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A Two-Echelon Capacitated Vehicle Routing Problem With Sharing Satellite Resources

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第9期25卷 12216-12227页

作者： Zhang, Dezhi Zhou, Saiqi Ji, Bin Li, Shuangyan Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Peoples R China Cent South Univ Forestry & Technol Coll Logist & Transportat Changsha 410004 Hunan Peoples R China

Collaboration vehicle routing has garnered increasing attention recently because it can help enterprises reduce costs by sharing resources. This paper addresses a novel two-echelon capacitated vehicle routing problem with sharing satellite resources (2E-CVRPSSR), which arises with the development of e-commerce and the Sharing economy in city logistics. In this problem, goods are delivered within a two-echelon routing network, where satellites are used to consolidate and transfer goods between first-and second-echelon routing. Moreover, the second-echelon vehicles can depart from and return to different satellites. A mixed integer linear programming model is first presented, and an adaptive large neighborhood search algorithm with several new search operators and strategies is proposed to solve this problem. The numerical results show that the proposed algorithm can effectively solve the 2E-CVRPSSR. Moreover, an average transportation cost savings of 6.29% can be obtained by sharing satellite resources. In addition, geographic analysis indicates that the layout of the depot, satellites, and customers largely impacts the economic advantages of sharing satellite resources. Specifically, the highest economic benefits are achieved when the satellites are distributed from nearest to farthest from the depot, with customers located between nearby and distant satellites and with satellites of large enough capacity close to the depot.

关键词： Two-echelon capacitated vehicle routing problem sharing satellite resources mixed integer linear programming model adaptive large neighborhood search

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modeling and solving the project selection and Scheduling

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COMPUTERS & INDUSTRIAL ENGINEERING 2015年 83卷 30-38页

作者： Tofighian, Ali Asghar Naderi, B. Kharazmi Univ Dept Ind Engn Fac Engn Tehran Iran

This paper considers the integrated bi-objective problem of projects selection and scheduling to optimize both total expected benefit and resource usage variation. The benefit is time-dependent. Although this integrated problem has become a very active field of research, the available model and algorithms suffer from serious shortcomings. This paper analyzes the available methods and develops a novel mathematical model, in form of a mixed integer linear program, for the problem. Then, it proposes an ant colony optimization algorithm employing four features of ant generation, colonial, Pareto front updating, and pheromone updating mechanisms. To evaluate the proposed algorithm, it is compared with two available genetic algorithm anotscatter search. Using comprehensive numerical experiments and statistical tools, it is shown that the proposed ant colony optimization outperforms the two available algorithms. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Project selection and scheduling mixed integer linear programming model Multi-objective ant colony optimization

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Stochastic Bi-level Trading model for an Active Distribution Company with DGs and Interruptible Loads

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IET RENEWABLE POWER GENERATION 2017年第2期11卷 277-287页

作者： Cai, Yu Lin, Jin Wan, Can Song, Yonghua Tsinghua Univ Dept Elect Engn State Key Lab Control & Simulat Power Syst & Gene Beijing 100084 Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China

With the rapid development of distributed generations (DGs) and interruptible loads (ILs), distribution network company can actively purchase electricity in market instead of playing as a traditional passive purchaser. This study proposes a stochastic bi-level model-based strategic trading model for an active distribution company (ADisCo) which operates the active distribution network (ADN) to maximise its profit in electricity market. Uncertainties pertaining to bidding and offering prices of other market rivals', the imbalance prices in the balance market and the productions of DGs are considered via stochastic programming. Besides, a linear ADN operation model is proposed to ensure ADN operation security within the stochastic programming model. The proposed model is initially formulated as a stochastic bi-level model, where the upper-level problem represents the maximisation of the profit of ADN operator, whereas the lower-level model represents the maximisation of the social welfare in clearing of market from the perspective of independent system operator. On the basis of the complementarity theory, the proposed model can be transformed into a mixed integer linear programming model. Case studies demonstrate the efficiency and effectiveness of the proposed strategic trading model for an ADisCo with DGs and ILs.

关键词： power markets distributed power generation power generation economics power distribution economics stochastic programming pricing power system security profitability ADisCo DG ILs distributed generations interruptible loads distribution network company passive purchaser stochastic bi-level model-based strategic trading model active distribution company active distribution network electricity market imbalance prices stochastic programming linear ADN operation model ADN operation security upper-level problem social welfare maximisation independent system operator complementarity theory mixed integer linear programming model profit maximization

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Enhancing supply chain resilience using ontology-based decision support system

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2019年第7期32卷 642-657页

作者： Singh, Sube Ghosh, Soumava Jayaram, Jayanth Tiwari, Manoj Kumar Indian Inst Technol Kharagpur Dept Ind & Syst Engn Kharagpur W Bengal India Indian Inst Technol Kharagpur Dept Mech Engn Kharagpur W Bengal India Univ South Carolina Darla Moore Sch Business Dept Management Sci Columbia SC 29208 USA

