Iterative approaches for solving a multi-objective 2-dimensional vector packing problem

In this paper, we address a bi-objective 2-dimensional vector packing problem (Mo2-DBPP) that calls for packing a set of items, each having two sizes in two independent dimensions, say, a weight and a height, into the minimum number of bins. The weight corresponds to a “hard” constraint that cannot be violated while the height is a “soft” constraint. The objective is to find a trade-off between the number of bins and the maximum height of a bin. This problem has various real-world applications (computer science, production planning and logistics). Based on the special structure of its Pareto front, we propose two iterative resolution approaches for solving the Mo2-DBPP. In each approach, we use several lower bounds, heuristics and metaheuristics. Computational experiments are performed on benchmarks inspired from the literature to compare the effectiveness of the two approaches.     

Shipper collaboration

The interest in collaborative logistics is fuelled by the ever increasing pressure on companies to operate more efficiently, the realization that suppliers, consumers, and even competitors, can be potential collaborative partners in logistics, and the connectivity provided by the Internet. Logistics exchanges or collaborative logistics networks use the internet as a common computing platform to implement strategies designed to reduce “hidden costs” such as asset reposition costs. Through collaboration shippers may be able to identify and submit tours with little or no asset repositioning to a carrier, as opposed to submitting individual lanes, in return for more favorable rates. In this paper, we focus on finding a set of tours connecting regularly executed truckload shipments so as to minimize asset repositioning. Mathematically, the truckload shipper collaboration problem translates into covering a subset of arcs in a directed Euclidean graph by a minimum cost set of constrained cycles. We formulate the lane covering problem, propose several solution algorithms, and conduct a computational study on the effectiveness of these methodologies.     

A system dynamics model for dynamic capacity planning of remanufacturing in closed-loop supply chains

Product recovery operations in reverse supply chains face continually and rapidly changing product demand characterized by an ever increasing number of product offerings with reduced lifecycles due to both technological advancements and environmental concerns. Capacity planning is a strategic issue of increased complexity importance for the profitability of reverse supply chains due to their highly variable return flows. In this work we tackle the development of efficient capacity planning policies for remanufacturing facilities in reverse supply chains, taking into account not only economic but also environmental issues, such as the take-back obligation imposed by legislation and the “green image” effect on customer demand. The behavior of the generic system under study is analyzed through a simulation model based on the principles of the system dynamics methodology. The simulation model provides an experimental tool, which can be used to evaluate alternative long-term capacity planning policies (“what-if” analysis) using total supply chain profit as measure of policy effectiveness. Validation and numerical experimentation further illustrate the applicability of the developed methodology, while providing additional intuitively sound insights.     

Monte Carlo simulations of the 2d-Ising model in the geometry of a long stripe

The two-dimensional Ising model in the geometry of a long stripe can be regarded as a model system for the study of nanopores. As a quasi-one-dimensional system, it also exhibits a rather interesting “phase behavior”: At low temperatures the stripe is either filled with “liquid” or “gas” and “densities” are similar to those in the bulk. When we approach a “pseudo-critical point” (below the critical point of the bulk) at which the correlation length becomes comparable to the length of the stripe, several interfaces emerge and the systems contains multiple “liquid” and “gas” domains. The transition depends on the size of the stripe and occurs at lower temperatures for larger stripes. Our results are corroborated by simulations of the three-dimensional Asakura–Oosawa model in cylindrical geometry, which displays qualitatively similar behavior. Thus our simulations explain the physical basis for the occurrence of “hysteresis critical points” in corresponding experiments.     

Robotic vocabulary building using extension inference and implicit contrast

TWIG (“Transportable Word Intension Generator”) is a system that allows a robot to learn compositional meanings for new words that are grounded in its sensory capabilities. The system is novel in its use of logical semantics to infer which entities in the environment are the referents (extensions) of unfamiliar words; its ability to learn the meanings of deictic (“I,” “this”) pronouns in a real sensory environment; its use of decision trees to implicitly contrast new word definitions with existing ones, thereby creating more complex definitions than if each word were treated as a separate learning problem; and its ability to use words learned in an unsupervised manner in complete grammatical sentences for production, comprehension, or referent inference. In an experiment with a physically embodied robot, TWIG learns grounded meanings for the words “I” and “you,” learns that “this” and “that” refer to objects of varying proximity, that “he” is someone talked about in the third person, and that “above” and “below” refer to height differences between objects. Follow-up experiments demonstrate the system's ability to learn different conjugations of “to be”; show that removing either the extension inference or implicit contrast components of the system results in worse definitions; and demonstrate how decision trees can be used to model shifts in meaning based on context in the case of color words.     

