作者:
Wu, YulunTanaka, ShunjiKyoto Univ
Grad Sch Engn Dept Elect Engn Nishikyo Ku Kyoto 6158510 Japan Okayama Univ
Fac Environm Life & Nat Sci & Technol 3-1-1 Tsushima NakaKita Ku Okayama 7008530 Japan
This study proposes a novel approximate mixed-integerlinearprogramming (MILP) model for the perishable inventory control problem considering non-stationary demands and backlogging penalties. Because of the existence...
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This study proposes a novel approximate mixed-integerlinearprogramming (MILP) model for the perishable inventory control problem considering non-stationary demands and backlogging penalties. Because of the existence of the waste costs incurred by outdated products in the cost function, it is difficult to apply the linearization technique employed for the non-perishable inventory control problem directly to our problem. To address this difficulty, we develop a two-step approximation method. In the first step, we approximate each expected cost to simplify the cost function, making it easy to handle. In the second step, we apply an existing linearization technique to linearize this function and then obtain the MILP model. We evaluate the proposed model in computer simulations by comparing it with other existing methods. The results show that our model closely matches a benchmark method capable of obtaining near-optimal solutions in solution quality, and it achieves abetter trade-off between solution quality and computational efficiency than existing heuristics.
Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based *** engineering management,UAV-based inspection has emerged as a highly ...
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Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based *** engineering management,UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments,surpassing traditional inspection *** on this foundation,this paper delves into the optimization of UAV inspection routing and scheduling,addressing the complexity introduced by factors such as no-fly zones,monitoring-interval time windows,and multiple monitoring *** tackle this challenging problem,we propose a mixed-integerlinearprogramming(MILP)model that optimizes inspection task assignments,monitoring sequence schedules,and charging *** comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem(VRP),leading to a mathematically intractable model for commercial solvers in the case of large-scale *** overcome this limitation,we design a tailored variable neighborhood search(VNS)metaheuristic,customizing the algorithm to efficiently solve our *** numerical experiments are conducted to validate the efficacy of our proposed algorithm,demonstrating its scalability for both large-scale and real-scale *** experiments and a case study based on an actual engineering project are also conducted,providing valuable insights for engineering managers to enhance inspection work efficiency.
In actual industrial production, several operations of a job may not have precedence relationships and can be placed at any point in the process route. However, traditional flexible job shop scheduling problems (FJSP)...
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In actual industrial production, several operations of a job may not have precedence relationships and can be placed at any point in the process route. However, traditional flexible job shop scheduling problems (FJSP) often assume that all operations of each job must be processed in strict linear order. Therefore, this research addresses the FJSP with discrete operation sequence flexibility (FJSPDS) with the objective of minimizing the makespan. Based on existing models, two novel mixed-integerlinearprogramming (MILP) models are formulated by improving the description methods of variables and constraints, significantly enhancing the models' performance. Additionally, a hybrid evolutionary algorithm (HEA) is proposed to solve large-scale instances through the following three aspects. An improved encoding method is proposed, which makes the search space of the HEA and solution space of the problem more compatible and reduces the possibility of optimal solutions being missed. A special neighborhood structure is designed according to the characters of sequence-free operations, and an iterative local search method is introduced to improve the quality of the solution. A knowledge-driven reinitialization operator is developed, which generates new individuals based on the features of the historical elite population, guiding the evolution of populations, avoiding premature convergence while also avoiding falling into local optima. Finally, a total of 110 benchmark problem instances are utilized to verify the superior effectiveness of the MILP models and the HEA in solving FJSPDS.
With the development of economic globalization, distributed hybrid flow shop scheduling problem (DHFSSP) has become prevalent in realistic manufacturing systems. Moreover, to accord with the actual production scenario...
