Scientia Iranica
Volume:25 Issue: 5, Sep - Oct 2018

This paper focuses on determining ordering and pricing policies in a single-period closed loop supply chain. The assumed supply chain includes a number of manufacturers who provide their different but substitutable products for their customers via a common retailer; however the manufacturers can have their own internet-based sales channel in order to provide products for the customers. The customers can return products if they are not satisfied with. The return products are collected in a repair center, are repaired and sold as second-hand product through the retailer channel or through an internet-based sale channel. The customer demand is assumed to be stochastic. This study aims to determine the optimum prices for the internet-based and retailer sales channels for the initial and second hand products. It also tries to determine the optimal values of retailer order and production rates of manufacturers and repair centers. Three types of game strategies for the supply chain including Nash, Stackelberg game with retailer as the leader and Stackelberg game with manufacturers and repair center as the leaders are studied in decentralized condition. The analytical equilibrium solutions, equations and constraints are extracted for these strategies. Finally, the effect of variations in key parameter is investigated.

This paper incorporates capital flow constraints and trade credit to lot sizing problems. Capital flow constraint is different from traditional capacity constraints: when a manufacturer begins to produce a certain number of products, its present capital should not be less than its total production costs of that period; otherwise, the manufacturer must decrease production quantity or suspend production, or it could delay payment using trade credit. Moreover, the capital of each period should also be greater than zero to avoid bankruptcy. We formulate a mathematical model for the single-item lot sizing problem. Based on dynamic programming, we approximate this mixed integer problem to a traveling salesman problem finding the longest route, divide the model into sub-linear problems without integer variables, and propose a dynamic programming algorithm with heuristic adjustment to solve it. The sub-linear problems can be easily solved by interior point algorithm. Our algorithm could obtain optimal solutions under certain situations. Numerical analysis shows our algorithm has small optimality deviation percentage under other situations and holds computation efficiency advantage compared with CPLEX 12.6.2. It also indicates capital flow constraints and the application of trade credit in lot sizing problems could affect optimal production decisions.

This research investigates joint scheduling of maintenance and production planning. This novel integrated problem takes benefit of reliability-centered maintenance (RCM) for monitoring and managing maintenance function of a stochastic complex production-planning problem namely flexible job shop scheduling problem (FJSP). The developed RCM works based on stochastic shocking of machines during their process time. In fact, it implements condition based maintenance approach regulated according to stochastic reliability concept. Comparison of the system reliability with critical levels determines the failure statues of the machines. It activates two main types of reaction called preventive and corrective maintenance. Considering breakdown of the system between inspection intervals makes the proposed model more realistic. Moreover, maintenance activity times and their duration are considered stochastically. Because of the high complexity level for this joint system, simulation-based optimization (SBO) approach is proposed for solving the problem. This SBO searches the feasible area through genetic algorithm (GA) and biogeography based optimization (BBO) algorithm. Different test problems, statistical methods, novel visualizations are used to discuss the problem and the algorithm explicitly.

A dynamic integrated solution for three main problems through integrating all metrics using SCOR are proposed in this research. This dynamic solution comprises strategic decisions in high-level, operational decisions in low-level and alignment of these two decision levels. In this regard, a human intelligence-based process for high level decisions and machine-intelligence based decision support systems (DSSs) for low-level decisions is then proposed using a novel approach. The operational presented model considers important supply chain features thoroughly such as different echelons, several suppliers, several manufacturers and several products during multiple periods. A multi-objective mathematical programming model is then developed to yield the operational decisions with Pareto efficient performance values and solved using a well-known meta-heuristic algorithm, i.e., NSGAII where its parameters is tuned using Taguchi method. Afterward, an intermediate machine-intelligence module is used to determine the best operational solution based on the strategic decision maker’s idea. The efficiency of the proposed framework is shown through numerical example where a sensitivity analysis is then conducted over the obtained results so as to show the impact of the strategic scenario planning on the considered supply chain’s performance.

In recent decades, many researchers have studied the cellular manufacturing system with consideration of various issues such as scheduling, production planning, layout, reliability, etc. However, limited research papers have investigated this problem in an uncertain environment. The present paper addresses a stochastic problem in cellular manufacturing systems considering simultaneous multiple routings and subcontracting. In the developed problem, each part can be simultaneously produced in multiple processing routes. It is also assumed that the unsatisfied part demands as a result of limited machine capacity or high manufacturing cost could be outsourced. A two-stage stochastic programming approach is employed to take the uncertainty into consideration and to formulate the problem. The objective function is to minimize the summation of production, subcontracting, material handling, and machine idleness costs. A sample average approximation method is applied as a solution method. Also, for further illustration of the problem, a numerical example is solved and sensitivity analyses are conducted. Finally, through some numerical examples extracted from related literature, the advantages of constructing a stochastic optimization model for the problem are demonstrated.

