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Location model for a remanufacturing reverse logistics network based on adaptive genetic algorithm

SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2019年第11期95卷 1069-1084页

作者： Yan, Rui Yan, Bo Univ Calif Los Angeles Dept Math Los Angeles CA 90024 USA South China Univ Technol Sch Econ & Commerce Guangzhou Guangdong Peoples R China

Energy saving and environmental protection are important issues of today. Concerning the environmental and social need to increase the utilization of used products, this paper introduces two remanufacturing reverse logistics (RL) network models, namely, the open-loop model and the closed-loop model. In an open-loop RL system, used products are recovered by outside firms, while in a closed-loop RL system, they are returned to their original producers. The open-loop model features a location selection with two layers. For this model, a mixed-integer linear program (MILP) is built to minimize the total costs of the open-loop RL system, including the fixed cost, the freight between nodes, the operation cost of storage and remanufacturing centers, the penalty cost of unmet or remaining demand quantity, and the government-provided subsidy given to the enterprises that protect the environment. This MILP is solved using an adaptive genetic algorithm with MATLAB simulation. For a closed-loop RL network model, a special demand function considering the relationship between new and remanufactured products is developed. Remanufacturing rate, environmental awareness, service demand elasticity, value-added services, and their impacts on total profit of the closed-loop supply chain are analyzed. The closed-loop RL network model is proved effective through the analysis of a numerical example.

关键词： Reverse logistics remanufacturing adaptive genetic algorithm mixed-integer linear programming

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Scheduling identical jobs on uniform machines with a conflict graph

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COMPUTERS & OPERATIONS RESEARCH 2019年 111卷 357-366页

作者： Mallek, Amin Bendraouche, Mohamed Boudhar, Mourad USTHB Univ RECITS Lab Fac Math BP 32 El Alia Algiers Algeria Saad Dahleb Univ Fac Sci Route Soumaa BlidaBP Blida 270 Blida Algeria

This paper addresses the problem of scheduling n identical jobs on a set of m parallel uniform machines. The jobs are subjected to conflicting constraints modelled by an undirected graph G, in which adjacent jobs are not allowed to be processed on the same machine. Minimising the maximum completion time in the schedule (makespan C-max) is known to be NP-hard. We prove that when G is restricted to complete bipartite graphs the problem remains NP-hard for arbitrary number of machines, however, if m is fixed an optimal solution can be obtained in polynomial time. To solve the general case of the problem, we propose mixed-integer linear programming (MILP) formulations alongside with lower bounds and heuristic approaches. Furthermore, computational experiments are carried out to measure the performance of the proposed methods. (C) 2019 Published by Elsevier Ltd.

关键词： Scheduling Identical jobs Incompatible jobs Uniform machines Conflict graph mixed-integer linear programming

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A Benders' decomposition algorithm with combinatorial cuts for the multi-manned assembly line balancing problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第3期278卷 796-808页

作者： Michels, Adalberto Sato Lopes, Thiago Cantos Stall Sikora, Celso Gustavo Magatao, Leandro Fed Univ Technol Parana UTFPR Grad Program Elect & Comp Engn CPGEI Curitiba Parana Brazil Univ Hamburg Hamburg Business Sch Inst Operat Res Hamburg Germany

Multi-manned assembly lines are commonly found in industries that manufacture large-size products (e.g. automotive industry), in which multiple workers are assigned to the same station in order to perform different operations simultaneously on the same product. Although the balancing problem of multi manned assembly lines had been modelled before, the previously presented exact mathematical formulations are only able to solve few small-size instances, while larger cases are solved by heuristics or metaheuristics that do not guarantee optimality. This work presents a new mixed-integer linear programming model with strong symmetry break constraints and decomposes the original problem into a new Benders' Decomposition Algorithm to solve large instances optimally. The proposed model minimises the total number of workers along the line and the number of opened stations as weighted primary and secondary objectives, respectively. Besides, feasibility cuts and symmetry break constraints based on combinatorial Benders' cuts and model's parameters are applied as lazy constraints to reduce search-space by eliminating infeasible sets of allocations. Tests on a literature dataset have shown that the proposed mathematical model outperforms previously developed formulations in both solution quality and computational processing time for small-size instances. Moreover, the proposed Benders' Decomposition Algorithm yielded 117 optimal results out of a 131-instances dataset. Compared to previously presented methods, this translates to 19 and 25 new best solutions reached for medium and large-size instances, respectively, of which 19 and 23 are optimal solutions. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Combinatorial optimisation Multi-manned assembly line balancing Benders' decomposition Combinatorial Benders' cuts mixed-integer linear programming

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Improving Energy Efficiency in Discrete Parts Manufacturing System Using an Ultra-Flexible Job Shop Scheduling Algorithm

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING-GREEN TECHNOLOGY 2019年第2期6卷 349-365页

作者： Liu, Ning Zhang, Y. F. Lu, Wen Feng Natl Univ Singapore Dept Mech Engn 9 Engn Dr 1 Singapore 117576 Singapore

