Realistic Variant Research Articles

A realistic variant of bi-objective unrelated parallel machine scheduling problem: NSGA-II and MOACO approaches

The problem investigated in this study involves an unrelated parallel machine scheduling problem with sequence-dependent setup times, different release dates, machine eligibility and precedence constraints. This problem has been inspired from a realistic scheduling problem in the shipyard. The optimization criteria are to simultaneously minimize mean weighted flow time and mean weighted tardiness. To formulate this complicated problem, a new mixed-integer programming model is presented. Considering the NP-complete characteristic of this problem, two famous meta-heuristics including a non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective ant colony optimization (MOACO) which is a modified and adaptive version of BicriterionAnt algorithm are developed. Obviously, the precedence constraints increase the complexity of the scheduling problem in strong sense in order to generate feasible solutions, especially in parallel machine environment. Therefore a new corrective algorithm is proposed to obtain the feasibility in all stages of the algorithms. Due to the fact that appropriate design of parameter has a significant effect on the performance of algorithms, we calibrate the parameters of these algorithms by using new approach of Taguchi method. The performances of the proposed meta-heuristics are evaluated by a number of numerical examples. The results indicated that the suggested MOACO statistically outperformed the proposed NSGA-II in solving the test problems. In addition, the application of the proposed algorithms is justified by a real block erection scheduling problem in the shipyard.

An AO* Based Exact Algorithm for the Canadian Traveler Problem

The Canadian traveler problem (CTP) is a simple, yet challenging, stochastic optimization problem wherein an agent is given a graph where some edges are blocked with certain probabilities and the status of these edges can be disambiguated dynamically upon reaching an incident vertex. The goal is to devise a traversal policy that results in the shortest expected walk length between a given starting vertex and a termination vertex. CTP has been shown to be intractable in many broad settings. In this paper, we introduce an optimal algorithm for the problem based on a Markov decision process formulation, which is a new improvement on AO* search that takes advantage of the special problem structure in CTP. We call our algorithm CAO*, which stands for AO* with caching. CAO* uses a caching mechanism to avoid re-expansion of previously visited states and makes use of admissible upper bounds at a node level for dynamic state-space pruning. CAO* is not polynomial time, but it can dramatically shorten the execution time needed to find an exact solution for moderately sized instances. We present computational experiments on a realistic variant of the problem involving an actual maritime minefield data set.

Efficient Metaheuristics for the Mixed Team Orienteering Problem with Time Windows

Given a graph whose nodes and edges are associated with a profit, a visiting (or traversing) time and an admittance time window, the Mixed Team Orienteering Problem with Time Windows (MTOPTW) seeks for a specific number of walks spanning a subset of nodes and edges of the graph so as to maximize the overall collected profit. The visit of the included nodes and edges should take place within their respective time window and the overall duration of each walk should be below a certain threshold. In this paper we introduce the MTOPTW, which can be used for modeling a realistic variant of the Tourist Trip Design Problem where the objective is the derivation of near-optimal multiple-day itineraries for tourists visiting a destination which features several points of interest (POIs) and scenic routes. Since the MTOPTW is a NP-hard problem, we propose the first metaheuristic approaches to tackle it. The effectiveness of our algorithms is validated through a number of experiments on POI and scenic route sets compiled from the city of Athens (Greece).

A BRILS metaheuristic for non-smooth flow-shop problems with failure-risk costs

This paper analyzes a realistic variant of the Permutation Flow-Shop Problem (PFSP) by considering a non-smooth objective function that takes into account not only the traditional makespan cost but also failure-risk costs due to uninterrupted operation of machines. After completing a literature review on the issue, the paper formulates an original mathematical model to describe this new PFSP variant. Then, a Biased-Randomized Iterated Local Search (BRILS) algorithm is proposed as an efficient solving approach. An oriented (biased) random behavior is introduced in the well-known NEH heuristic to generate an initial solution. From this initial solution, the algorithm is able to generate a large number of alternative good solutions without requiring a complex setting of parameters. The relative simplicity of our approach is particularly useful in the presence of non-smooth objective functions, for which exact optimization methods may fail to reach their full potential. The gains of considering failure-risk costs during the exploration of the solution space are analyzed throughout a series of computational experiments. To promote reproducibility, these experiments are based on a set of traditional benchmark instances. Moreover, the performance of the proposed algorithm is compared against other state-of-the-art metaheuristic approaches, which have been conveniently adapted to consider failure-risk costs during the solving process. The proposed BRILS approach can be easily extended to other combinatorial optimization problems with similar non-smooth objective functions.

