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Multi-objective metaheuristics for solving a type II robotic mixed-model assembly line balancing problem

JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2016年第7期33卷 472-484页

作者： Rabbani, Masoud Mousavi, Zahra Farrokhi-Asl, Hamed Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Iran Univ Sci & Technol Sch Ind Engn Tehran Iran

Nowadays, robots are used extensively in robotic assembly line balancing system because of the capabilities of the robots. Robotic assembly lines are used to manufacture high volume product in customization and specialization production. In this paper, type II robotic mixedmodel assembly line balancing is considered. The goals are to minimize robot purchasing costs, robot setup costs, sequence dependent setup costs, and cycle time. The proposed model tries to determine an optimal or near-optimal configuration of tasks, workstations in U-shaped assembly line balancing. In this model, two types of tasks including the special task for one product model and the common task for several products models exist. The problem with the aforementioned conditions is NP-hard problem. So, we used two different multi-objective evolutionary algorithms (MOEAs) to solve the problem. First algorithm is non-dominated sorting genetic algorithm (NSGA-II) and the second one is multi-objective particle swarm optimization (MOPSO). Also, we used GAMS software to solve the problem in small size problem to validate our proposed model. Then, some numerical examples are presented and the experimental results and the performance of the algorithms are compared with each other.

关键词： Robotic mixed-model assembly line balancing mixed-integer linear programming U-shaped assembly line multiobjective optimization

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A computational framework and solution algorithms for two-stage adaptive robust scheduling of batch manufacturing processes under uncertainty

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AICHE JOURNAL 2016年第3期62卷 687-703页

作者： Shi, Hanyu You, Fengqi Northwestern Univ Dept Chem & Biol Engn Evanston IL 60208 USA

A novel two-stage adaptive robust optimization (ARO) approach to production scheduling of batch processes under uncertainty is proposed. We first reformulate the deterministic mixed-integer linear programming model of batch scheduling into a two-stage optimization problem. Symmetric uncertainty sets are then introduced to confine the uncertain parameters, and budgets of uncertainty are used to adjust the degree of conservatism. We then apply both the Benders decomposition algorithm and the column-and-constraint generation (C&CG) algorithm to efficiently solve the resulting two-stage ARO problem, which cannot be tackled directly by any existing optimization solvers. Two case studies are considered to demonstrate the applicability of the proposed modeling framework and solution algorithms. The results show that the C&CG algorithm is more computationally efficient than the Benders decomposition algorithm, and the proposed two-stage ARO approach returns 9% higher profits than the conventional robust optimization approach for batch scheduling. (c) 2015 American Institute of Chemical Engineers AIChE J, 62: 687-703, 2016

关键词： two-stage adaptive robust optimization batch manufacturing processes short-term scheduling problem Benders decomposition algorithm column-and-constraint generation algorithm mixed-integer linear programming

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Robust Optimization for Strategic Energy Planning

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INFORMATICA 2016年第3期27卷 625-648页

作者： Moret, Stefano Bierlaire, Michel Marechal, Francois Ecole Polytech Fed Lausanne Ind Proc & Energy Syst Engn Grp IPESE CH-1015 Lausanne Switzerland Ecole Polytech Fed Lausanne Transport & Mobil Lab CH-1015 Lausanne Switzerland

Long-term planning for energy systems is often based on deterministic economic optimization and forecasts of fuel prices. When fuel price evolution is underestimated, the consequence is a low penetration of renewables and more efficient technologies in favour of fossil alternatives. This work aims at overcoming this issue by assessing the impact of uncertainty on energy planning decisions. A characterization of uncertainty in energy systems decision-making is performed. Robust optimization is then applied to a mixed-integer linear programming problem, representing the typical trade-offs in energy planning. It is shown that in the uncertain domain investing in more efficient and cleaner technologies can be economically optimal.

关键词： energy planning robust optimization uncertainty characterization mixed-integer linear programming

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A Characterization of the SSD-Efficient Frontier of Portfolio Weights by Means of a Set of mixed-integer linear Constraints

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MANAGEMENT SCIENCE 2016年第12期62卷 3549-3554页

作者： Longarela, Inaki R. Stockholm Univ Stockholm Business Sch SE-10691 Stockholm Sweden UiT Arctic Univ Norway Sch Business & Econ N-9037 Tromso Norway

In this paper, the set of all second-order stochastic dominance (SSD)-efficient portfolios is characterized by using a series of mixed-integer linear constraints. Our derivation employs a combination of the first-order conditions of the utility maximization problem together with a judicious use of binary variables. This result opens the door to the formulation of optimizations whose objective function is free to select a particular portfolio out of the entire SSD-efficient set.

