Showing papers in "International Journal of Operational Research in 2016"
TL;DR: An attempt has been made to give systematic review of supplier selection and evaluation process from 2005 to 2012 to answer three main questions: which method is more appropriate for supplier selection, which evaluating criteria were most cited and is present trend of research adequate enough to support proactive buying.
Abstract: Sole purpose of supplier selection is not limited to get supply at low cost and at right time. Supplier selection is a strategic decision to fulfil company's goal for long period of time at low risk. To accomplish this objective companies are moving from reactive buying to proactive buying to give more priority to co-creation of wealth with supplier/s. Considering this issue an attempt has been made in this paper to give systematic review of supplier selection and evaluation process from 2005 to 2012 to answer three main questions: 1) Which method is more appropriate for supplier selection? 2) Which evaluating criteria were most cited? 3) Is present trend of research is adequate enough to support proactive buying? In this regard, 78 papers are classified into ten categories to identify factors affecting supplier selection and evaluation process. Statistical analysis has been conducted with software 'R' to have better insight on the trend of research. Recommendations and future work is also included to verify inadequacy of existing methods, if any, to support proactive buying process.
29 citations
TL;DR: In this article, the authors present a review which highlights the operations research contribution to recent green supply chain and logistics literature which specifically focuses on planning and control of supply chain activities with respect to CO2 emission.
Abstract: Global warming impacts are becoming more visible in our daily life. Supply chain activities and many logistics activities are the leading sources of carbon dioxide (CO2) emission and environmental pollutions. These issues have raised concerns to reduce CO2 emissions amount through design and planning of supply chain networks. Operations research has been recognised by many studies as an effective tool to deal with CO2 emission in design and planning of green supply chains. To date, a number of literature reviews have highlighted the contribution of operations research to green supply chain management with broader areas of focus. In this paper, we present a review which highlights the operations research contribution to recent green supply chain and logistics literature which specifically focuses on planning and control of supply chain activities with respect to CO2 emission. Finally, we propose some possible areas for further developments of current studies and directions for future research.
22 citations
TL;DR: This study presents the reliability assessment of two unit active parallel systems connected to an external supporting device for its operation under two types of preventive maintenance, online and offline preventive maintenance.
Abstract: This study presents the reliability assessment of two unit active parallel systems connected to an external supporting device for its operation. The system is under two types of preventive maintenance, online and offline preventive maintenance. The online preventive maintenance (PM) is done before units/system failure while offline preventive maintenance is done to the external supporting device at units'/system failure. Using Kolmogorov forward equations method, explicit expressions related to system effectiveness important to reliability engineers, maintenance managers, system designers, etc. have been obtained. Based on assumed numerical values given to system parameters, graphical illustrations are given to highlight important results. Comparisons are performed to highlight the impact of online and offline preventive maintenance. Reliability of the system is improved with online and offline preventive maintenance.
21 citations
TL;DR: This article formulate the transportation problem in which costs, supplies and demands all are triangular intuitionistic fuzzy numbers, andIntuitionistic fuzzy modified distribution method is proposed to find optimal solution.
Abstract: In general, fuzzy numbers are used to study the transportation problem. In this article, fully fuzzy transportation problem is extended to fully intuitionistic fuzzy transportation problem. We formulate the transportation problem in which costs, supplies and demands all are triangular intuitionistic fuzzy numbers. Intuitionistic fuzzy methods are proposed to find starting basic feasible solution in terms of triangular intuitionistic fuzzy numbers. Intuitionistic fuzzy modified distribution method is proposed to find optimal solution. Then the method is illustrated by a numerical example.
19 citations
TL;DR: The approaches of revised multi-choice goal programming (RMCGP) and utility function into the MOTP are proposed and then compared the solution between them and a real-life problem on TP is considered to show the feasibility and usefulness of this paper.
