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In this work, some counterexamples are given to refute some results in the paper by Kohli [RAIRO:OR 53 (2019) 1617–1632]. We correct the fault in some of his results.

This paper deals with the Extended Resource Constrained Project Scheduling Problem (ERCPSP) which is defined by events, nonrenewable resources and precedence constraints between pairs of events. The availability of a resource is depleted and replenished at the occurrence times of a set of events. The decision problem of ERCPSP consists of determining whether an instance has a feasible schedule or not. When there is only one nonrenewable resource, this problem is equivalent to find a feasible schedule that minimizes the number of resource units initially required. It generalizes the maximum cumulative cost problem and the two-machine maximum completion time flow-shop problem. In this paper, we consider this problem with some specific precedence constraints: parallel chains, series-parallel and interval order precedence constraints. For the first two cases, polynomial algorithms based on a linear decomposition of chains are proposed. For the third case, a polynomial algorithm is introduced to solve it. The priority between events is defined using the properties of interval orders.

This paper studies the equality generalized symmetric traveling salesman problem (EGSTSP). A salesman has to visit a predefined set of countries. S/he must determine exactly one city (of a subset of cities) to visit in each country and the sequence of the countries such that s/he minimizes the overall travel cost. From an academic perspective, EGSTSP is very important. It is NP-hard. Its relaxed version TSP is itself NP-hard, and no exact technique solves large difficult instances. From a logistic perspective, EGSTSP has a broad range of applications that vary from sea, air, and train shipping to emergency relief to elections and polling to airlines' scheduling to urban transportation. During the COVID-19 pandemic, the roll-out of vaccines further emphasizes the importance of this problem. Pharmaceutical firms are challenged not only by a viable production schedule but also by a flawless distribution plan especially that some of these vaccines must be stored at extremely low temperatures. This paper proposes an approximate tree-based search technique for EGSTSP. It uses a beam search with low and high level hybridization. The low-level hybridization applies a swap based local search to each partial solution of a node of a tree whereas the high-level hybridization applies 2-Opt, 3-Opt or Lin-Kernighan to the incumbent. Empirical results provide computational evidence that the proposed approach solves large instances with 89 countries and 442 cities in few seconds while matching the best known cost of 8 out of 36 instances and being less than 1.78% away from the best known solution for 27 instances. © The authors. Published by EDP Sciences, ROADEF, SMAI 2021.

Renewable energy represented by wind energy plays an increasingly important role in China’s national energy system. The accurate prediction of wind power generation is of great significance to China’s energy planning and power grid dispatch. However, due to the late development of the wind power industry in China and the lag of power enterprise information, there are little historical data available at present. Therefore, the traditional large sample prediction method is difficult to be applied to the forecasting of wind power generation in China. For this kind of small sample and poor information problem, the grey prediction method can give a good solution. Thus, given the seasonal and long memory characteristics of the seasonal wind power generation, this paper constructs a seasonal discrete grey prediction model based on collaborative optimization. On the one hand, the model is based on moving average filtering algorithm to realize the recognition of seasonal and trend features. On the other hand, based on the optimization of fractional order and initial value, the collaborative optimization of trend and season is realized. To verify the practicability and accuracy of the proposed model, this paper uses the model to predict the quarterly wind power generation of China from 2012Q1 to 2020Q1, and compares the prediction results with the prediction results of the traditional GM(1,1) model, SGM(1,1) model and Holt-Winters model. The results are shown that the proposed model has a strong ability to capture the trend and seasonal fluctuation characteristics of wind power generation. And the long-term forecasts are valid if the existing wind power expansion capacity policy is maintained in the next four years. Based on the forecast of China’s wind power generation from 2021Q2 to 2024Q2 in the future, it is predicted that China’s wind power generation will reach 239.09 TWh in the future, which will be beneficial to the realization of China’s energy-saving and emission reduction targets.