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With the development of economic globalization, distributed hybrid flow shop scheduling problem (DHFSSP) has become prevalent in realistic manufacturing systems. Moreover, to accord with the actual production scenarios and satisfy the requirement of manufacturing market, it is imperative to comprehensively explore various complex manufacturing scenarios (e.g., production assembly) and production-constrained resources (e.g., worker resources) in DHFSSP. However, the integration mode of DHFSSP, assembly shop problem (ASP), and dual-resource constraints (DRC) has not been reported in existing literature. Thus, to fill out this research gap, this paper first attempts to investigate DAHFSSP-DRC with minimization the total tardiness (TTD). To solve this problem, a mixed-integerlinearprogramming (MILP) model and a knowledge-based iterated greedy algorithm (KBIG) are presented. The novelties of KBIG are as follows: (1) An efficient decoding is developed to improve the solution's quality;(2) A knowledge-based NEH (KB-NEH) initialization strategy is presented to generate an initial solution;(3) A knowledge-based destruction and construction is designed to improve the exploration capability;(4) A product-based local search is proposed to enhance the exploitation capability. Additionally, to validate the proposed model, we implement CPLEX to solve it on 24 small-sized instances. To verify the effectiveness of the proposed KBIG, extensive experiments are conducted to compare with other 7 comparison algorithms on 405 large-sized instances. Experimental results illustrate that KBIG is superior to its competitors.
This paper presents a stochastic mixed-integerlinear mathematical model for finding the optimal placement and sizing of distributed generation in a DC distribution network, considering the uncertainty of electrical d...
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This paper presents a stochastic mixed-integerlinear mathematical model for finding the optimal placement and sizing of distributed generation in a DC distribution network, considering the uncertainty of electrical demand and distributed renewable sources. The proposed model accurately represents the original mixed-integer nonlinearmodel, obtaining a globally optimal solution in less computational time with low errors. The mathematical model allows for considering constraints related to the maximum limits for the penetration of distributed generation, such as those specified by Resolution CREG 174 of 2021. Furthermore, the uncertainties of the electrical demand, wind energy-based distributed generation (DG), and solar energy-based DG are considered in the mathematical models using a two-stage stochastic programming approach. The accuracy and efficiency of the proposed model were tested and validated on a 21-node DC test system from the specialized literature, and the effectiveness and robustness were assessed on a 69-node DC test system. The obtained results show that the proposed stochastic mixed-integerlinear mathematical model performs well.
Bunkering costs constitute the largest portion of operational expenses in the shipping industry, directly influencing both economic efficiency and environmental impact. In line with Sustainable Development Goals (SDGs...
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Bunkering costs constitute the largest portion of operational expenses in the shipping industry, directly influencing both economic efficiency and environmental impact. In line with Sustainable Development Goals (SDGs) 12 and 13, this study develops a decision model that jointly optimizes bunkering and sailing speed in liner shipping, with the aim of minimizing fuel costs and reducing carbon emissions. The model explicitly incorporates two often-overlooked aspects of the bunkering process: the requirement for empty tanks before refueling and the fuel inspection process. Due to the presence of infinite-dimensional and nonlinear terms, solving the model is computationally challenging. To address this complexity, we employ approximation algorithms and linearization techniques to transform the model into a mixed-integerlinearprogramming (MILP) formulation. Additionally, we implement a Branch-and-Cut algorithm to enhance computational efficiency. Numerical experiments are conducted to evaluate the model's performance, along with sensitivity analyses to assess the impact of key parameters. The results demonstrate that both the empty tank requirement and fuel inspection significantly influence bunkering decisions and sailing strategies, with the latter having a more pronounced effect. Moreover, our findings highlight the potential for sustainable fuel management practices to contribute to carbon reduction in maritime transportation. This study provides valuable insights for policymakers and industry stakeholders seeking to balance cost efficiency and environmental sustainability in shipping operations.
The three-stage remanufacturing system scheduling problem (3T-RSSP) has been a hot research topic recently. The remanufacturing system in this paper is equipped with a novel configuration of unrelated parallel disasse...
