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Retail design concepts are complex designs meeting functional and aesthetic demands from various constraint generators. However, the literature on this topic is sparse and offers only little support for store designers to deal with such challenges. To address this issue, the purpose of this paper is to identify the most important constraint generators, investigating the types of constraints they generate, and providing guidelines for how to deal with constraint elicitation.

The three contributions mentioned above are developed through discussions of the literature and eight case studies of fashion store design projects.

The paper shows that the influence of the constraint generators decreases during the design process except for supplier-generated constraints, which increase in the final stages of the design process. The paper argues that constraints should be elicited close to their occurrence, and that doing so requires a solid understanding of relevant constraint generators.

The paper provides a structured basis for further research and identifies areas warranting further study. Although, the paper’s focus is on fashion store design, the findings may, to some degree, be applicable to other types of store design projects.

The understandings provided by this paper may help designers to deal proactively with constraints, reducing the use of resources to alter design proposals.

The paper: defines the most important constraint generators from the perspective of retail store designers, clarifies the types of constraints they generate, and provides guidelines for how to deal with constraint elicitation.

Market liberalisation has resulted in significant changes not only in the way electricity is traded, but also for the market participants themselves. The bidding behaviour of market participants who are active in a liberalised UK‐like market has been modelled. Both operational and technical parameters associated with the market and its participants are accounted for. Explicit characterization of risk (value at risk) is made with respect to market participants and their attitude to trading. Profit maximization strategies for market participants are then developed based on the minimization of price‐risk under uncertainty. Results are presented for a selected case study and the effect of alternative strategies is compared. The case study concerns several generators who need to determine what proportion of their production they should sell to the market. The results show that based on cost and price forecasts there is scope for generators to profitably take advantage of both contractual and within‐day market trades.

The purpose of this paper is to deal with the economic requirements of power system loading dispatch and reduce the fuel cost of generation units. In order to optimize the scheduling of power load, an improved chicken swarm optimization (ICSO) is proposed to be adopted, for solving economic load dispatch (ELD) problem.

The ICSO increased the self-foraging factor to the chicks whose activities were the highest. And the evolutionary operations of chicks capturing the rooster food were increased. Therefore, these helped the ICSO to jump out of the local extreme traps and obtain the global optimal solution. In this study, the generation capacity of the generation unit is regarded as a variable, and the fuel cost is regarded as the objective function. The particle swarm optimization (PSO), chicken swarm optimization (CSO), and ICSO were used to optimize the fuel cost of three different test systems.

The result showed that the convergence speed, global search ability, and total fuel cost of the ICSO were better than those of PSO and CSO under different test systems. The non-linearity of the input and output of the generating unit satisfied the equality constraints; the average ratio of the optimal solution obtained by PSO, CSO, and ICSO was 1:0.999994:0.999988. The result also presented the equality and inequality constraints; the average ratio of the optimal solution was 1:0.997200:0.996033. The third test system took the non-linearity of the input and output of the generating unit that satisfied both equality and inequality constraints; the average ratio was 1:0.995968:0.993564.

This study realizes the whole fuel cost minimization in which various types of intelligent algorithms have been applied to the field of load economic scheduling. With the continuous evolution of intelligent algorithms, they save a lot of fuel cost for the ELD problem.

The ICSO is applied to solve the ELD problem. The quality of the optimal solution and the convergence speed of ICSO are better than that of CSO and PSO. Compared with PSO and CSO, ICSO can dispatch the generator more reasonably, thus saving the fuel cost. This will help the power sector to achieve greater economic benefits. Hence, the ICSO has good performance and significant effectiveness in solving the ELD problem.

This study aims to minimize operating cost, adhere to pollution norms and maintain reserve and voltage levels subject to various operational concerns, including non linear characteristics of generators and fuel limitation issues, which are useful for the current power system applications.

Improved control settings are required while considering multiple conflicting operational objectives that necessitate using the modern bio-inspired algorithm ant lion optimizer (ALO) as the main optimization tool. Fuzzy decision-making mechanism is incorporated in ALO to extract the best compromise solution (BCS) among set of non-dominated solutions.

