-shop scheduling problems have become increasingly significant. This paper deals with two NP-hard optimization problems: flexib...
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As a result of rapid developments in production technologies in recent years, flexible job-shop scheduling problems have become increasingly significant. This paper deals with two NP-hard optimization problems: flexible job-shop scheduling problems (FJSPs) that encompass routing and sequencing sub-problems, and the FJSPs with process plan flexibility (FJSP-PPFs) that additionally include the process plan selection sub-problem. The study is carried out in two steps. In the first step, a mixed-integer linear programming model (MILP-1) is developed for FJSPs and compared to an alternative model in the literature (Model F) in terms of computational efficiency. In the second step, one other mixed-integer linear programming model, a modification of MILP-1, for the FJSP-PPFs is presented along with its computational results on hypothetically generated test problems. (C) 2009 Elsevier Inc. All rights reserved.
This paper proposes a hybrid combined-cycle gas turbine (CCGT) model for day-ahead market clearing, in order to enhance the operation flexibility of CCGTs in practice. The proposed hybrid model, by taking benefits of ...
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This paper proposes a hybrid combined-cycle gas turbine (CCGT) model for day-ahead market clearing, in order to enhance the operation flexibility of CCGTs in practice. The proposed hybrid model, by taking benefits of combined offers from market participants on both configurations and individual physical turbines, can more accurately reflect physical operation features of CCGTs than existing CCGT models. A comprehensive review on existing CCGT models in academia and industry practice with their advantages and shortcomings is conducted. By taking benefits of the two most investigated models, i.e., configuration-based model and component-based model, the mapping relationship between these two models is revealed for deriving the proposed hybrid model. Tight formulations are further discussed for achieving the better computational performance. The proposed hybrid model is tested and compared with other CCGT models via the modified IEEE 118-bus system and the midcontinent independent system operator system. Results show notable benefits in maintaining operation flexibility and enhancing social welfare.
When products are sold by multiple vendors in various locations, the purchaser must decide what to order from each vendor and where to send it. To solve this decision problem, a novel optimization model is developed a...
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When products are sold by multiple vendors in various locations, the purchaser must decide what to order from each vendor and where to send it. To solve this decision problem, a novel optimization model is developed and applied to a situation involving the nationwide wholesale distribution of grocery products. Comparing the model's solution with the actual record of shipments reveals instances in which the model selected higher-priced vendors in order to capitalize on truckload cost savings, which are seen to be an important factor in vendor selection. Additional models are developed to reduce computation time and assign shipments to vehicles. (c) 2007 Elsevier Ltd. All rights reserved.
Due to new business models and technological advances, dynamic vehicle routing is gaining increasing interest. Especially solving dynamic vehicle routing problems with stochastic customer requests becomes increasingly...
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Due to new business models and technological advances, dynamic vehicle routing is gaining increasing interest. Especially solving dynamic vehicle routing problems with stochastic customer requests becomes increasingly important, for example, in e-commerce and same-day delivery. Solving these problems is challenging, because it requires optimization along two dimensions. First, as a reaction to new customer requests, current routing plans need to be reoptimized. Second, potential future requests need to be anticipated in current decision making. Decisions need to be derived in real-time. The limited time often prohibits extensive optimization in both dimensions and the question arises how to utilize the limited calculation time effectively. In this paper, we analyze the merits of reactive route reoptimization and anticipation for a dynamic vehicle routing problem with stochastic requests. To this end, we compare an existing method from each dimension as well a policy allowing for a tunable combination of the two approaches. We show how the appropriate optimization combination is strongly connected to the degree of dynamism, the percentage of unknown requests. We also show that our combination does not provide significant benefit compared to the respectively best optimization dimension.
In this work, we present mixedinteger linear programming methods for the synthesis of processes that involve complex reaction networks. Specifically, we consider the modeling of reactors and interconnecting streams i...
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In this work, we present mixedinteger linear programming methods for the synthesis of processes that involve complex reaction networks. Specifically, we consider the modeling of reactors and interconnecting streams in systems where the composition of the reactor inlet streams can vary substantially, thereby making the determination of the limiting component as well as the calculation of the stream heating/cooling and power requirements challenging. First, towards the modeling of reactors, we develop an extent-based method which detects the limiting reactant of each reaction occurring in parallel with others, based on the inlet flows of the reactants. Second, we develop a computationally tractable method for the calculation of the work and heating/cooling duty needed to condition any stream of a process based on simple calculations that can be performed offline. Finally, we present how the two aforementioned components can be integrated in an optimization model generated based on a process superstructure. We demonstrate the application of the developed methods for the synthesis of a biorefinery. (C) 2018 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
We propose a new mathematical model for transport optimization in logistics networks on the tactical level. The main features include accurately modeled tariff structures and the integration of spatial and temporal co...
