A memetic algorithm for bi-objective integrated forward/reverse logistics network design

Logistics network design is a major strategic issue due to its impact on the efficiency and responsiveness of the supply chain. This paper proposes a model for integrated logistics network design to avoid the sub-optimality caused by a separate, sequential design of forward and reverse logistics networks. First, a bi-objective mixed integer programming formulation is developed to minimize the total costs and maximize the responsiveness of a logistics network. To find the set of non-dominated solutions, an efficient multi-objective memetic algorithm is developed. The proposed solution algorithm uses a new dynamic search strategy by employing three different local searches. To assess the quality of the novel solution approach, the quality of its Pareto-optimal solutions is compared to those generated by an existing powerful multi-objective genetic algorithm from the recent literature and to exact solutions obtained by a commercial solver.     

MamMoeT: An intelligent agent-based communication support platform for multimodal transport

In this paper, an intelligent agent-based communication support platform for multimodal transport is developed. The rationale for doing so is found in the potential of such a system to increase cost efficiency, service and safety for different transport-related actors. Although, at present several comparable systems exist, their current implementation is far from successful because of technological and economic obstacles. The new expert communication platform put forward here (called MamMoeT) addresses these two issues by using a software agent-based approach. Software agents are pieces of software representing a single user. They are autonomous, communicative and intelligent. The MamMoeT system developed can be described as a real-time decision support system in which intelligent software agents handle communicative tasks, exchange desired amounts of information among different users using common exchange protocols which act as translators between different systems.     

Hybrid Evolutionary Metaheuristics for Concurrent Multi-Objective Design of Urban Road and Public Transit Networks

This paper addresses a bi-modal multi-objective discrete urban road network design problem with automobile and bus flow interaction. The problem considers the concurrent urban road and bus network design in which the authorities play a major role in designing bus network topology. The road network design deals with the decision making for new street constructions, lane additions to existing streets, lane allocations for two-way streets, and the orientations and locations of one-way streets. The bus network design is performed by keeping the terminal stations of the existing bus lines unchanged and redesigning the forth and back routes of each line. Four measures, namely user benefit, the demand coverage of the bus network, the demand share of the bus mode, and the average travel generalized cost of bus passengers, are used to evaluate the network design scenarios. The problem is formulated as a multi-objective optimization model in which a modal-split/assignment model is included to depict the mode and route choice behaviors of travelers. The model is solved by the hybrid genetic algorithm and the hybrid clonal selection algorithm. The performance of these algorithms is presented and investigated by solving a number of test networks.     

A mechanistic study of the effect of pigment loading on the appearance of powder coatings: The effect of surface topography on the optical properties of powder coatings: Modelling and experimental results

Gloss is a critical property for many powder coating applications and is related to the amount of light reflected by the coating layer on a substrate. Gloss of powder coatings can, depending on the composition, vary from excellent to matt. It is well known in the powder coatings industry that increasing loadings of pigment, especially TiO₂, causes a detrimental loss of gloss. In order to understand the cause of this phenomenon two questions have to be addressed: firstly, what is the relation between the optical properties and the surface topography of the powder coating and, secondly, how do the powder coating composition and curing conditions affect the surface topography? In order to answer the first question, the typical features of the surface of a cured powder coating have been studied in detail. Using a white light interferometer, it has been shown that the surface topography consists of both short wave and long wave patterns. Each of these patterns could be described by using two statistical parameters only, the root mean square height of the roughness and its correlation length. The effect of both the short and long wave roughness on the gloss has been simulated with a single wave pattern model, based on an approximation of the Kirchoff scattering theory [9]. These simulations illustrated that neither the short nor the long wave roughness on itself determines the optical properties. In order to quantify the combined effect of the long and short wave surface features a two-scale modelling approach was followed. The predictions of this model were in good agreement with experimental gloss data of coatings containing different amounts and types of pigments.     