关键词： stochastic dominance mixed-integer linear programming portfolio theory

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Optimal location-multi-allocation-routing in capacitated transportation networks under population-dependent travel times

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2016年第6期29卷 652-676页

作者： Shiripoura, Saber Mahdavi-Amiri, Nezam Mahdavi, Iraj Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran Sharif Univ Technol Fac Math Sci Tehran Iran

A capacitated location-multi-allocation-routing model is presented for a transportation network with travel times between the nodes represented by links on the network. The concept of multi-allocation arises from the possibility of allocating the population in a demand node to more than one server node. In normal conditions, travel time between two nodes is a fixed value. However, since the flow of population in a link can affect the travel time, here the impact of the population flow on link time is considered to be simultaneous. This way, distribution of the population over the network has a direct influence on the travel link times. It is assumed that all links are two-way and capacities of the server nodes and arcs for accepting population are limited. Our aim is to ?nd optimal locations of server node(s), optimal allocation of the population in demand nodes to the server(s) and optimal allocation of the population of the nodes to different routes to reach the assigned servers so that total transportation time is minimised. First, the proposed problem is formulated as a mixed-integer non-linear programming model, followed by its suitable transformation into a mixed-integer linear programming problem. Then, a standard genetic algorithm (GA) and a heuristic algorithm combining genetic algorithm and local search (GALS) are presented to solve large instances of the problem. Finally, three sets of numerical experiments are made to compare the results obtained by CPLEX, standard GA and GALS. Numerical results show outperformance of GALS over CPLEX and the standard GA.

关键词： genetic algorithm mixed-integer linear programming local search population-dependent travel times capacitated location-multi-allocation-routing problem

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A joint chance-constrained programming approach for call center workforce scheduling under uncertain call arrival forecasts

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COMPUTERS & INDUSTRIAL ENGINEERING 2016年 96卷 16-30页

作者： Excoffier, M. Gicquel, C. Jouini, O. Univ Paris 11 Rech Informat Lab Campus OrsayBatiment 650 F-91405 Orsay France Ecole Cent Paris Lab Genie Ind F-92290 Chatenay Malabry France

We consider a workforce management problem arising in call centers, namely the shift-scheduling problem. It consists in determining the number of agents to be assigned to a set of predefined shifts so as to optimize the trade-off between manpower cost and customer quality of service. We focus on explicitly taking into account in the shift-scheduling problem the uncertainties in the future call arrival rates forecasts. We model them as independent random variables following a continuous probability distribution. The resulting stochastic optimization problem is handled as a joint chance-constrained program and is reformulated as an equivalent large-size mixed-integer linear program. One key point of the proposed solution approach is that this reformulation is achieved without resorting to a scenario generation procedure to discretize the continuous probability distributions. Our computational results show that the proposed approach can efficiently solve real-size instances of the problem, enabling us to draw some useful managerial insights on the underlying risk-cost trade-off. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Personnel planning Call center shift scheduling Customer abandonment Stochastic programming Probabilistic constraints mixed-integer linear programming

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Stochastic Unit Commitment in Isolated Systems With Renewable Penetration Under CVaR Assessment

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IEEE TRANSACTIONS ON SMART GRID 2016年第3期7卷 1356-1367页

作者： Asensio, Miguel Contreras, Javier Univ Castilla La Mancha ETS Ingenieros Ind E-13071 Ciudad Real Spain

Isolated regions and islands are facing imported fossil-fuel dependency, higher electricity prices, and vulnerability to climate change. At the same time, they are increasing their renewable penetration and, therefore, risk for electric utilities. Integrating stochastic energy resources in noninterconnected systems may take advantage of an intelligent and optimized risk-averse unit commitment (UC) model. This paper presents a two-stage stochastic UC model with high renewable penetration including reserve requirements for the efficient management of uncertainty. In order to account for the uncertainty around the true outcomes of load, wind, and photovoltaic (PV) generation, a minimum conditional value at risk term has been included in the model formulation. A stochastic measure of the value of the stochastic solution is used to evaluate the benefits of using stochastic programming. The model considers the need for reserves dependent on the forecasting horizon and the amount of renewable generation. Active power demand, and wind and PV generations are considered as probability distribution functions. The model is applied to the Lanzarote-Fuerteventura system in the Canary Islands, Spain, and Crete, Greece.