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We propose a new mathematical model for transport optimization in logistics networks on the tactical level. The main features include accurately modeled tariff structures and the integration of spatial and temporal consolidation effects via a cyclic pattern expansion. Using several graph-based gadgets, we are able to formulate our problem as a capacitated network design problem. To solve the model, we propose a local search procedure that reroutes flow of multiple commodities at once. Initial solutions are generated by various heuristics, relying on shortest path augmentations and LP techniques. As an important subproblem we identify the optimization of tariff selection on individual links, which we prove to be NP-hard and for which we derive exact as well as fast greedy approaches. We complement our heuristics by lower bounds from an aggregated mixed-integer programming formulation with strengthened inequalities. In a case study from the automotive, chemical, and retail industries, we prove that most of our solutions are within a single-digit percentage of the optimum.
Effective SKU rationalization is advantageous when applied to businesses with a high variety of product offerings. Advantages may include lower production costs, inventory simplifications, and system-wide reductions i...
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Effective SKU rationalization is advantageous when applied to businesses with a high variety of product offerings. Advantages may include lower production costs, inventory simplifications, and system-wide reductions in transportation costs. We apply SKU rationalization in the form of a variant of product substitution, towards an industrial packaged gas supply chain problem which includes production, allocation, and distribution decisions. An effective mixed-integer programming formulation is developed, capable of handling additional line investment, varying degrees of substitution, economies-of-scale in production, as well as network-wide planning decisions in the supply chain. A case study based on historical data is used for testing, followed by computational results and policy implications in the form of customer incentivization.
A leading manufacturer of forest products with several production facilities located in geographical proximity to each other has recently acquired a number of new production plants in other regions/countries to increa...
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A leading manufacturer of forest products with several production facilities located in geographical proximity to each other has recently acquired a number of new production plants in other regions/countries to increase its production capacity and expand its national and international markets. With the addition of this new capacity, the company wanted to know how to best allocate customer orders to its various mills to minimize the total cost of production and transportation. We developed mixed-integer programming models to jointly optimize production allocation and transportation of customer orders on a weekly basis. The models were run with real order files and the test results indicated the potential for significant cost savings over the company's current practices. The company further customized the models, integrated them into their IT system and implemented them successfully. Besides the actual cost savings for the company, the whole process from the initial step of analyzing the problem, to developing, testing, customizing, integrating and finally implementing the models provided enhanced intelligence to sales staff. (C) 2008 Elsevier Ltd. All rights reserved.
Influence maximization problems aim to identify key players in (social) networks and are typically motivated from viral marketing. In this work, we introduce and study the Generalized Least Cost Influence Problem (GLC...
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Influence maximization problems aim to identify key players in (social) networks and are typically motivated from viral marketing. In this work, we introduce and study the Generalized Least Cost Influence Problem (GLCIP) that generalizes many previously considered problem variants and allows to overcome some of their limitations. A formulation that is based on the concept of activation functions is proposed together with strengthening inequalities. Exact and heuristic solution methods are developed and compared for the new problem. Our computational results also show that our approaches outperform the state-of-the-art on relevant, special cases of the GLCIP.
We analyze the joint optimization of spare parts inventories and workforce allocation in a single-site maintenance system. In this system, for each failure, a service engineer with a necessary replacement part has to ...
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We analyze the joint optimization of spare parts inventories and workforce allocation in a single-site maintenance system. In this system, for each failure, a service engineer with a necessary replacement part has to be allocated. If one of the required resources is not available, the incoming failure request is routed to an external provider, such as a centralized repair facility or a sub-contractor. We study multiple failure types (related to failing components) with exponentially distributed inter-failure times. The system repair times and the replenishment times of the spare parts inventory are also exponentially distributed. The inventory replenishment is done according to a Base-Stock policy. The objective is to minimize the total system cost consisting of annual holding costs of the spare parts and the service engineers, and incidental outsourcing costs. For the joint optimization of the resources, we propose a mixed-integer programming (MIP) formulation using the balance equations of the Markov Chain representation of the system. Furthermore, we provide a simple and efficient heuristic that produces close-to-optimal (<0.3% difference) results, for solving larger instances. Using the proposed optimization methods and real-life data, we analyze the optimal balance between the costs of the resources and the outsourcing costs and show how the outsourcing rates and the total costs behave for different system parameters. (C) 2018 Elsevier B.V. All rights reserved.
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