In this paper, we study probabilistically constrained problems involving individual chance constraints, random univariate right-hand sides, and risk tolerances defined as decision variables which affect part of the ob...
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In this paper, we study probabilistically constrained problems involving individual chance constraints, random univariate right-hand sides, and risk tolerances defined as decision variables which affect part of the objective function. Built on the concept of efficient points, we formulate the problems as mixed-integer programs by using binary variables to determine an optimal risk tolerance for each chance constraint. We develop two benchmark approaches, both of which solve chance-constrained programs with fixed risk values in a bisection algorithm or by enumeration. We specify our approaches for a minimum cost flow problem and a network capacity design problem, both of which involve chance constraints for bounding the risk of demand shortages. We test instances with diverse size and complexity of the two network problems, and demonstrate the computational efficacy as well as give managerial insights. (C) 2014 Elsevier Ltd. All rights reserved.
Shorter product life cycles in the chemical industry have led to uncertain demand in terms of volume, location and type. Modular production concepts can be used to serve such volatile customer demand more efficiently....
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Shorter product life cycles in the chemical industry have led to uncertain demand in terms of volume, location and type. Modular production concepts can be used to serve such volatile customer demand more efficiently. Modular plants increase the tactical flexibility of the supply chain substantially compared to centralized production with large-scale plants. Furthermore, production facilities can be flexibly opened, deactivated and reactivated. Modular plants can be relocated in the medium term, and their production capabilities can be reconfigured via process module changes. This work proposes new mixed-integer programming formulations for the tactical planning of production networks in the chemical industry;these formulations prescribe the volume, location, and process of modular plants in the production network over a multi-period planning horizon. The economic value of modular flexibility is investigated in a case study from the chemical industry under the consideration of diverse demand structures. The results have several managerial implications for the technical implementation and development of modular production technology in the chemical industry. (C) 2019 Elsevier B.V. All rights reserved.
Decentralised in-house logistics areas, known as supermarkets, are widely used in the manufacturing industry for parts feeding to assembly lines. In contrary to the literature and inspired by observation in a real cas...
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Decentralised in-house logistics areas, known as supermarkets, are widely used in the manufacturing industry for parts feeding to assembly lines. In contrary to the literature and inspired by observation in a real case, this study relaxes the assumption of using identical transport vehicles when deciding on the supermarkets' location by considering the availability of different vehicles. In this regard, this study deals with the integrated supermarket location and transport vehicles selection problems (SLTVSP). A mixed-integer programming (MIP) model of the problem is developed. Due to the complexity of the problem, a hybrid genetic algorithm (GA) with variable neighborhood search (GA-VNS) is also proposed to address large-sized problems. The performance of GA-VNS is compared against the MIP, the basic GA, and simulated annealing (SA) algorithm. The computational results from the real case and a set of generated test problems show that GA-VNS provides a very good approximation of the MIP solutions at a much shorter computational time while outperforming the other compared algorithms. The analysis of the results reveals that it is beneficial to apply different transport vehicles rather than identical vehicles for SLTVSP.
In the fixed-charge transportation problem, the goal is to optimally transport goods from depots to clients when there is a fixed cost associated to transportation or, equivalently, to opening an arc in the underlying...
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In the fixed-charge transportation problem, the goal is to optimally transport goods from depots to clients when there is a fixed cost associated to transportation or, equivalently, to opening an arc in the underlying bipartite graph. We further motivate its study by showing that it is both a special case and a strong relaxation of the big-bucket multi-item lot-sizing problem, and a generalization of a simple variant of the single-node flow set. This paper is essentially a polyhedral analysis of the polynomially solvable special case in which the associated bipartite graph is a path. We give a O(n(3))-time optimization algorithm and a O(n(2))-size linear programming extended formulation. We describe a new class of inequalities that we call "path-modular" inequalities. We give two distinct proofs of their validity. The first one is direct and crucially relies on sub-and super-modularity of an associated set function. The second proof is by showing that the projection of the extended linear programming formulations onto the original variable space yields exactly the polyhedron described by the path-modular inequalities. Thus we also show that these inequalities suffice to describe the convex hull of the set of feasible solutions.
This paper addresses a routing and scheduling problem from two different perspectives: economic and environmental. From economic perspective, we aim to optimize the vehicle routing plan to reduce the operating cost, b...
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This paper addresses a routing and scheduling problem from two different perspectives: economic and environmental. From economic perspective, we aim to optimize the vehicle routing plan to reduce the operating cost, but from environmental perspective, we aim to optimize the vehicle routing and speed decisions to reduce the carbon emissions. This research can provide two different decision plans under these two different perspectives, and the comparison of the results from the two different perspectives will be very meaningful and helpful to the logistics decision-makers. We formulate the problem using two mixed-integer programming (MIP) models with different objectives. However, this problem is very challenging, with medium-sized instances already difficult for the MIP solver. In order to solve it with larger scale instances, we propose an exact branch-and-price (BAP) algorithm. The BAP algorithm relies on efficiently solving the pricing sub-problem with different objectives. We design two different tailored labeling algorithms to solve it. Extensive computational experiments demonstrate the effectiveness of the proposed BAP algorithm, comparing with the MIP formulation solved by CPLEX with a time limit of 2 h.