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The three-stage remanufacturing system scheduling problem (3T-RSSP) has been a hot research topic recently. The remanufacturing system in this paper is equipped with a novel configuration of unrelated parallel disassembly/reassembly workstations and parallel dedicated flow-shop-type reprocessing lines. To this end, a mixedintegerlinearprogramming (MILP) model based on the adjacent sequence-based modeling idea is first proposed to address the 3T-RSSP for a makespan minimization. Compared with other ideas, the adjacent sequence-based modeling idea is effective in deciding precedence relationship between two adjacent operations, especially for the investigated 3T-RSSP. The 3T-RSSP is NP (non-deterministic polynomial)-hard, we also design 18 composed heuristics for large-sized problems to gain a better performance, compared to traditional isolated heuristics. Simulation experiments are carried out on a publicly available dataset to test the performance the MILP model and composed heuristics. Results imply that the MILP model solved by CPLEX can seek optimum solutions within a short time when the problem size is small. It is found that when problem size becomes 2.0, 4.0, 8.0 times large, performance indicators NCs (number of constraints) and NBVs (number of binary variables) of the model become 3.29, 11.81, 44.60 and 3.43, 12.57, 48.00 times large. Besides, compared with other composed heuristics, LTRTF (longest total reprocessing time-first available machine) gains the best performance. Instance P5-C3-D2/A2 is selected to quantitatively analyze the MILP model by presenting the detailed 0-1 binary variable values. Finally, by comparing with position-based MILP model, the adjacent sequence-based MILP model has better performance in characterizing the investigated 3T-RSSP.
The method described for production scheduling in this study is a simultaneous use of a clustering algorithm with a genetic algorithm (GA). The aggregating algorithm presented in this study aims to control the concent...
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The method described for production scheduling in this study is a simultaneous use of a clustering algorithm with a genetic algorithm (GA). The aggregating algorithm presented in this study aims to control the concentration of operations and the cluster size, which is evaluated using the Silhouette criterion. The fitness function and the chromosome length in the GA have differences from the usual one. The results showed the number of binary variables in a mixed-integer linear programming model was reduced by 78.5% based on the created clusters. Although the aggregated model's net present value (NPV) is decreased by 7%, the solution time significantly dropped from 3 h to 43.1 s. Also, compared to the non-clustering block model, the aggregated block model's NPV, obtained by GA, was improved.
The rapid development of automated vehicle (AV) technologies enables us to explore how to update the urban road infrastructure to cater for the upcoming mixed-autonomy traffic with both AVs and human-driven vehicles (...
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The rapid development of automated vehicle (AV) technologies enables us to explore how to update the urban road infrastructure to cater for the upcoming mixed-autonomy traffic with both AVs and human-driven vehicles (HVs). In this study, we aim to seek an optimal solution to integrate the deployment of AV-dedicated lane and roadside unit assisting automated driving subject to a limited budget by considering the route choice behaviors of AVs and HVs. An AV-dedicated lane segregates AVs from the mixed traffic to create a fully automated driving envi-ronment and eliminate disturbances from HVs. The deployment of roadside units assisting automated driving could overcome the connectivity gap for AVs and lower their headway when they follow an HV. We seek to develop a robust and optimal integrated deployment solution that accommodates the uncertain road capacity caused by the stochastic mixed AV and HV fleet sequence. We first establish the stochastic network equilibrium conditions for the mixed traffic, and formulate the integrated deployment problem as a mathematical program with comple-mentarity constraints (MPCC). The MPCC is approximated by a mixed-integerlinear programing (MILP) model, which allows existing algorithms for its global optimum. We further develop two interesting strategies, including domain reduction and breakpoint selection, to enhance the effectiveness of the MILP model. Numerical experiments are finally carried out to evaluate the feasibility of research methodology proposed in this study and find some valuable insights.
Passage is an essential logistics carrying structure in the layout, rational planning of its location can effectively improve production efficiency and safety of the workshop. In this paper, we introduce the concept o...
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Passage is an essential logistics carrying structure in the layout, rational planning of its location can effectively improve production efficiency and safety of the workshop. In this paper, we introduce the concept of man-vehicle diversion and propose a novel corridor allocation problem with a separated man-vehicle logistics passage (CAP_SMVP). CAP_SMVP needs to sequence the facilities and determine the final exact location of the changeable sidewalk. Subsequently, we construct two mixed-integer linear programming models to minimise the material handling costs (MHC). The models for small-scale instances are solved precisely using GUROBI. We choose a two- stage method (combining meta-heuristic algorithm and linearprogramming, CMLP) to address large-scale problems. The correctness of the proposed model and CMLP is confirmed in solving the benchmark instances. Then, the calculation results of multiple algorithms for solving the initial CAP demonstrate the universality of the proposed algorithm. Finally, a real-world case is conducted, and managerial insights are provided. The comparison results show the proposed layout structure's advantages and highlight the efficiency of the two-stage method.
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