An ACO hybrid metaheuristic for close–open vehicle routing problems with time windows and fuzzy constraints

The close–open vehicle routing problem is a realistic variant of the “classical” vehicle routing problem where the routes can be opened and closed, i.e. all the vehicles are not required to return to the depot after completing their service. This variant is a planning model that is a standard practice in business nowadays. Companies are contracting their deliveries to other companies that hire vehicles, and payment is made based on the distance covered by the vehicles. Available information on parameters in real world situations is also imprecise, and must be included in the optimization model and method. The aims of this paper are to formulate a model of this novel variant with time windows and imprecise constraints and to propose a fuzzy optimization approach and a hybrid metaheuristic for its solutions. The full proposal is applied to a real route planning problem with outsourcing, obtaining promising practical results. Customer demands and travel times are imprecise, thus capacity and time windows constraints are considered flexible and modelled as fuzzy constraints.

Emergence of clustering in an acquaintance model without homophily

We introduce an agent-based acquaintance model in which social links are created by processes in which there is no explicit homophily. In spite of the homogeneous nature of the social interactions, highly-clustered social networks can arise. The crucial feature of our model is that of variable transitive interactions. Namely, when an agent introduces two unconnected friends, the rate at which a connection actually occurs between them depends on the number of their mutual acquaintances. As this transitive interaction rate is varied, the social network undergoes a dramatic clustering transition. Close to the transition, the network consists of a collection of well-defined communities. As a function of time, the network can also undergo an incomplete gelation transition, in which the gel, or giant cluster, does not constitute the entire network, even at infinite time. Some of the clustering properties of our model also arise, but in a more gradual manner, in Facebook networks. Finally, we discuss a more realistic variant of our original model in which network realizations can be constructed that quantitatively match Facebook networks.

Realistic variant of just-in-time flowshop scheduling: integration of L p -metric method in PSO-like algorithm

This study strives to schedule a just-in-time hybrid flowshop with sequence-dependent setup times by considering two performance measures, namely makespan and sum of the earliness and tardiness, simultaneously. The paper proposes a mixed integer programming model. However, since the simpler case with a single stage and with a single machine per stage is NP-hard, the utilization of the exact algorithms for the real-life problems is limited. Thus, this paper proposes a novel solving algorithm with a weighted Lp-metric-based framework. Since the particle swarm optimization is originally designed for continuous solution space, in this study, we modify the particle position based on our representation so that a particle position is decoded into a schedule using the largest processing time algorithm, Hadamard product, and swap operator. Furthermore, we apply a variable neighborhood search and a tabu search to improve the solution quality. This hybridization which combines the advantages of the individual components is the key innovative aspect of the approach. We investigate the performance of our algorithm in the comparison with several algorithms and show that it has a good performance.

An investigation into irreducible autocatalytic sets and power law distributed catalysis

RAF theory has been established as a useful and formal framework for studying the emergence and evolution of autocatalytic sets. Here, we present several new and additional results on RAF sets. In particular, we investigate in more detail the existence, expected sizes, and composition of the smallest possible, or irreducible, RAF sets. Furthermore, we study a more realistic variant of the well-known binary polymer model in which the catalysis events are assigned according to a power law distribution. Together, these results provide further insights into the existence and structure of autocatalytic sets in simple models of chemical reaction systems, with possible implications for theories on the origin of life.

Asymmetric Games in Monomorphic and Polymorphic Populations

Evolutionary game theory is an increasingly important way to model the evolution of biological populations. Many early models were in the form of matrix games, or bi-matrix games in asymmetric situations when individuals occupy distinct roles within the contest, where rewards are accrued through independent contests against random members of the population. More recent models have not had the simple linear properties of matrix games, and more general analysis has been required. In this paper we carry out a general analysis of asymmetric games, comparing monomorphic and polymorphic populations. We are particularly interested in situations where the strategies that individuals play influence which role that they occupy, for example in a more realistic variant of the classical Owner-Intruder game. We both prove general results and consider specific examples to illustrate the difficulties of these more complex games.

Asymmetric Games in Monomorphic and Polymorphic Populations

Evolutionary game theory is an increasingly important way to model the evolution of biological populations. Many early models were in the form of matrix games, or bi-matrix games in asymmetric situations when individuals occupy distinct roles within the contest, where rewards are accrued through independent contests against random members of the population. More recent models have not had the simple linear properties of matrix games, and more general analysis has been required. In this paper we carry out a general analysis of asymmetric games, comparing monomorphic and polymorphic populations. We are particularly interested in situations where the strategies that individuals play influence which role that they occupy, for example in a more realistic variant of the classical Owner-Intruder game. We both prove general results and consider specific examples to illustrate the difficulties of these more complex games.