Abstract: This paper presents the study of transportation problem (TP) with interval goal under multiple objective environment. Most of the multi-objective transportation problems (MOTP) are solved by goal programming (GP) approach. Using GP, the solution of MOTP may not be satisfied all time by the decision maker (DM) when the proposed problem contains interval-valued aspiration level. To overcome this difficulty, here we propose the approaches of revised multi-choice goal programming (RMCGP) and utility function into the MOTP, and then compared the solution between them. Finally, a real-life problem on TP is considered to show the feasibility and usefulness of our paper.
19 citations
Posted Content•
TL;DR: A simple and an effective fuzzy multi-objective linear programming method for solving matrix game in which the payoffs are expressed with fuzzy intervals, and upper and lower bounds of the value of the matrix game are obtained by this method.
Abstract: The conventional game theory is based on known payoffs. In the real situations, usually the payoffs are not known and have to be approximated. The aim of this paper is to develop a simple and an effective fuzzy multi-objective linear programming method for solving matrix game in which the payoffs are expressed with fuzzy intervals. Since, the payoffs of the matrix game are fuzzy intervals, the value of the matrix game is also fuzzy interval. Using upper and lower bounds of the payoffs, we obtain upper and lower bounds of the value of the matrix game by fuzzy multi-objective linear programming method. Lastly, a numerical example is given to illustrate the method.
18 citations
TL;DR: The extent to which lean manufacturing can be implemented given the various financial constraints encountered in the current economic environment of India is examined.
Abstract: To meet competitive requirements and reduce costs, many manufacturers are turning to lean manufacturing techniques. Various problems are usually faced in the implementation of lean manufacturing in the Indian micro, small and medium enterprises. This paper investigates the implementation of the lean manufacturing concept in the MSME sector. In particular, it examines the extent to which lean manufacturing can be implemented given the various financial constraints encountered in the current economic environment of India. To this end, lean implementations were conducted and studied in six Indian MSMEs. The study was conducted using structural equation modelling (SEM). The software, LISREL was used for calculating the ranking factors using TOPSIS and FUZZY TOPSIS technologies. The study was conducted with a view to elucidating the methodology of lean manufacturing that was successful, besides supporting the basic hypotheses. The final solution proposed is a systematic method postulated to improve the productivity and performance of the manufacturing industry.
17 citations
TL;DR: A fully fuzzy transportation problem is solved using the proposed methods and the obtained results are compared with the results of existing approaches like Kumar's method and Ezzati method.
Abstract: In the literature, there are several methods for finding a fuzzy optimal solution to fully fuzzy transportation problems (FFTP). In this paper, a new approach is proposed for solving a fully fuzzy transportation problem. Assuming that the transportation cost, total supply, total demands and also the transportation amount are imprecise in nature. The proposed approach is an extension of popular approach of solving transportation problem (i.e., North West corner method, least cost method, Vogel's approximation method, modified distribution method). The proposed methods are easy to understand and to apply for finding a fuzzy optimal solution to fuzzy transportation problems happening in real-life situations. To illustrate the proposed methods, a fully fuzzy transportation problem is solved using the proposed methods and the obtained results are compared with the results of existing approaches like Kumar's method (Kumar and Singh, 2012) and Ezzati method (Ezzati et al., 2013).
14 citations
TL;DR: In this article, the authors considered a portfolio selection problem wherein expected return of any asset, risk level and proportion of total investment on assets are in the form of interval, and obtained an optimum (best) portfolio.
Abstract: Uncertainty plays an important role in predicting the future earning of the assets in the financial market and it is generally measured in terms of probability. But in some cases, it would be a good idea for an investor to state the expected returns on assets in the form of closed intervals. Therefore, in this paper, we consider a portfolio selection problem wherein expected return of any asset, risk level and proportion of total investment on assets are in the form of interval, and obtain an optimum (best) portfolio. Such portfolio gives the total expected return and proportion of total investment on assets in the form of interval. The proposed portfolio model is solved by considering an equivalent linear programming problem, where all the parameters of the objective function and constraints as well as decision variables are expressed in form of intervals. The procedure gives a strongly feasible optimal interval solution of such problem based on partial order relation between intervals. Efficacy of the results is demonstrated by means of numerical examples.
