In an environment where markets go through a volatile process, and rapid fundamental changes occur due to technological advances, it is important to ensure and maintain a good performance measurement. Organizations, in their performance evaluation, should consider different types of financial and non-financial indicators. In systems like direct sales stores in which decision units have multiple inputs and outputs, all criteria influencing on performance must be combined and examined in a system, simultaneously. The purpose of this study is to evaluate the performance of different products through direct sales of a firm named Shirin Asal with a combination of Balanced Scorecard, fuzzy AHP and TOPSIS so that the weaknesses of subjectivity and selective consideration of evaluators in evaluating the performance indicators are reduced and evaluation integration is provided by considering the contribution of each indicator and each indicator group of balanced scorecard. The research method of this case study is applied. The data collection method is a questionnaire from the previous studies, the use of experts' opinions and the study of documents in the organization. MATLAB and SPSS were used to analyze the data. During this study, the customer and financial perspectives are of the utmost importance to assess the company branches. Among the sub-criteria, the rate of new customer acquisition in the customer dimension and the net income to sales ratio in financial dimension are of the utmost importance.

One subcategory of project scheduling is the resource constrained project scheduling problem (RCPSP). The present study proposes a differential evolution algorithm for solving the RCPSP making a small change in the method to comply with the model. The RCPSP is intended to program a group of activities of minimal duration while considering precedence and resource constraints. The present study introduces a differential evolution algorithm and local search was added to improve the performance of the algorithm. The problems were then solved to evaluate the performance of the algorithm and the results are compared with genetic algorithm. Computational results confirm that the differential evolution algorithm performs better than genetic algorithm.

Multi-period models of portfolio selection have been developed in the literature with respect to certain assumptions. In this study, for the first time, the portfolio selection problem has been modeled based on mean-semi variance with transaction cost and minimum transaction lots considering functional constraints and fuzzy parameters. Functional constraints such as transaction cost and minimum transaction lots were included. In addition, the returns on assets parameters were considered as trapezoidal fuzzy numbers. An efficient genetic algorithm (GA) was designed, results were analyzed using numerical instances and sensitivity analysis were executed. In the numerical study, the problem was solved based on the presence or absence of each mode of constraints including transaction costs and minimum transaction lots. In addition, with the use of sensitivity analysis, the results of the model were presented with the variations of minimum expected rate of programming periods.

One of the most important questions in outsourcing management is to decide on the allocation of the activities, processes or services either from the inside or from the outside of the organizations through outsourcing operations. It is necessary to determine the factors influencing outsourcing decision making as well as their impacts on decisions. In this paper, the existing approaches and models and their related criteria are studied and the necessary criteria are explored based on different conditions in marine manufacturing industries. These criteria are categorized in four dimensions; namely Capacity, Technology, Strategy and financial, and Risk called CTSR. After determining the weights of the criteria based on paired-wise comparisons, the related calculations are carried out using PROMETHEE method. Finally, a Black, White, Gray (BWG) analysis is executed to determine whether a candidate should be outsourced or not.

Machine failures during production period may impose thousands to millions of dollars to a manufacturing system. In this paper, the impact of machine failures on production lines in a closed-loop supply chain systems is examined. For this purpose, a new method is proposed for scheduling manufacturing workshops in a supply chain systems. The aim is to determine the best production plans in a manufacturing system by considering alternative preventive maintenance programs while machine failures can affect system performance. To solve the model, a hybrid Ant Colony and Simulated Annealing algorithms is developed and the results are compared with branch and bound method. Our findings show that the condition of emerging machine failure affects machines’ capacity which yields to lost sale. The impacts of using appropriate preventive maintenance on reducing lost sale is also examined. The results indicate that the proposed method can significantly reduce the level of sale variation in supply chain systems.

