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24th International Symposium for Production Research, ISPR 2024

作者： Özveri, Melis Üstün, Yasemin Eda Gürsesli, Dilara Baysal, Koray Dirik, Şükran Özdemir, Yağmur Leman Paldrak, Mert Staiou, Efthymia Industrial Engineering Yaşar University İzmir Turkey

ISBN: (纸本)9783031836107

This study, conducted for a leading dairy company in Türkiye, aimed to optimize the milk collection process by addressing the Vehicle Routing Problem (VRP), which is essential for the efficient collection of perishable products like milk in the dairy industry. Key constraints considered included vehicle capacity, the number of vehicles, milk quality, collection time windows, delivery speed, route optimization, and cost minimization. A mathematical model was initially developed and solved using IBM ILOG CPLEX;however, as the number of collection locations increased, solution times became impractical. To overcome this challenge, the benders decomposition algorithm was employed, significantly improving solution efficiency. The application of this method led to enhanced collection efficiency and cost-effectiveness, delivering substantial operational benefits to the dairy company. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： benders decomposition algorithm Cost optimization Dairy industry Milk collection Vehicle routing problem

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Integrated optimization of container allocation and yard cranes dispatched under delayed transshipment

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 158卷

作者： Hu, Hongtao Mob, Jiao Zhen, Lu Shanghai Maritime Univ Sch Logist Engn Shanghai 201306 Peoples R China Ningbo Univ Technol Sch Econ & Management Ningbo 315211 Peoples R China Shanghai Univ Sch Management Shanghai 200444 Peoples R China

Due to variations in carrying capacity and visiting periods of vessels calling at terminals, certain transshipped containers may not be able to be transported immediately after arrival. Instead, they must be temporarily stored in the yard and scheduled for later transportation. Increased container storage time in the yard as a consequence of transit delays not only incurs additional costs for shipping companies, but also introduces new issues for terminal yard management. Therefore, this study investigates the daily storage yard management challenges that arise in marine container terminals that integrate space allocation and yard crane deployment decisions while considering the delayed transshipment of containers. A mixed-integer linear programming model is proposed to describe the problem, whose objectives are to minimize the transportation costs for container loading and discharge operations, penalty costs associated with the delayed transshipment of containers, transportation costs associated with containers not transshipped within the planning horizon, and yard crane inter-block movement costs. The benders decomposition algorithm is applied to address this problem, and the traditional decomposition approach is improved by adding characteristics such as the development of strong optimality cuts and the generation of a powerful initial solution to the master problem. Extensive experiments are performed to validate the effectiveness of the model and efficiency of the proposed improved benders decomposition algorithm.

关键词： Container terminal Delayed transshipment Multi-period liner shipping Yard resources dispatching benders decomposition algorithm

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Research on Source-load Coordinated Optimal Scheduling of Integrated Energy System 9

Research on Source-load Coordinated Optimal Scheduling of In...

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9th International Forum on Electrical Engineering and Automation (IFEEA)

作者： Zhou, Dongxu Zhu, Zhengyi State Grid Jiangsu Elect Power Co Ltd Nanjing Power Supply Branch Nanjing Peoples R China

ISBN: (纸本)9781665464215

With the penetration of renewable energy such as wind and photovoltaic in the energy system, the intermittent and fluctuating nature of their output leads to the uncertainty of energy supply of the energy system, and the modeling and optimal scheduling of the energy system face great challenges. Aiming at the above problems, this paper constructs a source-load coordinated optimal scheduling model for the integrated energy system. The model takes into account the uncertainty of the system scenery output and the variable working conditions of system equipment, and it uses the benders decomposition algorithm and the alternating direction multiplier method (ADMM) to solve the model, realizing the cooperative optimal scheduling of source-load on both sides. Finally, the proposed method is tested in a community in Nanjing, and the results show that the proposed method can promote the positive source-load interaction and improve the economy, environmental protection and stability of system operation.

关键词： integrated energy system variable working condition source-load co-optimization benders decomposition algorithm alternating direction multiplier method

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Adjustable robust balanced hub location problem with uncertain transportation cost

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COMPUTATIONAL & APPLIED MATHEMATICS 2021年第1期40卷 1-28页

作者： Rahmati, Reza Neghabi, Hossein Ferdowsi Univ Mashhad Dept Ind Engn Mashhad Razavi Khorasan Iran

In this paper, an adjustable robust optimization with a polyhedral uncertainty set is used to deal with uncertain transportation cost in an uncapacitated multiple allocation balanced hub location problem. Adjustable robust optimization is modeled as two-stage or multi-stage problems in which decisions are determined in two or multi-separated stages. In two-stage robust optimization, first, the location of hubs is determined in the absence of uncertain parameters;then, the second-stage decision determined flows path in the presence of uncertainty. Two new mathematical models are proposed for this problem with mixed-integer linear and non-linear structures. benders decomposition algorithm with stronger cut (Pareto-optimal cut) is used to solve proposed models. Adjustable robust models and accelerated benders decomposition algorithms are analyzed using well-known AP data set with different levels of uncertainty. Also, a size reduction method is introduced to solve medium and large instances with good solution quality and shorter computation time. The numerical experiment shows the superiority of the Pareto-optimal cut benders decomposition algorithm comparing with a classic one. Also, the mixed-integer non-linear model has better results in CPU time and the gap in comparison with the linear integer one. Flow balancing affects hub configuration with a decreasing number of hub facilities. Also by increasing the uncertainty budget, more hubs are established and with increasing discount factor, number of hub facilities are decreased.

