Port can be viewed as a complex system containing several entities with interfering attributes. The whole image is very complex and special care should be considered to model such systems.Several works for investigating, analysing evaluating and improving port activities are carried out, each of them is concerned with a specific area related to the port functions, the key tool for most of these works is the simulation.The purpose of this paper is to give an overview of a computer simulation program used as a management decision support tool helping in evaluating and improving the comprehensive port activities. The proposed simulation model consists from a statistical module, an input module, ship generator, cargo generator, ship operation planner, cargo handling equipment planner, warehouse controller, and the transportation means planner. All the planning and controlling modules related to ships, cargoes, warehousing and transportation means contain special optimizers the optimizers constitute operations research packages to be used whenever possible to optimize the specific process related to port operations.The computer model will have several output results; evaluating different port performance indicators, performing port improvement analysis, studying port expansion possibilities, estimating future view of the port and making an economic impact evaluation.
Intensive competition and rapid technology development in an increasingly global marketplace has left no room for competing manufacturers to harbour system inefficiencies. Moreover, customers are becoming more demanding and meeting their expectations represents an increasing challenge. TPC are used in various communication and networks hardware applications; their manufacturing facilities face many challenges through the many phases of their product life cycle including various product configurations with different equipment settings, different product flows and work in process (WIP) space limitations. The quest for internal efficiency and external effectiveness mandates that companies have to align their internal settings and resources with external requirements/orders, or in other words, significant factors must be identified prior to manufacturing process. Simulation, as one of the most flexible and powerful tools which provides a comprehensive understanding of manufacturing process variations, is increasingly aiding management and production team's decisions. An IDEF0 model in conjunction with a simulation model and a design of experiments (DOE) have been developed to characterize the Twisted-Pair Cable (TPC) production system and examine various production setting scenarios aiming to get the best product flow time.
Determining the optimum location of facilities is critical in many fields, particularly in healthcare. This study proposes the application of a suitable location model for field hospitals during the novel coronavirus 2019 (COVID-19) pandemic. The used model is the most appropriate among the three most common location models utilized to solve healthcare problems (the set covering model, the maximal covering model, and the P-median model). The proposed nonlinear binary constrained model is a slight modification of the maximal covering model with a set of nonlinear constraints. The model is used to determine the optimum location of field hospitals for COVID-19 risk reduction. The designed mathematical model and the solution method are used to deploy field hospitals in eight governorates in Upper Egypt. In this case study, a discrete binary gaining-sharing knowledge-based optimization (DBGSK) algorithm is proposed. The DBGSK algorithm is based on how humans acquire and share knowledge throughout their life. The DBGSK algorithm mainly depends on two junior and senior binary stages. These two stages enable DBGSK to explore and exploit the search space efficiently and effectively, and thus it can solve problems in binary space.
This article proposes a new problem which is called the Stochastic Travelling Advisor Problem (STAP) in network optimization, and it is defined for an advisory group who wants to choose a subset of candidate workplaces comprising the most profitable route within the time limit of day working hours. A nonlinear binary mathematical model is formulated and a real application case study in the occupational health and safety field is presented. The problem has a stochastic nature in travelling and advising times since the deterministic models are not appropriate for such real-life problems. The STAP is handled by proposing suitable probability distributions for the time parameters and simulating the problem under such conditions. Many application problems like this one are formulated as nonlinear binary programming models which are hard to be solved using exact algorithms especially in large dimensions. A novel binary version of the recently developed gaining-sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems is given. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. The binary version of GSK (BGSK) depends mainly on two stages that enable BGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. The generated simulation runs of the example are solved using the BGSK, and the output histograms and the best-fitted distributions for the total profit and for the route length are obtained.
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