Today's scenario of manufacturing and supply chain is full of uncertainty because of numerous types of disruptions and failures such as fire, storm, machine failure, are a few names. Always supply chain disruptions present disastrous impacts, although probabilities of happening are low. In the recent robust as well as flexible supply chain has captured the focus of researchers in designing a supply chain network with consideration of disruption's risk. In this work, an ontology-based decision support system is proposed to intensify the supply chain resilience during a disruption. The concept of semantic and ontology is adopted in developing the knowledge base for the entire supply chain network including manufacturing units. Protege is used for defining the classes and sub-classes along with numerous types of properties and expressed in a rule-based system using semantic web rule language (SWRL). Furthermore, a mixed integer linear programming model with an objective of maximising quantified resilience to fulfil the demand. A hybrid particle swarm optimisation - differential evolution (PSO-DE) is utilised as an optimisation technique for the defined problem. In performing the study, a set of simulated data is formed and then interpreted in the ontology for an optimal selection of recovery activity.

关键词： Supply chain resilience ontology mixed integer linear programming model hybrid particle swarm optimisation semantic web rule language

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Integrated optimization of traffic signals and vehicle trajectories at isolated urban intersections

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2018年 112卷 89-112页

作者： Yu, Chunhui Feng, Yiheng Liu, Henry X. Ma, Wanjing Yang, Xiaoguang Tongji Univ Minist Educ Key Lab Rd & Traff Engn 4800 Caoan Rd Shanghai Peoples R China UMTRI 2901 Baxter Rd Ann Arbor MI 48109 USA Univ Michigan Dept Civil & Environm Engn 2320 GG Brown2350 Hayward St Ann Arbor MI 48109 USA

Existing traffic signal control systems only allocate green time to different phases to avoid conflicting vehicle movements. With advances in connected and automated vehicle (CAV) technologies, CAV trajectories not only provide more information than existing infrastructure-based detection systems, but also can be controlled to further improve mobility and sustainability. This paper presents a mixed integer linear programming (MILP) model to optimize vehicle trajectories and traffic signals in a unified framework at isolated signalized intersections in a CAV environment. A new planning horizon strategy is applied to conduct the optimization. All vehicle movements such as left-turning, right turning and through are considered. Phase sequences, green start and duration of each phase, and cycle lengths are optimized together with vehicle lane-changing behaviors and vehicle arrival times for delay minimization. Vehicles are split into platoons and are guaranteed to pass through the intersection at desired speeds and avoid stops at stop bars. Exact vehicle trajectories are determined based on optimized vehicle arrival times. For the trajectory planning of platoon leading vehicles, an optimal control model is implemented to minimize fuel consumption/emission. For following vehicles in a platoon, Newell's car following model is applied. Simulation results validate the advantages of the proposed control method over vehicle-actuated control in terms of intersection capacity, vehicle delays, and CO2 emissions. A sensitivity analysis is conducted to show the potential benefits of a short minimum green duration as well as the impacts of no-changing zones on the optimality of the proposed model. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Connected and automated vehicle Integrated optimization Vehicle trajectory planning mixed integer linear programming model Isolated urban intersection

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linear daily UC model to improve the transient stability of power system

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第13期13卷 2877-2888页

作者： Naghdalian, Salar Amraee, Turaj Kamali, Sadegh KN Toosi Univ Technol Fac Elect Engn Tehran Iran

As coherency of generators decreases, the risk of rotor angle instability increases, especially under severe contingencies. The slow coherency as a network characteristic may be controlled by the locations of committed generators. Unit commitment (UC) problem is conventionally carried out regarding operational and network constraints. In this study, a two-step strategy is developed to promote the slow coherency via the network constrained UC (NCUC) model on a daily horizon. First, conventional NCUC is executed. The most important generators with both economic and coherency merits are then determined as representative generators. In the second step, the Slow Coherency Based Unit Commitment (SCBUC) is re-optimisedaccording to the results obtained from the first step, using a multi-objective function. The first part of the multi-objective function is devoted to the cost of generation, start-up, and shutdown of generators. The goal of the second part of the multi-objective function is to maximise the coherency between the committed generators to reach a transient stability margin. The proposed model is converted to a mixed integer linear programming model. The performance of the proposed method of promoting transient stability is investigated using the dynamic IEEE 118-bus test system.

关键词： linear programming power generation dispatch IEEE standards power generation economics power generation scheduling integer programming power system transient stability load flow power system security linear daily UC model power system generators decreases rotor angle instability increases severe contingencies slow coherency network characteristic committed generators unit commitment problem two-step strategy daily horizon conventional NCUC important generators representative generators multiobjective function transient stability margin mixed integer linear programming model

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Simultaneous balancing, sequencing, and workstation planning for a mixed model manual assembly line using hybrid genetic algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 119卷 370-387页

作者： Defersha, Fantahun M. Mohebalizadehgashti, Fatemeh Univ Guelph Sch Engn 50 Stone Rd East Guelph ON N1G 2W1 Canada