12 citations
TL;DR: The goal of this paper is to survey the optimisation techniques that support the petrol companies in improving their delivery performance over a multi-period planning horizon and present the mathematical optimisation models that have been developed for both the t-day and periodic variants of the problem.
Abstract: The problem of planning the petrol station replenishment problem (PSRP) consists in making simultaneously several decisions, such as determining the minimum number of trucks required, assigning the stations to the available trucks, defining a feasible route for each tank-truck, etc. The objective to be achieved is usually defined as the minimisation of the travelled distance by the tank-trucks to serve all of the distribution stations. Traditional studies in the literature model and solve this problem over a time period of one single day. Only few works have recognised the fact that extending the time horizon to several days may yield important savings for the delivering company. The goal of this paper is to survey the optimisation techniques that support the petrol companies in improving their delivery performance over a multi-period planning horizon. We present the mathematical optimisation models that have been developed for both the t-day and periodic variants of the problem and discuss the heuristic methods so far developed for their solution.
12 citations
Posted Content•
TL;DR: In this article, a joint economic lot sizing model (JELS) in the context of a two-stage supply chain comprising of single-vendor and single-buyer is presented.
Abstract: This paper deals with joint economic lot sizing model (JELS) in the context of two stage supply chain comprising of single-vendor and single-buyer. The shipment from vendor to buyer is conducted in equally sized and the demand in buyer side is assumed to be normally distributed. In this paper, freight cost is explicitly incorporated in the model and formulated by all-weight freight discount model and incremental freight discount model. We provide two procedures to find the optimal shipment size, safety factor, production batch and discount level on both models. Numerical examples are presented to illustrate the benefit of the model. The result shows that incorporating freight discount into inventory model may result in significant saving on total cost. The increase in the number of discount levels will lead to the decrease in buyer cost and total cost. Moreover, it also shows that the changes in ordering cost, purchase cost, setup cost and production rate in incremental freight discount model give significant impacts on shipment size and safety factor.
TL;DR: Due to NP-hardness of the problem a parameters-tuned simulated annealing (SA) is proposed to obtain a satisfying solution and the efficiency of the proposed SA is evaluated on 180 test problems.
Abstract: Extensive research has been devoted to the vehicle routing problem (VRP). Most of these researches assume a homogenous fleet with constant travel times and single depot. In this paper, we consider the time dependent multi-depot vehicle routing problem. The objective is to minimise the total heterogeneous fleet cost assuming that the travel time between two locations depends on the departure time of vehicles. Also, hard time window constraints for the customers and transit restrictions should be satisfied. The problem is formulated as a mixed integer programming model. Due to NP-hardness of the problem a parameters-tuned simulated annealing (SA) is proposed to obtain a satisfying solution. The efficiency of the proposed SA is evaluated on 180 test problems. The obtained computational results indicate that the proposed procedure is efficient.
Posted Content•
TL;DR: In this paper, the technical and scale efficiencies of 36 public sector hospitals of Uttarakhand, a State of India, for the year 2011 (calendar year) using data envelopment analysis (DEA) technique was examined.
Abstract: This paper examines the technical and scale efficiencies of 36 public sector hospitals of Uttarakhand, a State of India, for the year 2011 (calendar year) using data envelopment analysis (DEA) technique. Number of beds, number of doctors and number of paramedical staff are taken as input variables and number of outdoor-patients and number of indoor-patients as output variables along with two case-mix outputs, i.e., number of major surgery and number of minor surgery received. The data for the study have been collected from the Directorate of Medical Health and Family Welfare, Government of Uttarakhand, Dehradun, India. The study concludes that overall efficiency of hospitals is 77.20%. However, the efficiency scores vary across regions and categories. The hospitals located in the Garhwal region are found to perform better than their counterparts located in the Kumaon region. Category-wise comparison of efficiencies indicates that the district male/female hospitals have relatively higher efficiency scores than the combined/base hospitals. Further, hospitals situated in the plain/partially plain areas are found to have higher efficiency than those situated in the hilly areas of the state. Sensitivity analysis is conducted to examine the robustness of results and Tobit regression is applied to study the impact of various background/environmental factors on the efficiency scores of the hospitals.