Today’s complicated business environment has underscored the importance of integrated decision-making in supply chains. In this paper, a novel mixed-integer nonlinear mathematical model is proposed to integrate cellular manufacturing systems into a three-stage supply chain to deal with customers’ changing demands, which has been little explored in the literature. This model determines the types of vehicles to transport raw materials and final parts, the suppliers to procure, the priorities of parts to be processed, and the cell formation to configure work centers. In addition, queueing theory is used to formulate the uncertainties in demands, processing times, and transportation times in the model more realistically. A linearization method is employed to facilitate the tractability of the model. A genetic algorithm is also developed to deal with the NP-hardness of the problem. Numerous instances are used to validate the effectiveness of the modeling and the efficiency of solution procedures. Finally, a sensitivity analysis and a real case study are discussed to provide important management insights and evaluate the applicability of the proposed model.

In this paper, we develop an integrated two-echelon supply chain inventory model with a single-manufacturer and multi-retailers in which each retailer’s demand is dependent on selling price of the product. The manufacturer produces a single product and dispatched the order quantities of the retailers in some equal batches. The production process is imperfect and produces imperfect quality of products with a defective percentage which is random in nature and follows binomial distribution. Inspection process is performed by the retailers to classify the defective items in each lot delivered from the manufacturer. The defective items that were found by the retailer will be returned to the manufacturer at the next delivery. Lead time is random and it follows an exponential distribution. We also assume that shortages are allowed and are completely backlogged at each retailer’s end. A closed form solution to maximize the expected average profit for both the centralized and the decentralized scenarios are obtained. The developed models are illustrated with the help of some numerical examples using stochastic search genetic algorithm (GA). It is found that integration of the supply chain players results an impressive increment in the profit of the whole supply chain. Sensitivity analysis is also performed to explore the impacts of key-model parameters on the expected average profit of the supply chain.

This paper addresses the allocation and scheduling of the relief teams as one of the main issues in the response phase of the disaster management. In this study, a bi-objective mixed-integer programming (BOMIP) model is proposed to assign and schedule the relief teams in the disasters. The first objective function aims to minimize the sum of weighted completion times of the incidents. The second objective function also minimizes the sum of weighted tardiness of the relief operations. In order to be more similar to the real world, time windows for the incidents and damaged routes are considered in this research. Furthermore, the actual relief time of an incident by the relief team is calculated according to the position of the corresponding relief team and the fatigue effect. Due to NP-hardness of the considered problem, the proposed model cannot present the Pareto solution in a reasonable time. Thus, NSGA-II and PSO algorithms are applied to solve the problem. Furthermore, the obtained results of the proposed algorithms are compared with respect to different performance metrics in large-size test problems. Finally, the sensitivity analysis and the managerial suggestions are provided to investigate the impact of some parameters on the Pareto frontier.

Binding audiovisual content into multimedia applications requires the specification of each media item, including its size and position, to define a screen layout. The multimedia application author must plan the application’s screen layout (ASL), considering a variety of screen sizes where the application shall be executed. An ASL that maximizes the area occupied by media items on the screen is essential, given that screen space is a valuable asset for media broadcasters. In this paper, we introduce the Application Screen Layout Optimization Problem, and present its 𝒩P-hardness. Besides, two integer programming formulations and an Iterated Local Search (ILS) metaheuristic are proposed to solve it. The efficiency of the proposed methods is evaluated, showing that the metaheuristic achieves better results and is at least 12 times faster, on average, than the mathematical formulations. Also, the proposed approaches were compared to a layout design algorithm, showing their effectiveness.

This paper deals with scalarization and stability aspects for a unified set optimization problem. We provide characterization for a unified preference relation and the corresponding unified minimal solution in terms of a generalized oriented distance function of the sup-inf type. We establish continuity of a function associated with the generalized oriented distance function and provide an existence result for the unified minimal solution. We establish Painlevé–Kuratowski convergence of minimal solutions of a family of scalar problems to the minimal solutions of the unified set optimization problem.

This paper considers a class of simultaneous optimization scheduling with two competitive agents on an unbounded serial-batching machine. The cost function of each agent depends on the completion times of its jobs only. According to whether the jobs from different agents can be processed in a common batch, compatible model and incompatible model are investigated. For the incompatible model, we consider batch availability and item availability. For each problem, we provide a polynomial-time algorithm that can find all Pareto optimal schedules.

