penalty cost
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2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Lishuan Hu ◽  
Caihong Xiang ◽  
Chengming Qi

Recently, fresh agricultural cold-chain logistics have been greatly developed with the increasing needs of people’s life. Reducing costs of cold-chain distribution has become the main object of loss control in logistics enterprises. The objective of this research is to find a set of optimal routes that minimize the total loss, including fuel cost, refrigeration cost, soft time window penalty cost, and cargo damage cost over transit time. In this paper, the definition and model construction of vehicle routing problem (VRP) with multiobjective minimum lost are introduced first. Then, an ant colony optimization (ACO) algorithm combined with Pareto local search (PLS) is put forward to solve the minimum loss model. In order to avoid the influence of complex road conditions during distribution, the distance matrix and the transit time matrix are both derived from the recommended navigation road based on E-map API. At last, a compare experiment between the traditional method and our proposed method is performed. The results indicate that our method has strong applicability and potential advantages in cold-chain logistic and has important practical significance and application value.


2021 ◽  
Vol 13 (22) ◽  
pp. 12838
Author(s):  
Yang Zhang ◽  
Yuehong Lu ◽  
Changlong Wang ◽  
Zhijia Huang ◽  
Tao Lv

Net-zero energy buildings (ZEB/NZEB) have been greatly encouraged and are considered to be a promising approach for energy conservation as well as environmental protection. However, a lack of incentive mechanisms can hinder the fast development and application of ZEB. This study thus focuses on the design of a daily reward–penalty mechanism (RPM) by considering the performance of the building, aiming to enable a lower penalty cost for the building where there is a better match between energy consumption and energy generation. The impact of the degree of freedom of the building load (k) is investigated on building performance based on a single-family house located in Shanghai city, China. It is observed that a higher value of k is preferred since the building users can adjust its energy consumption profile to better match with its energy generation. A higher k value enables lower annual energy consumption, lower penalty cost, better stability, and an average daily zero energy level of around 1.0. In addition, four quadratic fit curves are derived to describe the relationship between building performance (i.e., annual energy consumption, the average daily zero energy level, stability, and annual penalty cost) and the degree of freedom. Meanwhile, the uncertainty of ZEB performance is quantified, which provides flexibility for building users in selecting the appropriate degree of freedom.


Author(s):  
Xiao Xue ◽  
Yangbing Zheng ◽  
Chao Lu

In order to improve the economical performance of distributed energy supply system under uncertainty, the improved gray wolf algorithm is constructed for optimal allocation of distributed energy supply system. The relating research progress is summarized firstly, and effect of improved gray wolf algorithm on optimal allocation of distributed energy supply system are studied. The optimal allocation model of distributed energy supply system is constructed considering fuel consumption, operation and maintenance cost, environment penalty cost, and power grid energy exchange function, and the uncertain factor is processed based on scienario method. And then the improved gray wolf algorithm is designed, and the initial strategy of population and the regulated method of main parameters are improved. Finally, simulation analysis is carried out, simulation results show that the proposed model can obtain best optimal allocation effect of system.


2021 ◽  
Author(s):  
Xian Yu ◽  
Siqian Shen ◽  
Huizhu Wang

In this paper, we consider an integrated vehicle routing and service scheduling problem for serving customers in distributed locations who need pick-up, drop-off, or delivery services. We take into account the random trip time, nonnegligible service time, and possible customer cancellations, under which an ill-designed schedule may lead to undesirable vehicle idleness and customer waiting. We build a stochastic mixed-integer program to minimize the operational cost plus expected penalty cost of customers’ waiting time, vehicles’ idleness, and overtime. Furthermore, to handle real-time arrived service requests, we develop K-means clustering-based algorithms to dynamically update planned routes and schedules. The algorithms assign customers to vehicles based on similarities and then plan schedules on each vehicle separately. We conduct numerical experiments based on diverse instances generated from census data and data from the Ford Motor Company’s GoRide service, to evaluate result sensitivity and to compare the in-sample and out-of-sample performance of different approaches. Managerial insights are provided using numerical results based on different parameter choices and uncertainty settings.


2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Peixin Zhao ◽  
Fanfan Liu ◽  
Yuanyuan Guo ◽  
Xiaoyang Duan ◽  
Yunshu Zhang

With the growing interest in environmental protection and congestion, electric vehicles are increasingly becoming the important transportation means. However, electric vehicles currently face several adoption barriers including high purchasing price and limited travelling range, so the fleets where electric vehicles and conventional vehicles coexist are closer to the current fleet management status. Considering the impact of charging facilities and carbon emission, this paper proposes a vehicle routing problem with a mixed fleet of conventional and electric vehicles and soft time windows. A bi-objective programming model is established to minimize total operational cost and time penalty cost. Finally, the nondominated sorting genetic algorithm II (NSGA-II) is employed to deal with this problem. Furthermore, single-objective optimization is carried out for the two objectives, respectively, and the linear weighting method is also used to solve the problem. Through the contrast of these results and the NSGA-II results, the effectiveness of the algorithm in this paper is further verified. The results indicate that two objectives are contradictory to some extent and decision-makers need a trade-off between two objectives.


Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1521
Author(s):  
Guangyan Xu ◽  
Zailin Guan ◽  
Lei Yue ◽  
Jabir Mumtaz ◽  
Jun Liang

This paper investigates the nonidentical parallel production line scheduling problem derived from an axle housing machining workshop of an axle manufacturer. The characteristics of axle housing machining lines are analyzed, and a nonidentical parallel line scheduling model with a jumping process operation (NPPLS-JP), which considers mixed model production, machine eligibility constraints, and fuzzy due dates, is established so as to minimize the makespan and earliness/tardiness penalty cost. While the physical structures of the parallel lines in the NPPLS-JP model are symmetric, the production capacities and process capabilities are asymmetric for different models. Different from the general parallel line scheduling problem, NPPLS-JP allows for a job to transfer to another production line to complete the subsequent operations (i.e., jumping process operations), and the transfer is unidirectional. The significance of the NPPLS-JP model is that it meets the demands of multivariety mixed model production and makes full use of the capacities of parallel production lines. Aiming to solve the NPPLS-JP problem, we propose a hybrid algorithm named the multi-objective grey wolf optimizer based on decomposition (MOGWO/D). This new algorithm combines the GWO with the multi-objective evolutionary algorithm based on decomposition (MOEA/D) to balance the exploration and exploitation abilities of the original MOEA/D. Furthermore, coding and decoding rules are developed according to the features of the NPPLS-JP problem. To evaluate the effectiveness of the proposed MOGWO/D algorithm, a set of instances with different job scales, job types, and production scenarios is designed, and the results are compared with those of three other famous multi-objective optimization algorithms. The experimental results show that the proposed MOGWO/D algorithm exhibits superiority in most instances.


Author(s):  
Chunying Ren ◽  
Dachuan Xu ◽  
Donglei Du ◽  
Min Li

Abstract In the k-means problem with penalties, we are given a data set ${\cal D} \subseteq \mathbb{R}^\ell $ of n points where each point $j \in {\cal D}$ is associated with a penalty cost p j and an integer k. The goal is to choose a set ${\rm{C}}S \subseteq {{\cal R}^\ell }$ with |CS| ≤ k and a penalized subset ${{\cal D}_p} \subseteq {\cal D}$ to minimize the sum of the total squared distance from the points in D / D p to CS and the total penalty cost of points in D p , namely $\sum\nolimits_{j \in {\cal D}\backslash {{\cal D}_p}} {d^2}(j,{\rm{C}}S) + \sum\nolimits_{j \in {{\cal D}_p}} {p_j}$ . We employ the primal-dual technique to give a pseudo-polynomial time algorithm with an approximation ratio of (6.357+ε) for the k-means problem with penalties, improving the previous best approximation ratio 19.849+∊ for this problem given by Feng et al. in Proceedings of FAW (2019).


Author(s):  
Fabricio Previgliano ◽  
Gustavo Vulcano

Problem definition: We study the problem of managing uncertain capacities for revenue optimization over a network of resources. The uncertainty could be due to (i) the need to reallocate initial capacities among resources or (ii) the random availability of physical capacities by the time of service execution. Academic/practical relevance: The analyzed control policy is aligned with the current industry practice, with a virtual capacity and a bid price associated with each network resource. The seller collects revenues from an arriving stream of customers. Admitted requests that cannot be accommodated within the final, effective capacities incur a penalty cost. The objective is to maximize the total cumulative net revenue (sales revenue minus penalty cost). The problem arises in practice, for instance, when airlines are subject to last-minute change of aircrafts and in cargo revenue management where the capacity left by the passengers’ load is used for freight. Methodology: We present a stochastic dynamic programming formulation for this problem and propose a stochastic gradient algorithm to approximately solve it. All limit points of our algorithm are stationary points of the approximate expected net revenue function. Results: Through an exhaustive numerical study, we show that our controls are computed efficiently and deliver revenues that are almost consistently higher than the ones obtained from benchmarks based on the widely adopted deterministic linear programming model. Managerial implications: We obtain managerial insights about the impact of the timing of the capacity uncertainty clearance, the capacity heterogeneity, the network congestion, and the penalty for not being able to accommodate the previously accepted demand. Our approach tends to offer the best performance across different parameterizations of the problem.


Author(s):  
Ji Hwan Cha ◽  
Maxim Finkelstein

A new renewable warranty policy is suggested that increases the probability of its success and can decrease the corresponding costs for certain ranges of parameters. It deals with heterogeneous populations of items from two subpopulations (‘weak’ and ‘strong’) and is aimed at elimination and further replacement of weak items after screening at some optimal time. This elimination is performed when degradation described by the corresponding mixed degradation process reaches some optimally predetermined level. Probabilistic and cost analyses of the model are performed and the illustrative example is presented. It is shown that the proposed warranty policy with inspection outperforms the conventional one in a probabilistic sense. Furthermore, the proposed policy becomes economically beneficial especially when the additional penalty cost caused by a sudden failure is large.


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