TL;DR: In this article, an economic order quantity model for the retailer is proposed, where the supplier provides an up-stream trade credit and the retailer also offers a down-stream credit, and the retailers' optimal cycle time not only exists but also is unique.
Abstract: In a supply chain, the supplier frequently offers the retailer a trade credit period, and the retailer in turn provides a trade credit period to her/his customer to stimulate sales and reduce stock inventory. Also, many products such as fruits, vegetables, high-tech products, pharmaceuticals, and volatile liquids not only deteriorate continuously due to evaporation, obsolescence and spoilage but also have their expiration dates. However, only a few researchers take the expiration date of a deteriorating item into consideration. This paper proposes an economic order quantity model for the retailer where: a) the supplier provides an up-stream trade credit and the retailer also offers a down-stream trade credit; b) deteriorating items not only deteriorate continuously but also have their expiration dates. We then show that the retailer's optimal cycle time not only exists but also is unique. Furthermore, we discuss several special cases including for non-deteriorating items. Finally, we run some numerical examples to illustrate the problem and provide managerial insights.
TL;DR: In this paper, a comparative study is proposed to identify similarities and divergences between the most used multi-criteria decision-making (MCDM) procedures, including ELECTRE III, PROMETHEE I and II, TOPSIS, AHP and PEG.
Abstract: Various multi-criteria decision-making (MCDM) procedures have been developed over the last few decades to help decision-making in complex and seemingly intractable decision tasks. A major criticism of various MCDM methods is that they may yield different results when applied to the same problem. A comparative study is proposed here to identify similarities and divergences between the most used MCDM methods. Compared approaches include: ELECTRE III, PROMETHEE I and II, TOPSIS, AHP and PEG. Two multi-criteria case studies are presented. Studied methods are employed to establish an arrangement of a number of alternatives based on two and eight conflicting criteria, respectively. A Gini index is used to quantify rankings dispersion of Pareto optima obtained through studied MCDM methods. Results highlight the sensitivity of the Pareto-compromise design and ranking to the applied MCDM method.
TL;DR: In this article, the authors present a two-level load balancing problem that consists in loading items into containers which are then stowed in cargo holds of an aircraft, where two objectives are maximised: the total weight and the total priority of loaded cargo.
Abstract: To make air cargo carrier operations cost-effective, a challenging task consists on making profitable the stowage step of the transported freight. This can be accomplished by maximising the weight of the loaded cargo. Owing to the nature and the urgency of the carried cargo, a priority level can also constitute a relevant potential objective to be maximised. In this paper, we present a two level load balancing problem that consists in loading items into containers which are then stowed in cargo holds of an aircraft. Two objectives are maximised: the total weight and the total priority of loaded cargo. In order to minimise fuel consumption and satisfy stability requirements, a load balancing constraints are expressed in terms of the deviation between the gravity centre after loading and its ideal position. An integer linear programming-based formulation is presented for the problem at hand. The loading process performs a discrete multi-objective particle swarm optimisation approach. In order to show the effectiveness of our algorithm and due to the importance of satisfying the load balancing constraints, a practical case study is addressed. An experimental investigation of our approach shows that the proposed approach performs well.
Posted Content•
TL;DR: In this article, a contract compensation on disposal cost of deteriorated products is proposed aiming at coordinating the chain, where the players divide the surplus through bargaining that does not need any knowledge about the negotiation powers of the players.
