This paper deals with the cyclic robotic flowshop scheduling problem with time window constraints, where parts are processed successively on multiple machines with upper and lower bounds on processing times and the tr...
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This paper deals with the cyclic robotic flowshop scheduling problem with time window constraints, where parts are processed successively on multiple machines with upper and lower bounds on processing times and the transportation of parts among the machines is executed by a robot. in contrast with most previous studies, we consider the multi-cyclic scheduling problem in which two or more identical parts enter and leave the robotic flowshop during each cycle. We present the first mixed integer programming (MIP) model for the multi-cyclic robotic flowshop scheduling problem based on the description and analysis of the problem. We solve the model using commercial software CPLEX. Computational experiment on benchmark and randomly generated instances indicates that the proposed MIP approach can effectively solve real-life scheduling problems. (C) 2011 Elsevier Inc. All rights reserved.
The problem addressed in this paper focuses on the reduction of electrical energy costs for the operation of hydraulic pumps, which are used for catchment and distribution of water in water supply systems. As the elec...
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The problem addressed in this paper focuses on the reduction of electrical energy costs for the operation of hydraulic pumps, which are used for catchment and distribution of water in water supply systems. As the electricity tariffs are different according to the time of day, it is necessary to decide when and how many pumps must be used to collect and distribute water, in each period of time, and, when there must be water transfer among tanks so that the demand of each consumer center is met without a lack of resources. A mixed integer programming (MIP) model is proposed and a competitive MIP-based heuristic is developed to solve this problem. Numerical tests in a case study carried out with the water supply system of a Brazilian city demonstrate the efficiency of the proposed mathematical model and solution method.
In the container terminals of seaports, the container handling system consists of a variety of container handling machines such as quay cranes, internal yard trucks, and yard cranes. This study applies a holistic appr...
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In the container terminals of seaports, the container handling system consists of a variety of container handling machines such as quay cranes, internal yard trucks, and yard cranes. This study applies a holistic approach to the integrated scheduling of these machines for the container handling operations of a single vessel. We formulate this special hybrid flow shop scheduling problem through both mixed integer programming (MIP) and constraint programming (CP) techniques. Then we develop an easily-implemented approach that combines the strengths of MIP and CP. First, the MIP model, which only considers quay crane scheduling, is solved by an MIP solver, and a quay crane allocation plan is retrieved from the MIP solution. Then, this quay crane allocation plan is fed to the CP model, warm-starting the branch-and-prune algorithm built in a CP optimizer. Our numerical experiments reveal that this hybrid MIP/CP approach can solve the large-sized instances with up to 10 00 containers, 6 quay cranes, 36 yard trucks, and 15 yard cranes to optimality with a gap of less than 3.31%, within a solution time of 2 minutes. If we increase the solution time to 5 minutes, this hybrid approach solves larger instances with up to 1400 containers to optimality with a gap of less than 1.41%. The state-of-the-art dedicated algorithms reported in the literature (which address an easier version of the same problem by ignoring non-crossing constraints and safety margins between quay cranes) are only able to find solutions for real-life instances with up to 500 containers within the solution time of 2930 or 5221 seconds, leaving a 4% or an unknown optimality gap. Thus, this study improves the solution of this integrated scheduling problem in terms of the instance size, solution efficiency, and solution optimality. (C) 2020 Elsevier B.V. All rights reserved.
This paper presents a mixed-integerprogramming formulation to find optimal solutions for the block layout problem with unequal departmental areas arranged in flexible bays. The nonlinear department area constraints a...
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This paper presents a mixed-integerprogramming formulation to find optimal solutions for the block layout problem with unequal departmental areas arranged in flexible bays. The nonlinear department area constraints are modeled in a continuous plane without using any surrogate constraints. The formulation is extensively tested on problems from the literature. (c) 2005 Elsevier B.V. All rights reserved.
Piecewise affine functions are widely used to approximate nonlinear and discontinuous functions. However, most, if not all existing models, only deal with fitting a continuous function. In this paper, we investigate t...
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Piecewise affine functions are widely used to approximate nonlinear and discontinuous functions. However, most, if not all existing models, only deal with fitting a continuous function. In this paper, we investigate the problem of fitting a discontinuous piecewise affine function to a given function defined on an arbitrary subset of an integer lattice, where no restriction on the partition of the domain is enforced (i.e., its geometric shape can be nonconvex). This is useful for segmentation and denoising when the given function corresponds to a mapping from pixels of a bitmap image to their color depth values. We propose a novel mixedinteger Program (MIP) formulation for the piecewise affine fitting problem, where binary edge variables determine the boundary between two partitions of the function domain. To obtain a consistent partitioning (e.g., image segmentation), we include multicut constraints in the formulation. The resulting problem is NP-hard, and two techniques are introduced to improve the computation. One is to adopt a cutting plane method to add the exponentially many multicut inequalities on-the-fly. The other is to provide initial feasible solutions using a tailored heuristic algorithm. We show that the MIP formulation on grid graphs is approximate, while on king's graph, it is exact under certain circumstances. We conduct initial experiments on synthetic images as well as real depth images, and discuss the advantages and drawbacks of the two models.
Among the many challenges involved in efficient healthcare resource planning, the Patient Admission Scheduling Problem is of particular significance, impacting organizational decisions at all planning levels. The prob...
