With advancements in technology, commercial aircraft formation flying is becoming increasingly feasible as an efficient and environmentally friendly flight method. However, gaps remain in practical implementation, par...
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This research proposes an optimization approach to enhance the stencil printing process (SPP) in surface mount printed circuit board (PCB) assembly. Stencil printing behavior is affected by many variables including st...
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In this paper, we propose a new vehicle routing problem variant. The new problem is a type of selective vehicle routing model in which it is not necessary to visit all nodes, but to visit enough nodes in such a way th...
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ISBN:
(纸本)9783030003531;9783030003524
In this paper, we propose a new vehicle routing problem variant. The new problem is a type of selective vehicle routing model in which it is not necessary to visit all nodes, but to visit enough nodes in such a way that all clusters are visited and from which it is possible to cover all nodes. Here, a mixed-integer linear programming formulation (MILP) is proposed in order to model the problem. The MILP is tested by using adapted instances from the generalized vehicle routing problem (GVRP). The model is also tested on small size GVRP instances as a special case of our proposed model. The results allow to evaluate the impact of clusters configuration in solver efficacy.
The lower hedging problem with a minimal expected surplus risk criterion in incomplete markets is studied for American claims in finite state financial markets. It is shown that the lower hedging problem with linear e...
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The lower hedging problem with a minimal expected surplus risk criterion in incomplete markets is studied for American claims in finite state financial markets. It is shown that the lower hedging problem with linear expected surplus criterion for American contingent claims in finite state markets gives rise to a non-convex bilinearprogramming formulation which admits an exact linearization. The resulting mixed-integerlinear program can be readily processed by available software. (c) 2011 Elsevier B.V. All rights reserved.
We introduce the nested p-center problem, which is a multi-period variant of the well-known pcenter problem. The use of the nesting concept allows to obtain solutions, which are consistent over the considered time hor...
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This study addresses a production planning problem in a remanufacturing system, where the objective is to determine the amount of used products of different qualities that need to be recovered in order to produce rema...
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ISBN:
(纸本)9798350373981;9798350373974
This study addresses a production planning problem in a remanufacturing system, where the objective is to determine the amount of used products of different qualities that need to be recovered in order to produce remanufactured products that meet customer demands, while minimizing total costs, which include used product return costs, remanufacturing costs, start-up costs and storage costs. To achieve this objective, a new mixed-integer linear programming (MILP) model is developed for this problem. This model is evaluated through computational experiments conducted under different scenarios, considering limited or unlimited quantities of used products and different period lengths. The results of this study illustrate the effectiveness and efficiency of the proposed model in minimizing costs and meeting customer demands of remanufactured products.
A mixed-integer linear programming (MILP) model is proposed for solving a one dimension cutting stock problem (1D-CSP) in the steel industry. A case study of a metallurgical company is presented and the objective is t...
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ISBN:
(纸本)9783030867027;9783030867010
A mixed-integer linear programming (MILP) model is proposed for solving a one dimension cutting stock problem (1D-CSP) in the steel industry. A case study of a metallurgical company is presented and the objective is to minimize waste in the cutting process of steel bars, considering inventory constraints and the potential use of the resulting leftovers. The computational results showed that an optimal solution was always found with an average improvement in waste reduction of 80%. There was no significant difference when comparing results between the complete model and the model without inventory constraints.
Presolving has become an important component of modern mixed-integer linear programming (MILP) solvers. Empirically, it has been observed that the performance of the solver is significantly influenced by the presolvin...
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ISBN:
(纸本)9798400709234
Presolving has become an important component of modern mixed-integer linear programming (MILP) solvers. Empirically, it has been observed that the performance of the solver is significantly influenced by the presolving algorithm (presolver), and selecting an appropriate combination of presolvers can improve solving efficiency. In industry, it is common to manually control the switch of presolvers to find a more efficient combination. However, with the emergence of new presolvers, it has become increasingly challenging to manually implement an optimal selection strategy in the vast combination space. Therefore, this paper proposes presolver selection, which needs to consider two key issues: (P1) How many presolvers should be selected? (P2) Which presolvers should be preferred among them? To address this challenge, this paper uses a hierarchical sequence model (HEM) to learn the presolver selection strategy through reinforcement learning. Specifically, the high-level model learns how many presolvers should be selected, and the low-level model learns to select a subset of presolvers within the determined size. The experimental results show that the method used can solve (P1) and (P2) better scompared to the designed baseline. It effectively improves the performance of the solver on real-world and synthetic MILPs.
Purpose To maximize the network total profit by calculating the difference between costs and revenue (first objective function). To maximize the positive impact on the environment by integrating GSCM factors in RL (se...
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Purpose To maximize the network total profit by calculating the difference between costs and revenue (first objective function). To maximize the positive impact on the environment by integrating GSCM factors in RL (second objective function). To calculate the efficiency of disassembly centers by SDEA method, which are selected as suppliers and maximize the total efficiency (third objective function). To evaluate the resources and total efficiency of the proposed model to facilitate the allocation resource process, to increase resource efficiency and to improve the efficiency of disassembly centers by Inverse DEA. Design/methodology/approach The design of a closed-loop logistics network for after-sales service for mobile phones and digital cameras has been developed by the mixed-integer linear programming method (MILP). Development of MILP method has been performed by simultaneously considering three main objectives including: total network profit, green supply chain factors (environmental sustainability) and maximizing the efficiency of disassembly centers. The proposed model of study is a six-level, multi-objective, single-period and multi-product that focuses on electrical waste. The efficiency of product return centers is calculated by SDEA method and the most efficient centers are selected. Findings The results of using the model in a case mining showed that, due to the use of green factors in network design, environmental pollution and undesirable disposal of some electronic waste were reduced. Also, with the reduction of waste disposal, valuable materials entered the market cycle and the network profit increased. Originality/value (1) Design a closed-loop reverse logistics network for after-sales services;(2) Introduce a multi-objective multi-echelon mixedintegerlinearprogramming model;(3) Sensitivity analysis use Inverse-DEA method to increase the efficiency of inefficient units;(4) Use the GSC factors and DEA method in reverse logistics network.
This paper aims to investigate the capability of mixed-integer linear programming (MILP) method and genetic algorithm (GA) to solve binary problem (BP). A comparative study on the MILP method and GA with default and t...
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ISBN:
(纸本)9781538657485
This paper aims to investigate the capability of mixed-integer linear programming (MILP) method and genetic algorithm (GA) to solve binary problem (BP). A comparative study on the MILP method and GA with default and tuned setting to find out an optimal solution is presented. The mixed-integerprogramming library (MIPLIB 2010) is used to test and evaluate algorithms. The evaluation is shown in quality of the solution and the execution time of computation. The results show that GA is superior to MILP in execution time with inconsistent results. However, MILP is superior to GA in quality of the solution with more stable results.
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