Improving energy efficiency has been one of main objectives in modern manufacturing enterprises. Various approaches aiming at efficient energy management have been proposed/developed, among which minimizing energy consumption by energy-sensible production scheduling techniques has emerged as a promising one. However, reported workshop models are quite simple and unrealistic. This paper studies a more realistic workshop model called ultra-flexible job shop (uFJS). In an uFJS, the sequence among operations for a job can be changed within certain constraints. To formulate this energy-efficient scheduling problem, a mixed-integer linear programming model was developed. To deal with large-sized problems, a specially designed genetic algorithm (GA) was subsequently proposed and implemented. Numerical results showed the proposed GA worked with decent effectiveness and efficiency. Finally, several comparative studies are carried out to further demonstrate its efficacy in terms of energy efficiency improvement. The advantage of the uFJS as compared to other relative simple workshop models is also shown. By considering the flexibility in operation sequencing in each job, the uFJS effectively integrates process planning and scheduling in discrete parts manufacturing system, thus providing a much larger solution space for more energy-efficient solutions. It therefore provides an excellent platform for decision-makers when developing energy-efficient techniques and strategies

关键词： Energy efficiency Discrete parts manufacturing system Production scheduling Ultra-flexible job shop mixed-integer linear programming Genetic algorithm

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Model reduction by time aggregation for optimal design of energy supply systems by an MILP hierarchical branch and bound method

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ENERGY 2019年第0期181卷 782-792页

作者： Yokoyama, Ryohei Shinano, Yuji Wakayama, Yuki Wakui, Tetsuya Osaka Prefecture Univ Dept Mech Engn Naka Ku 1-1 Gakuen Cho Sakai Osaka 5998531 Japan Zuse Inst Berlin Dept Optimizat Takustr 7 D-14195 Berlin Germany

mixed-integer linear programming (MILP) methods have been applied widely to optimal design of energy supply systems in consideration of multi-period operation. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. An original problem has been solved by dividing it into a relaxed optimal design problem at the upper level and optimal operation problems which are independent of one another at the lower level. In addition, some strategies have been proposed to enhance the computation efficiency furthermore. In this paper, a method of reducing model by time aggregation is proposed as a novel strategy to search design candidates efficiently in the relaxed optimal design problem at the upper level. In addition, the previous strategies are modified in accordance with the novel strategy. This method is realized only by clustering periods and averaging energy demands for clustered periods, while it guarantees to derive the optimal solution. Thus, it may decrease the computation time at the upper level. Through a case study on the optimal design of a gas turbine cogeneration system, it is clarified how the model reduction is effective to enhance the computation efficiency in comparison and combination with the modified previous strategies. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Energy supply Optimal design mixed-integer linear programming Hierarchical optimization Model reduction Time aggregation

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A multiobjective hub-airport location problem for an airline network design

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第2期277卷 412-425页

作者： Soylu, Banu Katip, Hatice Erciyes Univ Dept Ind Engn TR-38039 Kayseri Turkey

This paper presents a biobjective multiple allocation p-hub median problem, discusses the properties of the Pareto frontier and proposes exact and heuristic algorithms for finding the Pareto frontier. Our motivation emanates from airline networks and their new hub investment strategies. The first objective minimizes the total transportation cost of the network, while the second one minimizes 2-stop journeys in order to improve customer satisfaction, which is negatively affected by the multiple-transit routes of airlines. Although using hubs reduces operating costs in networks, a cost-effective hub network may not imply minimum individual travel times for passengers, or happy passengers. It is well-known that airline customers prefer flights with fewer stops. However, reducing 2-stop routes increases the number of arcs, non-stop and 1-stop routes, and thus the total cost in the network. We analyzed the tradeoff between these objective functions. We performed experiments on well-Known data sets from the literature. We were able to find the Pareto frontier exactly for small/medium size instances. A variable neighborhood search (VNS) heuristic is presented to approximate the Pareto frontier of large size instances. We also performed an application on the current Turkish aeronautics network. The results are presented and discussed. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Multiple objective programming p-hub median problem mixed-integer linear programming Multiobjective VNS Hub location

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Improving access to housing and supportive services for runaway and homeless youth: Reducing vulnerability to human trafficking in New York City

A I I E Transactions

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A I I E Transactions 2024年第3期56卷

作者： Yaren Bilge Kaya Kayse Lee Maass Geri L. Dimas Renata Konrad Andrew C. Trapp Meredith Dank a Mechanical and Industrial Engineering Northeastern University Boston MA USA b Data Science Program Worcester Polytechnic Institute Worcester MA USA c WPI Business School Worcester Polytechnic Institute Worcester MA USA b Data Science Program Worcester Polytechnic Institute Worcester MA USAc WPI Business School Worcester Polytechnic Institute Worcester MA USA d New York University New York City NY USA

Recent estimates indicate that there are over a million runaway and homeless youth and young adults (RHY) in the United States (US). Exposure to trauma, violence, and substance abuse, coupled with a lack of community support services, puts homeless youth at high risk of being exploited and trafficked. Although access to safe housing and supportive services such as physical and mental healthcare is an effective response to the vulnerability of RHY towards being trafficked, the number of youth experiencing homelessness exceeds the capacity of available housing resources in most US communities. We undertake a RHY-informed, systematic, and data-driven approach to project the collective capacity required by service providers to adequately meet the needs of RHY in New York City, including those most at risk of being trafficked. Our approach involves an integer linear programming model that extends the multiple multidimensional knapsack problem and is informed by partnerships with key stakeholders. The mathematical model allows for time-dependent allocation and capacity expansion, while incorporating stochastic youth arrivals and length of stays, services provided in a periodic fashion, and service delivery time windows. Our RHY and service provider-centered approach is an important step toward meeting the actual, rather than presumed, survival needs of vulnerable youth.