Nowadays, energy-efficient scheduling has assumed a key role in ensuring the sustainability of manufacturing processes. In this context, we focus on the bi-objective problem of scheduling a set of jobs on identical pa...
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Nowadays, energy-efficient scheduling has assumed a key role in ensuring the sustainability of manufacturing processes. In this context, we focus on the bi-objective problem of scheduling a set of jobs on identical parallel machines to simultaneously minimize the maximum completion time and the total energy consumption over a time horizon partitioned into a set of discrete slots. The energy costs are determined by a time-of-use pricing scheme, which plays a crucial role in regulating energy demand and flattening its peaks. First, we uncover a symmetry-breaking property that characterizes the structure of the solution space of the problem. As a consequence, we provide a novel, compact mixed-integer linear programming formulation at the core of an efficient exact solution algorithm. A thorough experimental campaign shows that the use of the novel mathematical programming formulation enables the solution of larger-scale instances and entails a reduction in the computational times as compared to the formulation already available in the literature. Furthermore, we propose a new heuristic that improves the state-of-the-art in terms of required computational effort and quality of solutions. Such a heuristic outperforms the existing heuristics for the problem and is also capable of speeding up the exact solution algorithm when used for its initialization. Finally, we introduce a novel dynamic programming algorithm that is able to compute the optimal timing of the jobs scheduled on each machine to further improve the performance of the new heuristic.& COPY;2023 Elsevier B.V. All rights reserved.
A parallel Simulated Annealing algorithm with multi-threaded architecture is proposed to solve a real bi-objective maintenance scheduling problem with conflicting objectives: the minimisation of the total equipment do...
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A parallel Simulated Annealing algorithm with multi-threaded architecture is proposed to solve a real bi-objective maintenance scheduling problem with conflicting objectives: the minimisation of the total equipment downtime caused by maintenance jobs and the minimisation of the multi-skilled workforce requirements over the given horizon. The maintenance jobs have different priorities with some precedence relations between different skills. The total weighted flow time is used as a scheduling criterion to measure the equipment availability. The multi-threaded architecture is used to speed up a multi-objective Simulated Annealing algorithm to solve the considered problem. Multi-threading is a form of parallelism based on shared memory architecture where multiple logical processing units, so-called threads, run concurrently and communicate via shared memory. The performance of the parallel method compared to the exact method is verified using a number of test problems. The obtained results imply the high efficiency and robustness of the proposed heuristic for both solution quality and computational effort.
This paper is concerned with the problem of assigning employees to a number of work centres taking into account employees' expressed preferences for specific shifts, off-days, and work centres. This particular pro...
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This paper is concerned with the problem of assigning employees to a number of work centres taking into account employees' expressed preferences for specific shifts, off-days, and work centres. This particular problem is faced by the Kuwait National Petroleum Corporation that hires a firm to prepare schedules for assigning employees to about 86 stations distributed all over Kuwait. The number of variables in a mixed-integer programming model formulated for this problem is overwhelming, and hence, a direct solution to even the continuous relaxation of this model for relatively large-scale instances is inconceivable. However, we demonstrate that a column generation method, which exploits the special structures of the model, can readily solve the continuous relaxation of the model. Based on this column generation construct, we develop an effective heuristic to solve the problem. Computational results indicate that the proposed approach can facilitate the generation of good-quality schedules for even large-scale problem instances in a reasonable time.
The steady-state economic optimum in chemical process plants generally lies at the intersection of constraints. However, in order to maintain feasible operation in the face of disturbances. the steady-state operating ...
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The steady-state economic optimum in chemical process plants generally lies at the intersection of constraints. However, in order to maintain feasible operation in the face of disturbances. the steady-state operating point needs to be moved some distance from the constraints into the feasible region. The optimal back-off is a function of the plant dynamics, the type and magnitude of the expected disturbances, and the plant control system. This paper considers calculation of the optimal back-off with regulation under constrained predictive control. The resulting optimization problem is multilevel in nature. and is formulated and solved as a mixed-integer quadratic or linear programming problem for which global optimality is guaranteed. Case studies comprising CSTRs in series and a fluid catalytic cracking unit illustrate the application of the strategy. (c) 2007 Elsevier Ltd. All rights reserved.
We consider the problem of decomposing Intensity Modulated Radiation Therapy (IMRT) fluence maps using rectangular apertures. A fluence map can be represented as an integer matrix, which denotes the intensity profile ...
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We consider the problem of decomposing Intensity Modulated Radiation Therapy (IMRT) fluence maps using rectangular apertures. A fluence map can be represented as an integer matrix, which denotes the intensity profile to be delivered to a patient through a given beam angle. We consider IMRT treatment machinery that can form rectangular apertures using conventional jaws, and hence, do not need sophisticated multi-leaf collimator (MLC) devices. The number of,apertures used to deliver the fluence map needs to be minimized in order to treat the patient efficiently. From a mathematical point of view, the problem is equivalent to a minimum cardinality matrix decomposition problem. We propose a combinatorial Benders decomposition approach to solve this problem to optimality. We demonstrate the efficacy of our approach on a set of test instances derived from actual clinical data. We also compare our results with the literature and solutions obtained by solving a mixed-integer programming formulation of the problem. (C) 2011 Elsevier Ltd. All rights reserved.
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