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Among the many challenges involved in efficient healthcare resource planning, the Patient Admission Scheduling Problem is of particular significance, impacting organizational decisions at all planning levels. The problem of scheduling patient admissions involves assigning patients to beds over a given time horizon so as to maximize treatment efficiency, patient comfort and hospital utilization, while satisfying all necessary medical constraints and taking into consideration patient preferences as much as possible. A number of different variants of the Patient Admission Scheduling Problem exist at the strategic, tactical, and operational levels. In this paper, we consider a static offline operational level variant for which we propose a comprehensive mixed integer programming formulation and advance an exact solution method. We generate new best found solutions for 9 out of 13 benchmark instances from a publicly available repository. Additionally, we prove the optimality of two best known solutions reported in the literature. (C) 2018 Elsevier B.V. All rights reserved.
The last milestone achievement for the roundoff-error-free solution of general mixedinteger programs over the rational numbers was a hybrid-precision branch-and-bound algorithm published by Cook, Koch, Steffy, and Wo...
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The last milestone achievement for the roundoff-error-free solution of general mixedinteger programs over the rational numbers was a hybrid-precision branch-and-bound algorithm published by Cook, Koch, Steffy, and Wolter in 2013. We describe a substantial revision and extension of this framework that integrates symbolic presolving, features an exact repair step for solutions from primal heuristics, employs a faster rational LP solver based on LP iterative refinement, and is able to produce independently verifiable certificates of optimality. We study the significantly improved performance and give insights into the computational behavior of the new algorithmic components. On the MIPLIB 2017 benchmark set, we observe an average speedup of 10.7x over the original framework and 2.9 times as many instances solved within a time limit of two hours.
Despite the fact that waste rock hauling and dumping comprises a large proportion of the cost of materials handling in an open pit mine, little detailed planning is devoted to optimising waste rock dumping. The lack o...
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Despite the fact that waste rock hauling and dumping comprises a large proportion of the cost of materials handling in an open pit mine, little detailed planning is devoted to optimising waste rock dumping. The lack of such planning could result in long-term exposure of potential acid forming (PAF) waste rock, causing acid mine drainage (AMD), which would incur ongoing collection and treatment costs. A waste rock dumping plan that not only minimises hauling costs, but also involves the encapsulation of PAF waste rock to minimise AMD, would benefit a mining operation economically and environmentally. In order to create such a plan, two new mixed integer programming (MIP) models are formulated and tested with a five-year mining schedule. Optimised dumping plans are automatically generated, which detail the dumping location for each mining block. A manual approach is also attempted, and comparison shows that the MIP models have obvious advantages in error prevention, faster solution time and cost saving, by up to 9.5%.
When a company is integrated vertically, it can manage and plan its overall value chain in one direct and integrated approach. However in many cases, companies follow a decoupled approach where forests and production ...
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When a company is integrated vertically, it can manage and plan its overall value chain in one direct and integrated approach. However in many cases, companies follow a decoupled approach where forests and production plants optimize separately their processes in a supply-driven strategy. In Chile, the two largest forest companies are vertically integrated (i.e., they own forest and mills that produce logs, lumber, plywood, pulp, paper, and bioenergy, etc.). Historically, they have coordinated their value chains using a make-to-stock strategy, for which the forest is the main driver of the value chain activities. In this paper, we propose an integrated planning approach to show the impacts of a demand-driven integration of the value chain in the forest industry. To compare this strategy with the decoupled strategy, we propose a mixed integer programming (MIP) model for the integrated strategy. To illustrate our proposal, we use forest and production information from a Chilean forest company. The decoupled strategy, where the forest and industry planning are planned separately, uses two models. The first model deals with the forest management and harvesting decisions and maximizes the expected net present value (NPV) of logs. In this model, the planning horizon covers one full forest rotation, which in Chile corresponds to about 25 years. The second model maximizes the NPV of the downstream operations for a shorter business planning horizon (five years) constrained by the availability of the logs from the first model. In the integrated approach, all parts of the value chain (forest, transportation, and mills) are driven by final product demandand where the objective is to maximize the profit of the company (NPV of the entire value chain). The demand is only given for the shorter business planning horizon. The two strategies are evaluated using the MIP model, and NPV is used to determine the best practice. According to the results, the NPV can increase up to 5.0% when
Heterogeneous wireless sensor network has the advantages of completing complex communication tasks and is widely applied to military, medical, environmental, industrial and commercial, family life and so on. But heter...
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Heterogeneous wireless sensor network has the advantages of completing complex communication tasks and is widely applied to military, medical, environmental, industrial and commercial, family life and so on. But heterogeneous nodes are expensive, it is necessary to consider how to balance the energy consumption and prolong network lifetime by deploying minimum number of heterogeneous nodes. In this paper, a clustering routing algorithm for heterogeneous wireless sensor network (CHRA) is presented to balance energy and prolong the network lifetime. The proposed routing algorithm computes the optimal number of heterogeneous nodes and selects the cluster heads per round. In CHRA, all common nodes are divided into two kinds. The first kind includes the nodes which transmit data to Sink by heterogeneous node and the others belong to the second kind. Furthermore, the common nodes are clustered per round by LEACH-C. The performance analysis and numerical results show that the proposed routing algorithm can availably prolong network lifetime and stable period. And it also can balance the energy consumption significantly. (C) 2018 Elsevier B.V. All rights reserved.
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