关键词： Human trafficking homelessness capacity expansion mixed-integer linear programming homeless shelters

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The impact of various carbon reduction policies on green flowshop scheduling

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APPLIED ENERGY 2019年 249卷 300-315页

作者： Foumani, Mehdi Smith-Miles, Kate Univ Melbourne Sch Math & Stat Parkville Vic 3010 Australia

In this paper we consider, from an environmental policy-maker perspective, how carbon reduction policies impact the economic competitiveness of the manufacturing sector. Specifically, we focus on flowshop scheduling - which typically aims to minimize makespan for purely economic objectives - and consider how three common carbon reduction policies - namely, taxes on emissions, baselines on emissions, and emissions trading schemes - can create competitive green flowshops that balance minimization of makespan and carbon emissions. The goal is to enable policy-makers to understand how to set policies and control parameters to achieve environmental objectives while ensuring global economic competitiveness of industry. We initially present a set of mixed integer linear programming (MILP) models for flowshop scheduling problems operating in a regulated environment in which each carbon reduction policy is adopted. We then introduce a bi-objective scheduling framework for the corresponding problem to obtain alternative solutions under each policy. These models and their computational results however, are not the main focus of the study, but are presented as a means to demonstrate how green policies co-exist with economic objectives, with policy-makers in control of the balance. To this end, based on financial data from Australia's carbon emissions profile, we provide a policy-oriented analysis of the models, and some managerial insights into the effect of scheduling strategies on carbon emissions under different reduction policies. These insights offer support to both environmental policy-makers and corporate production and sustainability managers to determine whether it is technically feasible and profitable to replace traditional scheduling strategies with environmentally friendly scheduling strategies.

关键词： Green flowshop scheduling Carbon reduction policy Energy consumption mixed-integer linear programming Bi-objective optimization Sensitivity analysis

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A strategy for transmission network expansion planning considering multiple generation scenarios

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ELECTRIC POWER SYSTEMS RESEARCH 2019年 172卷 22-31页

作者： Freitas, Patricia F. S. Macedo, Leonardo H. Romero, Ruben Sao Paulo State Univ Dept Elect Engn Ave Brasil 56 BR-15385000 Ilha Solteira SP Brazil

Transmission network expansion planning (TNEP) is subject to several constraints that are driven by new market rules, such as generation uncertainty, demand growth, seasonal climate changes and technological advances. When considering multiple generation scenarios, due to different operational conditions, it is necessary to find an expansion plan that allows the expanded system to operate properly for each scenario. This paper presents a strategy that provides several expansion plans that operate efficiently in all the previously defined generation scenarios and that keep reasonable values for the expansion costs. Thus, the developed mathematical model allows us to find adequate expansion plans with expansion costs that can be controlled, with small infeasibilities in the operation of the system that may not be significant in the long-term planning. The proposed approach is applied to the 24-bus system and the Colombian 93-bus system. The results indicate that the plans obtained using the proposed strategy present much lower expansion costs compared to the conventional TNEP model with multiple generation scenarios, with very low infeasibilities in the operation of the system, which can be corrected in the short-term expansion planning.

关键词： Generation scenarios mixed-integer linear programming Power systems optimisation Transmission network expansion planning

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A MILP Model for Incorporating Reliability Indices in Distribution System Expansion Planning

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IEEE TRANSACTIONS ON POWER SYSTEMS 2019年第3期34卷 2453-2456页

作者： Jooshaki, Mohammad Abbaspour, Ali Fotuhi-Firuzabad, Mahmud Farzin, Hossein Moeini-Aghtaie, Moein Lehtonen, Matti Sharif Univ Technol Elect Engn Dept Tehran *** Iran Aalto Univ Dept Elect Engn & Automat Espoo 00076 Finland Aalto Univ Espoo 00076 Finland Shahid Chamran Univ Ahvaz Elect Engn Dept Ahvaz *** Iran Sharif Univ Technol Dept Energy Engn Tehran *** Iran

Reliability cost is considered as an inevitable criterion in expansion planning studies of distribution systems. However, nonlinear expressions of reliability indices aggravate complexity of planning studies. To address this issue, this letter proposes a novel method to linearize mathematical model of the reliability-based distribution expansion planning problem. Using this variant of reliability indices, reliability costs can easily be involved in mixed-integer linear programming model of distribution expansion planning. Validity of the derived expressions is tested by simulation results.

关键词： Distribution network reliability distribution system expansion planning mixed-integer linear programming

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