In this paper, we study a class of manufacturing systems which consist of multiple plants and each of the plants has capability of producing multiple distinct products. The production lines of a certain plant may swit...In this paper, we study a class of manufacturing systems which consist of multiple plants and each of the plants has capability of producing multiple distinct products. The production lines of a certain plant may switch between producing different kinds of products in a time-sharing mode. We optimize the capacity configuration of such a system s production lines with the objective to maximize the overall profit in the capacity planning horizon. Uncertain demand is incorporated in the model to achieve a robust configuration solution. The optimization problem is formulated as a nonlinear polynomial stochastic programming problem, which is difficult to be efficiently solved due to demand uncertainties and large search space. We show the NP-hardness of the problem first, and then apply ordinal optimization(OO) method to search for good enough designs with high probability. At lower level, an mixed integer programming(MIP) solving tool is employed to evaluate the performance of a design under given demand profile.展开更多
Vehicle routing problem with time windows(VRPTW)is a core combinatorial optimization problem in distribution tasks.The electric vehicle routing problem with time windows under demand uncertainty and weight-related ene...Vehicle routing problem with time windows(VRPTW)is a core combinatorial optimization problem in distribution tasks.The electric vehicle routing problem with time windows under demand uncertainty and weight-related energy consumption is an extension of the VRPTW.Although some researchers have studied either the electric VRPTW with nonlinear energy consumption model or the impact of the uncertain customer demand on the conventional vehicles,the literature on the integration of uncertain demand and energy consumption of electric vehicles is still scarce.However,practically,it is usually not feasible to ignore the uncertainty of customer demand and the weight-related energy consumption of electronic vehicles(EVs)in actual operation.Hence,we propose the robust optimization model based on a route-related uncertain set to tackle this problem.Moreover,adaptive large neighbourhood search heuristic has been developed to solve the problem due to the NP-hard nature of the problem.The effectiveness of the method is verified by experiments,and the influence of uncertain demand and uncertain parameters on the solution is further explored.展开更多
This paper considers a closed-loop supply chain comprising one retailer and two suppliers.It assumes one supplier as the main supplier and another as the backup.The coordination issue of the supply chain has been disc...This paper considers a closed-loop supply chain comprising one retailer and two suppliers.It assumes one supplier as the main supplier and another as the backup.The coordination issue of the supply chain has been discussed in this work.The main supplier’s yield is considered subject to disruption.The demand considered here is stochastic.We aim to calculate the supplier’s optimum production quantity.Similarly,the retailer’s optimal ordering quantity is found out.Additionally,in the centralized supply chain model,we want to maximize the expected profit under certain restriction.Numerical illustrations are discussed to the benefit some characteristic insights over the supply chain model.展开更多
In this paper, the joint production and recycling problem is investigated for a hybrid manufacturing and remanufacturing system where brand-new products are produced in the manufacturing plant and recycled products ar...In this paper, the joint production and recycling problem is investigated for a hybrid manufacturing and remanufacturing system where brand-new products are produced in the manufacturing plant and recycled products are remanufactured into as-new products in the remanufacturing facility. Both the brand-new products and remanufactured products are used to satisfy customer demands. Returns of used products that are recycled from customers are assumed to be stochastic and nonlinearly price-dependent. A mathematical model is proposed to maximize the overall profit of the system through simultaneously optimizing the production and recycling decisions, subject to two capacity constraints ? the manufacturing capacity and the remanufacturing capacity. Based on Lagrangian relaxation method, subgradient algorithm and heuristic algorithm, a solution approach is developed to solve the problem. A representative example is presented to illustrate the system, and managerial analysis indicates that the uncertainties in demand and return have much influence on the production and recycling policy. In addition, twenty randomly produced examples are solved, and computational results show that the solution approach can obtain very good solutions for all examples in reasonable time.展开更多
基金supported by a contract between General Motors Company and Tsinghua University,National Natural Science Foundation of China(61425027,60736027,61021063,61074034,61174105)
文摘In this paper, we study a class of manufacturing systems which consist of multiple plants and each of the plants has capability of producing multiple distinct products. The production lines of a certain plant may switch between producing different kinds of products in a time-sharing mode. We optimize the capacity configuration of such a system s production lines with the objective to maximize the overall profit in the capacity planning horizon. Uncertain demand is incorporated in the model to achieve a robust configuration solution. The optimization problem is formulated as a nonlinear polynomial stochastic programming problem, which is difficult to be efficiently solved due to demand uncertainties and large search space. We show the NP-hardness of the problem first, and then apply ordinal optimization(OO) method to search for good enough designs with high probability. At lower level, an mixed integer programming(MIP) solving tool is employed to evaluate the performance of a design under given demand profile.
文摘Vehicle routing problem with time windows(VRPTW)is a core combinatorial optimization problem in distribution tasks.The electric vehicle routing problem with time windows under demand uncertainty and weight-related energy consumption is an extension of the VRPTW.Although some researchers have studied either the electric VRPTW with nonlinear energy consumption model or the impact of the uncertain customer demand on the conventional vehicles,the literature on the integration of uncertain demand and energy consumption of electric vehicles is still scarce.However,practically,it is usually not feasible to ignore the uncertainty of customer demand and the weight-related energy consumption of electronic vehicles(EVs)in actual operation.Hence,we propose the robust optimization model based on a route-related uncertain set to tackle this problem.Moreover,adaptive large neighbourhood search heuristic has been developed to solve the problem due to the NP-hard nature of the problem.The effectiveness of the method is verified by experiments,and the influence of uncertain demand and uncertain parameters on the solution is further explored.
文摘This paper considers a closed-loop supply chain comprising one retailer and two suppliers.It assumes one supplier as the main supplier and another as the backup.The coordination issue of the supply chain has been discussed in this work.The main supplier’s yield is considered subject to disruption.The demand considered here is stochastic.We aim to calculate the supplier’s optimum production quantity.Similarly,the retailer’s optimal ordering quantity is found out.Additionally,in the centralized supply chain model,we want to maximize the expected profit under certain restriction.Numerical illustrations are discussed to the benefit some characteristic insights over the supply chain model.
基金supported by NSERC Discovery grant, Canada Foundation for Innovation, and China State Scholarship Fund
文摘In this paper, the joint production and recycling problem is investigated for a hybrid manufacturing and remanufacturing system where brand-new products are produced in the manufacturing plant and recycled products are remanufactured into as-new products in the remanufacturing facility. Both the brand-new products and remanufactured products are used to satisfy customer demands. Returns of used products that are recycled from customers are assumed to be stochastic and nonlinearly price-dependent. A mathematical model is proposed to maximize the overall profit of the system through simultaneously optimizing the production and recycling decisions, subject to two capacity constraints ? the manufacturing capacity and the remanufacturing capacity. Based on Lagrangian relaxation method, subgradient algorithm and heuristic algorithm, a solution approach is developed to solve the problem. A representative example is presented to illustrate the system, and managerial analysis indicates that the uncertainties in demand and return have much influence on the production and recycling policy. In addition, twenty randomly produced examples are solved, and computational results show that the solution approach can obtain very good solutions for all examples in reasonable time.