Recently, learning effects have been studied as an interesting topic for scheduling problems, however, most researches have considered single or two-machine settings. Moreover, learning factor has been considered for job times instead of setup times and the same learning effect has been used for all machines. This paper studies the m-machine no-wait flowshop scheduling problem considering truncated learning effect in no-wait flowshop environment. In this problem, setup time is a function of job position in the sequence with a learning truncation parameter and each machine has its own learning effect. In this paper, a mixed integer linear programming is proposed for the problem to solve such problem. This problem is NP-hard so an improved genetic algorithm (GA) and a simulated annealing (SA) algorithm are developed to find near optimal solutions. The accuracy and efficiency of the proposed procedures are tested against different criteria on various instances. Numerical experiments approve that SA outperforms in most instances.

In this paper, a stochastic model of a closed-loop supply chain (CLSC) with one risk-averse manufacturer, one risk-averse retailer and one risk-averse third party is developed. To analyze how the members make decisions about wholesale price, collection rate, retail price and sales effort under different decision-making structures, the optimal decision problem under uncertain price and sales effort-dependent demand is studied through development of four game theoretical models. The equilibrium results between various models are compared and the optimal decisions from each member’s perspective are investigated. According to the results, the third party-led model has better performance than manufacturer-led model. The cooperation mode of manufacturer and retailer is beneficial for the whole chain and customers and the cooperation mode of manufacturer and third party is the most effective model to collect the used-product. Finally, to increase the performance of decentralized CLSC compared with the centralized CLSC, a coordination contract is developed. The results indicate that this contract is advantageous for the members of CLSC, the customers, and the environmental issues.

Today, most industrial managers in the world are interested in protecting the environment and biological resources. On the other hand, current technologies are getting momentum towards specialization and globalization. Thus, in order to remain in a highly competitive world market, producers have to respond to the customers' demands under different circumstances. The leading role of distribution centers to deliver products to customers on time and to reduce the costs of stock maintenance has attracted the attention of many supply chain managers in current competitive conditions. Cross docking is a logistic strategy aiming to reduce the stock and increase the level of customer's satisfaction. Products are delivered from the supplier to the customers through cross docking. In this paper, a nonlinear multiproduct vehicle location-routing model is presented with heterogeneous vehicles. Each truck can carry one or more types of products. In other words, compatibility between product and vehicle has been accounted for here. This model aims to find out the possible minimum number of cross dockings among the existing set of discrete locations and minimize the total cost of opening cross docking centers as well as vehicle transportation (distribution and operation cost) costs. In sum, the model aims to find the number of cross docking centers, the number of vehicles and the best route in the distribution network. Since the model is mixed integer programming, to apply the model to medium and large scale problems, meta innovative genetic and particle swarm optimization algorithms are introduced. The results obtained from examining various problems show high efficiency of the proposed methods.

This paper provides a review on recent works in the field of competitive facility location models based on the following seven components: 1) Variables, 2) Competition type, 3) Solution space, 4) Customer behavior, 5) Demand type, 6) Number of new facilities and 7) Relocation and redesign possibility. First, the components are introduced and then based on these components; different studies are compared with each other via a proposed taxonomy and finally a review on work of each paper is provided.

In this article, a manpower allocation and cell loading problem is studied, where demand is sto-chastic. The inter-cell and intra-cell movements are considered and attention is focused on as-signing operators with different skill levels to operations, because cell performance in addition to load cell is dependent on manpower. The purpose of this article is manpower allocation in cellu-lar manufacturing with consideration to learning and training policies. The manpower skill levels are determined in order to enhance production rate. The main contribution of this approach is the scenarios of training and learning in addition to the combination of training and learning being simulated. By using these three scenarios, the skill level of workers increase which reduces the processing time. In this regard cell layout is static where processing times and customer demand follow a normal distribution. As one of the significant costs of industrial unit is related to pro-duction cost, this study has attempted to reduce these costs by increasing the skill level of opera-tor which causes to reduce the processing time. Scenarios are evaluated by using a simulation method that finally attained results indicate this simulation provides better manpower assign-ments.