One of the techniques, which are used for Multiple Criteria Decision Making (MCDM) is the permutation. In the classical form of permutation, it is assumed that weights and decision matrix components are crisp. However, when group decision making is under consideration and decision makers could not agree on a crisp value for weights and decision matrix components, fuzzy numbers should be used. In this article, the fuzzy permutation technique for MCDM problems has been explained. The main deficiency of permutation is its big computational time, so a Tabu Search (TS) based algorithm has been proposed to reduce the computational time. A numerical example has illustrated the proposed approach clearly. Then, some benchmark instances extracted from literature are solved by proposed TS. The analyses of the results show the proper performance of the proposed method.

The problem of portfolio optimization is a standard problem in financial world and it has received tremendous attentions. Portfolio optimization plays essential role in determining portfolio strategies for investors. Portfolio optimization is intrinsically a discrete optimization problem whose decision criteria are in conflict and the proposed study of this paper considers a portfolio optimization problem involving fuzzy random variables. To solve the proposed model, we first present the possibility and necessity-based model to reformulate the fuzzy random portfolio selection model into linear programming models and using the resulted linear programs, a multi-objective problem is constructed. To solve the multi-objective problem we propose some methods to consider decision makers’ optimistic and pessimistic views. A numerical example illustrates the whole idea on multiobjective fuzzy random portfolio optimization by possibility and necessity-based model.

In this paper, we evaluate the performance of a supply chains (SCs) under uncertainty with different components such as direct costs, operational costs, transaction expenses, order lead time, product flexibility and net profit. Data Envelopment Analysis (DEA) can be used for measuring the performance of supply chain problems. On the other hand, robust optimization approach is a powerful technique for handling problems faced with various environmental uncertainties. This paper combines these two concepts and proposes a method to evaluate SCs performance. The results of the proposed method, under any different environmental situation, show which ranking of SC’s performance is better in a network. The preliminary results of the implementation of a real-world case study indicates that the method could be successfully used for performance measurement.

Manpower planning using annualised hours is an effective tool where seasonal demand for staff in industry exists. In annualised hours (AH) workers are contracted to work for a certain number of hours per year. The workers are associated with relative efficiency for different types of tasks. This paper proposes a Mixed Integer linear Programming (MILP) model to solve an annualised working hours planning problem when spike in demand exists. The holiday weeks for the workers are considered as partially individualised. If a worker has been assigned with more than one type of working week in a week, this will be compensated with one or more holiday week. The performance of the model is demonstrated with an example. It can be seen that this type of modelling helps to meet the spikes in demand with less capacity shortage compared with one working week in a week.

Clustering plays an essential role for data analysis and it has been widely used in different fields like data mining, machine learning and pattern recognition. Clustering problem divides some data, which is more similar to each other in terms of parameters under consideration. One of available methods about this area is k-means algorithm. Despite dependency of this algorithm on initial condition and convergence to local optimal points, it classifies n data to k clusters with high speed. Since we encounter a huge volume of data in clustering problems, one of suitable methods for optimal clustering is to use a meta-heuristic algorithm, which improves clustering operation. In this paper, differential evolution algorithm is utilized for solving available problems in k-means algorithm. In this paper, meta-heuristic algorithm has been used for solving data clustering problems. The applied algorithm has been compared with k-means algorithm on six known dataset from UCI database. Results show that this algorithm achieves better clustering than k-means algorithm.

This paper considers preemption and idle time are allowed in a single machine scheduling problem with just-in-time (JIT) approach. It incorporates Earliness/Tardiness (E/T) penalties, interruption penalties and holding cost of jobs which are waiting to be processed as work-in-process (WIP). Generally in non-preemptive problems, E/T penalties are a function of the completion time of the jobs. Then, we introduce a non-linear preemptive scheduling model where the earliness penalty depends on the starting time of a job. The model is liberalized by an elaborately–designed procedure to reach the optimum solution. To validate and verify the performance of proposed model, computational results are presented by solving a number of numerical examples.

Determining the optimal inventory control and selling price for deteriorating items is of great significance. In this paper, a joint pricing and inventory control model for deteriorating items with price- and time-dependent demand rate and time-dependent deteriorating rate with partial backlogging is considered. The objective is to determine the optimal price, the replenishment time, and economic order quantity such that the total profit per unit time is maximized. After modeling the problem, an algorithm is proposed to solve the resulted problem. We also prove that the problem statement is concave function and the optimal solution is indeed global.

This paper studies capacitated facility location problem by considering green management perspectives. The proposed study considers reverse logistic problem as an alternative strategy for facility location in an attempt to take care of environmental characteristics. The resulted problem is formulated as mixed integer programming and it is classified as an NP-Hard problem. Therefore, a Lagrangian relaxation methodology is presented to reduce the complexity of the proposed problem and the solution has been implemented for some instances to examine the performance of the proposed study.

Supply chain is a network of organizations that work together and performs various business functions such as procurement of raw materials, converting the raw material into semi-finished or finished goods and distributing the same to their ultimate customers. Presence of bullwhip effect in a supply chain is costly and degrades the performance of the supply chain. Reduction in bullwhip effect can improve the efficiency or profitability of a supply chain. The objective of this paper is to know the impact of imperfect Advance Demand Information (ADI) sharing on bullwhip effect in a four-stage serial supply chain and to evaluate the supply chain performance by conducting an experiment similar to the beer distribution game. The performance measures used are variance of orders placed by each stage, fill rate, total inventory at each stage and total holding cost of the supply chain. Results show that imperfect ADI improves the performance of the supply chain. The performance of the supply chain is also evaluated under order up to level (R, S) policy with safety stock and it is found that the performance of supply chain is better when inventory policy used.

Electricity distribution systems are considered as the most critical sectors in countries because of the essentiality of power supplement security, socioeconomic security, and way of life. According to the central role of electricity distribution systems, risk analysis helps decision maker determine the most serious risk items to allocate the optimal amount of resources and time. Probability-impact (PI) matrix is one of the most popular methods for assessment of the risks involved in the system. However, the traditional PI matrix is criticized for its inability to take into account the inherent uncertainty imposed by real-world systems. On the other hand, fuzzy sets are capable of handling the uncertainty. Thus, in this paper, fuzzy risk assessment model is developed in order to assess risk and management for electricity distribution system asset protection. Finally, a comparison analysis is conducted to show the effectiveness and the capability of the new risk assessment model.