Safety stock placement problem in capacitated supply chains

Today's highly competitive business environment forces supply chain managers to maintain high service levels while keeping inventory-related costs as low as possible. Therefore, placing the right amount of safety stock at the right places in the supply chain is an important aspect of effective inventory management. This safety stock placement problem, for which some solution strategies have been proposed in the case of uncapacitated supply chains, becomes much more complicated when, in addition to the variability of the demand, capacity constraints also come into play. In this paper we propose a model to locate safety stocks in a capacitated supply chain with the objective of maintaining the required service level. The underlying relationships linking excess capacity, demand variability, and service levels are analysed to gain deeper understanding of the safety stock placement problem in capacitated supply chains. Based on these relationships a solution approach for the problem is proposed and is tested with Monte Carlo simulation.     

An enriched model for the integrated berth allocation and quay crane assignment problem

Given the increasing pressure to improve the efficiency of container terminals, a lot of research efforts have been devoted to optimizing container terminal operations. Most papers deal with either the berth allocation problem (BAP) or the (quay) crane assignment problem (CAP). In the literature on the BAP, handling times are often simplified to be berth dependent or proportional to vessel size, so the CAP can be ignored when scheduling vessels. This is unsatisfactory for real-life applications because the handling time primarily depends on the number of containers to be handled and the number of cranes deployed. Only a limited number of papers deals with the combination of berth allocation and crane assignment. In these papers however, authors often have resorted to algorithmic simplifications that limit the practical use of the models. This paper presents a MILP model for the integrated BAP–CAP taking into account vessel priorities, preferred berthing locations and handling time considerations. The model is used in a hybrid heuristic solution procedure that is validated on real-life data illustrating the potential to support operational and tactical decision-making.     

Multifractal features of spot rates in the Liquid Petroleum Gas shipping market

We investigate for the first time the spot rate dynamics of Very Large Gas Carriers (VLGCs) by means of multifractal detrended fluctuation analysis (MF-DFA) and rescaled range (R/S) analysis. Both non-parametric methods allow for a rigorous statistical analysis of the freight process by detecting correlation, scaling and fluctuation behavior regardless of nonlinearity issues. By applying different data-frequencies and a temporal framework, the Hurst exponents indicate that freight rates exhibit trend-reinforcement and persistence subject to limited time-dependency and controlled volatility. The found long-range dependence corroborates that a predictive freight model can be built undermining the efficient market hypothesis. Memory effects seem to each time build up until they are interrupted by seasonal transitions, stochastic events or cycles which all spark a sudden loss in correlations or increase in nonlinearities. The surrogate and shuffling data procedures demonstrate that, dependent on the data-frequency used, memory effects and fat-tail distributions should be contained differently in freight rate models.     

A penalty-based edge assembly memetic algorithm for the vehicle routing problem with time windows

In this paper, we present an effective memetic algorithm for the vehicle routing problem with time windows (VRPTW). The paper builds upon an existing edge assembly crossover (EAX) developed for the capacitated VRP. The adjustments of the EAX operator and the introduction of a novel penalty function to eliminate violations of the time window constraint as well as the capacity constraint from offspring solutions generated by the EAX operator have proven essential to the heuristic's performance. Experimental results on Solomon's and Gehring and Homberger benchmarks demonstrate that our algorithm outperforms previous approaches and is able to improve 184 best-known solutions out of 356 instances.     

A hybrid approach to designing inbound-resupply strategies

We model the behavior of the demand distribution and lead-time distribution separately and determine the value of demand during lead time by discrete-event simulation. A new methodology determines the optimal inbound-resupply strategy when suppliers ship goods to receivers. An evolutionary metaheuristic uses discrete-event simulation to calculate safety stock levels and minimize total logistics costs.     

A multi-attribute Systemic Risk Index for comparing and prioritizing chemical industrial areas

Measures taken to decrease interdependent risks within chemical industrial areas should be based on quantitative data from a holistic (cluster-based) point of view. Therefore, this paper examines the typology of networks representing industrial areas to formulate recommendations to more effectively protect a chemical cluster against existing systemic risks. Chemical industrial areas are modeled as two distinct complex networks and are prioritized by computing two sub-indices with respect to existing systemic safety and security risks (using Domino Danger Units) and supply chain risks (using units from an ordinal expert scale). Subsequently, a Systemic Risk Index for the industrial area is determined employing the Borda algorithm, whereby the systemic risk index considers both a safety and security network risk index and a supply chain network risk index. The developed method allows decreasing systemic risks within chemical industrial areas from a holistic (inter-organizational and/or inter-cluster) perspective. An illustrative example is given.