Assessment of the impact of lifting device use on low back pain and musculoskeletal injury claims among nurses

ObjectivesThe aims of this study were: (1) to evaluate the effect of manually lifting patients on the occurrence of low back pain (LBP) among nurses, and (2) to estimate the...

A continuous approximation model for the fleet composition problem

This paper presents a continuous approximation model to determine the long-term vehicle fleet composition needed to perform distribution activities. The problem is a realistic variant of the vehicle routing problem, in which the fleet size and mix are also decision variables. The types of vehicles differ in terms of their capacities, fixed costs and variable costs. The objective is to minimize the total cost, subject to capacity and route duration constraints. We assume customers are distributed over a circular service region partitioned into zones, each of which is serviced by a single vehicle. The routing costs are assessed through a continuous approximation model. We present a mixed integer non-linear formulation for the problem, followed by computationally efficient upper and lower bounding procedures. The performance of the model and of its bounds is assessed on several test instances.

You Are My Only (review)

Reviewed by: You Are My Only Deborah Stevenson Kephart, Beth. You Are My Only. Geringer/Egmont, 2011. [256p]. Trade ed. ISBN 978-1-60684-272-0 $16.99 E-book ed. ISBN 978-1-60684-285-0 $16.99 Reviewed from galleys R Gr. 7–10. At fourteen, Sophie is starting to get fed up with her isolated life as the overprotected, homeschooled daughter of a single mother, and she illicitly develops a friendship with her new neighbor, Joey, and his guardian aunts. Her narration alternates with that of Emmy, a young woman who suffers a breakdown and hospitalization after her baby daughter disappears from the family backyard. Warmed by the affection of Joey’s family, an emboldened Sophie investigates her mother’s private papers and realizes that her life has been built on lies, a discovery that brings her story into conjunction with Emmy’s. This has a very different style from classic child-abduction melodramas such as Mazer’s Taking Terri Mueller (BCCB 6/83) and Ehrlich’s Where It Stops, Nobody Knows (BCCB 1/89); Kephart’s writing is a thing of beauty in its own right, and Sophie’s story earns its frequent and apt allusions to Rapunzel with its own fairy-tale quality. Emmy’s story is also compelling, but it’s effective mainly as a suspenseful counterpoint to Sophie’s journey toward recognition and reunion. There’s a Sleeping Beauty element as well in Sophie’s gradual awakening to the possibilities of life beyond her mother’s restrictions, and Joey’s aunts make excellent not-quite-fairy godmothers; Joey himself, whose wooing starts with a game of catch, is an appealingly accessible handsome prince. Readers will eat up this realistic variant of the youthful fantasy about finally finding one’s real parents and being properly appreciated. [End Page 87] Copyright © 2011 The Board of Trustees of the University of Illinois

Scheduling open shops with parallel machines to minimize total completion time

This paper explores scheduling a realistic variant of open shops with parallel machines per working stage. Since real production floors seldom employ a single machine for each operation, the regular open shop problem is very often in practice extended with a set of parallel machines at each stage. The purpose of duplicating machines in parallel is to either eliminate or to reduce the impact of bottleneck stages on the overall shop efficiency. The objective is to find the sequence which minimizes total completion times of jobs. We first formulate the problem as an effective mixed integer linear programming model, and then we employ memetic algorithms to solve the problem. We employ Taguchi method to evaluate the effects of different operators and parameters on the performance of memetic algorithm. To further enhance the memetic algorithm, we hybridize it with a simple form of simulated annealing as its local search engine. To assess the performance of the model and algorithms, we establish two computational experiments. The first one is small-sized instances by which the model and general performance of the algorithms are evaluated. The second one consists of large-sized instances by which we further evaluate the algorithms.

Including different kinds of preferences in a multi-objective ant algorithm for time and space assembly line balancing on different Nissan scenarios

Most of the decision support systems for balancing industrial assembly lines are designed to report a huge number of possible line configurations, according to several criteria. In this contribution, we tackle a more realistic variant of the classical assembly line problem formulation, time and space assembly line balancing. Our goal is to study the influence of incorporating user preferences based on Nissan automotive domain knowledge to guide the multi-objective search process with two different aims. First, to reduce the number of equally preferred assembly line configurations (i.e., solutions in the decision space) according to Nissan plants requirements. Second, to only provide the plant managers with configurations of their contextual interest in the objective space (i.e., solutions within their preferred Pareto front region) based on real-world economical variables. We face the said problem with a multi-objective ant colony optimisation algorithm. Using the real data of the Nissan Pathfinder engine, a solid empirical study is carried out to obtain the most useful solutions for the decision makers in six different Nissan scenarios around the world.

