Abstract-Many random events usually are associated with executions of operational plans at various companies and organizations. For example, some tasks might be delayed and/or executed earlier. Some operational constraints can be introduced due to new regulations or business rules. In some cases, there might be a shift in the relative importance of objectives associated with these plans. All these potential modifications create a huge pressure on planning staff for generating plans that can adapt quickly to changes in environment during execution. In this paper we address adaptation in dynamic environments.Many researchers in evolutionary community addressed the problem of optimization in dynamic environments. Through an overview on applying evolutionary algorithms for solving dynamic optimization problems, we classify the work into two main categories: (1) finding/tracking optima and (2) adaptation and we discuss their relevance for solving planning problems. Based on this discussion, we propose a computational approach to adaptation within the context of planning. This approach models the dynamic planning problem as a multi-objective optimization problem and an evolutionary mechanism is incorporated, this adapts the current solution to new situations when a change occurs.As the multi-objective model is used, the proposed approach produces a set of non-dominated solutions after each planning cycle. This set of solutions can be perceived as an informationrich data set which can be used to support the adaptation process against the effect of changes. The main question is how to exploit this set efficiently? In this paper we propose a method based on the concept of centroids over a number of changing-time steps, at each step we obtain a set of non-dominated solutions.We carried out a case study on this proposed approach. Mission planning was used for our experiments and experimental analysis. We selected mission planning as our test environment because battlefields are always highly dynamic and uncertain and can be conveniently used to demonstrate different types of changes, especially time-varying constraints. The obtained results support the significance of our centroid-based approach.

In this paper, we discuss interactions between XP (eXtreme Programming) practices. We discuss 2 case studies of introducing XP practices selectively from the 13 practices which are defined in XP, and we analyze how to select practices. Our analysis is based on interviews with developers. While it is difficult to introduce all the XP practices at once, our knowledge makes it easier to determine more effective combinations of practices.

Motivated by a real world supply chain planning problem, this paper examines the impacts of exchange rate volatility and different shipping pricing structures on the overall performance for a multinational enterprise (MNEs). A unified optimisation model is developed that minimises the major costs incurred in an MNE, incorporating important factors such as manufacturing localisation, exchange rates, and quantity-based shipping pricing structures offered by transport vendors. Numerical results from the model implementation show that (a) shipping pricing structures can have pronounced impacts on manufacturing/assembly and distribution decisions both in the parent and host countries, (b) different shipping pricing structures might not be equally profitable as the cost benefits may only offset the losses from a discriminatory tariff or substantial storage expenses, and (c) increased manufacturing in the host country can be decelerated to a large extent by the localisation policy in the parent country and tight transport and storage limitations.