This paper provides a framework to study the integration of network marketing in a dual channel distribution system. We develop an approach to optimize the main decision variables of this system simultaneously. These decision variables include the price paid by the customers of both channels, confidence level, the effort level of active distributors of network marketing and also whole sale price. Although both channels compete, it is vital to have a balanced pricing system to make both channels motivated. However, the price in network marketing and traditional retailer system is not necessarily equal, due to the difference of their nature. Furthermore, it is also required to develop an appropriate system of commissioning for the payoff of distributers at different levels of network marketing to make them motivated. We also examine different scenarios of dual distribution systems, centralized and decentralized operation of network marketing. Furthermore, in case of decentralized system, we also investigate revenue or profit sharing for all parties involved (manufacturer; retailer and network marking distributers). To illustrate the proposed approach, we present some numerical studies and also investigate the impact of customer loyalty degree to retail channel on decisions.

The analysis of efficiency is conducted for two vital purposes firstly, in order to evaluate the current level of efficiency; and secondly, to provide information on how to improve the level of efficiency, which is to provide benchmarking information. The inefficient Decision Making Units (DMUs) are usually able to improve their performance and Data Envelopment Analysis (DEA) projections provide a prescription for improvement. However, sometimes an inefficient DMU cannot move its performance toward best practice by either decreasing its inputs or increasing its outputs. On the other hand, it can scarcely reach its efficient benchmark. This research suggests a method to find an improved region of efficiency through DEA-efficient hyperplanes by providing an algorithm for detecting an improved efficiency path. In addition to the production of reasonable benchmarking information, the proposed algorithm provides the general requirements that, satisfy the demands which every professional decision-maker should meet. Finally, we provide a more detailed description of some of the new issues, extending the insights from this analysis of the benchmark region from the under-evaluated inefficient DMU. Finally, numerical examples are provided to demonstrate the results of the analysis

Grey modeling is an alternative approach to time series forecasting with growing popularity. There is no theoretical limitation for grey prediction models to adapt to almost every process by taking the appropriate order. However, deficiencies of traditional higher-order models have made researchers overlook such flexibility and make use of first-order models by default. In order to bridge the mentioned gap, this paper makes two contributions. First, a novel discrete modeling is developed with the basic form equation at its heart, which reconciles estimation and prediction processes. Second, the traditional least-squares estimation technique is modified by shifting the focus from nominal parameters to parameters practically employed in the prediction process. The new approach named ‘Basic Form’-focused Grey Model (BFGM) is applied to first-order, second-order, and Verhulst grey models. Then, it is validated through comparing its performance with the traditional approach. Results show that in most cases BFGM makes considerable improvements in simulation and prediction accuracy, while it has reasonable computational complexity. Improvements are especially dramatic when BFGM is applied to GM (2, 1). The resultant BFGM (2, 1) is superior in simulation and short-term prediction and, therefore, can be regarded as the basis for developing efficient higher-order grey formulations.

Closed loop supply chain design is to provide an optimal platform for efficient and effective supply chain management. It is an essential and strategic operation management problem in supply chain management, and usually includes multiple and conflicting objectives. A new mixed integer non-linear programming model for a multi-objective closed loop supply chain network design problem in the paper industry is developed under uncertainty. The objective functions are to minimize the total cost, maximize the total volume flexibility and minimize the total number of vehicles hired in order to fulfill the paper industry’s policies towards a cleaner and green environment. Also, a novel hybrid solution is presented based on stochastic programming, robust optimization and fuzzy goal programming. A numerical example utilizing the real data from the paper industry in East Azerbaijan of Iran is designed and the model performance is assessed. Furthermore, a recently developed Dragonfly Algorithm (DA) employed to solve the given problem in large scales and compared with Genetic Algorithm (GA). The results indicated that the DA achieved better performance compared with the GA.

Data envelope analysis (DEA) is an approach to estimate the relative efficiency of decision making units (DMUs). Several studies were conducted in order to prioritize efficient units and some useful models such as cross-efficiency matrix (CEM) were presented. Besides, a number of DEA models with interval data have been developed and ranking DMUs with such data was solved. However, presenting an obtained crisp data derived interval data is a critical problem, so that many researches were implemented so as to compute weights and averaging the interval data. In this paper we propose the new algorithm to find more suitable weight applying a data mining approach of DMU’s data. For this purpose, we employed clustering and pair-wise comparison matrix on given relative efficiency from CEM. Results indicate there is meaningful different between efficiency of DMUs with lower bound and that of DMUs with upper bound.