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This study develops a inventory model to determine ordering policy for deteriorating items with shortages under markovian inflationary conditions. Markov processes include process whose future behavior cannot be accurately predicted from its past behavior (except the current or present behavior) and which involves random chance or probability. Behavior of business or economy, flow of traffic, progress of an epidemic, all are examples of Markov processes. Since the far previous inflation rate don’t have a great impact on the current inflation rate, so, It is logical to consider changes of the inflation rate as a markov process. In addition, It is assumed that the cost of the items changes as a Continuous – Time - Markov Process too. The inventory model is described by differential equations over the time horizon along with the present value method. The objective is minimization of the expected present value of costs over the time horizon. The numerical example and a sensitivity analysis are provided to analyze the effect of changes in the values of the different parameters on the optimal solution.

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Scheduling problem for the hybrid flow shop scheduling problem (HFSP) followed by an assembly stage considering aging effects additional preventive and maintenance activities is studied in this paper. In this production system, a number of products of different kinds are produced. Each product is assembled with a set of several parts. The first stage is a hybrid flow shop to produce parts. All machines can process all kinds of parts in this stage but each machine can process only one part at the same time. The second stage is a single assembly machine or a single assembly team of workers. The aim is to schedule the parts on the machines and assembly sequence and also determine when the preventive maintenance activities get done in order to minimize the completion time of all products (makespan). A mathematical modeling is presented and its validation is shown by solving an example in small scale. Since this problem has been proved strongly NP-hard, in order to solve the problem in medium and large scale, four heuristic algorithms is proposed based on the Johnson’s algorithm. The numerical experiments are used to run the mathematical model and evaluate the performance of the proposed algorithms.

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Material selection is a challenging issue in manufacturing processes while the inappropriate selected material may lead to fail the manufacturing process or end user experience especially in high-tech industries such as aircraft and shipping. Every material has different quantitative and qualitative criteria which should be considered simultaneously when assessing and selecting the right material. A weighted linear optimization method (WLOM) in the class of data envelopment analysis which exists in literature is adopted to address material selection problem while accounting for both qualitative and quantitative criteria. However, it is demonstrated the adopted WLOM method is not able to produce a full ranking vector for the material selection problems borrowed from the literature. Thus, an augmented common weight data envelopment analysis model (ACWDEA) is developed in this paper with the aim of eliminating deficiencies of WLOM model. The proposed ACWDEA is able to produce full ranking vector in decision making problems with less computational complexities in superior to the WLOM. Two material selection problems are solved and results are compared with WLOM and previous methods. Finally, the robustness and effectiveness of the proposed ACWDEA method are evaluated through Spearman’s correlation tests.

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A fundamental problem is the use of DEA in multistep or multilevel processes such as supply chain, lack of attention to processes’ internal communications in a way that the recent studies on DEA in the context of serial processes have focused on closed systems that the outputs of one level become the inputs of the next level and none of the inputs enter the mediator process. The present study aimed to examine the general dimensions of an open multilevel process. Here, some of the data such as inputs and outputs are supposed to leave the system while other outputs turn into the inputs of the next level. The new inputs can enter the next level as well. We expand this mode for network structures. The overall performance of such a structure is considered as a weighted average of sectors’ performance or distinct steps. Therefore, this suggested model in this study, not only provides the possibility to evaluate the performance of the entire network, but creates the performance analysis for each of the sub-processes. On the other hand, considering the data with undesirable structure leads to more correct performance estimation. In the real world, all productive processes do not comprise desirable factors. Therefore, presenting a structure that is capable of taking into account the undesirable structure is of crucial importance. In this study, a new model in the DEA by network structure is offered that can analyze the performance considering undesirable factors.

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In the previous decade, fossil energy resources shortage and environmental challenges such as air and water pollution, global warming, and greenhouse-gas emissions, etc. have increased environmental concerns considerably. Since, one of the most practical and useful solutions to decrease environmental pollutants is to deploy green purchasing and clean energies by organizations or even governments. Thus, the construction of renewable-energy power plants and, consequently, the green supplier selection for these plants’ equipment has become more important. With this respect, this article presents a novel approach to assess and select green suppliers of a solar power plant. The proposed approach integrates Fuzzy Analytic Hierarchy Process and VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) methodologies. The results demonstrate the efficiency of the proposed approach as a practical tool to assist managers and CEOs (Chief Executive Officers) of electric power industry in assessing suppliers of solar power plant’s equipment.

