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 Annals of Operations Research   [SJR: 1.186]   [H-I: 78]   [8 followers]  Follow         Hybrid journal (It can contain Open Access articles)    ISSN (Print) 1572-9338 - ISSN (Online) 0254-5330    Published by Springer-Verlag  [2345 journals]
• Preface: logistics, optimization and transportation—in memory of the
late Arne Løkketangen
• Authors: Geir Hasle; Lars Magnus Hvattum; David L. Woodruff
Pages: 709 - 711
PubDate: 2017-06-01
DOI: 10.1007/s10479-017-2504-5
Issue No: Vol. 253, No. 2 (2017)

• An examination of job interchange relationships and induction-based proofs
in single machine scheduling
• Authors: J. J. Kanet; C. E. Wells
Pages: 345 - 351
Abstract: We provide a generalization of Lawler’s (Mathematical programming the state of the art. Springer, Berlin, pp 202–234, 1983) Theorem on solutions to permutation scheduling problems when the objective function admits a particular job interchange relation. We complete Lawler’s result with a straight-forward proof by induction on n, the number of jobs. A notable application is $$1 \varSigma {w}_{j} C_{j}$$ where the objective of total weighted completion time admits WSPT (i.e., scheduling jobs in non-decreasing order of $$p_{j}/w_{j}$$ ). We provide new proofs by induction for the optimality of WSPT as well as for SPT in the unweighted case.
PubDate: 2017-06-01
DOI: 10.1007/s10479-016-2289-y
Issue No: Vol. 253, No. 1 (2017)

• Two short proofs regarding the logarithmic least squares optimality in
Chen, K., Kou, G., Tarn, J. M., Song, Y. (2015): Bridging the gap between
missing and inconsistent values in eliciting preference from pairwise
comparison matrices, Annals of Operations Research 235(1):155–175
• Authors: Sándor Bozóki
Pages: 707 - 708
PubDate: 2017-06-01
DOI: 10.1007/s10479-016-2396-9
Issue No: Vol. 253, No. 1 (2017)

• The multiple lot sizing problem of a serial production system with
interrupted geometric yields, rigid demand and Pentico’s heuristic
• Authors: Sy-Ming Guu; Chun-Yi Lin
Abstract: We consider the optimal lot sizing decision problem for a serial production system with the interrupted geometric yields and rigid demand. Such decisions are well-known for analytical difficulty due to often complicated cost expressions and necessity of deciding optimal lotsizes to stages/machines in the system. Pentico proposed a simple and effective heuristic that all usable items exiting a stage will be processed at the next stage till the end of the system. Pentico’s heuristic requires only the decision on the initial lot size. Based on Pentico’s heuristic, Bez-Zvi and Grosfeld-Nir considered the optimal “P-policies” to yield an optimal initial lot size so as to minimize the expected cost of the system while fulfilling the order. They showed the optimal initial lot size is always smaller than or equal to the outstanding demand. In this paper, we provide a finite upper bound and narrow searching range for the optimal initial lot sizes. It is well known that the worst case for minimizing the expected cost is to have an optimal initial lotsize equal to 1 for any outstanding demand. We characterize conditions in terms of the average expected costs for the worst case of the production system. An efficient algorithm for finding the optimal initial lot size is given which utilizes the recursive feature among the expected cost elements. For intellectual curiosity, we study a two-stage serial production system with a uniform yield in stage 1 and an interrupted geometric yield in stage 2. We propose an algorithm to derive an optimal initial lot size to enter this two-stage problem under Pentico’s heuristic. We show that for small outstanding demands (equal to 1 or 2) the optimal initial lot sizes are often greater than the outstanding demands. We prove that for large outstanding demands the optimal initial lot sizes are smaller than or equal to the outstanding demands. We also prove the existence of a finite upper bound for all optimal initial lot sizes. Our numerical example illustrates the existence of a threshold such that if the demand is smaller (greater) than it then the optimal lot size is larger (smaller) than the demand. Our analysis and numerical observation are very interesting to contrast with the most commonly seen binomial or uniform yield settings that the optimal lot size is always larger than the outstanding demand while for interrupted geometric yield the optimal lot size is always smaller than the outstanding demand.
PubDate: 2017-06-20
DOI: 10.1007/s10479-017-2558-4

