In this paper, we present a new stochastic mixed-integer linear programming model for the Stochastic Outpatient Procedure Scheduling Problem (SOPSP). In this problem, we schedule a day's worth of procedures for a ...
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In this paper, we present a new stochastic mixed-integer linear programming model for the Stochastic Outpatient Procedure Scheduling Problem (SOPSP). In this problem, we schedule a day's worth of procedures for a single provider, where each procedure has a known type and associated probability distribution of random duration. Our objective is to minimize the expectation of a weighted sum of patient waiting time, provider idling, and clinic overtime. We present computational results to show the size and characteristics of problem instances that can be solved with our model. We also compare this model to other formulations in the literature and analyze them both empirically and theoretically, demonstrating where significant improvements in performance can be gained with our proposed model. This work is motivated by our research on developing scheduling templates for endoscopic procedures at a major medical center. More broadly, however, the SOPSP is a stochastic single-resource sequencing and scheduling problem and therefore has applications both within and outside of healthcare operations. (C) 2019 Elsevier B.V. All rights reserved.
作者：
Bard, JFUNIV TEXAS
DEPT MECH ENGNGRAD PROGRAM OPERAT RESAUSTINTX 78712 USA
This paper reports on the results of an effort to design and analyse the rail car unloading area of Procter & Gamble's principal laundry detergent (soap powder) plant. In the first part of the study the design...
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This paper reports on the results of an effort to design and analyse the rail car unloading area of Procter & Gamble's principal laundry detergent (soap powder) plant. In the first part of the study the design team established daily requirements for the number of raw material rail cars unloaded per day. The related combinatorial optimisation problem of assigning rail cars to positions on the platform and unloading equipment to rail cars was modelled as a mixed-integer nonlinear program. The inability of two standard commercial codes to find optimal solutions led to the development of a greedy randomised adaptive search procedure (GRASP). Accounting for the operational and physical limitations of the system, GRASP was used to determine the maximum performance that could be achieved under normal conditions. In the second part of the study alternative designs were proposed for meeting an expected 14% increase in demand over the next few years. The analytic hierarchy process in conjunction with a standard scoring model was used to rank the evaluation criteria and to select the preferred alternative. A worst-case analysis of the top candidate confirmed its performance capabilities.
Solving nesting problems involves the waste minimisation in cutting processes, and therefore it is not only economically relevant for many industries but has also an important environmental impact, as the raw material...
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Solving nesting problems involves the waste minimisation in cutting processes, and therefore it is not only economically relevant for many industries but has also an important environmental impact, as the raw materials that are cut are usually a natural resource. However, very few exact approaches have been proposed in the literature for the nesting problem (also known as irregular packing problem), and the majority of the known approaches are heuristic algorithms, leading to suboptimal solutions. The few mathematical programming models known for this problem can be divided into discrete and continuous models, based on how the placement coordinates of the pieces to be cut are dealt with. In this paper, we propose an innovative semi-continuous mixed-integer programming model for two-dimensional cutting and packing problems with irregular shaped pieces. The model aims to exploit the advantages of the two previous classes of approaches and discretises the [GRAPHICS] -axis while keeping the [GRAPHICS] -coordinate continuous. The board can therefore be seen as a set of stripes. Computational results show that the model, when solved by a commercial solver, can deal with large problems and determine the optimal solution for smaller instances, but as it happens with discrete models, the optimal solution value depends on the discretisation step that is used.
Distribution systems are most commonly operated in a radial configuration for a number of reasons. In order to impose radiality constraint in the optimal network reconfiguration problem, an efficient algorithm is intr...
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Distribution systems are most commonly operated in a radial configuration for a number of reasons. In order to impose radiality constraint in the optimal network reconfiguration problem, an efficient algorithm is introduced in this paper based on graph theory. The paper shows that the normally followed methods of imposing radiality constraint within a mixed-integer programming formulation of the reconfiguration problem may not be sufficient. The minimum-loss network reconfiguration problem is formulated using different ways to impose radiality constraint. It is shown, through simulations, that the formulated problem using the proposed method for representing radiality constraint can be solved more efficiently, as opposed to the previously proposed formulations. This results in up to 30% reduction in CPU time for the test systems used in this study. (C) 2014 Elsevier Ltd. All rights reserved.
This paper introduces various models for optimal and maximal utility-based distributed generation penetration in the radial distribution systems. Several problems with different probabilistic indices as objective func...
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This paper introduces various models for optimal and maximal utility-based distributed generation penetration in the radial distribution systems. Several problems with different probabilistic indices as objective functions constrained by power flow equations, distributed generation penetration, voltage, and thermal limits are proposed to obtain the optimal penetration of distributed generations on rural distribution networks. There are tradeoffs between interests and risks that the distribution network operators or distribution companies may be willing to take on. Thus, to have an effective method for maximal allocation of distributed generations, new indices are proposed, and the problems are formulated as a risk-constrained optimization model. The obtained problems have mixed-integer nonlinear programming and nonconvex forms because of nonlinearity and nonconvexity of the optimal power flow(OPF) equations and indices, leading to computationally nondeterministic polynomial-time-hard problems. Accordingly, in this paper, convex relaxations of OPF are introduced instead of the conventional nonlinear equations. Efficient linear equivalents of the objective function and constraints are introduced to reduce the computational burden. Test results of the proposed models on a radial distribution system are presented and discussed.
