This paper investigates scheduling of jobs with deadlines across a serial multi-factory supply chain which involves minimizing sum of total tardiness and total transportation costs. Jobs can be transported among facto...
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This paper investigates scheduling of jobs with deadlines across a serial multi-factory supply chain which involves minimizing sum of total tardiness and total transportation costs. Jobs can be transported among factories and can be delivered to the customer in batches which have limited capacity. The aim of this optimization problem is threefold: (1) determining the number of batches, (2) assigning jobs to batches,and (3) scheduling the batches production and delivery in each factory. The proposed problem formulated as a mixed-integer linear program. Then the model's performance is analyzed and evaluated through two examples. Moreover, a knowledge-based imperialistic competitive algorithm (KBICA) is also presented to find an approximate optimum solution for the problem. Computational experiments of the proposed problem investigate the efficiency of the method through different sizes of the test problems. (C) 2016 Elsevier Ltd. All rights reserved.
The Master Surgery Scheduling problem consists of finding a suitable allocation of operating resources to surgical groups. A myriad of variants of the problem has been addressed in literature. Here we focus on two maj...
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The Master Surgery Scheduling problem consists of finding a suitable allocation of operating resources to surgical groups. A myriad of variants of the problem has been addressed in literature. Here we focus on two major variants, arising during a cooperation with Sykehuset Asker og B'rum HF, a large hospital in the city of Oslo. The first variant asks for balancing patient queue lengths among different specialties, whereas the second for minimizing resort to overtime. To cope with these problems we introduce a new mixedinteger linear formulation and show its beneficial properties. Both problems require the estimation of demand levels. As such estimation is affected by uncertainty, we also develop a light robustness approach to the second variant. Finally we present computational results on a number of real-world instances provided by our reference hospital.
This article presents a novel algorithm for the generation of multiple shortterm production schedules for an open-pit mine, in which several objectives, of varying priority, characterize the quality of each solution. ...
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This article presents a novel algorithm for the generation of multiple shortterm production schedules for an open-pit mine, in which several objectives, of varying priority, characterize the quality of each solution. A shortterm schedule selects regions of a mine site, known as 'blocks', to be extracted in each week of a planning horizon (typically spanning 13 weeks). Existing tools for constructing these schedules use greedy heuristics, with little optimization. To construct a single schedule in which infrastructure is sufficiently utilized, with production grades consistently close to a desired target, a planner must often run these heuristics many times, adjusting parameters after each iteration. A planner's intuition and experience can evaluate the relative quality and mineability of different schedules in a way that is difficult to automate. Of interest to a short-term planner is the generation of multiple schedules, extracting available ore and waste in varying sequences, which can then be manually compared. This article presents a tool in which multiple, diverse, short-term schedules are constructed, meeting a range of common objectives without the need for iterative parameter adjustment.
The capacitated facility location problem (CFLP)is a well-known combinatorial optimization problem with applications in distribution and production planning. It consists in selecting plant sites from a finite set of p...
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The capacitated facility location problem (CFLP)is a well-known combinatorial optimization problem with applications in distribution and production planning. It consists in selecting plant sites from a finite set of potential sites and in allocating customer demands in such a way as to minimize operating and transportation costs. A number of solution approaches based on Lagrangean relaxation and subgradient optimization has been proposed for this problem. Subgradient optimization does not provide a primal (fractional) optimal solution to the corresponding master problem. However, in order to compute optimal solutions to large or difficult problem instances by means of a branch-and-bound procedure information about such a primal fractional solution can be advantageous. In this paper, a (stabilized) column generation method is, therefore, employed in order to solve a corresponding master problem exactly. The column generation procedure is then employed within a branch-and-price algorithm for computing optimal solutions to the CFLP. Computational results are reported for a set of larger and difficult problem instances. (c) 2006 Elsevier B.V. All rights reserved.
In liberalized electricity markets, generation companies bid their hourly generation in order to maximize their profit. The optimization of the generation bids over a short-term weekly period must take into account th...
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In liberalized electricity markets, generation companies bid their hourly generation in order to maximize their profit. The optimization of the generation bids over a short-term weekly period must take into account the action of the competing generation companies and the market-price formation rules and must be coordinated with long-term planning results. This paper presents a three stage optimization process with a data analysis and parameter calculation, a linearized unit commitment, and a nonlinear generation scheduling refinement. Although the procedure has been developed from the experience with the Spanish power market, with minor adaptations it is also applicable to any generation company participating in a competitive market system. (C) 2006 Elsevier Ltd. All rights reserved.
Driven by fast advancements in wind and photovoltaic (PV) technologies, onsite renewable electricity generation is becoming attractive to manufacturers since they are able to reduce electricity purchases from the grid...
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Driven by fast advancements in wind and photovoltaic (PV) technologies, onsite renewable electricity generation is becoming attractive to manufacturers since they are able to reduce electricity purchases from the grid and may lower their electricity costs. This paper proposes a methodology to minimize the electricity cost of a grid-connected factory that also has onsite solar power generation and battery storage. Purchases from the grid are subject to time-of-use electricity rate schedules. The problem is formulated as a mixed-integer programming problem and GAMS is used to find the optimal manufacturing and onsite energy flow schedules that have the minimal electricity cost. A case study with one hybrid flow shop, onsite PV power generation, and a battery was used to test the proposed method. Testing results showed that the factory's electricity cost can be reduced by 54.0% under summer TOU rate on a typical day while a 0.7% electricity cost reduction can be achieved for a representative day under a winter TOU rate. An annual electricity cost savings of 28.1% can be obtained with the optimal schedules. In addition, a parametric study incorporating the optimal schedules was performed to understand the economic performances associated with different PV capacity and battery bank size for the factory.