Volume discounting coordinates a supply chain effectively when demand is sensitive to both price and sales effort

In this paper, we use a simple and parsimonious model to investigate the performance of volume discounting schemes (hereafter “[VD]”) in a supply chain where the market demand is sensitive to both retail price “p” and sales effort “e” — hereafter called a “(p,e)-channel.” The problem is analyzed as a manufacturer-leading Stackelberg game. We first present, for the deterministic-system-parameter situation, contract-designing procedures under two contract formats; namely, a “regular” version of [VD] (hereafter “[RVD]”) and a “continuous” version of [VD] (hereafter “[CVD]”). Our solutions show that [RVD] cannot perfectly coordinate this (p,e)-sensitive channel; moreover, very often [RVD] leads to a lower channel efficiency than the simple price-only contract. In contrast, we show that [CVD] leads to perfect channel coordination — a significant result since most contract formats have been shown in the literature to be unable to coordinate a (p,e)-channel. Next, we consider the more realistic situations in which the manufacturer is uncertain about one of the system parameters — specifically, either the market size “a” or the effort cost “η”. Our results show that, if Manu is uncertain about a, [RVD] becomes useless but the manufacturer can still use [CVD] to benefit himself. When the manufacturer is uncertain about η, [CVD] remains useful (as expected); however, surprisingly, [RVD] can outperform [CVD] when both the mean value and the uncertainty of η are sufficient high. These results underline the necessity of evaluating a contract format under various forms of system-parameter uncertainties — often at the expense of analytical tractability.     

Simultaneous performance achievement via compensator blending

In this paper we consider the problem of designing a state-feedback controller that simultaneously achieves different optimality criteria defined on different input-output pairs. Precisely, if r “optimal” target transfer functions are given (as the result of local “optimal” controllers), it is shown that (under mild assumptions) there exists a unique controller capable of replicating these transfer functions in the closed-loop system, so simultaneously achieving the performances inherited by the chosen local transfer functions. An explicit and constructive procedure (we refer to such procedure as “compensator blending”) is provided. The possibility of designing a stable blending compensator or the generalization to dynamic local controllers or time varying systems are also discussed. We finally consider the dual version of the problem, precisely, we show how to achieve simultaneous optimality by filter blending.     

OWA trees and their role in security modeling using attack trees

We introduce the idea of an OWA node as an extension of the “and/or” node and use them to generalize “and/or” trees to OWA trees. We provide a semantics for an OWA node. Specifically while an “or” node requires only one of the children to be satisfied and an “and” node requires “all” the children be satisfied the OWA node allows us to model situations in which there is some probabilistic uncertainty in the number of children that need be satisfied. We then use these OWA nodes in the security related problem of constructing attack trees. Techniques for the evaluation of an OWA attack tree for the overall probability of success and cost of an attack are provided. A method is provided for describing different types of attackers.     

A system dynamics framework for integrated reverse supply chain with three way recovery and product exchange policy

With the technological advancements and rapid changes in demand pattern, diverse ranges of products are entering into the market with reduced lifecycle which leads to the environmental disasters. The awareness of product take-back and recovery has been increasing in various supply chains not only due to the obligation imposed by legislation but also competitive economics worldwide. In this paper, we develop a system dynamics framework for a closed-loop supply chain network with product exchange and three way recovery policy, namely; product remanufacturing, component reuse and remanufacturing, and raw material recovery. In the simulation study, we investigate the significance of various factors including product exchange, collection and remanufacturing; their interactions and the type of their impact on bullwhip and profitability through sensitivity and statistical analysis. Our results suggest that the inclusion of three way recovery in reverse channel and product exchange policy in the forward channel reduce the order variation and bullwhip effect at both retailer and distributor level and increases the profitability of RL operations. In addition, we redefine the reverse logistics framework with “open-loop” in which the remanufactured products are redistributed only in the secondary market and compare the performance of open-loop model with that of closed-loop. Extended numerical investigation provides insights to the decision makers regarding the actions which can lead to better performance of the system.     

