Slack Due-Window Assignment Scheduling Problem with Deterioration Effects and a Deteriorating Maintenance Activity

This paper studies the slack due-window assignment scheduling problem with deterioration effects and a deterioration maintenance activity on a single-machine. The machine deteriorates during the machining process, and at a certain moment performs a deterioration maintenance activity, that is, the duration time of the maintenance activity is a linear function of the maintenance starting time. It is needed to make a decision on when to schedule the deteriorating maintenance activity, the optimal common flow allowances and the sequence of jobs to minimize the weighted penalties for the sum of earliness and tardiness, weighted number of early and delayed, and weighted due-window starting time and size. This paper proposes a polynomial time algorithm to solve this problem.

Approximation Algorithms for the Capacitated Min–Max Correlation Clustering Problem

In this paper, we propose a so-called capacitated min–max correlation clustering model, a natural variant of the min–max correlation clustering problem. As our main contribution, we present an integer programming and its integrality gap analysis for the proposed model. Furthermore, we provide two approximation algorithms for the model, one of which is a bi-criteria approximation algorithm and the other is based on LP-rounding technique.

Nonsubmodular Constrained Profit Maximization in Attribute Networks

The number of social individuals who interact with their friends through social networks is increasing, leading to an undeniable fact that word-of-mouth marketing has become one of the useful ways to promote sale of products. The Constrained Profit Maximization in Attribute network (CPMA) problem, as an extension of the classical influence maximization problem, is the main focus of this paper. We propose the profit maximization in attribute network problem under a cardinality constraint which is closer to the actual situation. The profit spread metric of CPMA calculates the total benefit and cost generated by all the active nodes. Different from the classical Influence Maximization problem, the influence strength should be recalculated according to the emotional tendency and classification label of nodes in attribute networks. The profit spread metric is no longer monotone and submodular in general. Given that the profit spread metric can be expressed as the difference between two submodular functions and admits a DS decomposition, a three-phase algorithm named as Marginal increment and Community-based Prune and Search(MCPS) Algorithm frame is proposed which is based on Louvain algorithm and logistic function. Due to the method of marginal increment, MPCS algorithm can compute profit spread more directly and accurately. Experiments demonstrate the effectiveness of MCPS algorithm.

Does Gold Still Shelter Inflation, and, if so, When? Evidence From Four Countries

This study investigates gold as a hedge or a safe haven against inflation in four countries. We propose two standard and quantile techniques in the volatility models, with a time-varying conditional variance of regression residuals based on TGARCH specifications. Gold exhibits considerable evidence of a strong hedge in the US and China. Nevertheless, gold provides shelter at different times and not consistently across countries. With regards to be a safe haven, gold retains its status as a key investment in China. On the other hand, gold only plays a minor role in the UK and India. These findings indicate that gold can secure Chinese investment during the high inflationary periods, while gold is a profitable asset to hold over a long period of time in the US. In contrast, UK and Indian investors should hold a well-diversified portfolio for sustainable return and protection from purchasing power loss.

Online-Retail Supply Chain Optimization with Credit Period and Selling Price-Dependent Demand

Credit payment strategies have been implemented widely in the online retail industry. This work studies an online-retail supply chain involving credit period and selling price-dependent demands. The participants of the supply chain form a Stackelberg game where the supplier as a follower sells products to the customers through an online platform provider, who as a leader provides a credit period to customers and charges the supplier based on the quantity of goods sold. We study and compare the supply chains when the online platform provider adopts the cash payment and credit payment strategies, respectively, to investigate the effects of the credit period, the selling price and the default risk on supply chain system performance. We also investigate these supply chains under both the centralized and decentralized settings and provide an example to illustrate a simple allocation mechanism to coordinate the decentralized supply chain. Finally, an extension of the supply chain with credit payment is given.

Distributionally Robust Joint Chance Constrained Vessel Fleet Deployment Problem

This work investigates the problem of vessel fleet deployment for liner shipping. The objective is to minimize the total cost, i.e., the sum of vessel chartering cost and vessel-route operating cost. In the considered problem, the shipment demand for each route is uncertain and its distribution is unknown. Due to lacking historical data, we use the moment-based ambiguity set to characterize the unknown distributions of demands. We then introduce a distributionally robust model and propose a new approximation approach to solve this problem. Finally, numerical experiments are conducted to demonstrate the performance of our approximation approach.

Heuristics for Finding Sparse Solutions of Linear Inequalities

In this paper, we consider the problem of finding a sparse solution, with a minimal number of nonzero components, for a set of linear inequalities. This optimization problem is combinatorial and arises in various fields such as machine learning and compressed sensing. We present three new heuristics for the problem. The first two are greedy algorithms minimizing the sum of infeasibilities in the primal and dual spaces with different selection rules. The third heuristic is a combination of the greedy heuristic in the dual space and a local search algorithm. In numerical experiments, our proposed heuristics are compared with the weighted-[Formula: see text] algorithm and DCA programming with three different non-convex approximations of the zero norm. The computational results demonstrate the efficiency of our methods.

Majorized iPADMM for Nonseparable Convex Minimization Models with Quadratic Coupling Terms

In this paper, we consider nonseparable convex minimization models with quadratic coupling terms arised in many practical applications. We use a majorized indefinite proximal alternating direction method of multipliers (iPADMM) to solve this model. The indefiniteness of proximal matrices allows the function we actually solved to be no longer the majorization of the original function in each subproblem. While the convergence still can be guaranteed and larger stepsize is permitted which can speed up convergence. For this model, we analyze the global convergence of majorized iPADMM with two different techniques and the sublinear convergence rate in the nonergodic sense. Numerical experiments illustrate the advantages of the indefinite proximal matrices over the positive definite or the semi-definite proximal matrices.

A Novel Two-Stage Game Model for Pricing Cloud/Fog Computing Resource in Blockchain Systems

Cloud/fog computing resource pricing is a new paradigm in the blockchain mining scheme, as the participants would like to purchase the cloud/fog computing resource to speed up their mining processes. In this paper, we propose a novel two-stage game to study the optimal price-based cloud/fog computing resource management, in which the cloud/fog computing resource provider (CFP) is the leader, setting the resource price in Stage I, and the mining pools act as the followers to decide their demands of the resource in Stage II. Since mining pools are bounded rational in practice, we model the dynamic interactions among them by an evolutionary game in Stage II, in which each pool pursues its evolutionary stable demand based on the observed price, through continuous learning and adjustments. Backward induction method is applied to analyze the sub-game equilibrium in each stage. Specifically in Stage II, we first build a general study framework for the evolutionary game model, and then provide a detailed theoretical analysis for a two-pool case to characterize the conditions for the existence of different evolutionary stable solutions. Referring to the real world, we conduct a series of numerical experiments, whose results validate our theoretical findings for the case of two mining pools. Additionally, the impacts from the size of mining block, the unit transaction fee and the price of token on the decision makings of participants are also discussed.