The Flow-Refueling Location Model (FRLM) locates a given number of refueling stations on a network to maximize the traffic flow among origin-destination pairs that can be refueled given the driving range of alternativ...
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The Flow-Refueling Location Model (FRLM) locates a given number of refueling stations on a network to maximize the traffic flow among origin-destination pairs that can be refueled given the driving range of alternative-fuel vehicles. Traditionally, the FRLM has been formulated using a two-stage approach: the first stage generates combinations of locations capable of serving the round trip on each route, and then a mixed-integer programming approach is used to locate p facilities to maximize the flow refueled given the feasible combinations created in the first stage. Unfortunately, generating these combinations can be computationally burdensome and heuristics may be necessary to solve large-scale networks. This article presents a radically different mixed-binary-integerprogramming formulation that does not require pre-generation of feasible station combinations. Using several networks of different sizes, it is shown that the proposed model solves the FRLM to optimality as fast as or faster than currently utilized greedy and genetic heuristic algorithms. The ability to solve real-world problems in reasonable time using commercial math programming software offers flexibility for infrastructure providers to customize the FRLM to their particular fuel type and business model, which is demonstrated in the formulation of several FRLM extensions.
This paper studies beamforming techniques for energy efficiency maximization (EEmax) in multiuser multiple-input single-output (MISO) downlink system. For this challenging nonconvex problem, we first derive an optimal...
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This paper studies beamforming techniques for energy efficiency maximization (EEmax) in multiuser multiple-input single-output (MISO) downlink system. For this challenging nonconvex problem, we first derive an optimal solution using branch-and-reduce-and-bound (BRB) approach. We also propose two low-complexity approximate designs. The first one uses the well-known zero-forcing beamforming (ZFBF) to eliminate inter-user interference so that the EEmax problem reduces to a concave-convex fractional program. Particularly, the problem is then efficiently solved by closed-form expressions in combination with the Dinkelbach's approach. In the second design, we aim at finding a stationary point using the sequential convex approximation (SCA) method. By proper transformations, we arrive at a fast converging iterative algorithm where a convex program is solved in each iteration. We further show that the problem in each iteration can also be approximated as a second-order cone program (SOCP), allowing for exploiting computationally efficient state-of-the-art SOCP solvers. Numerical experiments demonstrate that the second design converges quickly and achieves a near-optimal performance. To further increase the energy efficiency, we also consider the joint beamforming and antenna selection (JBAS) problem for which two designs are proposed. In the first approach, we capitalize on the perspective reformulation in combination with continuous relaxation to solve the JBAS problem. In the second one, sparsity-inducing regularization is introduced to approximate the JBAS problem, which is then solved by the SCA method. Numerical results show that joint beamforming and antenna selection offers significant energy efficiency improvement for large numbers of transmit antennas.
In this paper, we propose a method for N - 1 contingency constrained transmission capacity expansion planning (TCEP), which is formulated as a mixed-integer programming (MIP) problem. In relatively well-designed power...
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In this paper, we propose a method for N - 1 contingency constrained transmission capacity expansion planning (TCEP), which is formulated as a mixed-integer programming (MIP) problem. In relatively well-designed power systems, a single outage of a majority of lines will not usually cause overload on other lines in most loading conditions. Thus they will not affect the feasible region and the optimal answer of the TCEP optimization problem, and can be safely removed from contingency analysis if we can identify them. A contingency identification index is developed to detect these lines and create variable contingency lists (VCL) for different network loading conditions. In our proposed method, we use results of a relaxed version of the original problem as a lower bound answer in the first step, and integrate contingencies into TCEP in the next steps to solve this optimization problem faster while still satisfying N - 1 criterion. For solving TCEP with contingencies, two options are offered, i. e., option A that uses an updated system as its base case (original existing network together with selected lines by the relaxed problem) and option B that uses the original existing network as its base case (without results of the relaxed problem). Option A is faster than option B because it usually should select fewer new lines compared to B, but cannot guarantee optimality. Option B provides the optimal answer while taking more computational time. An ERCOT case study is used to show capabilities of the proposed method for solving large scale problems, and the numerical result demonstrates this method is much faster than the integrated MIP method that directly incorporates all contingencies.
Consider optimizing a periodic schedule for an automated production plant as a last step of a more comprehensive design process. In our scenario, each robot's cyclic sequence of operations and trajectories between...
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Consider optimizing a periodic schedule for an automated production plant as a last step of a more comprehensive design process. In our scenario, each robot's cyclic sequence of operations and trajectories between potential waiting points have already been fully specified. Further given are those precedences that fix sequence requirements on operations between different robots. It remains to determine the starting time for each operation or movement of each robot within a common cyclic time period so as to avoid collisions of robots that operate in the same space simultaneously. So the task is to find a conflict-resolving schedule that minimizes this common periodic cycle time while observing all precedence relations and collision avoidance constraints. The proposed cycle time minimization problem for robot coordination has, to the best of our knowledge, not been studied before. We develop an approach for solving it by employing binary search for determining the smallest feasible period time of an iso-periodic event scheduling problem (IPESP). This is a variant of the periodic event scheduling problem in which the objects that have to be scheduled need to obey exactly the same period time. The possibility to wait arbitrarily long at waiting points turns out to be essential to justify the use of binary search for identifying the minimum cycle time, thereby avoiding bilinear mixedinteger formulations. Special properties of the given scenario admit bounds on the periodic tension variables of an integerprogramming formulation. Although the IPESP subproblems remain NP-complete in general, these bounds allow solving real-world instances sufficiently fast for the approach to be applicable in practice. Numerical experiments on real-world and randomly generated data are supplied to illustrate the potential and limitations of this approach. Summary of Contribution: When designing automated production plants, a crucial step is to identify the smallest possible per unit perio
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