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Determining the proper sequence of interrelated activities in the design of a complex product is an irrefutable challenge for project management. The presence of components with different levels of technology in a complex product can disrupt project planning and cause cost overruns or schedule delays. This article presents a multiobjective model for organizing interrelated activities by taking into account the impact of technological capability and financial aspect of the project with a periodic perspective. First, a multidomain matrix is devised based on design structure matrix and technology readiness level to demonstrate the impact of components technology maturity level. Second, a mathematical model is developed according to rework caused by information dependence (minimizing total feedback value), project financial aspect (minimizing the difference between the budget and project costs), and technology maturity level of components (minimizing technology risk). Finally, a possibilistic programming approach is applied to cope with the uncertainty of input data; in addition, to deal with multiple objective functions, an interactive fuzzy solution approach is implemented. To validate the model and solution approach, a numerical experiment and a case study are conducted. Testing results illustrate the importance of technology maturity level in sequencing activities and indicate that the model can be introduced as a management tool for the financial feasibility study of the project due to the balanced use of budget.

Purpose The purpose of this study is to investigate the benefits of the turning point layout; a simulation model being applicable for strategic level is designed that compares systems with and without turning points. Specifically, the avoidance of deadlocks and prevention of conflicts is substantial. Design/methodology/approach Optimization process for different layouts and configuration of autonomous guided vehicles (AGVs) are worked out using statistical methods for design parameters. Regression analysis is used to find effective design parameters and analysis of variance is applied for adjusting critical factors. Also, the optimal design is then implemented in a manufacturing system for an industrial automation and the results are reported. Findings The outputs imply the effectiveness of the proposed approach for real industrial cases. This research will combine both simulation-based method and optimization technique to improve the quality of solutions. Originality/value In AGV systems, the begin-end combinations are usually connected by using a fixed layout, which is not the optimal path. The capability of these configurations is limited and often the conflict of multiple AGVs and deadlock are inevitable. By appearing more flexible layouts and advanced technology, the positioning and dispatching of AGVs increased. A new concept would be to determine each path dynamically. This would use the free paths for AGVs leading to overcome the conflicts and deadlocks.

The feasibility of a photovoltaic project depends on strategic decisions such as the location of the power plant. This article contributes to the literature by proposing a novel algorithm employing...

Motivated by a real problem, in this paper, a new multi-period production-distribution planning (PDP) for perishable products with a fixed lifetime in a seller-buyer system is developed. The objective is to maximize the seller’s profit conditional on the optimality of the buyer in a three-level supply chain (SC), including the factories, distribution centers, and retailers. The factories and distribution centers act cooperatively at the leader level and, as the seller, make the location, production, inventory, and distribution decisions. While the retailers operate as the follower; therefore, a vertical competition is held in this SC. In addition to price, freshness owing to the perishability of the product is considered as an important competitive factor. Also, to encourage the retailers, three strategies in the development of a new demand function, including the return, discount policies, and credit period, are employed. Since the problem is NP-hard, a hierarchical heuristic approach based on Benders Decomposition Algorithm (BDA) and Genetic Algorithm (GA) is proposed. Finally, to demonstrate the applicability and efficiency of the developed model, it is applied to a real industrial case. The computational results of the problems in different sizes confirm the efficient performance of the proposed model and solution method.

Growing ecological concerns has led governments to utilize mechanisms such as Renewable Identification Number (RIN) in promoting biofuels as an alternative for fossil fuels. In this paper, ...

The objective of this study was to perform a sensitivity analysis on the factors affecting wind site localization. A case study is selected based on 13 cities in the province of Fars in Iran which is equally applicable in any other wind sites. Then, the cities are ranked using the dual form of the data envelopment analysis model. Next, six criteria are adopted including wind conditions, population, Available Land condition, distance to distribution networks, rate of natural disasters, and the cost of land. In the sensitivity analysis of each criterion, first, the criterion under analysis is omitted from the model, and then the dual model is applied again to obtain a new ranking. Evaluating the results of the ranking for 13 studied cities indicate that the city of Shiraz is just sensitive to the population criterion and fell 11 places in the ranking by omitting this criterion. But the city is insensitive to any other criteria.

An integrated decision in supply chain is a significant principle in order to compete in today’s market. This paper proposes a novel mathematical model in a two-stage supply chain scheduling to cooperate procurement and manufacturing activities. The supply chain scheduling along with the production approach of cellular manufacturing under demand, processing time, and transportation time uncertainties makes business environment sustainably responsive to the changing needs of customers. Uncertainties are formulated by queuing theory. In this paper, a new mixed-integer nonlinear programming formulation is used to determine types of vehicles to carry raw materials, suppliers to procure, priority of each part in order to process, and cell formation to configure work centers. The goal is to minimize total tardiness. A linearization method is used to ease tractability of the model. A genetic algorithm is developed due to the NP-hard nature of the problem. The parameters of the genetic algorithm are set and estimated by Taguchi’s experimental design. Numerous test problems are employed to validate the effectiveness of the modeling and the efficiency of solution approaches. Finally, a real case study and a sensitivity analysis are discussed to provide significant managerial insights and assess the applicability of the proposed model.

This paper develops a bi-objective optimization model for the integrated production-distribution planning of perishable goods under uncertainty. The first objective seeks to maximize the profit in a specific supply chain with three levels: plants, distribution centers, and in the last level, customers. Since transportation is one of the major pollution sources in a distribution problem, the second objective is to minimize their emission. In the considered problem, the decisions of production, location, inventory, and transportation are made in an integrated structure. In developing the demand function, the effect of the product freshness and the price is formulated. Besides, to encourage customers, three strategies, including perished product return, discount, and credit policies, are proposed. Also, robust optimization is utilized to cope with the operational uncertainty of some cost parameters. To prove the applicability of this research and the feasibility of the environmental aspect, a case study is conducted. Finally, the numerical computations on the case study provide a trade-off between the environmental and economic goals and indicate a 37.5 percent increase in the profit using the developed model.