The BCS records of IEEE-30 bus and JEAS-118 bus systems are updated in this work. Numerical simulation results comparison and comprehensive performance analysis justify the applicability of the intended algorithm to solve multi-objective dynamic optimal power flow (DOPF) problem over the state-of-art methods.

Optimal control settings are obtained for IEEE-30 and JEAS-118 bus systems with the objectives of minimizing fuel cost and emission in dynamic environment considering take-or-pay fuel contract issue. The fuzzy supported ALO (FSALO) is applied first time to solve the DOPF problem.

This study aims to intend and implement the optimal power flow, where tuning the production cost is done with the inclusion of stochastic wind power and different kinds of flexible AC transmission systems (FACTS) devices. Here, the speed with fitness-based krill herd algorithm (SF-KHA) is adopted for deciding the FACTS devices’ optimal sizing and placement integrated with wind power. Here, the modified SF-KHA optimizes the sizing and location of FACTS devices for attaining the minimum average production cost and real power depletions of the system. Especially, the objective includes reserve cost for overestimation, cost of thermal generation of the wind power, direct cost of scheduled wind power and penalty cost for underestimation. The efficiency of the offered method over several popular optimization algorithms has been done, and the comparison over different algorithms establishes proposed KHA algorithm attains the accurate optimal efficiency for all other algorithms.

The proposed FACTS devices-based power system with the integration of wind generators is based on the accurate placement and sizing of FACTS devices for decreasing the actual power loss and total production cost of the power system.

Through the cost function evaluation of the offered SF-KHA, it was noted that the proposed SF-KHA-based power system had secured 13.04% superior to success history-based adaptive differential evolution, 9.09% enhanced than differential evolution, 11.5% better than artificial bee colony algorithm, 15.2% superior to particle swarm optimization and 9.09% improved than flower pollination algorithm.

The proposed power system with the accurate placement and sizing of FACTS devices and wind generator using the suggested SF-KHA was effective when compared with the conventional algorithm-based power systems.

Economic load dispatch (ELD) is one of the crucial optimization problems in power system planning and operation. The ELD problem with valve point loading (VPL) and multi-fuel options (MFO) is defined as a non-smooth and non-convex optimization problem with equality and inequality constraints, which obliges an efficient heuristic strategy to be addressed. The purpose of this study is to present a new and powerful heuristic optimization technique (HOT) named as squirrel search algorithm (SSA) to solve non-convex ELD problems of large-scale power plants.

The suggested SSA approach is aimed to minimize the total fuel cost consumption of power plant considering their generation values as decision variables while satisfying the problem constraints. It confers a solution to the ELD issue by anchoring with foraging behavior of squirrels based on the dynamic jumping and gliding strategies. Furthermore, a heuristic approach and selection rules are used in SSA to handle the constraints appropriately.

Empirical results authenticate the superior performance of SSA technique by validating on four different large-scale systems. Comparing SSA with other HOTs, numerical results depict its proficiencies with high-qualitative solution and by its excellent computational efficiency to solve the ELD problems with non-smooth fuel cost function addressing the VPL and MFO. Moreover, the non-parametric tests prove the robustness and efficacy of the suggested SSA and demonstrate that it can be used as a competent optimizer for solving the real-world large-scale non-convex ELD problems.

This study has compared various HOTs to determine optimal generation scheduling for large-scale ELD problems. Consequently, its comparative analysis will be beneficial to power engineers for accurate generation planning.

To the best of the authors’ knowledge, this manuscript is the first research work of using SSA approach for solving ELD problems. Consequently, the solution to this problem configures the key contribution of this paper.

The purpose of this paper is to solve the problem of committing electric power generators (unit commitment, UC), considering network constraints.