• Flow shop scheduling problem with conflict graphs
• Authors: Nour El Houda Tellache; Mourad Boudhar
Abstract: This paper addresses the problem of scheduling jobs on flow shops subject to constraints given by an undirected graph, in which each edge joins a pair of conflicting jobs that cannot be processed simultaneously on different machines. We first show that the problem of minimizing the maximum completion time (makespan) is NP-hard for several versions. Then, we present polynomial-time solvable cases. On the other hand, we propose heuristic approaches and lower bounds alongside with an experimental study.
PubDate: 2017-06-20
DOI: 10.1007/s10479-017-2560-x

• Photovoltaic power plant design considering multiple uncertainties and
risk
• Authors: Yasemin Merzifonluoglu; Eray Uzgoren
Abstract: The objective of this study was to develop stochastic optimization tools for determining the best strategy of photovoltaic installations in a campus environment with consideration of uncertainties in load, power generation and system performance. In addition to a risk neutral approach, we used Conditional Value-at-Risk to estimate the risk in our problem. The resulting Mixed Integer Programming models were formulated using a scenario-based approach. To minimize the mismatch between supply and demand, hourly solar resource and electricity demand levels were characterized via refined models. A sample-average approximation (SAA) method was proposed to provide high-quality solutions efficiently. The SAA problems were solved using exact and heuristic methods. A complete numerical study was conducted to examine the performance of the proposed solution methods, identify optimal selection strategies and consider the sensitivity of the solution to varying levels of risk.
PubDate: 2017-06-20
DOI: 10.1007/s10479-017-2557-5

• A DEA ranking method based on cross-efficiency intervals and
signal-to-noise ratio
• Authors: Shiang-Tai Liu
Abstract: Cross-efficiency evaluation is a useful approach to ranking decision making units (DMUs) in data envelopment analysis (DEA). The possible existence of multiple optimal weights for the DEA may reduce the usefulness of the cross-efficiency evaluation since the ranking is according to the choice of weights that different DMUs make. Most of existing approaches for cross-efficiency evaluation employ the average cross-efficiency to further discriminate among the DEA efficient units or focus on how to determine input and output weights uniquely, but lay little emphasis on the consideration of the ranges and variances of cross-efficiencies as alternative ranking factors. In this paper we consider cross-efficiency intervals and their variances for ranking DMUs. The aggressive and benevolent formulations are taken into account at the same time. Consequently, a number of cross-efficiency intervals is obtained for each DMU. The signal-to-noise (SN) ratio, originally designed for optimizing the robustness of a process, is constructed as a numerical index for ranking DMUs. A nonlinear fractional program with bound constraints is formulated to find the optimal value of the SN ratio. By model reduction and variable substitution, this nonlinear fractional program is transformed into a quadratic one for deriving the global optimum solution. With the derived SN ratios, we are able to fully rank all DMUs accordingly. Two examples are given to illustrate the effectiveness of the methodology proposed in this paper.
PubDate: 2017-06-19
DOI: 10.1007/s10479-017-2562-8

• Structure of optimal policies to periodic-review inventory models with
convex costs and backorders for all values of discount factors
• Authors: Eugene A. Feinberg; Yan Liang
Abstract: This paper describes the structure of optimal policies for discounted periodic-review single-commodity total-cost inventory control problems with fixed ordering costs for finite and infinite horizons. There are known conditions in the literature for optimality of $$(s_t,S_t)$$ policies for finite-horizon problems and the optimality of (s, S) policies for infinite-horizon problems. The results of this paper cover the situation, when such assumption may not hold. This paper describes a parameter, which, together with the value of the discount factor and the horizon length, defines the structure of an optimal policy. For the infinite horizon, depending on the values of this parameter and the discount factor, an optimal policy either is an (s, S) policy or never orders inventory. For a finite horizon, depending on the values of this parameter, the discount factor, and the horizon length, there are three possible structures of an optimal policy: (1) it is an $$(s_t,S_t)$$ policy, (2) it is an $$(s_t,S_t)$$ policy at earlier stages and then does not order inventory, or (3) it never orders inventory. The paper also establishes continuity of optimal value functions and describes alternative optimal actions at states $$s_t$$ and s.
PubDate: 2017-06-17
DOI: 10.1007/s10479-017-2548-6

