Bi-objective optimization of a multi-product multi-period three-echelon supply chain network consisting of manufacturing plants, distribution centers (DCs) each with uncertain services, and customer nodes is aimed in ...
详细信息
Bi-objective optimization of a multi-product multi-period three-echelon supply chain network consisting of manufacturing plants, distribution centers (DCs) each with uncertain services, and customer nodes is aimed in this paper. The two objectives are minimization of the total cost while maximizing the average number of products dispatched to customers. The decision variables are: (1) the number and the locations of reliable DCs in the network, (2) the optimum number of items produced by plants, (3) the optimum quantity of transported products, (4) the optimum inventory of products at DCs and plants, and (5) the optimum shortage quantity of the customer nodes. The problem is first formulated into the framework of a constrained bi-objective mixed integer linear programming model. Then, to solve the problem using the GAMS software, six multi-objective decision-making (MODM) methods are investigated in order to select the best in terms of total supply chain cost, total expected number of products dispatched to customers, and their required CPU time, simultaneously. At the end, some numerical illustrations are provided to show the applicability of the proposed methodology. (C) 2014 Elsevier Ltd. All rights reserved.
The maximum consensus problem is fundamentally important to robust geometric fitting in computer vision. Solving the problem exactly is computationally demanding, and the effort required increases rapidly with the pro...
详细信息
ISBN:
(纸本)9781467388528
The maximum consensus problem is fundamentally important to robust geometric fitting in computer vision. Solving the problem exactly is computationally demanding, and the effort required increases rapidly with the problem size. Although randomized algorithms are much more efficient, the optimality of the solution is not guaranteed. Towards the goal of solving maximum consensus exactly, we present guaranteed outlier removal as a technique to reduce the runtime of exact algorithms. Specifically, before conducting global optimization, we attempt to remove data that are provably true outliers, i.e., those that do not exist in the maximum consensus set. We propose an algorithm based on mixed integer linear programming to perform the removal. The result of our algorithm is a smaller data instance that admits a much faster solution by subsequent exact algorithms, while yielding the same globally optimal result as the original problem. We demonstrate that overall speedups of up to 80% can be achieved on common vision problems.
Summary form only given: Private investors, flexibility, efficiency and environmental requirements from deregulated markets have led the existence and building of a significant number of combined-cycle gas turbines (C...
详细信息
Summary form only given: Private investors, flexibility, efficiency and environmental requirements from deregulated markets have led the existence and building of a significant number of combined-cycle gas turbines (CCGTs) in many power systems. These plants represent a complex optimization problem for the short-term planning unit commitment (UC) carried out by independent system operators due to their multiple operating configurations. Accordingly, this paper proposes a mixed-integerlinearprogramming (MIP) formulation of the configuration-based model of CCGTs, which is commonly utilized for bid/offering market processes. This formulation is simultaneously tighter and more compact than analogous MIP-based models; hence, it presents a lower computational burden. The computational efficiency of the proposed formulation is demonstrated by solving network-constrained UC case studies, of different size and complexity, using three of the leading commercial MIP solvers: CPLEX, GUROBI, and XPRESS.
This paper considers a dairy industry problem on integrated planning and scheduling of set yoghurt production. A mixed integer linear programming formulation is introduced to integrate tactical and operational decisio...
详细信息
This paper considers a dairy industry problem on integrated planning and scheduling of set yoghurt production. A mixed integer linear programming formulation is introduced to integrate tactical and operational decisions and a heuristic approach is proposed to decompose time buckets of the decisions. The decomposition heuristic improves computational efficiency by solving big bucket planning and small bucket scheduling problems. Further, mixed integer linear programming and constraint programming methodologies are combined with the algorithm to show their complementary strengths. Numerical studies using illustrative data with high demand granularity (i.e., a large number of small-sized customer orders) demonstrate that the proposed decomposition heuristic has consistent results minimizing the total cost (i.e., on average 8.75% gap with the best lower bound value found by MILP) and, the developed hybrid approach is capable of solving real sized instances within a reasonable amount of time (i.e., on average 92% faster than MILP in CPU time). (C) 2015 Elsevier Ltd. All rights reserved.
Monoculture induced threats such as "pass the hash" attacks can spread more easily through a system of similar components. Introducing a few different and more robust components in the system has the potenti...
详细信息
Monoculture induced threats such as "pass the hash" attacks can spread more easily through a system of similar components. Introducing a few different and more robust components in the system has the potential to mitigate such situations. In this paper, we propose a constrained resource allocation optimization framework exploring the binary decision diagram (BDD) and mixed integer linear programming techniques. An illustrative example is provided.
Although several optimization models have been proposed for chemical production scheduling, there is still a need for effective solution methods. Accordingly, the goal of this work is to present different reformulatio...