Abstract: Coordination among supply chain members is imperative for improving chain wide performance. In this paper, we focus on coordination and profit division in a two-echelon supply chain that consists of a manufacturer and a retailer. The manufacturer supplies a perishable product to the retailer in a single lot. The product cannot be reworked and the retailer disposes it without any salvage value. A contract-compensation on disposal cost of deteriorated products is proposed aiming at coordinating the chain. Two situations are explored. In the first, the players divide the surplus through bargaining that does not need any knowledge about the negotiation powers of the players, whereas in the second, the players divide the surplus through bargaining by applying their negotiation powers. Proposed mechanisms for coordination and surplus sharing are illustrated through a numerical example.
TL;DR: In this paper, a constrained continuous review inventory system with a mixture of backorders and lost sales is investigated, where the lead time is assumed to be constant while the lead-time demand is connected to the annual fuzzy random demand through the length of the leadtime.
Abstract: The article investigates a constrained continuous review inventory system with a mixture of backorders and lost sales. The proposed model is developed with the annual customer demand incorporated as a fuzzy random variable. The lead-time is assumed to be constant while the lead-time demand is assumed to be connected to the annual fuzzy random demand through the length of the lead-time. A budget constraint is imposed on the model in the form of an 'imprecise' chance constraint to present a possible way of quantifying fuzzily defined uncertain information of the constraint. A methodology is proposed to determine the optimal order quantity and the reorder point such that the total cost incurred is minimised subject to the constraint. A numerical example is given to illustrate the proposed methodology.
Posted Content•
TL;DR: A new version of the well-known TOPSIS method is introduced: similarity-based TopSIS, which uses a similarity measure to replace the commonly used distance measure.
Abstract: In this paper, we introduce a new version of the well-known TOPSIS method: similarity-based TOPSIS. The new method uses a similarity measure to replace the commonly used distance measure. Similarity measure is formed under generalised Łukasiewicz structure that allows us to form the measure in a more general structure and this way enhances (patent) ranking results. At the same time, however, the selection of a similarity parameter becomes a problem. To fix this new problem, a new method that we call histogram ranking is introduced. Histogram ranking is usable for relaxing the dependence of ranking on parameter value; it is designed to be a complement to parameter dependent ranking methods and is usable, when it is difficult to select precise parameter values. Histogram ranking is based on calculating the centre of gravity points from the histograms and this information is then used to form parameter value independent ranking of the object.
TL;DR: In this paper, a new definition of a relevant variable in a DEA model is proposed for variable selection, and the selection procedure is the conventional iterative backward elimination procedure with multiple statistical comparisons.
Abstract: In this study, a new definition of a relevant variable in a DEA model is proposed for variable selection. The selection procedure is the conventional iterative backward elimination procedure with multiple statistical comparisons. The multiple tests of null hypothesis are reduced to a simple hypothesis test using either the binomial probability or the McNemar test with Bonferroni correction of significant level. From the results based on two simulation populations of moderately and lowly correlated input variables, the proposed procedure using either one of the suggested statistical tests can identify the relevant variables with high accuracy and eliminate the irrelevant variables effectively. In the dataset from a large scale experiment in the US public school education, the reduced model selected by the proposed procedure is shown to be the better approximation of the full model than the ones selected by the Pastor et al. method.
Posted Content•
TL;DR: The probability generating function is obtained in terms of Laplace transforms and the corresponding steady state results explicitly are obtained.
Abstract: We consider an M[X]/G/1 queue with Poisson arrivals, random server breakdowns and Bernoulli schedule server vacation. Both the service time and vacation time follow general distribution. After completion of a service, the server may go for a vacation with probability θ or continue staying in the system to serve a next customer, if any, with probability 1 − θ. With probability p, the customer feedback to the tail of original queue for repeating the service until the service becomes successful. With probability 1 − p = q, the customer departs the system if service be successful. The system may breakdown at random following Poisson process and the repair time follows exponential distribution. Also, we assume that at the end of a busy period, the server needs a random setup time before giving proper service. We obtain the probability generating function in terms of Laplace transforms and the corresponding steady state results explicitly.