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This work investigates the effect of different inventory policies of a supply chain model using the system dynamics approach which belongs to the class of Vendor Managed Inventory (VMI), automatic pipeline, inventory and order based production control systems (VMI-APIOBPCS). This work helps management to investigate the effect of different policies such as adding the VMI system or third party logistic (TPL) on the whole cost of the supply chain. To this end, this work applies system dynamics in supply chain with two supplier and one retail channel which consists of VMI system. Moreover, this work studies the performance of the proposed model via three metrics: Bullwhip effect; satisfaction of the end-customer; the amount of the whole inventory of chain.

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In most manufacturing processes, each product may contain a variety of quality characteristics which are of the interest to be optimized simultaneously through determination of the optimum setting of controllable factors. Although, classic experimental design presents some solutions for this regard, in a fuzzy environment, and in cases where the response data follow non-normal distributions, the available methods do not apply any more. In this paper, a general framework is introduced in which NORTA inverse transformation technique and fuzzy goal programming are used to deal with non-normality distribution of the response data and the fuzziness of response targets respectively. Moreover, the presented framework uses a simulation approach to investigate the effectiveness of the determined setting of controllable factors obtained from statistical analysis, for optimization of sink mark index, deflection rate and volumetric shrinkage in plastic molding manufacturing processes. The accuracy of the proposed method is verified through a real case study.

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This paper considers the multi-depot vehicle routing problem with time window in which each vehicle starts from a depot and there is no need to return to its primary depot after serving customers. The mathematical model which is developed by new approach aims to minimizing the transportation cost including the travelled distance, the latest and the earliest arrival time penalties. Furthermore, in order to reduce the problem searching space, a novel GA clustering method is developed. Finally, Experiments are run on number problems of varying depots and time window, and customer sizes. The method is compared to two other clustering techniques, fuzzy C means (FCM) and K-means algorithm. Experimental results show the robustness and effectiveness of the proposed algorithm.

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This paper presents the formulation and solution of the Combinatorial Multi-Mode Resource Constrained Multi-Project Scheduling Problem. The focus of the proposed method is not on finding a single optimal solution, instead on presenting multiple feasible solutions, with cost and duration information to the project manager. The motivation for developing such an approach is due in part to practical situations where the definition of optimal changes on a regular basis. The proposed approach empowers the project manager to determine what is optimal, on a given day, under the current constraints, such as, change of priorities, lack of skilled worker. The proposed method utilizes a simulation approach to determine feasible solutions, under the current constraints. Resources can be non-consumable, consumable, or doubly constrained. The paper also presents a real-life case study dealing with scheduling of ship repair activities.

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In this study, a two-objective mixed-integer linear programming model (MILP) for multi-product re-entrant flow shop scheduling problem has been designed. As a result, two objectives are considered. One of them is maximization of the production rate and the other is the minimization of processing time. The system has m stations and can process several products in a moment. The re-entrant flow shop scheduling problem is well known as NP-hard problem and its complexity has been discussed by several researchers. Given that NSGA-II algorithm is one of the strongest and most applicable algorithm in solving multi-objective optimization problems, it is used to solve this problem. To increase algorithm performance, Taguchi technique is used to design experiments for algorithm’s parameters. Numerical experiments are proposed to show the efficiency and effectiveness of the model. Finally, the results of NSGA-II are compared with SPEA2 algorithm (Strength Pareto Evolutionary Algorithm 2). The experimental results show that the proposed algorithm performs significantly better than the SPEA2.

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In this paper, a comprehensive model is proposed to design a network for multi-period, multi-echelon, and multi-product inventory controlled the supply chain. Various marketing strategies and guerrilla marketing approaches are considered in the design process under the static competition condition. The goal of the proposed model is to efficiently respond to the customers’ demands in the presence of the pre-existing competitors and the price inelasticity of demands. The proposed optimization model considers multiple objectives that incorporate both market share and total profit of the considered supply chain network, simultaneously. To tackle the proposed multi-objective mixed-integer nonlinear programming model, an efficient hybrid meta-heuristic algorithm is developed that incorporates a Taguchi-based non-dominated sorting genetic algorithm-II and a particle swarm optimization. A variable neighborhood decomposition search is applied to enhance a local search process of the proposed hybrid solution algorithm. Computational results illustrate that the proposed model and solution algorithm are notably efficient in dealing with the competitive pressure by adopting the proper marketing strategies.