关键词： Conditional value at risk (CVaR) mean-risk value of the stochastic solution (MRVSS) mixed-integer linear programming risk aversion two-stage stochastic programming unit commitment (UC)

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Using cross-docking operations in a reverse logistics network design: a new approach

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PRODUCTION ENGINEERING-RESEARCH AND DEVELOPMENT 2016年第2期10卷 175-184页

作者： Kheirkhah, Amirsaman Rezaei, Saeid Bu Ali Sina Univ Sch Engn Dept Ind Engn Hamadan Iran

Increasing development of competitive market has forced organizations to make great efforts in supplying, production and distribution of goods in their company so that they are capable of responding the customers different needs at the minimum delivery time and lowest cost. Cross-docking is a practical strategy in distribution cycle which has significantly attracted the attention of experts and industrialists in different areas. In this paper, the problem of designing a multi-echelon reverse logistics network with applying cross-docking centers is presented-as the first attempt to propose the new approach of using cross-docking centers in reverse logistics network. In this regard, a mixed-integer linear programming is utilized to model the problem for the goals of increasing shipment rate, decreasing fixed, variable costs and better management of returned products. Finally, the validation and sensitivity analysis are done by using the GAMS software. Considered the above requirements, the model facilitates objective-oriented reverse logistic performance.

关键词： Reverse logistics Cross-docking operations Multi-echelon logistics network design mixed-integer linear programming

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A robust optimization model for agile and build-to-order supply chain planning under uncertainties

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ANNALS OF OPERATIONS RESEARCH 2016年第2期240卷 435-470页

作者： Lalmazloumian, Morteza Wong, Kuan Yew Govindan, Kannan Kannan, Devika Univ Teknol Malaysia Fac Mech Engn Dept Mfg & Ind Engn Skudai Malaysia Univ Southern Denmark Dept Econ & Business Odense Denmark Aalborg Univ Dept Mech & Mfg Engn DK-2450 Copenhagen Denmark

Supply chain planning as one of the most important processes within the supply chain management concept, has a great impact on firms' success or failure. This paper considers a supply chain planning problem of an agile manufacturing company operating in a build-to-order environment under various kinds of uncertainty. An integrated optimization approach of procurement, production and distribution costs associated with the supply chain members has been taken into account. A robust optimization scenario-based approach is used to absorb the influence of uncertain parameters and variables. The formulation is a robust optimization model with the objective of minimizing the expected total supply chain cost while maintaining customer service level. The developed multi-product, multiperiod, multi-echelon robust mixed-integer linear programming model is then solved using the CPLEX optimization studio and guidance related to future areas of research is given.

关键词： Agile manufacturing Build-to-order Supply chain planning Robust optimization mixed-integer linear programming

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Integrated strategic and tactical supply chain planning with price-sensitive demands

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ANNALS OF OPERATIONS RESEARCH 2016年第2期242卷 423-456页

作者： Fattahi, M. Mahootchi, M. Husseini, S. M. Moattar Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran

This paper deals with the supply chain network design and planning for a multi-commodity and multi-layer network over a planning horizon with multiple periods in which demands of customer zones are considered to be price dependent. These prices determine the demands using plausible price-demand relationships of customer zones. The net income of the supply chain is maximized, while satisfying budget constraints for investment in network design. In addition, a new approach is considered for capacity planning to make the problem more realistic. In this regard, when production plants are opened and expanded, capacity options are taken into account for manufacturing operations. Furthermore, several aspects relevant to real world applications are captured in the problem. Different interconnected time periods in the planning horizon are considered for strategic and tactical decisions in the problem and then, a mixed-integer linear programming (MILP) model is developed. The performance and applications of the model are investigated by several test problems with reasonable sizes. The numerical results illustrate that obtained solutions after solving the MILP model by using CPLEX solver are acceptable. Moreover, using an alternative pricing approach, a tight upper bound is provided for the problem. In addition, a deep sensitivity analysis is conducted to show the validity and performance of the proposed model.

关键词： Supply chain network design Strategic and tactical decisions Capacity expansion mixed-integer linear programming Dynamic pricing approach

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