• Satisficing data envelopment analysis: a Bayesian approach for peer mining
in the banking sector
• Authors: Vincent Charles; Ioannis E. Tsolas; Tatiana Gherman
Abstract: Over the past few decades, the banking sectors in Latin America have undergone rapid structural changes to improve the efficiency and resilience of their financial systems. The up-to-date literature shows that all the research studies conducted to analyze the above-mentioned efficiency are based on a deterministic data envelopment analysis (DEA) model or econometric frontier approach. Nevertheless, the deterministic DEA model suffers from a possible lack of statistical power, especially in a small sample. As such, the current research paper develops the technique of satisficing DEA to examine the still less explored case of Peru. We propose a Satisficing DEA model applied to 14 banks operating in Peru to evaluate the bank-level efficiency under a stochastic environment, which is free from any theoretical distributional assumption. The proposed model does not only report the bank efficiency, but also proposes a new framework for peer mining based on the Bayesian analysis and potential improvements with the bias-corrected and accelerated confidence interval. Our study is the first of its kind in the literature to perform a peer analysis based on a probabilistic approach.
PubDate: 2017-06-17
DOI: 10.1007/s10479-017-2552-x

• Dynamic pricing and inventory control with delivery flexibility
• Authors: Wen Chen; Ying He
Abstract: We study a multi-period inventory system with price-sensitive demand and uncertain supplier, focusing on the advantage of delivery flexibility. The optimal pricing and inventory replenishment decisions are explored. We also investigate the changes of marginal profit, optimal order quantities and optimal prices over time horizon with additive demand noise. By comparing our system with delivery flexibility with the other two traditional systems numerically in different scenarios, we show that the delivery flexibility can improve the total profit and mitigate the supply risk.
PubDate: 2017-06-14
DOI: 10.1007/s10479-017-2555-7

• Ascent–descent variable neighborhood decomposition search for community
detection by modularity maximization
Abstract: In this paper we propose a new variant of the Variable Neighborhood Decomposition Search (VNDS) heuristic for solving global optimization problems. We call it Ascent-Descent VNDS since it performs “boundary effect”, or local search step, even if the improvement in solving the subproblem has not been obtained. We apply it in detecting communities in large networks by modularity maximization, the criterion which is, despite of some recent criticism, most widely used. Computational analysis is performed on 22 instances from the 10th DIMACS Implementation Challenge. On 13 instances where optimal solutions were not known, we got the improved best known solutions on 9 instances and on 4 instances the solution was equal to the best known. Thus, the proposed new heuristic outperforms the current state-of-the-art algorithms from the literature.
PubDate: 2017-06-13
DOI: 10.1007/s10479-017-2553-9

• Optimization of sample size and order size in an inventory model with
quality inspection and return of defective items
• Authors: Naoufel Cheikhrouhou; Biswajit Sarkar; Baishakhi Ganguly; Asif Iqbal Malik; Rafael Batista; Young Hae Lee
Abstract: To ensure all products as perfect, inspection is essential, even though it is not possible to inspect all products after producing them like some special type products as plastic joint for the water pipe. In this direction, this paper develops an inventory model with lot inspection policy. With the help of lot inspection, all products need not to be verified still the retailer can decide the quality of products during inspection. If retailer founds products as imperfect quality, the products are sent back to supplier. As it is lot inspection, mis-clarification errors (Type-I error and Type-II error) are introduced to model the problem. Two possible cases are discussed for sending back products as defective lots are immediately withdrawn from the system and send back to supplier with retailer’s payment and for second case, retailer sends defective products during receiving next lot from supplier with supplier’s investment, like in food industry or in hygiene product industry. The model is solved analytically and results indicate that optimal order size and sample size are intrinsically linked and maximize the total profit. Numerical examples, graphical representations, and sensitivity analysis are given to illustrate the model. The results suggest that sending defective products maintaining the first case is the more profitable than the second case.
PubDate: 2017-06-12
DOI: 10.1007/s10479-017-2511-6