This paper presents a mathematical programming model for designing a sustainable continuous-time multi-period hub network considering time-dependent demand. The present model can be used in situations where the distribution of parameters related to the demand function is unknown, and we only can determine the range of changes of these parameters. To model these conditions, we consider interval uncertainty for the demand function parameters. The proposed model is a nonlinear multi-objective model. The objectives of the model cover economic, environmental, and social aspects of sustainability. These objectives include minimizing total costs, minimizing emissions, and maximizing fixed and variable job opportunities. We linearize the model by using some linearization techniques, and then, with the help of Bertsimas and Sim’s method, we construct a robust counterpart of the model. We also present some valid inequalities to strengthen the formulation. To solve the proposed model, we use Torabi and Hassini method. From solving the proposed model, network design decisions and the best time to implement decisions during the planning horizon are determined. To validate the model, we solve a sample problem based on the Turkish dataset and compare the designed network in two cases: in the first case, the demand function parameters take nominal values, and in the second case, the value of these parameters can change up to 20% of their nominal values. The results show that in the second case, the total capacity selected for hubs and hub links is greater than the first case. To investigate changes in objective functions to parameters level of conservatism and probability of constraints violation, we perform sensitivity analysis on these parameters in both single-objective and multi-objective optimization cases and report the results.

In this paper, a single-vendor and multiple-buyers’ integrated production inventory model is investigated where demand of the item at the buyers’ location is considered as interval type-2 fuzzy number (IT2FN). Deterioration rate of the item is assumed to change in accordance with the weather conditions of a particular region. It relies upon the values of certain attributes that have a direct influence on the extent of deterioration. These parameter values are easily forecasted and thereby can be utilized to determine the item depletion rate, which is executed here using Mamdani fuzzy inference scheme. Besides, a nearest interval approximation formula for the defuzzification of IT2FN is developed and applied in the proposed integrated production inventory model. The model optimizes the total number of shipments to be made to the buyers within a complete cycle so as to minimize the overall integrated cost incurred. A detailed illustration of the theoretical results is further demonstrated with the help of numerical example, followed by sensitivity analysis which provides insights into better decision making.

This paper considers the equilibrium balking behavior of customers in a single-server Markovian queue with variable vacation and vacation interruption, where the server can switch across four states: vacation, working vacation, idle period, and busy period. Once the queue becomes empty, the server commences a working vacation and slows down its service rate. However, this period may be interrupted anytime by the vacation interruption. Upon the completion of a working vacation, the server takes a vacation in a probability-based manner and stops service if the system is empty. The system stays idle after a vacation until a new customer arrives. The comparisons between the equilibrium balking strategy of customers and the optimal expected social benefit per time unit for each type of queue are elucidated and the inconsistency between the individual optimization and the social optimization is revealed. Moreover, the sensitivity of the expected social benefit and the equilibrium threshold with respect to the several parameters as well as diverse precision levels is illustrated through numerical examples in a competitive cloud environment.

Popular measures of product availability in inventory systems seek to control different aspects of stock shortages. However, none of them simultaneously control all aspects of shortages, because stock shortages in inventory systems are complex random events. This paper analyzes the performance ofαLservice measure, defined as the probability that stockouts do not occur during a replenishment cycle, to cover different aspects of stock shortages when used to design an optimal continuous review (Q,r) policy. We show that explicitly controlling the frequency of replenishment cycle stockouts, using theαLservice-level, allows to implicitly control the size of the stockouts at an arbitrary time, the size of accumulated backorders at an arbitrary time, and the duration of the replenishment cycle stockouts. However, the cost of controlling the frequency of replenishment cycle stockouts is greater than the cost of controlling the size of stockouts and the duration of the replenishment cycle stockouts.

