Heat-integrated process synthesis is fundamental to achieve higher energy efficiency. The well-known sequential-conceptual methods have been widely adopted to solve the synthesis problem in a hierarchical manner. Howe...
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Heat-integrated process synthesis is fundamental to achieve higher energy efficiency. The well-known sequential-conceptual methods have been widely adopted to solve the synthesis problem in a hierarchical manner. However, the natural hierarchy fails to consider complex interactions between the unit operation and the heat integration. To address this issue, a surrogate-based optimization framework is proposed for simultaneous synthesis of chemical process and heat exchanger network. An artificial neural network (ANN)-based surrogate model, derived from the simulation data generated via rigorous mechanism modelling approach, is established for process units to replace their complex realistic models. With surrogate model formulation incorporated into heat integration, an enhanced transshipment-based mixed integer nonlinear programming model is introduced to synthesize heat exchanger network with variable flowrates and temperatures, aiming at the maximized annual profit. Finally, two example studies are investigated to demonstrate the effectiveness of the proposed framework. (c) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
The dynamic production scheduling is a very complex process that may arise from the occurrence of unpredictable situations such as the arrival of new orders besides the ones already accepted. As a consequence, compani...
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The dynamic production scheduling is a very complex process that may arise from the occurrence of unpredictable situations such as the arrival of new orders besides the ones already accepted. As a consequence, companies may often encounter several difficulties to make decisions about the new orders acceptance and sequencing along with the production of the existing ones. With this recognition, a mathematical programming model for the regenerative scheduling problem with deterministic processing times is formulated in the present paper to evaluate the economic advantage of accepting a new order in an engineer to order (ETO) manufacturing organization. The real case of an Italian ETO company which produces hydraulic marine and offshore cranes is afterwards presented.
Process capacity and the associated notions of bottleneck activities and bottleneck resources-which are responsible for limiting process capacity to its present value-are fundamental concepts in the operations managem...
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Process capacity and the associated notions of bottleneck activities and bottleneck resources-which are responsible for limiting process capacity to its present value-are fundamental concepts in the operations management literature. However, for processes that involve collaboration and multitasking, there is little clarity in the literature on what bottlenecks are, what they look like, and how they can be identified. In this paper, we formulate and analyze graph-theoretic optimization problems that determine bottleneck structures of activities and the associated bottleneck sets of resources in deterministic, single-productprocesses with possibly m ul tiple copies of one or more resources and possibly multiple sets of resouites that can perform each activity. In the presence of both collaboration and multitasking, sets of activities that are interconnected in a specific manner via shared resources form bottleneck structures that are responsible for limiting capacity. We use the collaboration graph of the process to either characterize bottleneck structures completely or identify graphical structures that must necessarily be part of any bottleneck structure. Our analysis reveals a natural hierarchy in the algorithmic approach for identifying bottleneck structures as processes become increasingly sophisticated, ranging from the "easy" case where the simple bottleneck formula correctly identifies bottlenecks to more complex cases where one needs to solve progressively complicated mathematical programs. In turn, this understanding helps us obtain prescriptive answers to several questions of interest to managers, for example, the budget-constrained procurement of resources to maximize capacity improvement and the design of processes to increase capacity without procuring additional resources.
In this work we investigate a supply chain design problem with uncertain final demands for the end products produced by a set of manufacturers. This network of manufacturers has the organizational decisions made inter...
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In this work we investigate a supply chain design problem with uncertain final demands for the end products produced by a set of manufacturers. This network of manufacturers has the organizational decisions made internally by pooling resources in a cooperative manner and externally by determining the dominant strategic actors that are characterized by the retailer. A non-linear production game (NLPG) is formulated as a mathematical programming problem to describe coalition formation among the manufacturers based on initial contractual agreements. We show that NLPG is a grand coalition game when demand distribution has an increasing generalized failure rate. The conditions that impact the profit allocation in the game including core set, fairness, stability, superadditivity, least-core and SMALL ELEMENT OF-core are defined. A core allocation solution is generated by using an algorithmic approach. This algorithmic solution is tested in a distributed work station network and resulted in attaining strong computational results for the proposed mathematical programming problem;optimization results with 1000 players are determined in 6 min 40 s on a university computer network using parallel programming with 52 powerful work stations. The numerical results indicated O(log(-N|)) complexity of the algorithmic solution up to 800 players. To the best of our knowledge, this work is the first of its kind in which distributed parallel processing is implemented in a university work station network with distributed parallel programming and processing for solving a production cooperative game.
作者：
Castro, Pedro M.Univ Lisbon
Ctr Matemat Aplicacoes Fundamentais & Invest Oper Fac Ciencias P-1749016 Lisbon Portugal
Scheduling of the transportation of liquids by multiproduct pipelines has been receiving considerable attention in the literature. The preferred methods for solving this problem rely on mathematical programming and us...
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Scheduling of the transportation of liquids by multiproduct pipelines has been receiving considerable attention in the literature. The preferred methods for solving this problem rely on mathematical programming and use continuous-time formulations that move the virtual entities of batches instead of the real entities of products. Such batch-centric formulations require the user to convert the products initially in the pipeline into batches, which is trivial to do for linear or branched systems with a single input node but not for straight systems with multiple input nodes. 'While the dependence of solution quality on the initial batch sequence has been recognized early, no systematic procedure covering all possibilities has been presented yet. In this paper, we propose a novel scheduling algorithm that starts generating all possible initial batch sequences with a new planning formulation, before exploring all generated sequences with either a sequential or simultaneous approach, using the scheduling formulation of Liao, Q;et al. [AIChE J. 2019, 65, e16712]. We then generalize this formulation to consider automatic batch selection, by including the logical constraints of the planning model.
