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In previous work, we proposed and studied a new dynamic formulation of the Location-allocation (LA) problem called the Dynamic-Customer Location-allocation (DC-LA) problem. DC-LA is based on the idea of changes in customer distribution over a defined period, and these changes have to be taken into account when establishing facilities to service changing customers distributions. This necessitated a dynamic stochastic evaluation function Which came with a high computational cost due to a large number of simulations required in the evaluation process.In this paper, we investigate the use of racing, an approach used in model selection, to reduce the high computational cost by employing the minimum number of simulations for solution selection. Our adaptation of racing uses the Friedman test to compare solutions statistically. Racing allows simulations to be performed iteratively, ensuring that the minimum number of simulations is performed to detect a statistical difference.We present experiments using Population-Based Incremental Learning (PBIL) to explore the savings achievable from using racing in this way. Our results show that racing achieves improved cost savings over the dynamic stochastic evaluation function. We also observed that on average, the computational cost of racing was about 4.5 times loWer than the computational cost of the full dynamic stochastic evaluation.

In this paper, we introduce a new stochastic Location-Allocation Problem which assumes the movement of customers over time. We call this new problem Dynamic Customer Location-Allocation Problem (DC-LAP). The problem is based on the idea that customers will change locations over a defined horizon and these changes have to be taken into account when establishing facilities to service customers demands. We generate 1440 problem instances by varying the problem parameters of movement rate which determines the possible number of times a customer will change locations over the defined period, the number of facilities and the number of customers. We propose to analyse the characteristics of the instances generated by testing a search algorithm using the stochastic dynamic evaluation (based on the replication of customer movement scenarios) and a deterministic static evaluation (based on the assumption that customer will not move over time). We show that the dynamic approach obtains globally better results, but the performances are highly related to the parameters of the problem. Moreover, the dynamic approach involves a significantly high computational overhead.

The Uncapacitated Location-Allocation problem (ULAP) is a major optimisation problem concerning the determination of the optimal location of facilities and the allocation of demand to them. In this paper, we present two novel problem variants of Non-Linear ULAP motivated by a real-world problem from the telecommunication industry: Uncapacitated Location-Allocation Resilience problem (ULARP) and Uncapacitated Location-Allocation Resilience problem with Restrictions (ULARPR). Problem sizes ranging from 16 to 100 facilities by 50 to 10000 demand points are considered. To solve the problems, we explore the components and configurations of four Genetic Algorithms [1]–[3] and [4] selected from the ULAP literature. We aim to understand the contribution each choice makes to the GA performance and so hope to design an Optimal GA configuration for the novel problems. We also conduct comparative experiments with Population-Based Incremental Learning (PBIL) Algorithm on ULAP. We show the effectiveness of PBIL and GA with parameter set: random and heuristic initialisation, tournament and fined_grained tournament selection, uniform crossover and bitflip mutation in solving the proposed problems.

In this paper, we introduce a dynamic variant of the Location-Allocation problem: Dynamic Location-Allocation Problem (DULAP). DULAP involves the location of facilities to service a set of customer demands over a defined horizon. To evaluate a solution to DULAP, we propose two holistic metric approaches: Static and Dynamic Approach. In the static approach, a solution is evaluated with the assumption that customer locations and demand remain constant over a defined horizon. In the dynamic approach, the assumption is made that customer demand, and demographic pattern may change over the defined horizon. We introduce a stochastic model to simulate customer population and distribution over time. We use a Genetic Algorithm and Population-Based Incremental Learning algorithm used in previous work to find robust and satisfactory solutions to DULAP. Results show the dynamic approach of evaluating a solution finds good and robust solutions.

Virtual reality technology is argued to be suitable to the simulation study of mass evacuation behavior, because of the practical and ethical constraints in researching this field. This article describes three studies in which a new virtual reality paradigm was used, in which participants had to escape from a burning underground rail station. Study 1 was carried out in an immersion laboratory and demonstrated that collective identification in the crowd was enhanced by the (shared) threat embodied in emergency itself. In Study 2, high-identification participants were more helpful and pushed less than did low-identification participants. In Study 3, identification and group size were experimentally manipulated, and similar results were obtained. These results support a hypothesis according to which (emergent) collective identity motivates solidarity with strangers. It is concluded that the virtual reality technology developed here represents a promising start, although more can be done to embed it in a traditional psychology laboratory setting.

International audience; In this work, we performed computer simulations of halothane embedded in a fully hydrated dimyristoylphosphatidylcholine (DMPC) membrane. Since the action of general anaesthetics was known to be pressure dependent, we carried out the simulations performed under physiological conditions and also at elevated pressures (i.e., 2 × 10 7 Pa and 4 × 10 7 Pa). The results clearly show that at high pressures the halothane molecules tend to cluster together. Based on these results, we propose a possible mechanism for the pressure reversal of anaesthesia.