• Hybrid clustering of data and vague concepts based on labels semantics
• Authors: Zengchang Qin; Tao Wan; Hanqing Zhao
Abstract: Data clustering is the process of dividing data elements into clusters so that items in the same cluster are as similar as possible, and items in different clusters are as dissimilar as possible. One of the key features for clustering is how to define a sensible similarity measure. Such measures usually handle data in one modality, but unable to cluster data from different modalities. Based on fuzzy set and prototype theory interpretations of label semantics, two (dis) similarity measures are proposed by which we can automatically cluster data and vague concepts represented by logical expressions of linguistic labels. Experimental results on a toy problem and one in image classification demonstrate the effectiveness of new clustering algorithms. Since our new proposed measures can be extended to measuring distance between any two granularities, the new clustering algorithms can also be extended to cluster data instance and imprecise concepts represented by other granularities.
PubDate: 2017-06-10
DOI: 10.1007/s10479-017-2541-0

• When is tail mean estimation more efficient than tail median? Answers and
implications for quantitative risk management
• Authors: Roger W. Barnard; Kent Pearce; A. Alexandre Trindade
Abstract: We investigate the relative efficiency of the empirical “tail median” versus “tail mean” as estimators of location when the data can be modeled by an exponential power distribution (EPD), a flexible family of light-tailed densities. By considering appropriate probabilities so that the quantile of the untruncated EPD (tail median) and mean of the left-truncated EPD (tail mean) coincide, limiting results are established concerning the ratio of asymptotic variances of the corresponding estimators. The most remarkable finding is that in the limit of the right tail, the asymptotic variance of the tail median estimate is approximately 36% larger than that of the tail mean, irrespective of the EPD shape parameter. This discovery has important repercussions for quantitative risk management practice, where the tail median and tail mean correspond to value-at-risk and expected shortfall, respectively. To this effect, a methodology for choosing between the two risk measures that maximizes the precision of the estimate is proposed. From an extreme value theory perspective, analogous results and procedures are discussed also for the case when the data appear to be heavy-tailed.
PubDate: 2017-06-10
DOI: 10.1007/s10479-017-2547-7

• Two-phase heuristic for SNCF rolling stock problem
• Authors: Mirsad Buljubašić; Michel Vasquez; Haris Gavranović
Abstract: A two-phase approach was adopted to solve the problem given during the ROADEF/EURO Challenge 2014 competition. The problem focuses on rolling stock management at railway sites, as defined by French Railways (SNCF). In the first phase, a train assignment problem is solved by combining a greedy heuristic procedure with integer programming. The objective is to maximize the number of assigned departures while meeting technical constraints. The second phase consists of scheduling train movements inside the station while minimizing the number of cancelled (uncovered) arrivals and departures. This schedule has to comply with resource constraints such as capacity, length, order of trains, etc. A constructive heuristic is used to build a feasible schedule, which is subject to improvement by an iterative procedure based on a local search. Experiment results, that demonstrate the effectiveness of our approach on the large scale instances provided by SNCF, are presented hereafter.
PubDate: 2017-06-08
DOI: 10.1007/s10479-017-2550-z

• Simulated annealing approach to nurse rostering benchmark and real-world
instances
• Authors: Frederik Knust; Lin Xie
Abstract: The nurse rostering problem, which addresses the task of assigning a given set of activities to nurses without violating any complex rules, has been studied extensively in the last 40 years. However, in a lot of hospitals the schedules are still created manually, as most of the research has not produced methods and software suitable for a practical application. This paper introduces a novel, flexible problem model, which can be categorized as ASBN RVNTO PLG. Two solution methods are implemented, including a MIP model to compute good bounds for the test instances and a heuristic method using the simulated annealing algorithm for practical use. Both methods are tested on the available benchmark instances and on the real-world data. The mathematical model and solution methods are integrated into a state-of-the-art duty rostering software, which is primarily used in Germany and Austria.
PubDate: 2017-06-07
DOI: 10.1007/s10479-017-2546-8