关键词： Balanced hub location benders decomposition algorithm Adjustable robust optimization Uncertain transportation cost Pareto-optimal cut

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A production and distribution planning of perishable products with a fixed lifetime under vertical competition in the seller-buyer systems: A real-world application

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JOURNAL OF MANUFACTURING SYSTEMS 2021年第PartA期58卷 223-247页

作者： Aazami, Adel Saidi-Mehrabad, Mohammad Iran Univ Sci & Technol Dept Ind Engn Tehran Iran

Motivated by a real problem, in this paper, a new multi-period production-distribution planning (PDP) for perishable products with a fixed lifetime in a seller-buyer system is developed. The objective is to maximize the seller's profit conditional on the optimality of the buyer in a three-level supply chain (SC), including the factories, distribution centers, and retailers. The factories and distribution centers act cooperatively at the leader level and, as the seller, make the location, production, inventory, and distribution decisions. While the retailers operate as the follower;therefore, a vertical competition is held in this SC. In addition to price, freshness owing to the perishability of the product is considered as an important competitive factor. Also, to encourage the retailers, three strategies in the development of a new demand function, including the return, discount policies, and credit period, are employed. Since the problem is NP-hard, a hierarchical heuristic approach based on benders decomposition algorithm (BDA) and Genetic algorithm (GA) is proposed. Finally, to demonstrate the applicability and efficiency of the developed model, it is applied to a real industrial case. The computational results of the problems in different sizes confirm the efficient performance of the proposed model and solution method.

关键词： Production distribution planning Seller-buyer problem Perishable product Bi-level programming benders decomposition algorithm Genetic algorithm

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THERMAL ENERGY CONTROL IN BUILDING ENERGY SYSTEM

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THERMAL SCIENCE 2021年第4期25卷 3123-3131页

作者： Chen, Yuan Hetao Coll Bayaner City Inner Mongolia Peoples R China

There is usually a waste of energy consumption in building systems. To help buildings reduce energy waste, the article established a building-sharing heat and power energy sharing system to achieve optimal energy allocation. Furthermore, the report determined the dual operation strategy model of using heat energy to determine power supply and electricity to determine heat energy. At the same time, we use stochastic programming and multi-objective optimization of the heating model and propose a two-level optimization model solution method based on the benders decomposition algorithm. At the end of the thesis, the process was applied to actual cases to verify the method's effectiveness.

关键词： combined cooling thermal energy storage stochastic planning building energy system benders decomposition algorithm Pareto optimal solution set heating and power system

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An ellipse-based locating method for flexible deployment of emergency UAVs

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SOCIO-ECONOMIC PLANNING SCIENCES 2024年 96卷

作者： Zhao, Jinqiu Yu, Le Xie, Binglei Harbin Inst Technol Shenzhen Sch Architecture Shenzhen 518000 Guangdong Peoples R China Shenzhen Technol Univ Coll Urban Transportat & Logist Shenzhen 518000 Guangdong Peoples R China

Unmanned Aerial Vehicles (UAVs), or drones, are gaining attention in emergency response for their rapid mobility in dynamic scenarios. Constrained by limited endurance and payload, UAVs typically operate in a "depot-customer-depot" paradigm. Thus, optimally locating multiple depots is critical to achieving operational efficiency and flexibility. Traditional location models, which rely on circular coverage, fail to capture the actual reachable area for UAV round-trips between multiple depots within a given endurance range. This drawback restricts deployment flexibility or results in excessive redundancy, even making it impractical. To address this limitation, we introduce an ellipse-based locating method for flexible UAV deployment, inspired by UAV reachability and process flexibility in manufacturing. This approach attempts to optimize the redundancy of multi-depot coverage for demand points to achieve a better balance between deployment flexibility and resource requirements. To tackle the model's computational challenge, we present an improved benders decomposition algorithm that speeds up the solution process by analytically addressing subproblems and implementing dominance rules to manage the master problem's size. Simulations show that the proposed model greatly improves the ability to handle uncertainties by incorporating slight redundancy in emergency resources, and the fulfillment rate of demand fluctuations is increased by 5%-20%, which shows the superiority of enhancing the mobility and flexibility of UAV deployment.