Most data envelopment analysis (DEA) studies on scale elasticity (SE) and returns to scale (RTS) of efficient units arise from the traditional definitions of them in economics, which is based on measuring radial changes in outputs caused by the simultaneous change in all inputs. In actual multiple inputs/outputs activities, the goals of expanding inputs are not only to obtain increases in outputs, but also to expect the proportions of such increases consistent with the management preference of decision-makers. However, the management preference is usually not radial changes in outputs. With the latter goal into consideration, this paper proposes the directional SE and RTS in a general formula for multi-output activities, and offers a DEA-based model for the formula of directional SE at any point on the DEA frontier, which is straightforward and requires no simplifying assumptions. Finally, the empirical part employs the data of 16 basic research institutions in Chinese Academy of Sciences (CAS) to illustrate the superiority of the proposed theories and methods.

The two-stage Data Envelopment Analysis (DEA) is widely applied to assess the efficiency of commercial banks in recent years. Even though this approach well simulates the sequence of banks production process, the independent operations within sub-stages are generally ignored, and the cooperative or non-cooperative relations between sub-stages are usually investigated separately.Please check whether short title on odd pages have been set correctly. Commercial banking production system, however, has complex internal structure within which parallel and series structure can co-exist, and cooperative relations may concurrently occur with non-cooperative ones. In this paper, we develop a hybrid two-stage DEA to consider simultaneously the series-parallel internal structure and the cooperative-Stackelberg relations between sub-stages. The data of 19 Chinese listed commercial banks are used to show the abilities of the proposed models. This approach represents a powerful and flexible efficiency measurement implement that can be applied when the system in question has a complex internal structure in terms of both sub-systems features and sub-systems relations.

Portfolio Optimization is based on the efficient allocation of several assets, which can get heavily affected by the uncertainty in input parameters. So we must look for such solutions which can give us steady results in uncertain conditions too. Recently, the uncertainty based optimization problems are being dealt with robust optimization approach. With this development, the interest of researchers has been shifted toward the robust portfolio optimization. In this paper, we study the robust counterparts of the uncertain mean-variance problems under box and ellipsoidal uncertainties. We convert those uncertain problems into bi-level optimization models and then derive their robust counterparts. We also solve a problem using this methodology and compared the optimal results of box and ellipsoidal uncertainty models with the nominal model.

Although pricing and inventory control are crucial decisions in each production system, these decisions are usually investigated separately. This paper considers pricing and inventory control decisions simultaneously in a hybrid production system. The hybrid production system has two recovery options, remanufacturing and refurbishing sites. The demand follows Poisson distribution, which depends on the sale price of each product. Returned products arrive according to a Poisson process. Each returned product can be remanufactured, refurbished, or disposed. The time to manufacturing, refurbishing, and remanufacturing a product also follows an exponential distribution. By modeling the system as a Markov chain, the long-run expected profit function is obtained in terms of the dispose-down-to level of returned products and the order-up-to level and the sale price of serviceable products. A three-dimensional state space of the Markov Chain dependent to the sale price is developed considering pricing and inventory control decisions simultaneously with remanufacturing and refurbishing returned products. Since the model is a mixed integer nonlinear programming and known as complex models, the Artificial Bee Colony (ABC) algorithm, simulation and complete search method are used to solve the problem. The results show that by increasing the purchase price of the returned products, the amount of returned products will increase. If the refurbishing cost of the returned products is high or the disposal cost is low, less inventory should be kept in the system with a high price of serviceable products. If the lost sale cost is high, the more inventory should be maintained. Moreover, by decreasing the price elasticity of demand, the customer’s demand increases, and then more inventory should be maintained in the system.

Hierarchical multilevel multi-leader multi-follower problems are non-cooperative decision problems in which multiple decision-makers of equal status in the upper-level and multiple decision-makers of equal status are involved at each of the lower-levels of the hierarchy. Much of solution methods proposed so far on the topic are either model specific which may work only for a particular sub-class of problems or are based on some strong assumptions and only for two level cases. In this paper, we have considered hierarchical multilevel multi-leader multi-follower problems in which the objective functions contain separable and non-separable terms (but the non-separable terms can be written as a factor of two functions, a function which depends on other level decision variables and a function which is common to all objectives across the same level) and shared constraint. We have proposed a solution algorithm to such problems by equivalent reformulation as a hierarchical multilevel problem involving single decision maker at all levels of the hierarchy. Then, we applied a multi-parametric algorithm to solve the resulting single leader single followers problem.

