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 considers the problem of scheduling n jobs on a single machine. A fixed processing time and an execution interval are associated with each job. Preemption is not allowed. The objective is to find a feasible...
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This paper considers the problem of scheduling n jobs on a single machine. A fixed processing time and an execution interval are associated with each job. Preemption is not allowed. The objective is to find a feasible job sequence that minimizes the number of tardy jobs. On the basis of an original mathematical integerprogramming formulation, this paper shows how good-quality lower and upper bounds can be computed. Numerical experiments are provided for assessing the proposed approach.
This article is based on a real-life problem of a global aluminium supply chain network driven by an aluminium smelter. At each echelon of the aluminium supply chain network, several members are involved which are sca...
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This article is based on a real-life problem of a global aluminium supply chain network driven by an aluminium smelter. At each echelon of the aluminium supply chain network, several members are involved which are scattered around the world. Producing aluminium begins with bauxite mining. Next, aluminium oxide is made from bauxite and finally aluminium is produced from aluminium oxide. A novel type of mixed-integer decision-making model, including a time-continuous representation of the planning period, is presented. The model enables coordination of production quantities and times of all supply chain members in order to minimise production and transportation costs of the whole supply chain minus bonus payments for early deliveries which are stipulated between the supply chain network and its customers. Material flows can take place with or without temporary storage of intermediate products at supplying and/or receiving sites. Furthermore, relax-and-fix heuristics are presented. A number of randomly generated scenarios are presented to demonstrate that the heuristics can find nearly optimal solutions along with drastically reduced computation times. The relax-and-fix heuristic enables iterative planning between centralised and decentralised decision makers.
In project scheduling under processing times uncertainty, the Anchor-Robust Project Scheduling Problem is to find a baseline schedule of bounded makespan and a max-weight subset of jobs whose starting times are guaran...
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In project scheduling under processing times uncertainty, the Anchor-Robust Project Scheduling Problem is to find a baseline schedule of bounded makespan and a max-weight subset of jobs whose starting times are guaranteed. The problem was proven NP-hard even for budgeted uncertainty. In the present work we design mixed-integer programming (MIP) formulations that are valid for a variety of uncertainty sets encompassing budgeted uncertainty. A new dominance among solutions is proposed, resulting into an MIP formulation. We further study the combinatorial structure of the problem. Non-trivial polynomial cases under budgeted uncertainty are exhibited, where the dominance-based formulation yields a polyhedral characterization of integer solutions. In more general cases, the dominance-based formulation is shown to be tighter than all previously known formulations. In numerical experiments we investigate how the formulation performs on instances around the polynomial cases, for both budgeted uncertainty sets and more elaborate uncertainty sets involving several budgets. (c) 2021 Elsevier B.V. All rights reserved.
Enterprise-wide Optimization (EWO) has become a major goal in the process industries due to the increasing pressures for remaining competitive in the global marketplace. EWO involves optimizing the supply, manufacturi...
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Enterprise-wide Optimization (EWO) has become a major goal in the process industries due to the increasing pressures for remaining competitive in the global marketplace. EWO involves optimizing the supply, manufacturing and distribution activities of a company to reduce costs, inventories and environmental impact, and to maximize profits and responsiveness. Major operational items include planning, scheduling, real-time optimization and control. We provide an overview of EWO in terms of a mathematical programming framework. We first provide a brief overview of mathematical programming techniques (mixed-integer linear and nonlinear optimization methods), as well as decomposition methods, stochastic programming and modeling systems. We then address some of the major issues involved in the modeling and solution of these problems. Finally, based on the EWO program at the Center of Advanced Process Decision-making at Carnegie Mellon, we describe several applications to show the potential of this area. (C) 2012 Elsevier Ltd. All rights reserved.
We introduce a modeling framework for stochastic rider-driver matching in many-to-one ridesharing systems, in which drivers have to be selected before the exact rider demand is known. The modeling framework allows for...
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We introduce a modeling framework for stochastic rider-driver matching in many-to-one ridesharing systems, in which drivers have to be selected before the exact rider demand is known. The modeling framework allows for the use of driver booking fees and penalties for unmatched drivers, therefore supporting different system operating modes. We model this problem as a two-stage stochastic set packing problem. To tackle the intractability of the stochastic problem, we introduce three model approximations and evaluate them on a large set of benchmark instances for three different system operating modes. Our computational experiments show the superiority of some model approximations over others and provide valuable insights on the impact of penalties and booking fees on the system's profitability and user satisfaction.
Reverse logistics networks reintroduce end-of-life products to remanufacturing, which is significant for sustainable development and environmental protection. In this paper, we investigate the current reverse logistic...
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Reverse logistics networks reintroduce end-of-life products to remanufacturing, which is significant for sustainable development and environmental protection. In this paper, we investigate the current reverse logistics network of household appliances in China and redesign a new network that introduces necessary facilities: disassembly centres and three types of remanufacturing plants to improve recycling rates. A mixed-integer linear programming model with multi-level capacity choices is proposed to determine the locations and capacities for disassembly centres and remanufacturing plants. An efficient decomposition-and-expansion heuristic is developed to solve the model. The redesigned network is evaluated using a real case. The results indicate that the recycling rate is largely improved by the new network in which almost all useful materials can be recycled, whereas, in the traditional network, only metals are efficiently recycled. A sensitivity analysis of model parameters is conducted using the Taguchi method to identify their effects on recycling decisions. The proposed algorithm is further evaluated using a set of randomly generated instances. The results show that the algorithm can yield high-quality solutions within a short time. The best configuration of the algorithm is suggested via sensitivity analysis of parameters using the generated instances.
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.
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