Posted Content•
TL;DR: In this article, a new extended version of the existing home healthcare service problems and two mixed integer linear programming formulations are proposed to solve the routing and scheduling problem in home healthcare services.
Abstract: In this paper, we address the home healthcare services problem in terms of routing and scheduling. The aim of the study is to determine a feasible working plan for nurses in order to offer patients the best possible solution in terms of quality of service and economy while satisfying the demands of patients and nurses as well as the related constraints. Besides giving a brief overview of related literature, we describe a new extended version of the existing home healthcare service problems and propose two mixed integer linear programming formulations. Computational results conducted based on a set of randomly generated home healthcare scenarios reveal that the proposed model based on Big-M method is more flexible and applicable in practice when compared to another model based on arc timing method.
TL;DR: This paper presents hybrid metaheuristics for resource constrained project scheduling problem to minimise makespan, a combination of variable neighbourhood search (VNS) and genetic algorithm (GA) and a local search meta heuristics.
Abstract: This paper presents hybrid metaheuristics for resource constrained project scheduling problem to minimise makespan. The hybrid metaheuristics is combination of variable neighbourhood search (VNS) and genetic algorithm (GA). Variable neighbourhood search, a local search metaheuristics strengthens the exploration process of GA. The chromosome of the population pool, generated using priority rule-based heuristics and GA operators, are processed using VNS. The schedules from GA and VNS together form the next generation population pool. Thus the hybrid mechanism provides a combination of exploration and exploitation process to achieve the desired objective. The solution scheme is tested using Kolisch library J30 and J60 datasets for multiple resource types and results are compared with the best available solutions. The computational experimentation presents the performance of the proposed hybrid metaheuristics.
TL;DR: In this paper, the various initiatives which can be taken by different industries are identified and by using the approach of principal component analysis, they are classified into four factors for better understanding and implementation.
Abstract: Remanufacturing is an important manufacturing activity for any economy as it helps to provide processing equipment and products at a lower cost and also helps to reduce environmental degradation and green house gasses. In spite of its economic, environmental and social importance it is not getting the due focus and thus there is a need to take initiatives by different domestic players in India. In this paper, the various initiatives which can be taken by different industries are identified and by using the approach of principal component analysis, they are classified into four factors for better understanding and implementation. The exercise resulted in the extraction of four factors.
TL;DR: A solution technique for UBQP is investigated that is based on perturbing a solution by drawing from the distribution of variables' estimated effect as determined via an unbiased design of experiments (DOE) sampling of the solution space.
Abstract: The unconstrained binary quadratic problem (UBQP) has been shown to be an excellent framework from which to solve many types of problems, both constrained and unconstrained. In this paper we investigate a solution technique for UBQP that is based on perturbing a solution by drawing from the distribution of variables' estimated effect as determined via an unbiased design of experiments (DOE) sampling of the solution space. Solution perturbation is followed by a steepest ascent local search with path relinking. A simple implementation on benchmark problems compares well in time and solution quality with published results on benchmark problems of size up to 7,000 variables. A new set of larger problems having up to 15,000 variables and with non-uniform magnitude distributions of the elements in Q are also investigated and provide evidence that magnitude distributions of Q values affect problem difficulty. These large difficult problem instances required a more sophisticated path relinking approach as well as dynamic adjustments to perturbation sampling.
TL;DR: An incident-based mixed-integer rescheduling model is proposed which is solved using CPLEX software which automatically generates optimal solutions, and an innovative method which decomposes the main problem to five smaller sub-problems, each of which is solve by branch-and-bound algorithm.
Abstract: This paper studies a double-track train rescheduling problem, when an un-foreseen incident over a specific time horizon occurs. We solve the problem by utilising a rescheduling technique named bi-operational approach. An incident-based mixed-integer rescheduling model is proposed which is solved using CPLEX software which automatically generates optimal solutions. To reduce the computation time, an innovative method is proposed which decomposes the main problem to five smaller sub-problems, each of which is solved by branch-and-bound algorithm. Moreover, a novel heuristic is proposed which divides the available computation time between sub-problems proportionately depending on their sizes. An experimental analysis, on two double-track railways of Iranian network, indicates that the decomposition method provides near-optimal solutions with much shorter computation times compared with CPLEX. The analysis also provides evidence for effectiveness of the proposed heuristic in tackling large-scale problems; so that good feasible solutions are achievable in limited times compatible with real-time use.