Algorithms for a realistic variant of flowshop scheduling

This paper deals with a realistic variant of flowshop scheduling, namely the hybrid flexible flowshop. A hybrid flowshop mixes the characteristics of regular flowshops and parallel machine problems by considering stages with parallel machines instead of having one single machine per stage. We also investigate the flexible version where stage skipping might occur, i.e., not all stages must be visited by all jobs. Lastly, we also consider job sequence dependent setup times per stage. The optimization criterion considered is makespan minimization. While many approaches for hybrid flowshops have been proposed, hybrid flexible flowshops have been rarely studied. The situation is even worse with the addition of sequence dependent setups. In this study, we propose two advanced algorithms that specifically deal with the flexible and setup characteristics of this problem. The first algorithm is a dynamic dispatching rule heuristic, and the second is an iterated local search metaheuristic. The proposed algorithms are evaluated by comparison against seven other high performing existing algorithms. The statistically sound results support the idea that the proposed algorithms are very competitive for the studied problem.

A hybrid artificial immune algorithm for a realistic variant of job shops to minimize the total completion time

This paper investigates an extended problem of job shop scheduling to minimize the total completion time. With aim of actualization of the scheduling problems, many researchers have recently considered realistic assumptions in their problems. Two of the most applied assumptions are to consider sequence-dependent setup times and machine availability constraints (MACs). In this paper, we deal with a specific case of MACs caused by preventive maintenance (PM) operations. Contrary to the previous papers considering fixed or/and conservative policies, we consider flexible PM operations, in which PM operations may be postponed or expedited as required. A simple technique is employed to schedule production jobs along with the flexible MACs caused by PM. To solve the given problem, we present a novel meta-heuristic method based on the artificial immune algorithm (AIA) incorporating some advanced features. For further enhancement, the proposed AIA is hybridized with a simple and fast simulated annealing (SA). To evaluate the proposed algorithms, we compare our proposed AIA with three well-known algorithms taken from the literature. Finally, we find that the proposed AIA outperforms other algorithms.

A variable neighborhood search for the capacitated arc routing problem with intermediate facilities

The capacitated arc routing problem (CARP) focuses on servicing edges of an undirected network graph. A wide spectrum of applications like mail delivery, waste collection or street maintenance outlines the relevance of this problem. A realistic variant of the CARP arises from the need of intermediate facilities (IFs) to load up or unload the service vehicle and from tour length restrictions. The proposed Variable Neighborhood Search (VNS) is a simple and robust solution technique which tackles the basic problem as well as its extensions. The VNS shows excellent results on four different benchmark sets. Particularly, for all 120 instances the best known solution could be found and in 71 cases a new best solution was achieved.

A transcription-based approach to determine the difficulty of a speech recognition task

A new parameter for estimating the difficulty of a continuous speech recognition task, called speech decoding difficulty, is presented. It is obtained from the language model defined for the recognition task and the phonetic similarity between the transcriptions of the words that make up the vocabulary used. Two variants of the proposed task difficulty measure are introduced: ideal speech decoding difficulty (ISDD), which is not influenced by practical considerations on the recognition system implemented, and a second, more realistic variant, called practical speech decoding difficulty (PSDD) to study the performance of a specific recognition system when confronting a given task.

A two-dimensional individual-based model of territorial behaviour: possible population consequences of kinship in red grouse

A two-dimensional simulation model of territory establishment and maintenance by individual male red grouse investigated whether differences in aggressive behaviour between kin and non-kin might cause population cycles or other demographic instability. Two behavioural phenotypes were envisaged. Intolerant cocks were equally aggressive to all neighbours, while kin-tolerant cocks were less aggressive to their close kin. The model's dynamics showed that: (i) pure populations of intolerants reached low but quite stable densities; whereas (ii) pure populations of kin-tolerant cocks attained higher densities with larger fluctuations; while (iii) populations of kin-tolerant cocks able to disperse showed even wider fluctuations. Mixed populations of intolerants and kin-tolerants co-existed at intermediate frequencies, with marked fluctuations. Thus kin-tolerant behaviour destabilised the model, giving it the potential to bifurcate to a cycle, but cycles were not observed. Two variants of the model altered grouse behaviour in response to changes in density, by changing either their kin tolerance or their number of dispersal attempts. Sensitivity tests showed these variants often produced density cycles, with periods around 6–15 years. We concluded that: differential aggressive behaviour between kin and non-kin during territorial disputes can destabilise a two-dimensional model population that has a life history similar to red grouse; dispersal may have a big effect on the stability of such model populations; some realistic variant models produced density cycles with periods and amplitudes comparable to those observed in some studies of wild red grouse.