• A greedy approach for a rolling stock management problem using
multi-interval constraint propagation
• Authors: Hugo Joudrier; Florence Thiard
Abstract: In this article we present our contribution to the Rolling Stock Unit Management problem proposed for the ROADEF/EURO Challenge 2014. We propose a greedy algorithm to assign trains to departures. Our approach relies on a routing procedure using multi-interval constraint propagation to compute the individual schedules of trains within the railway station. This algorithm allows to build an initial solution, satisfying a significant subset of departures.
PubDate: 2017-06-07
DOI: 10.1007/s10479-017-2543-y

• Computing conditional sojourn time of a randomly chosen tagged customer in
a $$\textit{BMAP/MSP/}1$$ BMAP / MSP / 1 queue under random order service
discipline
• Authors: Souvik Ghosh; A. D. Banik
Abstract: This paper deals with the analysis of a single server queue with non-renewal batch arrival and non-renewal service, where the customers are selected randomly for service. The Laplace–Stieltjes transform of the waiting time distribution of a randomly chosen k-type ( $$k{\ge }1$$ ) customer, i.e., the customer who finds k ( $${\ge }1$$ ) other customers in the system at his arrival epoch, is derived using matrix-analytic (RG-factorization) technique. The expression of the expected sojourn time of a k-type ( $$k\ge 0$$ ) customer is formulated. The detailed computational procedure along with the numerical results is presented in this paper. A comparison among the random order service (ROS), first-come first-serve, egalitarian processor sharing and generalized processor sharing discipline in terms of the expected sojourn time of a k-type ( $$k\ge 0$$ ) customer is presented in the numerical section. The present study indicates that the ROS discipline may be preferred over other scheduling policies for certain correlated arrival and/or service processes.
PubDate: 2017-06-06
DOI: 10.1007/s10479-017-2534-z

• How difficult is nonlinear optimization? A practical solver tuning
approach, with illustrative results
• Authors: János D. Pintér
Abstract: Nonlinear optimization (NLO) encompasses a vast range of problems, from very simple to theoretically intractable instances. For this reason, it is impossible to offer guaranteed—while practically meaningful—advice to users of NLO software. This issue becomes apparent, when facing exceptionally hard and/or previously unexplored NLO challenges. We propose a heuristic quadratic meta-model based approach, and suggest corresponding key option settings to use with the Lipschitz global optimizer (LGO) solver suite. These LGO option settings are directly related to estimating the sufficient computational effort to handle a broad range of NLO problems. The proposed option settings are evaluated experimentally, by solving (numerically) a representative set of NLO test problems which are based on real-world optimization applications and non-trivial academic challenges. Our tests include also a set of scalable optimization problems which are increasingly difficult to handle as the size of the model-instances increases. Based on our computational results, it is possible to offer generally valid, practical advice to LGO users. Arguably (and mutatis mutandis), comparable advice can be given to users of other NLO software products with a similarly broad mandate to LGO’s. An additional benefit of such aggregated tests is that their results can effectively assist the rapid evaluation and verification of NLO solver performance during software development phases.
PubDate: 2017-06-06
DOI: 10.1007/s10479-017-2518-z

• A systematic review of humanitarian operations, humanitarian logistics and
humanitarian supply chain performance literature 2005 to 2016
• Authors: Ruth Banomyong; Paitoon Varadejsatitwong; Richard Oloruntoba
Abstract: The purpose of this manuscript is to explore methodologies for conducting comprehensive literature review. The manuscript objectives are twofold: one is to identify a suitable methodology for conducting comprehensive literature review and two is to enable the identification of main research themes and clusters obtained from the literature. The domain of humanitarian operations, logistics and supply chain performance is selected as the review context. The main strength of a systematic literature review from other styles of literature review is that it provides a much higher level of methodology to the process. It further provides a rapid comprehensive identification of main research themes and clusters as illustrated from the humanitarian operations and logistics performance domain.
PubDate: 2017-06-05
DOI: 10.1007/s10479-017-2549-5

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