The safety norms for drug design are very strict with at least three stages of trials. One test, early on in the trials, is about the cardiotoxicity of the molecules, that is, whether the compound blocks any heart channel. Chemical libraries contain millions of compounds. Accurate a priori and in silico classification of non-blocking molecules, can reduce the screening for an effective drug, by half. The compound has to be checked for other risk factors alongside its therapeutic effect; these tests can also be done using a computer. Actual screening in a research laboratory is very expensive and time consuming. To enable the computer modelling, the molecules are provided in Simplified Molecular Input Line Entry (SMILE) format. In this study, they have been decoded using the chem-informatics development kit written in the Java language. The kit is accessed in the R statistical software environment through the rJava package, that is further wrapped in the rcdk package. The strings representing the molecular structure, are parsed by the rcdk functions, to provide structure-activity descriptors, that are known, to be good predictors of biological activity. These descriptors along with the known blocking behaviour of the molecule, constitute the input to the Decision Tree, Random Forest, Gradient Boosting, Support-Vector-Machine, Logistic Regression, and Artificial Neural Network algorithms. This paper reports the results of the data analysis project with shareware tools, to determine the best subset of molecular descriptors, from the large set that is available.

Additive manufacturing (AM) enables cost-effective and efficient production toward sustainability. However, a rigorous evaluation method is required to further investigate the measurement method and efficiency before AM can be well-positioned in sustainable manufacturing and become the industry mainstream. Cost savings play a key role in the manufacturing industry. Compared to conventional manufacturing (CM), the cost of AM is volume-independent. In contrast, CM production requires a certain volume to share the initial tooling costs to achieve cost reduction. This constraint limits CM from service on demand and leaves ambiguity in the threshold setting of that critical batch volume. In addition, the invisibility of AM advantages in cost factors blocks AM technologies from appropriate processes and affects its applications. To address these issues, this paper proposes a business model. The major issues encountered by AM are the scaling, speed, and size of products. The enhancement of cost modeling and addressing speed, scale, and size issues are the novelties of this study and provide a breakthrough in AM issues. Generic equations are derived using the convergence effect and cost–volume intersection calculation between AM and CM. Furthermore, the divide-and-conquer approach is proposed to support scaling factors and dependencies for both AM and CM. Consequently, appropriate AM technologies can be compared with the CM convergence threshold to contribute to decision-making. Next, the advantages and weaknesses of AM are identified, and a collaboration pattern is proposed to connect large enterprises, small-and medium-sized enterprises, and home-based manufacturers into an AM society. Through this society, the advantages of AM can be fully exploited, scaling and speed issues can be addressed, and AM's dominant role in sustainable manufacturing can be made feasible.

Constant technology control has been widely used in modern agriculture and industry and daily life, control performance affects the safety, the industry productivity and product quality. Based on MATLAB/Simulink software, take the electric kettle as the control object, the simulation model of constant temperature control system is proposed in the paper, the PID controller is used to calibrate the system, and realize constant temperature control of electric kettle. The simulation results show that the PID controller is used to control temperature, which reduces the time of overshooting and adjusting, improves the control accuracy and system reliability, and has practical reference value.

This paper considered the design of a stand-alone PV system that would be adequate to power a single residence and estimate the appropriate size of the solar panel. This system converts solar energy directly into electricity using photovoltaic principle in PV panel arrays. The electricity produced can be used to power most ac and dc electrical appliances. Inverter is used to convert the dc generated by the PV panels to ac for most domestic and industrial use. For continuous availability of power during days of autonomy (low insolation or cloudy days), battery storage system and charge controller (for battery charge and discharge control) are required. inverter, charge controller, battery, components interconnection wires. The sizing processes considered the quality of solar irradiation of the geographical location, effect of temperature de-rating, efficiency of components, system voltage selection, days of autonomy and load demand (in watt-hour). A residence in Gaza town was chosen as a case study. The minimum electrical load of 7.875kWh per day, household, Finally excel program simulation was designed to satisfy calculation equations process and generalize the program.