详细信息
Although several optimization models have been proposed for chemical production scheduling, there is still a need for effective solution methods. Accordingly, the goal of this work is to present different reformulations of representative continuous-time models by introducing an explicit variable for the number of batches of a given task. This idea, which has been successfully applied to discrete-time models, results in significant computational enhancement. We discuss how different objective functions benefit from particular reformulations and show significant improvements by means of an extensive computational study that includes several instances containing different process networks and scheduling horizons.
Purpose: Network design of the supply chain is an important and strategic aspect of logistics management. In this paper, we address the network design problem specific to packaged gases (cylinder) supply chain. We pro...
详细信息
Purpose: Network design of the supply chain is an important and strategic aspect of logistics management. In this paper, we address the network design problem specific to packaged gases (cylinder) supply chain. We propose an integrated framework that allows for the determination of the optimal facility locations, the filling plant production capacities, the inventory at plants and hubs, and the number of packages to be routed in primary and secondary transportation. Design/methodology/approach: We formulate the problem as a mixedinteger program and then develop a decomposition approach to solve it. We illustrate the proposed framework with numerical examples from real-life packaged gases supply chain. The results show that the decomposition approach is effective in solving a broad range of problem sizes. Findings: The main finding of this paper is that decomposing the network design problem into two sub-problems is very effective to tackle the real-life large scale network design problems occurring in cylinder gas distribution by optimizing strategic and tactical decisions and approximating the operational decisions. We also benchmark the results from the decomposition approach by solving the complete packaged gases network design model for smaller test cases. Originality/value: The main contribution of our work is that it integrates supply chain network design decisions without fixing the fillings plant locations with inventory and resource allocation decisions required at the plants. We also consider the transportation costs for the entire supply chain including the transhipment costs among different facilities by deciding the replenishment frequency.
Khezrimotlagh et al. (2013) pointed out the Kuosmanen and Kazemi Matin's model (hereafter referred to as, the KKM model) may not be stronger than the Lozano and Villa's model (hereafter referred to as, the LVM...
详细信息
Khezrimotlagh et al. (2013) pointed out the Kuosmanen and Kazemi Matin's model (hereafter referred to as, the KKM model) may not be stronger than the Lozano and Villa's model (hereafter referred to as, the LVM model), and they used a counter example to show that the input targets of the KKM model may even be greater than that of the LVM model. In this paper, we improved the KKM model into a rectified KKM model (hereafter referred to as, the RKKM model), and showed that the RKKM model can successfully solve the problem in the counter example. (C) 2015 Elsevier Ltd. All rights reserved.
Automation and flexibility are often mentioned as key concepts in modern production industry. To increase the level of flexibility, deterministic finite automata (DFA) can be used to model, specify and verify the prod...
详细信息
Automation and flexibility are often mentioned as key concepts in modern production industry. To increase the level of flexibility, deterministic finite automata (DFA) can be used to model, specify and verify the production systems. Often, it is also desirable to optimize some production criteria, such as for example the cycle time of a manufacturing cell. In this paper, a method for automatic conversion from DFA to a mixed integer linear programming (MILP) formulation is first presented. This conversion is developed for a number of DFA structures that have shown to be useful in practical applications. Special attention is paid to reducing the search region explored by the MILP solver. Second, a conversion from the MILP solution to a DFA supervisor is described. This allows to combine the advantages of DFA modeling with the efficiency of MILP and supervisory control theory to automatically generate time-optimal, collision-free and non-blocking working schedules for flexible manufacturing systems.
Piecewise polynomial functions are extensively used to approximate general nonlinear functions or sets of data. In this work, we propose a mixed integer linear programming (MILP) framework for generating optimal piece...
详细信息
Piecewise polynomial functions are extensively used to approximate general nonlinear functions or sets of data. In this work, we propose a mixed integer linear programming (MILP) framework for generating optimal piecewise polynomial approximations of varying degrees to nonlinear functions of a single variable. The nonlinear functions may be represented by discrete exact samplings or by data corrupted by noise. We studied two distinct approaches to the problem: (1) the generation of interpolating piecewise polynomial functions, in which the approximating function values in the extremes of each polynomial segment coincide with the original function values;and (2) a de facto approximation strategy in which the polynomial segments are free, except for the enforcement of continuity of the overall approximation. Our results from the implemented models show that the procedure is capable of efficiently approximating nonlinear functions and it has the added capability of allowing for the straightforward implementation of further constraints on solutions, such as the convexity of polynomial segments. Finally, the models for the generation of piecewise linear approximations and interpolations were applied for the linearization of mixedinteger nonlinearprogramming (MINLP) models extracted from the MINLP library. These models were linearized by the reformulation of their nonlinearities as piecewise linear functions with varying numbers of segments, resulting in MILP models that were solved to optimality and the solutions from the linearized models were compared with global optimal solutions from the original problems.
暂无评论