In the present work, a recently developed advanced optimization algorithm named as teaching–learning-based optimization (TLBO) is used for the parameters optimization of fabric finishing system of a textile industry. Fabric Finishing System has four main subsystems, arranged in hybrid configuration. For performance modeling and analysis of availability, a performance evaluating model of fabric finishing system has been developed with the help of mathematical formulation based on Markov-Birth-Death process using Probabilistic Approach. Then, the overall performance of the concerned system has first analyzed and then, optimized by using teaching–learning-based optimization (TLBO). The results of optimization using the proposed algorithm are validated by comparing with those obtained by using the genetic algorithm (GA) on the same system. Improvement in the results is obtained by the proposed algorithm. The results of effect of variation of the algorithm parameters on fitness values of the objective function are reported.

This study uses an alternative fabrication scheme to study the effect of rework of nonconforming items and delayed differentiation on a multiproduct supply-chain system. Traditional economic production quantity model focuses on a single-product inventory system where all products made are assumed to be perfect quality and finished products are issued continuously. To increase machine utilization, lower quality costs in production, and reflect the real-world vendor-buyer integrated systems Chiu et al. (2016a) [Chiu, Y-S.P., Kuo, J-S., Chiu, S. W., Hsieh, Y-T. (2016a). Effect of delayed differentiation on a multiproduct vendor–buyer integrated inventory system with rework. Advances in Production Engineering & Management, 11(4), 333-344.] employed a single-machine two-stage production scheme to study the effects of rework and delayed differentiation on a multi-product supply-chain system. With the intention of further reducing fabrication cycle time, this study considers an alternative two-machine two-stage fabrication scheme to re-explore the problem in Chiu et al. (2016a). Machine one solely produces all common parts for multiple end products. Then, machine two fabricates the customized multiproduct using a common cycle time strategy. Through the use of mathematical modeling and analyses, the optimal production cycle length and distribution policy are derived. Numerical examples are provided to demonstrate practical usage of the research results, and show its significant benefit in reducing fabrication cycle time compared to that obtained from prior studies that used different schemes.

Capacitated maximal covering location problems (MCLP) have considered capacity constraint of facilities but these models have been studied in only one direction. In this paper, capacitated MCLP and dynamic MCLP are integrated with each other and dynamic capacity constraint is considered for facilities. Since MCLP is NP-hard and commercial software packages are unable to solve such problems in a rational time, Genetic algorithm (GA) and bee algorithm are proposed to solve this problem. In order to achieve better performance, these algorithms are tuned by Taguchi method. Sample problems are generated randomly. Results show that GA provides better solutions than bee algorithm in a shorter amount of time.

Nowadays, process optimization has been an interest in engineering design for improving the performance and reducing cost. In practice, in addition to uncertainty or noise parameters, a comprehensive optimization model must be able to attend other circumstances which might be imposed in problems under real operational conditions such as dynamic objectives, multi-responses, various probabilistic distribution, discrete and continuous data, physical constraints to design parameters, performance cost, computational complexity and multi-process environment. The main goal of this paper is to give a general overview on topics with brief systematic review and concise discussions about the recent development on comprehensive robust design optimization methods under hybrid aforesaid circumstances. Both optimization methods of mathematical programming based on Taguchi approach and robust optimization based on scenario sets are briefly described. Metamodels hybrid robust design is discussed as an appropriate methodology to decrease computational complexity in problems under uncertainty. In this context, the authors’ policy is to choose important topics for giving a systematic picture to those who wish to be more familiar with recent studies about robust design optimization hybrid metamodels, also by attending real circumstances in practice. In particular, production and project management are considered as two important methodologies that could be improved by applications of advanced robust design combining with metamodel methods.

The multi-objective problem of multi-depot vehicle routing (MOMDVRP) is proposed by considering the minimization of the traveled arc costs and the balance of routes. Seven mathematical models were reviewed to determine the route balance equation and the best-performing model is selected for this purpose. The solution methodology consists of three stages; in the first one, beginning solutions are built up by means of a constructive heuristic. In the second stage, fronts are constructed from each starting solution using the iterated local search multi-objective metaheuristics (ILSMO). In the third stage, we obtain a single front by using concepts of dominance, taking as a base the fronts of the previous stage. Thus, the first two fronts are taken and a single front is formed that corresponds to the current solution of the problem; next the third front is added to the current Pareto front of the problem, the procedure is repeated until exhaustion of the list of the fronts initially obtained. The resulting front is the solution to the problem. To validate the methodology we use instances from the specialized literature, which have been used for the multi-depot routing problem (MDVRP). The results obtained provide very good quality. Finally, decision criteria are used to select the most appropriate solution for the front, both from the point of view of the balance and the route cost.

