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The present study is to evaluate the performance of different aggregate production planning (APP) strategies in presence of uncertainty. Therefore, the relevant models for APP strategies including the pure chase, the pure level, the modified chase, the modified level and the mixed chase and level strategies are constructed by using both multi-objective programming and simulation methods.The models constructed for these strategies are run with respect to the corresponding objectives/criteria in order to provide business insights to operations managers about the effectiveness and practicality of various APP strategies in presence of uncertainty. The real world operational data are collected from soft drink industry to validate and implement the models.In addition, multiple criteria decision making (MCDM) methods are used besides multi-objective optimisation to assess the overall performance of each APP strategy. A detailed sensitivity analysis is also conducted by changing the criteria weights in MCDM methods to evaluate the impacts that these weight changes can have on the final rank of each APP strategy.The results of the simulation models are compared to those of multi-objective optimisation models. In general, in both mathematical programming and simulation models, the pure chase and the modified chase strategies presented the best performance, followed by the pure level strategy.

One of the fundamental problems in supply chain management is to design the effective inventory control policies for models with stochastic demands because efficient inventory management can both maintain a high customers’ service level and reduce unnecessary over and under-stock expenses which are significant key factors of profit or loss of an organization. In this study, a new formulation of an inventory system is analyzed under discrete Markov-modulated demand. We employ simulation-based optimization that combines simulated annealing pattern search and ranking selection (SAPS&RS) methods to approximate near-optimal solutions of this problem. After determining the values of demand, we employ novel approach to achieve minimum cost of total SCM (Supply Chain Management) network. In our proposed approach, hybrid improved cuckoo search algorithm (ICS) and genetic algorithm (GA) are presented as main platform to solve this problem. The computational results demonstrate the effectiveness and applicability of the proposed approach.

From its generation to utilization, some of the electrical energy gets wasted in the process. This loss of energy occurs due to various reasons, one of which is energy loss in distribution networks. Considering the high cost of power generation, it is important to identify factors causing this loss. This study was carried out with the objective of identifying energy loss factors and the importance of each factor. Lack of identification for factors stealing energy, network deterioration, amount of electrical load and the impact of such factors that can have significant influence on energy loss could diverge the path of energy management. Thus, the main objective of this study was to reduce energy loss and its additional costs by developing the concept of identifying influential factors and measuring the effect of each factor especially in different regions. The statistical population of this study comprised of power and energy experts and university professors. The statistical sample included 12 energy experts and their opinions were collected using questionnaires and paired comparisons. Weights of criteria were determined using SWARA technique. COPRAS-G technique was used for measuring the importance of criteria for Bushehr province distribution networks. The importance of criteria are: energy theft, measurement error, amount of load, network deterioration, loose fittings, improper placement of equipment, the amount of voltage, conductor resistance, equipment casualty, location and size of the capacitor, geographical conditions, Size and dimensions of the conductor, leakage, and network arrangements respectively. Distribution network of Assaluyeh region had the highest energy losses.

The Project Management Body of Knowledge (PMBOK) is a group of processes and knowledge fields which are generally accepted as greatest practice within the project management discipline. The PMBOK Guide is also an internationally recognized standard which provides the fundamentals of project management as they apply to a wide range of projects. PRINCE2 is a project management methodology standard in the United Kingdom and Europe. This standard is the required method for all UK government commissioned projects. PRINCE2 is in the public domain and offers best practice guidance on how to manage a project. The aim of this study is to compare PRINCE2 and PMBOK to recognize the characteristics of these best practices in order to provide decision criteria for governments with regard to selecting IT project management methodology.