In this study, we reduce the uncertainty embedded in secondary possibility distribution of a type-2 fuzzy variable by fuzzy integral, and apply the proposed reduction method to p-hub center problem, which is a nonline...
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In this study, we reduce the uncertainty embedded in secondary possibility distribution of a type-2 fuzzy variable by fuzzy integral, and apply the proposed reduction method to p-hub center problem, which is a nonlinear optimization problem due to the existence of integer decision variables. In order to optimize p-hub center problem, this paper develops a robust optimization method to describe travel times by employing parametric possibility distributions. We first derive the parametric possibility distributions of reduced fuzzy variables. After that, we apply the reduction methods to p-hub center problem and develop a new generalized value-at-risk (VaR) p-hub center problem, in which the travel times are characterized by parametric possibility distributions. Under mild assumptions, we turn the original fuzzy p-hub center problem into its equivalent parametric mixed-integer programming problems. So, we can solve the equivalent parametric mixed-integer programming problems by general-purpose optimization software. Finally, some numerical experiments are performed to demonstrate the new modeling idea and the efficiency of the proposed solution methods. (C) 2014 Elsevier Inc. All rights reserved.
In a factory of automobile component primer painting, various automobile parts are attached to overhead hangers in a conveyor line and undergo a series of coating processes. Thereafter, the components are wrapped at a...
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In a factory of automobile component primer painting, various automobile parts are attached to overhead hangers in a conveyor line and undergo a series of coating processes. Thereafter, the components are wrapped at a packaging station. The packaging process should be fully balanced by an appropriate sequence of components to prevent the bottleneck effect because each component requires different packaging times and materials. An overhead hanger has a capacity limit and can hold varying numbers of components depending on the component type. Capacity loss can occur if the hanger capacity is not fully utilized. To increase hanger utilization, companies sometimes mix two or more component types on the same hangers, and these hangers are called mixed hangers. However, mixed hangers generally cause heavy workload because different items require additional setup times during hanging and packing processes. Hence, having many mixed hangers is not recommended. A good production schedule requires a small number of mixed hangers and maximizes hanger utilization and packaging workload balance. We show that the scheduling problem is NP-hard and develop a mathematical programming model and efficient solution approaches for the problem. When applying the methods to solve real problems, we also use an initial solution-generating method that minimizes the mixing cost, set a rule for hanging the items on hangers considering eligibility constraint, and decrease the size of tabu list in proportion to the remaining computational time for assuring intensification in the final iterations of the search. Experimental results demonstrate the effectiveness of the proposed approaches.
A production planning problem, known as the discrete lot sizing and scheduling problem with sequence-dependent changeover costs, is considered. We propose a new way of modelling the production system based on the use ...
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A production planning problem, known as the discrete lot sizing and scheduling problem with sequence-dependent changeover costs, is considered. We propose a new way of modelling the production system based on the use of a multi-attribute product structure encountered in many industrial situations. The basic idea is to describe the products as combinations of physical attributes and to exploit this description to reduce the size of the mixed-integer program to be solved. The results of our computational experiments show the practical usefulness of the proposed formulation which leads to significantly improved efficiency in the solution process. (C) 2008 Elsevier Ltd. All rights reserved.
Selection of supply contracts is a critical decision faced by manufacturing firms in a variety of industries. Manufacturing firms often have the option of selecting from several types of supply contracts that include ...
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Selection of supply contracts is a critical decision faced by manufacturing firms in a variety of industries. Manufacturing firms often have the option of selecting from several types of supply contracts that include long-term, medium-term, and short-term contracts. While extant literature has stressed the importance of such contracts, few methodologies have been proposed for optimally selecting contracts under various business conditions. To this end, this paper proposes a methodology for optimal contract selection based on a mixed-integer programming approach. We present specific insights to manufacturing managers on choosing the right contracts in the presence of market price uncertainty, supplier discounts, investment costs, and supplier capacity restrictions.
The employment of industrial robot systems especially in the automotive industry noticeably changed the view of production plants and led to a tremendous increase in productivity. Nonetheless, rising technological com...
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The employment of industrial robot systems especially in the automotive industry noticeably changed the view of production plants and led to a tremendous increase in productivity. Nonetheless, rising technological complexity, the parallelization of production processes, as well as the crucial need for respecting specific safety issues pose new challenges for man and machine. Our goal is to develop algorithms, guidelines, and tools that make the commissioning of industrial robot systems more reliable by verifying the programs of robots and logical controllers. This in particular includes optimizing the schedule of the robot systems in order to ensure desired period times as well as conflict-free timetables already in the planning stage. The applicability of the Periodic Event Scheduling Problem proposed by Serafini and Ukovich (SIAM J Discrete Math 2(4):550-581,1989) is investigated to tackle this cycle time minimization task, and we establish a variant of the classical formulation in order to cover the special characteristics of our scenario. We want to demonstrate how this key element forms a part of a range of developed software tools that support engineers and programmers throughout the commissioning of real-world robot production systems.
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