Abstract: Following the original interpretation of the Shapley value as a priori evaluation of the prospects of a player in a multi-person interaction situation, we intend to apply the Shapley generalized value (introduced formally in Marichal et al. in Discrete Appl Math 155:26–43, 2007) as a tool for the assessment of a group of players that act as a unit in a coalitional game. We propose an alternative axiomatic characterization which does not use a direct formulation of the classical efficiency property. Relying on this valuation, we also analyze the profitability of a group. We motivate this use of the Shapley generalized value by means of two relevant applications in which it is used as an objective function by a decision maker who is trying to identify an optimal group of agents in a framework in which agents interact and the attained benefit can be modeled by means of a transferable utility game. PubDate: 2019-06-01
Abstract: We address cumulative deterioration scheduling in which two agents compete to perform their respective jobs on a single machine. By cumulative deterioration we mean that the actual processing time of any job of the two agents is a linear increasing function of the total normal processing times of already processed jobs. Each agent desires to optimize some scheduling criterion that depends on the completion times of its own jobs only. We study several scheduling problems arising from different combinations of some regular scheduling criteria, including the maximum cost (embracing lateness and makespan as its special cases), the total completion time, and the (weighted) number of tardy jobs. The aim is to find an optimal schedule that minimizes the objective value of one agent while maintaining the objective value of the other agent not exceeding a fixed upper bound. For each problem under study, we design either a polynomial-time or a pseudo-polynomial-time algorithm to solve it. PubDate: 2019-06-01
Abstract: Based on an application in the field of server consolidation, we consider the one-dimensional cutting stock problem with nondeterministic item lengths. After a short introduction to the general topic we investigate the case of normally distributed item lengths in more detail. Within this framework, we present two lower bounds as well as two heuristics to obtain upper bounds, where the latter are either based on a related (ordinary) cutting stock problem or an adaptation of the first fit decreasing heuristic to the given stochastical context. For these approximation techniques, dominance relations are discussed, and theoretical performance results are stated. As a main contribution, we develop a characterization of feasible patterns by means of one linear and one quadratic inequality. Based on this, we derive two exact modeling approaches for the nondeterministic cutting stock problem, and provide results of numerical simulations. PubDate: 2019-06-01
Abstract: By considering the characteristics of fresh agricultural products, this article introduces the behavioral tendency of fairness concerns of manufacturer to examine the fresh agricultural product supply chain and the optimal decision-making problem. The effects of the manufacturer’s behavioral tendency of fairness concerns on the fresh agricultural product supply chain is analyzed within the traditional and Nash bargaining frameworks, respectively. Results indicate that the manufacturer’s behavioral tendencies can reduce the freshness-keeping effort, the freshness of the produce, and the market demand of fresh agricultural products. The optimal wholesale price and retail price also increase or decrease to meet the manufacturer’s fairness concerns. These are determined by the price elasticity and the level of fairness concern, along with the fairness concern condition. Whether within the traditional or Nash bargaining fairness concern framework, market demand declines significantly with the increase in the level of fairness concern, thereby leading to the reduction of the utility of retailer in the supply chain. Therefore, the revenue sharing contract is introduced to achieve the Pareto improvement of both the manufacturer and the retailer. PubDate: 2019-05-18
Abstract: We provide decision-making models for a manufacturer which plans to produce multiple short life cycle products with the one-shot decision theory. The obtained optimal production quantities are based on the most appropriate scenarios for the manufacturer. Since the models are the bilevel programming problems with the max–min or min–max operator in the lower levels, we propose two approaches to translate them into general single-level optimization problems such that they can be solved via the commonly used optimization methods. The effectiveness of our approaches is examined from the theoretical and computational aspects. PubDate: 2019-05-10
Abstract: We present an algorithm for minimizing locally Lipschitz functions being continuously differentiable in an open dense subset of \(\mathbb {R}^n\) . The function may be nonsmooth and/or nonconvex. The method makes use of a gradient sampling method along with a conjugate gradient scheme. To find search directions, we make use of a sequence of positive definite approximate Hessians based on conjugate gradient matrices. The algorithm benefits from a restart procedure to improve upon poor search directions or to make sure that the approximate Hessians remain bounded. The global convergence of the algorithm is established. An implementation of the algorithm is executed on a collection of well-known test problems. Comparative numerical results clearly show outperformance of the algorithm over some recent well-known nonsmooth algorithms using the Dolan–Moré performance profiles. PubDate: 2019-05-06
Abstract: In the context of big data, many scientific communities aim to provide efficient approaches to accommodate large-scale datasets. This is the case of the machine-learning community, and more generally, the artificial intelligence community. The aim of this article is to explain how data mining problems can be considered as combinatorial optimization problems, and how metaheuristics can be used to address them. Four primary data mining tasks are presented: clustering, association rules, classification, and feature selection. This article follows the publication of a book in 2016 concerning this subject (Dhaenens and Jourdan, Metaheuristics for big data, Wiley, New York, 2016); additionally, updated references and an analysis of the current trends are presented. PubDate: 2019-04-06
Abstract: We consider a newsvendor that can increase the mean demand with advertising and reduce the variability in the demand by forecasting or market research. We analyze the problem under uniform and normal demand distributions. We also study the distribution-free case by using a lower bound on the newsvendor profit function. We show that when the budget is unlimited, the forecasting expenditure increases with the production cost until the cost of holding an inventory is equal to the cost of a lost sales. Although both expenditures increase with the product price, it turns out that the advertising expenditure is more important for the newsvendor: it allocates more to the advertising for products with higher prices if the budget is limited. It turns out that a newsvendor can benefit more from advertising (forecasting) if the market size (variability) is larger. Moreover, it is more profitable to allocate the expenditures into a single large market rather than allocating it to small segmented markets. The numerical studies show that the ability of forecasting makes a newsvendor more robust to the variance, i.e., the variability level is reduced significantly with the forecasting expenditure. PubDate: 2019-03-01
Abstract: This note proposes and analyzes a posterior tight worst-case bound for the longest processing time (LPT) heuristic for scheduling independent jobs on identical parallel machines with the objective of minimizing the makespan. It makes natural remarks on the well-known posterior worst-case bounds, and shows that the proposed bound can complement the well-known posterior bounds to synergistically achieve a better posterior worst-case bound for the LPT heuristic. Moreover, it gives some insight on LPT asymptotical optimality. PubDate: 2019-03-01
Abstract: This paper surveys two key issues in closed-loop supply chain (CLSC) research: return functions and coordination mechanisms. The return function provides the rule according to which end-of-life/use products are returned to a collector. The coordination mechanisms consist of the adoption of a certain mechanism (e.g., a contract) to align the closed-loop supply chain members’ objectives. We describe latest thinking in these two major CLSC-related fields and suggest future research directions to be undertaken. PubDate: 2019-02-26
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