Chunky and equal-spaced polynomial multiplication

Finding the product of two polynomials is an essential and basic problem in computer algebra. While most previous results have focused on the worst-case complexity, we instead employ the technique of adaptive analysis to give an improvement in many “easy” cases. We present two adaptive measures and methods for polynomial multiplication, and also show how to effectively combine them to gain both advantages. One useful feature of these algorithms is that they essentially provide a gradient between existing “sparse” and “dense” methods. We prove that these approaches provide significant improvements in many cases but in the worst case are still comparable to the fastest existing algorithms.     

An investigation of mating and population maintenance strategies in hybrid genetic heuristics for product line designs

This research builds on prior work on developing near optimal solutions to the product line design problems within the conjoint analysis framework. In this research, we investigate and compare different genetic algorithm operators; in particular, we examine systematically the impact of employing alternative population maintenance strategies and mutation techniques within our problem context. Two alternative population maintenance methods, that we term “Emigration” and “Malthusian” strategies, are deployed to govern how individual product lines in one generation are carried over to the next generation. We also allow for two different types of reproduction methods termed “Equal Opportunity” in which the parents to be paired for mating are selected with equal opportunity and a second based on always choosing the best string in the current generation as one of the parents which is referred to as the “Queen bee”, while the other parent is randomly selected from the set of parent strings. We also look at the impact of integrating the artificial intelligence approach with a traditional optimization approach by seeding the GA with solutions obtained from a Dynamic Programming heuristic proposed by others. A detailed statistical analysis is also carried out to determine the impact of various problem and technique aspects on multiple measures of performance through means of a Monte Carlo simulation study. Our results indicate that such proposed procedures are able to provide multiple “good” solutions. This provides more flexibility for the decision makers as they now have the opportunity to select from a number of very good product lines. The results obtained using our approaches are encouraging, with statistically significant improvements averaging 5% or more, when compared to the traditional benchmark of the heuristic dynamic programming technique.     

An optimization model for reverse logistics network under stochastic environment by using genetic algorithm

Recovery of used products has become increasingly important recently due to economic reasons and growing environmental or legislative concern. Product recovery, which comprises reuse, remanufacturing and materials recycling, requires an efficient reverse logistic network. One of the main characteristics of reverse logistics network problem is uncertainty that further amplifies the complexity of the problem. The degree of uncertainty in terms of the capacities, demands and quantity of products exists in reverse logistics parameters. With consideration of the factors noted above, this paper proposes a probabilistic mixed integer linear programming model for the design of a reverse logistics network. This probabilistic model is first converted into an equivalent deterministic model. In this paper we proposed multi-product, multi-stage reverse logistics network problem for the return products to determine not only the subsets of disassembly centers and processing centers to be opened, but also the transportation strategy that will satisfy demand imposed by manufacturing centers and recycling centers with minimum fixed opening cost and total shipping cost. Then, we propose priority based genetic algorithm to find reverse logistics network to satisfy the demand imposed by manufacturing centers and recycling centers with minimum total cost under uncertainty condition. Finally, we apply the proposed model to a numerical example.     

Active fault tolerant control of discrete event systems using online diagnostics

The aim of this paper is to deal with the problem of fault tolerant control in the framework of discrete event systems modeled as automata. A fault tolerant controller is a controller able to satisfy control specifications both in nominal operation and after the occurrence of a fault. This task is solved by means of a parameterized controller that is suitably updated on the basis of the information provided by online diagnostics: the supervisor actively reacts to the detection of a malfunctioning component in order to eventually meet degraded control specifications. Starting from an appropriate model of the system, we recall the notion of safe diagnosability as a necessary step in order to achieve fault tolerant control. We then introduce two new notions: (i) “safe controllability”, which represents the capability, after the occurrence of a fault, of steering the system away from forbidden zones and (ii) “active fault tolerant system”, which is the property of safely continuing operation after faults. Finally, we show how the problem can be solved using a general control architecture based on the use of special kind of diagnoser, called “diagnosing controller”, which is used to safely detect faults and to switch between the nominal control policy and a bank of reconfigured control policies. A simple example is used to illustrate the new notions and the control architecture introduced in the paper.     

