An EOQ inventory model for items with Weibull distribution deterioration,ramp type demand rate and partial backlogging

In this paper, an EOQ inventory model is presented depleted not only by demand but also by Weibull distribution deterioration, in which the demand rate is assumed that with a ramp type function of time. In the model, shortages are allowed partial backlogging and the backlogging rate is variable and is dependent on waiting time for the next replenishment. The method is illustrated by three numerical examples, and sensitivity analysis of the optimal solution with respect to parameters of the system is carried out.     

Using tri-reference point theory to evaluate risk attitude and the effects of financial incentives in a gamified crowdsourcing task

Crowdsourcing has rapidly developed as a mechanism to accomplish tasks that are easy for humans to accomplish but are challenging for machines. However, unlike machines, humans need to be cajoled to perform tasks, usually through some type of incentive. Since participants from the crowd are typically anonymous and have no expectation of an ongoing work relationship with a task requester, the types of incentives offered to workers are usually short-term monetary bonuses, which have had an inconclusive impact on crowdsourcing worker quality. In this paper, we explore the notion that the risk attitude of crowdsourcing workers may play an important role in the effectiveness of incentives on task accuracy. Traditional utility theories, such as prospect theory, depend on decisions made relative to a singular reference point, whereas the tri-reference point (TRP) theory (Wang and Johnson, J Exp Psychol Gen 141:743–756, 2012) holds that three reference points impact decision making. Using the TRP theory as a guide, we develop a game that provides workers with three reference points and subsequently explores the role of multiple reference points on worker risk aversion and task accuracy.     

Minimum number of disjoint linear forests covering a planar graph

Graph coloring has interesting real-life applications in optimization, computer science and network design, such as file transferring in a computer network, computation of Hessians matrix and so on. In this paper, we consider one important coloring, linear arboricity, which is an improper edge coloring. Moreover, we study linear arboricity on planar graphs with maximum degree \(\varDelta \ge 7\) . We have proved that the linear arboricity of \(G\) is \(\lceil \frac{\varDelta }{2}\rceil \) , if for each vertex \(v\in V(G)\) , there are two integers \(i_v,j_v\in \{3,4,5,6,7,8\}\) such that any two cycles of length \(i_v\) and \(j_v\) , which contain \(v\) , are not adjacent. Clearly, if \(i_v=i, j_v=j\) for each vertex \(v\in V(G)\) , then we can easily get one corollary: for two fixed integers \(i,j\in \{3,4,5,6,7,8\}\) , if there is no adjacent cycles with length \(i\) and \(j\) in \(G\) , then the linear arboricity of \(G\) is \(\lceil \frac{\varDelta }{2}\rceil \) .     

Single-machine batch delivery scheduling with an assignable common due window

This paper addresses a batch delivery single-machine scheduling problem in which jobs have an assignable common due window. Each job will incur an early (tardy) penalty if it is early (tardy) with respect to the common due window under a given schedule. There is no capacity limit on each delivery batch, and the cost per batch delivery is fixed and independent of the number of jobs in the batch. The objective is to find the optimal size and location of the window, the optimal dispatch date for each job, as well as an optimal job sequence to minimize a cost function based on earliness, tardiness, holding time, window location, window size, and batch delivery. We show that the problem can be optimally solved in O(n⁸)$O(n^8)$ time by a dynamic programming algorithm under a reasonable assumption on the relationships among the cost parameters. A computational experiment is also conducted to evaluate the performance of the proposed algorithm. We also show that some special cases of the problem can be optimally solved by lower order algorithms.     

Some results on the target set selection problem

In this paper we consider a fundamental problem in the area of viral marketing, called Target Set Selection problem. We study the problem when the underlying graph is a block-cactus graph, a chordal graph or a Hamming graph. We show that if G is a block-cactus graph, then the Target Set Selection problem can be solved in linear time, which generalizes Chen’s result (Discrete Math. 23:1400–1415, 2009) for trees, and the time complexity is much better than the algorithm in Ben-Zwi et al. (Discrete Optim., 2010) (for bounded treewidth graphs) when restricted to block-cactus graphs. We show that if the underlying graph G is a chordal graph with thresholds θ(v)≤2 for each vertex v in G, then the problem can be solved in linear time. For a Hamming graph G having thresholds θ(v)=2 for each vertex v of G, we precisely determine an optimal target set S for (G,θ). These results partially answer an open problem raised by Dreyer and Roberts (Discrete Appl. Math. 157:1615–1627, 2009).