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In this paper, we study the problem of integrated capacitated hub location problem and seat inventory control considering concept and techniques of revenue management. We consider an airline company maximizes its revenue by utilizing the best network topology and providing proper booking limits for all itineraries and fare classes. The transportation system arises in the form of a star/star network and includes both hub-stop and non-stop flights. This problem is formulated as a two-stage stochastic integer program with mixed-integer recourse. We solve various instances carried out from the Turkish network data set. Due to the NP-hardness of the problem, we propose a hybrid optimization method, consisting of an evolutionary algorithm based on genetic algorithm and exact solution. The quality of the solutions found by the proposed meta-heuristic is compared with the original version of GA and the mathematical programming model. The results obtained by the proposed model imply that integrating hub location and seat inventory control problem would help to increase the total revenue of airline companies. Also, in the case of serving non-stop flights, the model can provide more profit by employing less number of hubs.

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This study examines inventory management practices of flour milling manufacturing firms and their effects on operational performance. Five flour milling manufacturing firms in Lagos were used for this study. Structured questionnaire was the major instrument for the collection of relevant primary data while descriptive statistics such as mean and standard deviation was deployed to analyzing the data gathered. The results obtained showed that exception of the large manufacturing companies, most of the medium-sized flour milling firms adopts different inventory management strategies from the scientific and best practice models. Their inventory management strategies and policies were rather based on factors such as changing level of customer demand, prevailing industry practices, forecast estimates and guesses, and available production capacity. Findings also revealed significant differences between the effective management of inventory and optimal operating performance. For instance, while firms that adopt best practice inventory management approaches reported efficiency in capacity utilization, increased service level, and reduced lead time, others with different strategies had minimal utilization of material resources. There is need for flour manufacturing firms to implement scientific inventory management models to adequately handle material shortages, product stock outs situations, component pile up and their associated penalties.

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Inventory models play an important role in determining the optimal ordering and pricing policies. Much work has been reported in literature regarding inventory models with finite or infinite replenishment. But in many practical situations the replenishment is governed by random factors like procurement, transportation, environmental condition, availability of raw material etc., Hence, it is needed to develop inventory models with random replenishment. In this paper, an EPQ model for deteriorating items is developed and analyzed with the assumption that the replenishment is random and follows a Weibull distribution. It is further assumed that the life time of a commodity is random and follows a generalized Pareto distribution and demand is a function of on hand inventory. Using the differential equations, the instantaneous state of inventory is derived. With suitable cost considerations, the total cost function is obtained. By minimizing the total cost function, the optimal ordering policies are derived. Through numerical illustrations, the sensitivity analysis is carried. The sensitivity analysis of the model reveals that the random replenishment has significant influence on the ordering and pricing policies of the model. This model also includes some of the earlier models as particular cases for specific values of the parameters.

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Considered supply chain in this article consists of one vendor and multiple retailers where the vendor applies vendor managed inventory. Considering vendor as a leader and retailers as followers, Stackelberg game theory is applied for modeling and analyzing this system. A general mixed integer nonlinear model is developed which can optimizes the performance of the system under revenue sharing contract, wholesale price contract and centralized structure. Based on this model, we numerically analyzed the performance of revenue sharing contract in the considered supply chain and four states for revenue sharing contract are analyzed at the end. Moreover, in each state, performance of the system under revenue sharing contract is compared with the performance of the system under wholesale price contract and centralized structure.