FOE estimation: Can image measurement errors be totally “corrected” by the geometric method?

For the FOE estimation, there are basically three kinds of estimation methods in the literature: algebraic, geometric, and the maximum likelihood-based ones. In this paper, our attention is focused on the geometric method. The computational complexity of the classical geometric method is usually very high because it needs to solve a non-linear minimum problem with many variables. In this work, such a minimum problem is converted into an equivalent one with only two variables and accordingly a simplified geometric method is proposed. Based on the equivalence of the classical geometric method and the proposed simplified geometric method, we show that the measurement errors can at most be “corrected” only in one of the two images by geometric methods. In other words, it is impossible to correct the measurement errors in both of the two images. In addition, we show that the “corrected” corresponding pairs by geometric methods cannot in general meet some of the inherent constraints of corresponding pairs under pure camera translations. Hence, it is not proper to consider the “corrected” corresponding pairs as “faithful” corresponding pairs in geometric methods, and the estimated FOE from such pairs is not necessarily trustworthier. Finally, a new geometric algorithm, which automatically enforces the inherent constraints, is proposed in this work, and better FOE estimation and more faithful corresponding pairs are obtained.     

On the tractability of coloring semirandom graphs

As part of the efforts to understand the intricacies of the k-colorability problem, different distributions over k-colorable graphs have been analyzed. While the problem is notoriously hard (not even reasonably approximable) in the worst case, the average case (with respect to such distributions) often turns out to be “easy”. Semi-random models mediate between these two extremes and are more suitable to imitate “real-life” instances than purely random models. In this work we consider semi-random variants of the planted k-colorability distribution. This continues a line of research pursued by Coja-Oghlan, and by Krivelevich and Vilenchik. Our aim is to study a more general semi-random framework than those suggested so far. On the one hand we show that previous algorithmic techniques extend to our more general semi-random setting; on the other hand we give a hardness result, proving that a closely related semi-random model is intractable. Thus we provide some indication about which properties of the input distribution make the k-colorability problem hard.     

Solving a non-linear model: The importance of model specification for deriving a suitable solution

In this paper, we consider a macroeconomic model with alternative linear and non-linear specifications. One version of the model, expressed in levels, is highly non-linear and has at least two steady-state equilibria. One of these equilibria has an economically meaningful interpretation, while the other does not have a sensible economic interpretation. A second version of the model, expressed in logarithms, is linear and has a unique steady-state equilibrium, which corresponds to the economically meaningful equilibrium of the non-linear version of the model. The dynamic solution of each model version has a combination of stable and unstable eigenvalues so that any dynamic solution requires the calculation of appropriate “jumps” in endogenous variables. Attempts to solve these models, using forward-shooting and reverse-shooting algorithms, show that the forward-shooting algorithm chooses the “wrong” solution for the non-linear model, but the “right” solution for the linear model. The reverse-shooting algorithm chooses the “right” solution in both cases. We demonstrate how this result is driven by particular properties of the two versions of the model.     

Clarifying the semantics of value in use cases through Jackson's Problem Frames

Use cases constitute a popular technique to problem analysis, partly due to their focus on thinking in terms of the user needs. However this is not a guarantee for discovering all the subproblems that compose the structure of a given software problem. Moreover, a rigorous application of the technique requires a previous consensus about the meaning of I. Jacobson's statement “a use case must give a measurable value to a particular actor” (The Rational Edge, March 2003). This paper proposes a particular characterisation of the concept of “value” with the purpose of problem structuring. To this aim we base on the catalogue of frames for real software problems proposed by M. Jackson (Problem Frames, 2001) and we reason about what could be valuable for the user on each problem class. We illustrate our technique with the analysis of a web auction problem.     

Monte Carlo simulations of the HP model (the "Ising model" of protein folding)

Using Wang–Landau sampling with suitable Monte Carlo trial moves (pull moves and bond-rebridging moves combined) we have determined the density of states and thermodynamic properties for a short sequence of the HP protein model. For free chains these proteins are known to first undergo a collapse “transition” to a globule state followed by a second “transition” into a native state. When placed in the proximity of an attractive surface, there is a competition between surface adsorption and folding that leads to an intriguing sequence of “transitions”. These transitions depend upon the relative interaction strengths and are largely inaccessible to “standard” Monte Carlo methods.