The UC is first solved with a local search based meta‐heuristic, following the assumption that all generators and loads are connected to a single network node. For evaluation purposes, the economical production levels of the units committed are computed by running a pre‐dispatch algorithm where network constraints are not included. If a good quality solution is reached, an economic dispatch (ED) with network constraints is performed, where the geographic location of generators and loads are considered. Therefore, the production level of each committed generator is performed that leads to the global lowest solution cost, regarding both the generators' costs and constraints and the power system network constraints.

The algorithm proposed is computationally efficient, given the time available for decision making. In addition, the solution for this algorithm, in terms of minimization of total costs, is generally better than the solution of the two phases approach. Some contractual and legal aspects related with the injection in network connections can also be included in the model.

UC with network constraints has a large potential of use, especially for small and medium size power systems. It reflects reality in a closer way and provides a more complete and realistic knowledge about the system in operation.

The paper presents an approach where the ED with network constraints is integrated with the UC procedure. The model described is currently implemented in an EMS package offered in the market – making it a case of successful transfer from science to industry.

The designs of salient pole generators may differ considerably from one hydroelectric plant to another. Automatic optimization procedure is highly desirable, because the designer may have little experience with a similar machine. The presented approach defines the design space by 12 variables which have the largest influence on the goal function. The design space is constrained by a number of linear and nonlinear constraints. The optimization process is based on successive linearizations of the goal function and the nonlinear constraints followed by a simplex procedure. The process is highly effective because the goal function is heavily constrained, so the optimum is virtually always on the boundary of the feasibility region. The procedure has been tested on a number of earlier designs. The goal function could have been reduced on average by some 8 percent, had this software been available at the time of the design of these machines.

Information seeking does not occur in a vacuum but invariably is motivated by some wider task. It is well accepted that to understand information seeking we must understand the task context within which it takes place. Writing is amongst the most common tasks within which information seeking is embedded. This paper considers how writing can be understood in order to account for embedded information seeking. Following Sharples, the paper treats writing as a design activity and explore parallels between the psychology of design and information seeking. Significant parallels can be found and ideas from the psychology of design offer explanations for a number of information seeking phenomena. Next, a design‐oriented representation of writing tasks as a means of providing an account of phenomena such as information seeking uncertainty and focus refinement is developed. The paper illustrates the representation with scenarios describing the work of newspaper journalists.

Responding to natural resource depletion and carbon dioxide (CO₂) emission problems, and also the stricter government’s energy regulations, the purpose of this paper is to develop a sustainable waste heat recovery optimal-profit-oriented management model especially targeting on the easily forgotten low- and medium-temperature waste heat in the industry. In the paper, a system is constructed to facilitate converting the low- and medium-grade waste heat in factories into electricity, and yields optimal profit.

This paper integrates an efficient Organic Rankine Cycle (ORC) system from both sustainable energy reservation and cost effectiveness approaches with an optimization model that adopts particle swarm optimization (PSO) algorithm to determine proper installation locations and feasible generator sets. The system is constructed to facilitate converting the low- and medium-grade waste heat in factories into electricity, and yields optimal profit. The model considers the environmental factors: temperature, heat amount, equipment configuration of the number of ORC sets, and detailed investment cost constraints.

The results show that annual investment return rate, annual increase in electricity, power generation efficiency, and annual CO₂ emission reduction are all highly improved, and investment recovery period is shortened. Also, the larger scale of the waste heat emission, the better the performance is achieved. Finally, the study also completes a sensitivity test under dynamic conditions of electricity price, generator sales price and factory budget constraints, and the results are consistently robust. More valuably, this paper demonstrates applications on two different manufacturing industries with various waste heat emission scales to prove the accountability.

The main contributions are in three aspects. First, it proves that applying PSO to a nonlinear mathematical model can help determine the optimal number and style configuration of generators for waste heat sources. Second, different from the prior research works focusing on power generation, this paper also deliberates the cost factors, cost of generators, costs of numerous peripheral components and future maintenance costs to ensure the factories not conflict with the financial limitations. Third, it is not only successfully applied in two industries with different scales, but also robust with various economic tests, electricity price change, generator sales price change, and investment budget adjustments.