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Occurrence of natural disaster inflicts irreparable injuries and symptoms on humans. In such conditions, affected people are waiting for medical services and relief commodities. Thus, quick reaction of medical services and relief commodities supply play important roles in improving natural disaster management. In this paper, a multi-objective non-linear credibility-based fuzzy mathematical programming model under uncertainty conditions is presented, which considers two vital needs in disaster time including medical services and relief commodities through location of hospitals, transfer points, and location routing of relief depots. The proposed model approaches reality by considering time, cost, failures probability in routes, and parameters uncertainty. The problem is first linearized and then global criterion method is applied for solving the multi objective model. Moreover, to illustrate model efficiency, a case study is performed on region 1 of Tehran city for earthquake disaster. Results demonstrate that if Decision-makers want to meet uncertainty with lowered risk, they have to choose a high minimum constraint feasibility degree even though the objective function will be worse.

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Cost of Quality analysis is emerged as an effective tool for the industrial managers for pinpointing the deficiencies in the system as well as for identifying the improvement areas by highlighting the cost reduction opportunities. However , this analysis will be fully effective only if it is further extended to identify the cost incurred in ensuring quality in all areas of the supply chain including the hidden costs and costs of missed out opportunities. Most of the hidden elements of quality costs are difficult to track and not accounted by the traditional accounting tools. An exploratory analysis is made in this research to identify the hidden elements of quality costs in manufacturing industry. Further, the identified cost elements are classified into various groups for better analysis and, finally, prioritized to identify the vital few among them. Analytic Hierarchy Process (AHP) technique which is one of the most popular Multi Criteria Decision Method (MCDM) and Pareto analysis were used in this study for prioritizing the hidden quality cost elements based on their degree of impact on overall cost of quality. By this analysis, the key cost elements which are to be addressed to reduce the overall cost of quality are identified.

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In this study, a bi-objective model for integrated planning of production-distribution in a multi-level supply chain network with multiple product types and multi time periods is presented. The supply chain network including manufacturers, distribution centers, retailers and final customers is proposed. The proposed model minimizes the total supply chain costs and transforming time of products for customers in the chain. The proposed model is in the class of linear integer programming problems. The complexity of the problem is large and in the literatur, this problem has been shown to be NP-hard. Therefore, for solving this problem, two multi objective meta-heuristic approaches based on Pareto method including non-dominated Sorting Genetic Algorithm-II (NSGA-II) and non-dominated Ranking Genetic Algorithm (NRGA) have been suggested. Since the output of meta- heuristic algorithms are highly dependent on the input parameters of the algorithm, Taguchi method (Taguchi) is used to tune the parameters. Finally, in order to evaluate the performance of the proposed solution methods, different test problems with different dimensions have been produced and the performances of the proposed algorithms on the test problems have been analyzed.

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This paper presents an optimized diamond structured automobile supply chain network towards a robust Business Continuity Management model. The model is necessitated by the nature of the automobile supply chain. Companies in tier two are centralized and numerically limited and have to supply multiple tier one companies with goods and services. The challenge with this supply chain structure is the inherent risks in the supply chain. Once supply chain disruption takes place at tier 2 level, the whole supply chain network suffers huge loses. To address this challenge, the paper replaces Risk Analysis with Risk Ranking and it introduces Supply Chain Cooperation (SCC) to the traditional Business Continuity Plan (BCP) concept. The paper employed three statistical analysis techniques (correlation analysis, regression analysis and Smart PLS 3.0 calculations). In this study, correlation and regression analysis results on risk rankings, SCC and Business Impact Analysis were significant, ascertaining the value of the model. The multivariate data analysis calculations demonstrated that SCC has a positive total significant effect on risk rankings and BCM while BIA has strongest positive effects on all BCP factors. Finally, sensitivity analysis demonstrated that company size plays a role in BCM.

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This paper presents the capacitated Windy Rural Postman Problem with several vehicles. For this problem, two objectives are considered. One of them is the minimization of the total cost of all vehicle routes expressed by the sum of the total traversing cost and another one is reduction of the maximum cost of vehicle route in order to find a set of equitable tours for the vehicles. Mathematical formulation is provided. The multi-objective simulated annealing (MOSA) algorithm has been modified for solving this bi-objective NP-hard problem. To increase algorithm performance, Taguchi technique is applied to design experiments for tuning parameters of the algorithm. Numerical experiments are proposed to show efficiency of the model. Finally, the results of the MOSA have been compared with MOCS (multi-objective Cuckoo Search algorithm) to validate the performance of the proposed algorithm. The experimental results indicate that the proposed algorithm provides good solutions and performs significantly better than the MOCS.