Faculty of Science

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Intelligent Optimization Algorithms: A Stochastic Closed-Loop Supply Chain Network Problem Involving Oligopolistic Competition for Multiproducts and Their Product Flow Routings.
(Hindawi Publishing Corporation, 2015-10-26) Zhou, Y.; Chan, C.K.; Wong, K.H.; Lee, Y.C.E.
Recently, the first oligopolistic competition model of the closed-loop supply chain network involving uncertain demand and return has been established. This model belongs to the context of oligopolistic firms that compete noncooperatively in a Cournot-Nash framework. In this paper, we modify the above model in two different directions. (i) For each returned product from demand market to firm in the reverse logistics, we calculate the percentage of its optimal product flows in each individual path connecting the demand market to the firm. This modification provides the optimal product flow routings for each product in the supply chain and increases the optimal profit of each firm at the Cournot-Nash equilibrium. (ii) Our model extends the method of finding the Cournot-Nash equilibrium involving smooth objective functions to problems involving nondifferentiable objective functions. This modification caters for more real-life applications as a lot of supply chain problems involve nonsmooth functions. Existence of the Cournot-Nash equilibrium is established without the assumption of differentiability of the given functions. Intelligent algorithms, such as the particle swarm optimization algorithm and the genetic algorithm, are applied to find the Cournot-Nash equilibrium for such nonsmooth problems. Numerical examples are solved to illustrate the efficiency of these algorithms.
Spatially nested niche partitioning between syntopic grazers at foraging arena scale within overlapping home ranges.
(Ecological Society of America, 2015-09) Owen-Smith, N.; Martin, J.; Yoganand, K.
Niche separation among species with similar resource requirements can be expressed at various spatiotemporal scales, from the resource components selected at feeding sites to habitat and home range occupation and ultimately geographic distribution ranges. African large herbivores present a challenge to niche theory because multiple species commonly overlap both spatially and in vegetation components consumed. Aided by GPS telemetry, we investigated the space use patterns of two large grazers that are frequently associated in mixed-species aggregations. Specifically, we compared a generalist grazer with hindgut fermentation (plains zebra) with a similar-sized grazing ruminant (blue wildebeest) in west-central Kruger National Park, South Africa. We found that herds of the two species overlapped substantially in the home ranges that they occupied, but exploited spatially distinct foraging arenas for periods lasting several days or weeks within these ranges. Moreover, wildebeest and zebra differed in duration of settlement, extent of areas occupied during settlement, consequent exploitation intensity per unit area, proportion of time spent within foraging arenas relative to roaming interludes, and movement rates while within these arenas. In particular, wildebeest herds concentrated within small areas for prolonged periods, while zebra herds used more foraging arenas but exploited them for briefer periods. Both species overlapped substantially in habitat use, although wildebeest more strongly favored gabbro uplands and sodic sites presenting short grass lawns while zebra made greater use of areas with a taller grass cover. Hence resource partitioning was expressed mainly through behavioral distinctions in patch exploitation at foraging arena scale rather than in home range or habitat separation. Although zebra may have been partially excluded from the grasslands kept short by wildebeest, these sites formed only a small part of the wider ranges utilized by zebra, thereby restricting the competitive consequences. Hence spatially nested resource partitioning of this form contributes to the coexistence of these two grazers, and may be a mechanism enabling niche separation among other species.

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