Posted Content•
TL;DR: Results of extensive simulation study with NS-2, confirms that the hit ratio of UDCR approaches that of JSSRPP in steady state.
Abstract: In this article, we formulate the problem of optimum joint replica server deployment and content placement in urban content delivery networks as a bi-objective binary integer programming model namely JSSRPP. The proposed formulation results in an optimum design such that the miss ratio of requests, the client response time and the cost of server deployment are minimised. In practice, the popularity of files may change over the course of time, thus, a novel adaptive file replacement algorithm namely UDCR is also proposed in which files are scored according to different criteria including the time and the number of recent requests and the size of requested files. Then, in discrete points of time, the files with the lowest score are replaced with the one experiencing the highest number of misses. Results of extensive simulation study with NS-2, confirms that the hit ratio of UDCR approaches that of JSSRPP in steady state.
Posted Content•
TL;DR: The experimental results show that the proposed probe guided mutation operator outperforms the classical polynomial mutation operator, based on a number of different performance metrics that evaluate both the proximity of the solutions to the Pareto front and their dispersion on it.
Abstract: This paper re-examines the classical polynomial mutation (PLM) operator and proposes a probe guided version of the PLM for more efficient exploration of the search space. The proposed probe guided mutation (PGM) operator applied to two well-known MOEAs, namely the non-dominated sorting genetic algorithm II (NSGAII) and strength Pareto evolutionary algorithm 2 (SPEA2), under two different sets of test functions. The relevant results are compared with the results derived by the same MOEAs by using their typical configuration with the PLM operator. The experimental results show that the proposed probe guided mutation operator outperforms the classical polynomial mutation operator, based on a number of different performance metrics that evaluate both the proximity of the solutions to the Pareto front and their dispersion on it.
TL;DR: F fuzzy logic is used to model the imperfections of maintenance actions due to operators and a minimisation of maintenance costs is considered and compared with a perfect maintenance model.
Abstract: This paper uses fuzzy logic to model the imperfections of maintenance actions due to operators. In general, enterprises using a perfect maintenance policy must introduce important maintenance improvement tools to ensure that maintenance actions are really perfect. In fact, the accuracy of such actions can never be pulled apart of the human factor and so, its inclusion in the maintenance programmes is crucial to reduce maintenance costs. Nevertheless, this human factor is very difficult to model and therefore it is generally not included in maintenance programmes. Accordingly, the interest in ruled-based fuzzy logic arises as an important tool to model system imperfections such as worker's skills. Based on this fuzzy model, a minimisation of maintenance costs is considered in this paper and compared with a perfect maintenance model.
TL;DR: A modified GA is devised to solve the intended problem, where risk and time-window constraints are also to be satisfied and the efficiency of the algorithm is tested by using some model datasets from TSPLIB and some existing benchmark test functions.
Abstract: In this paper, we have presented a solid travelling salesman problem (STSP) with introduction of time-window and constraints. In STSP, a traveller can avail different conveyance for travelling. Also as constraint, a risk factor is there joining a path between two cities. During a tour, the traveller must ensure that the entire risk of the tour is within a prearranged risk level. Costs, time, and risk factor of travel using different conveyances are dissimilar and interval in nature. Finding a complete tour with minimum cost is the goal of the problem, where risk and time-window constraints are also to be satisfied. The standard genetic algorithm (GA) is modified by adding three features, namely refinement, immigration, and refreshing population, and thus a modified GA is devised to solve the intended problem. The efficiency of the algorithm is tested by using some model datasets from TSPLIB and some existing benchmark test functions.