The steelmaking process scheduling problem by considering variable electricity price （SMSPVEP） was in- vestigated. A decomposition approach was proposed for the SMSPVEP. At the first stage, mathematical program-ming...
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The steelmaking process scheduling problem by considering variable electricity price （SMSPVEP） was in- vestigated. A decomposition approach was proposed for the SMSPVEP. At the first stage, mathematical program-ming was utilized to minimize the maximum completion time for each cast without considering variable electricity price. At the second stage, based on obtained relative schedules of all casts, a mathematical model was formulated with an objective of minimizing the energy cost for all casts scheduling problem. The two-stage models were tested on randomly generated instances based on the practical process in a Chinese steelmaking plant. Computational results demonstrate the effectiveness of the proposed approach.
A novel bilevel programming model for designing feeder bus routes to simultaneously consider bus stop selection, bus routing, and passenger route/trip choice behavior is developed in this work. The objective of the up...
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A novel bilevel programming model for designing feeder bus routes to simultaneously consider bus stop selection, bus routing, and passenger route/trip choice behavior is developed in this work. The objective of the upper-level model is to minimize the total in-vehicle travel time of passengers. The objective of the lower-level model is to determine bus stop locations and assign passenger demand to stops by minimizing the total passenger walking time. An ant colony optimization-based two-stage heuristic algorithm is developed to solve the bilevel programming model within an acceptance computation time. Different from conventional methods, the proposed methodology benefits from using real temporal and spatial characteristic datasets of travel demand aggregated from cellular data and traveling time and distance matrices under actual traffic conditions resulted from an open geographic information system (GIS) tool. Results of a real-life case study in Chongqing municipality, China, show that the proposed methodology could achieve a significant reduction of 2.9% total passenger travel time compared with a traditional method. Finally, sensitivity analyses are conducted to further understand the performance of the model. It is anticipated that the proposed methodology could be very useful in designing attractive and cost-effective feeder transit service in practice.
Purchasing decisions are playing a key role in business policy. In this regard, product qualities and popularities, company's reputation and enhancing quick-response are highly dependent on the raw material suppli...
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Purchasing decisions are playing a key role in business policy. In this regard, product qualities and popularities, company's reputation and enhancing quick-response are highly dependent on the raw material suppliers' performance. In garment supply chain, these decisions are based on competitive goals to provide the most customer satisfaction. In this article, quantitative and qualitative decision-making criteria are comprehensively developed to enable the logistician to systematically evaluate and select the suppliers. Then, a three-step integrated approach is proposed to deal with the supplier selection and evaluation problem in the garment industry. In the first phase, the criteria are reduced by maintaining the maximum information and adoption of principal component analysis. In the second phase, the additive model of data envelopment analysis is enhanced by the resultant principal components to determine the efficient suppliers. Finally, the third phase is concerned with ranking of efficient suppliers through Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method as a compromise ranking approach. A numerical study in a garment supply chain is conducted to show the applicability and utility of the model development.
The surgery rescheduling problem (SRP) for hospitals is a challenging combinatorial optimization problem due to the involvement of critical patient groups, scarce personnel and facility resources, and multiple stakeho...
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The surgery rescheduling problem (SRP) for hospitals is a challenging combinatorial optimization problem due to the involvement of critical patient groups, scarce personnel and facility resources, and multiple stakeholders, often with conflicting objectives. The SRP arises mainly due to the arrival of emergency surgeries request, medical personnel tardiness, staff availability, patients' health condition, and surgery duration variability. One or multiple of these factors may occur at a time and disrupts the planned schedule. To overcome the disruption hospital management needs to revise and resolve the SRP efficiently within a limited time frame. Though the SRP is a time-sensitive complex combinatorial problem, only limited studies have addressed this issue in the literature. The majority of surgery planning and scheduling research was focused on developing comprehensive models for robust offline surgery scheduling problems consideringuncertainties. These frameworks would help the hospitals to better prepare the surgery schedule for unforeseen disruptions, however, in some cases, revision of the online surgery schedule is inevitable to support the hospital's functionality. In this research, we have developed an optimization framework to tackle the SRP in the context of real-life scenarios. We have proposed via flexible multiple-stage multiobjective mixed-integer linear program (MILP) mathematical model for the SRP considering availability, compatibility, and workload bounds of associated *** SRP model considers six distinct objectives to minimize the cost incurred due to overtime, emergency surgery turndown, late start of emergency surgery, elective surgery cancellation, surgery turnaround wait between two stages, and mean of mean absolute deviation of resource activity among each resource group. When we considered large instances of the SRP, that would represent the environment of level-1 or level-2 trauma centers, the exact method would beinefficient to
Process Integration (PI) supporting Process Design, Integration, and Optimisation has been around from the early 1970s. PI was developed originally from Heat Integration, which remains the cornerstone for PI continuou...
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Process Integration (PI) supporting Process Design, Integration, and Optimisation has been around from the early 1970s. PI was developed originally from Heat Integration, which remains the cornerstone for PI continuous advance. It has been closely related to the development of Chemical, Mechanical and Power Engineering supported by the extended implementation of mathematical modelling, simulation and optimisation, and by the application of information technology. Its development has accelerated over the years as its methodology has been able to provide answers and support for important issues regarding economic development - better utilisation and savings regarding energy, water, and other resources. This contribution is targeting towards providing at least a short overview of its historical development, achievements, and future challenges.
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