Biomass, as a renewable material and a clean alternative fuel in generating electricity and heat, is of particular importance in the transportation industry for the production of biofuel instead of fossil fuels. So far, many models have been proposed for the Bioenergy Supply network design, all of which are local and focus on designing new configurations that conform to a specific country's economic policies. But since globalization of the economy and industry is unavoidable today, effort has been made in this paper to propose a global Bioenergy Supply network management model. Contrary to the present literature on the Bioenergy Supply network, the proposed model addresses the redesign of an existing biomass supply network through logistic decisions, financial problems, and related merits. The model objectives can help managers to make decisions using the best transfer prices to maximize the global after tax profit of the biomass company even under uncertain conditions. To validate the model, use has been made of the data of a case study in Iran and Armenia. At last, it is concluded when transfer price is considered at the model, the global after tax profit can increase more than 8% compared with the case of without transfer price.

Wind energy is becoming one of the most important sources of renewable energy for many countries in the future. The purpose of this study is to predict wind speed using different algorithms. In this study, a new hybrid algorithm is developed to predict the wind speed behavior, and 24 h predictions of changes in wind speed are obtained with the aid of Homer software. The proposed algorithm is a combination of a well-known artificial neural network predictor called extreme learning machine as an artificial neural network algorithm and the Grey model (1, 1) as a method of Grey systems theory. Long-term wind speed forecasts are obtained using three-year data (2013–2016) of eight variables: TMAX, TMIN, VP, RHMIN, RHMAX, WINDSPEED, SUNSHINE HOURS, and PERCIPITATION for the Zanjan city in Iran, and 24 h wind speed forecast is obtained using 10-year data (2005–2015) pertaining to this city. The results show that proposed algorithm with relative measure of fit R2 of 0.99376 and mean square error of 0.000376 provides better predictions of wind speed in the study area than ordinary extreme learning machine algorithm with R2 of 0.98075 and mean square error of 0.00720. Also, the 24 h prediction of changes in wind speed is done using Homer software. The methodology in this research is more efficient in terms of execution performance and accuracy.

Biodiesel has recently attracted much interest as an appropriate alternative for the fossil diesel which is mostly consumed in the transportation sector. Meanwhile, Jatropha Curcas L. has emerged as one of the most promising biofuel feedstocks due largely to its salient features such as compatibility with arid and semi-arid lands. In this regard, this paper unveils a two-stage stochastic programming model for the design of an integrated green biodiesel supply chain network from Jatropha Curcas feedstocks. The concerned biodiesel supply chain design is an environmentally friendly mixed-integer linear programming, multi-period and multi-product model. Furthermore, a flexible stochastic programming approach is developed and applied to the supply chain network model. This proposed approach is indeed an extension of a two-stage scenario-based stochastic programming approach which incorporates min-max relative regret in a soft worst-case framework. Given the natural variability of long-term decision-making, fuel demand and Jatropha Curcas trees yielding are hemmed in by uncertainty in this model. The performance of the proposed framework and biodiesel supply chain network design is corroborated through ten realizations and a myriad of various analyses in a real case study of Iran. The derived results and their analyses acknowledge the efficiency and applicability of the model.

This article proposes a mixed-integer programming (MILP) model to determine the strategic and tactical level decisions of lignocellulosic bioethanol supply chain subject to different sources and types of uncertainty. A comprehensive classification, including sources of uncertainty, corresponding parameters and possible reasons which may cause the uncertainty, as well as an up to date and systematic literature review of biofuel supply chain optimal design and planning studies which consider uncertain input data are presented. To handle different types of uncertainty, including randomness, epistemic and deep uncertainties, a hybrid robust optimization model is proposed. Uncertainty in technology is presented as imprecise conversion rates, which are expressed as probabilistic scenarios. Biomass yield is treated as fuzzy numbers while demand is assumed to vary in a known interval. Furthermore, fixed costs of the biorefineries are calculated according to the piecewise linear functions in which segments are capacity level intervals. In order to investigate the performance of the proposed models a case study is developed for bioethanol supply chain located in Iran. Computational results show that the proposed robust model outperforms deterministic model in terms of given performance measures.

In a virtual cell formation system, machine types are formed for one period during which the machine types of a cluster process the corresponding operations of part types. However, there is one major difference between a virtual cellular manufacturing and a cellular manufacturing and that is the fact that machine types of the same cluster are not necessarily brought to a physical proximity in virtual cellular manufacturing, contrary to a cellular manufacturing. Depending on the variations in the demand for part types, the virtual cells are clustered periodically and merged according to the demand for new part types in a planning horizon. On the other hand, optimal raw material quantity to purchase from qualified suppliers has gained importance recently; this phenomenon is greatly influenced by the significant portion of raw materials costs in a finished product. Consequently, most firms have to pay out most of their revenues on transportation and purchasing. In this paper, the dynamic virtual cellular manufacturing is formulated through a mathematical model developed for this purpose; it is also of utmost importance to consider machine layout and quantity of raw material purchased from qualified suppliers. To solve the real-sized problems of proposed model, a hybrid metaheuristic algorithm is extended. The results demonstrate that the proposed hybrid genetic algorithm is promising.