Balancing and sequencing are two important challenging problems in designing mixed-model assembly lines. A large number of studies have addressed these two problems both independently and simultaneously. However, several important aspects such as assignment of common tasks between models to different workstations, and minimizing the number and length of workstations are not addressed in an integrated manner. In this paper, we proposed a mixed integer linear programming mathematical model by considering the above aspects simultaneously for a continuously moving conveyor. The objective function of the model is to minimize the length and number of workstations, costs of workstations and task duplications. Since the proposed model cannot be efficiently solved using commercially available packages, a multi-phased linear programming embedded genetic algorithm is developed. In the proposed algorithm, binary variables are determined using genetic search whereas continuous variables corresponding to the binary variables are determined by solving linear programming subproblem using simplex algorithm. Several numerical examples with different sizes are presented to illustrate features of the proposed model and computational efficiency of the proposed hybrid genetic algorithm. A comparative study of genetic algorithm and simulated annealing is also conducted.

关键词： Balancing Sequencing mixed integer linear programming model mixed model assembly line Hybrid genetic algorithm

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Single Machine Scheduling Problem With a Weight-Modifying-Activity to Minimize the Total Weighted Completion Time

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IEEE ACCESS 2022年 10卷 45444-45456页

作者： Chen, Yarong Guan, Zailin Tsai, Ya-Chih Chou, Fuh-Der Huazhong Univ Sci & Technol Coll Mech Sci & Engn Wuhan 430074 Peoples R China Wenzhou Univ Coll Mech & Elect Engn Wenzhou 325035 Zhejiang Peoples R China Vanung Univ Dept Hotel Management Taoyuan 32061 Taiwan

The single-machine scheduling problem with a weight-modifying-activity (WMA) to minimize the total weighted completion time was initially addressed by Mosheiov and Oron in 2020, where the activity was an option, and once the activity was performed, the weights of the subsequent jobs become decreased. This problem has proven to be NP-hard. Following their study, we propose two mixed integer linear programming models (model_1 and model_2). Based on some optimality properties, a heuristic algorithm with swap and insert procedures is developed. The computation results indicate that model_2 can optimally solve problems of up to 40 jobs efficiently, while the average relative percentage of error and hit rate of the proposed heuristic is 0.0005% and 98.2%, respectively. The influence of parameters, such as the number of jobs, the adjusted coefficient for the job weight, and the time of the WMA, on the performance of the proposed methods, are also analyzed.

关键词： Heuristic algorithms Costs Computational modeling Single machine scheduling Schedules Production mixed integer linear programming Heuristic algorithm mixed integer linear programming model single-machine scheduling weight-modifying activity

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In the determination of the most efficient decision making unit in data envelopment analysis: A comment

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 104卷 216-218页

作者： Salahi, Maziar Toloo, Mehdi Univ Guilan Fac Math Sci Dept Appl Math Rasht Iran Tech Univ Ostrava Fac Econ Dept Syst Engn Ostrava Czech Republic

In order to deal with finding the most efficient unit problem, Lam (2015) recently built a new integrated mixed integer linear programming model which is nearly close to the super-efficiency model. The suggested model involves a non-Archimedean epsilon as the lower bound for the input and output weights. Selecting a suitable value for epsilon is a challenging issue in DEA (Data Envelopment Analysis). Lam (2015) suggested a value for epsilon which guarantees the feasibility of his model;however, this paper illustrates that the model may fail to find the most efficient unit due to unsuitable selected value for epsilon. To cope with this issue, a new model is formulated which provides the maximum epsilon value for the model of Lam (2015). The built model guarantees that when epsilon is maximum, then Lam's model gives exactly one DMU (Decision Making Unit) as the most efficient unit with the maximum discrimination distance from the other DMUs. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Data envelopment analysis Most efficient DMU Non-Archimedean epsilon mixed integer linear programming model

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Mathematical models and a hunting search algorithm for the no-wait flowshop scheduling with parallel machines

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2014年第9期52卷 2667-2681页

作者： Naderi, B. Khalili, Majid Khamseh, Alireza Arshadi Univ Kharazmi Dept Ind Engn Fac Engn Karaj Iran Islamic Azad Univ Dept Ind Engn Karaj Iran

Majority of researches in no-wait flowshop scheduling assume that there is only one machine at each stage. But, factories commonly duplicate machines in parallel for each operation. In this case, they balance the speed of the stages, increase the throughput of the shop floor and reduce the impact of bottleneck stages. Despite their importance, there is no paper to study the general no-wait flowshop with parallel machines. This paper studies this problem where the objective is to minimise makespan. Since there is no mathematical model for the problem, we first mathematically formulate it in form of two mixed integer linear programming models. By the models, the small instances are optimally solved. We then propose a novel hunting search metaheuristic algorithm (HSA) to solve large instances of the problem. HSA is derived based on a model of group hunting of animals when searching for food. A set of experimental instances are carried out to evaluate the algorithm. The algorithm is carefully evaluated for its performance against an available algorithm by means of statistical tools. The related results show that the proposed HSA provides sound performance comparing with other algorithms.

关键词： flowshop scheduling parallel machines mixed integer linear programming model hunting search metaheuristic algorithm

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