关键词： Emergency depot location Ellipse coverage UAV reachability Flexible deployment of UAVs benders decomposition algorithm

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Wasserstein distance-based distributionally robust parallel-machine scheduling *

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2023年 120卷

作者： Yin, Yunqiang Luo, Zunhao Wang, Dujuan Cheng, T. C. E. Univ Elect Sci & Technol China Sch Management & Econ Chengdu 611731 Peoples R China Sichuan Univ Business Sch Chengdu 610064 Peoples R China Hong Kong Polytech Univ Dept Logist & Maritime Studies Kowloon Hong Kong Peoples R China

Recent research on distributionally robust (DR) machine scheduling has used a variety of approaches to describe the region of ambiguity of uncertain processing times by imposing constraints on the moments of the probability distributions. One approach that has been employed outside machine scheduling research is the use of statistical metrics to define a distance function between two probability distributions. Adopting such an approach, we study Wasserstein distance-based DR parallel-machine scheduling, where the ambiguity set is defined as a Wasserstein ball around an empirical distribution of uncertain processing times corresponding to finitely many samples. The objective is to minimize a DR objective that concerns the worst-case expected total completion time-related cost over all the distributions arising from the Wasserstein ambiguity set, subject to DR chance constraints on the machine service capacity. We show that the problem can be equivalently re-formulated as a mixed-integer linear program (MILP), which has a more simplified formulation when the bounded support set reduces to a left bounded one. To solve the resulting model, we develop a tailored branch-and-benders-cut algorithm incorporating some enhancement strategies, including in-out benders cut generation, aggregated sample group cut generation, and two-stage benders cut generation, which significantly outperforms the CPLEX solver. Experiment results on comparing our model with the deterministic and stochastic counterparts and the model with first-order moment ambiguity set illustrate the benefits of considering distributional ambiguity and Wasserstein ambiguity set.

关键词： Parallel-machine scheduling Distributionally robust optimization Wasserstein metric benders decomposition algorithm

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A New Model for Sea-Rail Intermodal Transportation Network System Planning Considering the Arctic Route 4

A New Model for Sea-Rail Intermodal Transportation Network S...

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4th International Conference on Intelligent Autonomous Systems (ICoIAS)

作者： Han, Peixiu Sun, Zhuo Liu, Kaili Li, Bin Dalian Maritime Univ Coll Transportat Engn Dalian Peoples R China BMW Brilliance Auto Co Ltd Dept Supply Chain Shenyang Peoples R China

ISBN: (纸本)9781665441957

The opening of the Arctic route will greatly shorten the distance of navigation and save costs. This research was mainly focus on hierarchical hub locations for rail-sea container transportation network system planning considering the Arctic Route. According to the characteristics of integrated network including the Arctic route, a new mathematical model was established by juxtaposing the traditional idea of hierarchical hub and hub arc location. benders decomposition method was used to transform and solve the mixed integer nonlinear model. Based on the analysis of the Arctic route example, it could be indicated that the number of central hubs directly affects the transportation cost and location of hubs. This study provides a certain direction for a shipping company to choose hubs after the opening of the Arctic route in the future and also made sense for the rail-sea intermodal transportation system from a strategic consideration.

关键词： rail-sea transportation system planning the Arctic Route Hierarchical hub location benders decomposition algorithm

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Flexible two-stage robust model for moving the transmission and reactive power infrastructures expansion planning towards power system integration of renewables

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IET RENEWABLE POWER GENERATION 2020年第11期14卷 1921-1932页

作者： Olfatinezhad, Nader Vahidinasab, Vahid Ahmadian, Mohammad Arasteh, Hamidreza Aghaei, Jamshid Mehran, Kamyar Shahid Beheshti Univ Abbaspour Sch Engn Dept Elect Engn Tehran *** Iran Niroo Res Inst Power Syst Planning & Operat Grp Tehran Iran Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Queen Mary Univ London Sch Elect Engn & Comp Sci London England

This study proposes a two-stage robust optimisation framework to model uncertainties in construction cost, load and renewable power generations (RPGs) in the coordinated transmission and reactive power infrastructure expansion planning problem. The proposed planning model, based on the network AC model and its voltage constraints, is represented as a mixed-integer linear programing problem with the aim of minimising: (i) the construction costs of lines, (ii) installation cost of reactive power resources and (iii) active load shedding as RPGs curtailment. Using the suggested robust optimisation approach, the best expansion plan is obtained even in the worst case of the defined uncertainty sets and the plan robustness level is controlled via parameter definition for the individual uncertainty sets. The benders decomposition algorithm is used to solve the two-stage problem where the plan security level is also taken into consideration using theN-1 contingency criterion. The standard Garver 6-bus system and the IEEE 24-bus reliability test system are used to validate the proposed robust framework.

关键词： load shedding nonlinear programming power system security power transmission planning integer programming reactive power power generation planning power generation reliability power transmission reliability power system integration renewable power generations coordinated transmission network AC model mixed-integer nonlinear programing problem installation cost robust optimisation approach plan security level uncertainty sets reactive power infrastructure expansion planning benders decomposition algorithm load shedding RPG curtailment Garver 6-bus system

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