This study aims to design a new model for selecting most fitting new product development projects in a pool of projects. To catch the best model, we assume new products will be introduced to the competitive markets. Also, we suppose the revenue yielded by completed projects can be reinvested on implementation of other projects. Other sources of financing are borrowing loans from banks and initial capital of the firm. These limited resources determine most evaluated projects to be performed. Several types of interactions among different projects are considered to make the chosen projects more like a portfolio. In addition, some numerical examples from the real world are provided to demonstrate the applicability of the proposed model. These examples show how the particular considerations in the suggested model affect the results.

The present work deals with some machinability studies on tool wear and surface roughness, in finish hard turning of AISI D2 steel using PCBN, Mixed ceramic and coated carbide inserts. The machining experiments are conducted based on the response surface methodology (RSM). Combined effects of three cutting parameters viz., cutting speed, cutting time and tool hardness on the two performance outputs (i.e. VB and Ra), are explored employing the analysis of variance (ANOVA).The relationship(s) between input variables and the response parameters are determined using a quadratic regression model. The results show that the tool wear was influenced principally by the cutting time and in the second level by the cutting tool hardness. On the other hand, cutting time was the dominant factor affecting workpiece surface roughness followed by cutting speed. Finally, the multiple response optimizations of tool wear and surface roughness were carried out using the desirability function approach (DFA).

Infectious waste disposal remains one of the most serious problems in the medical, social and environmental domains of almost every country. Selection of new suitable locations and finding the optimal set of transport routes for a fleet of vehicles to transport infectious waste material, location routing problem for infectious waste disposal, is one of the major problems in hazardous waste management. Determining locations for infectious waste disposal is a difficult and complex process, because it requires combining both intangible and tangible factors. Additionally, it depends on several criteria and various regulations. This facility location problem for infectious waste disposal is complicated, and it cannot be addressed using any stand-alone technique. Based on a case study, 107 hospitals and 6 candidate municipalities in Upper-Northeastern Thailand, we considered criteria such as infrastructure, geology and social & environmental criteria, evaluating global priority weights using the fuzzy analytical hierarchy process (Fuzzy AHP). After that, a new multi-objective facility location problem model which hybridizes fuzzy AHP and goal programming (GP), namely the HGP model, was tested. Finally, the vehicle routing problem (VRP) for a case study was formulated, and it was tested using a hybrid genetic algorithm (HGA) which hybridizes the push forward insertion heuristic (PFIH), genetic algorithm (GA) and three local searches including 2-opt, insertion-move and interexchange-move. The results show that both the HGP and HGA can lead to select new suitable locations and to find the optimal set of transport routes for vehicles delivering infectious waste material. The novelty of the proposed methodologies, HGP, is the simultaneous combination of relevant factors that are difficult to interpret and cost factors in order to determine new suitable locations, and HGA can be applied to determine the transport routes which provide a minimum number of vehicles and minimum transportation cost under the actual situation efficiently in this case.

Optimum design and control of a Combined Cooling, Heating and Power generation (CCHP) system, in addition to the economic benefits, could be profitable in environmental and energy consumption aspects. The aim of this study is to determine the optimal capacity of equipment and define the best control strategy of a CCHP system. Since determination of optimal system control strategy has a huge impact on improving the objective functions, the system’s performance under five different strategies (developed based on well-known Following Electrical Load (FEL) and Following Thermal Load (FTL) strategies) is evaluated. In a real case study, a CCHP system is designed for an educational complex located in Mahmoudabad, Mazandaran, Iran. The objective is to minimize capital and operational costs, energy consumption, and CO2 emissions of the system. Due to the complexities of the model, genetic algorithm (GA) and particle swarm optimisation (PSO) algorithm are used to find the optimal values of the decision variables. The results show that using FEL strategy CO2 emissions reduces in compression to FTL strategy. Furthermore, using multiple power generation units under FTL strategy eventuates the least cost but increases CO2 emissions and energy consumption in compression to FEL strategy.

In this paper, the Multi-Depot Electric Vehicle Location Routing Problem with Time Windows (MDVLRP) is addressed. This problem is an extension of the MDVLRP, where electric vehicles are used instead of internal combustion engine vehicles. The recent development of this model is explained by the advantages of this technology, such as the diminution of carbon dioxide emissions, and the support that they can provide to the design of the logistic and energy-support structure of electric vehicle fleets. There are many models that extend the classical VRP model to take electric vehicles into consideration, but the multi-depot case for location-routing models has not been worked out yet. Moreover, we consider the availability of two energy supply technologies: the “Plug-in” Conventional Charge technology, and Battery Swapping Stations; options in which the recharging time is a function of the amount of energy to charge and a fixed time, respectively. Three models are proposed: one for each of the technologies mentioned above, and another in which both options are taken in consideration. The models were solved for small scale instances using C++ and Cplex 12.5. The results show that the models can be used to design logistic and energy-support structures, and compare the performance of the different options of energy supply, as well as measure the impact of these decisions on the overall distance traveled or other optimization objectives that could be worked on in the future.