International Journal of Supply and Operations Management
Volume:2 Issue: 3, Autumn 2015

Closed-loop supply chain network design is a critical issue due to its impact on both economic and environmental performances of the supply chain. In this paper, we address the problem of designing a multi-echelon, multi-product and capacitated closed-loop supply chain network. First, a mixed-integer linear programming formulation is developed to maximize the total profit. The main contribution of the proposed model is addressing two economic viability issues of closed-loop supply chain. The first issue is the collection of sufficient quantity of end-of-life products are assured by retailers against an acquisition price. The second issue is exploiting the benefits of colocation of forward facilities and reverse facilities. The presented model is solved by LINGO for some test problems. Computational results and sensitivity analysis are conducted to show the performance of the proposed model.

In this paper we address the VRPCD, in which a set of homogeneous vehicles are used to transport products from the suppliers to customers via a cross-dock. The products can be consolidated at the cross-dock but cannot be stored for very long as the cross-dock does not have long-term inventory-holding capabilities. The objective of the VRPCD is to minimize the total traveled distance while respecting time window constraints of suppliers and customers and a time horizon for the whole transportation operation. Rummaging through all the work of literature on vehicle routing problems with cross-docking, there is no work that considers that customer will receive its requests from several suppliers; this will be the point of innovation of this work. A heuristic and a memetic algorithm are used to solve the problem. The proposed algorithms are implemented and tested on data sets involving up to 200 nodes (customers and suppliers). The first results show that the memetic algorithm can produce high quality solutions.

Strategic alliance promotes enterprise resources sharing and enhances the competitiveness of the marketplace. Therefore, finding a mutually beneficial partner to make a strategic alliance is an important issue for various industries. The aim of this paper is to propose a suitable method based on Grey theory and Data Envelopment Analysis (DEA). A method predicts future business and measure operation efficiency, by the use of critical input and output variables. From this, firms can find out their appropriate candidates. This research was implemented with realistic public data from four consecutive financial years (2009-2012) of twenty Auto Manufactures. The study tries to help target firm find the right alliance partners. The results show the most priori candidates in recent years. The study will be of interest for managers of Auto Manufacture in utilizing alliance strategy.

In this paper, the flexible job shop scheduling problem with machine flexibility and controllable process times is studied. The main idea is that the processing times of operations may be controlled by consumptions of additional resources. The purpose of this paper to find the best trade-off between processing cost and delay cost in order to minimize the total costs. The proposed model, flexible job shop scheduling with controllable processing times (FJCPT), is formulated as an integer non-linear programming (INLP) model and then it is converted into an integer linear programming (ILP) model. Due to NP-hardness of FJCPT, conventional analytic optimization methods are not efficient. Hence, in order to solve the problem, a Scatter Search (SS), as an efficient metaheuristic method, is developed. To show the effectiveness of the proposed method, numerical experiments are conducted. The efficiency of the proposed algorithm is compared with that of a genetic algorithm (GA) available in the literature for solving FJSP problem. The results showed that the proposed SS provide better solutions than the existing GA.

This paper considers the fuzzy inventory model for deteriorating items for power demand under fully backlogged conditions. We define various factors which are affecting the inventory cost by using the shortage costs. An intention of this paper is to study the inventory modelling through fuzzy environment. Inventory parameters, such as holding cost, shortage cost, purchasing cost and deterioration cost are assumed to be the trapezoidal fuzzy numbers. In addition, an efficient algorithm is developed to determine the optimal policy, and the computational effort and time are small for the proposed algorithm. It is simple to implement, and our approach is illustrated through some numerical examples to demonstrate the application and the performance of the proposed methodology.

In this paper, a new genetic algorithm (GA) is presented for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) with minimization of project makespan as the objective subject to resource and precedence constraints. A random key and the related mode list (ML) representation scheme are used as encoding schemes and the multi-mode serial schedule generation scheme (MSSGS) is considered as the decoding procedure. In this paper, a simple, efficient fitness function is proposed which has better performance compared to the other fitness functions in the literature. Defining a new mutation operator for ML is the other contribution of the current study. Comparing the results of the proposed GA with other approaches using the well-known benchmark sets in PSPLIB validates the effectiveness of the proposed algorithm to solve the MRCPSP.

In this study, a new stochastic model is proposed to deal with a multi-product, multi-period, multi-stage, multi-site production and transportation supply chain planning problem under demand uncertainty. A two-stage stochastic linear programming approach is used to maximize the expected profit. Decisions such as the production amount, the inventory level of finished and semi-finished product, the amount of backorder and the quantity of products to be transported between upstream and downstream plants in each period are considered. The robustness of production supply chain plan is then evaluated using statistical and risk measures. A case study from a real textile and apparel industry is shown in order to compare the performances of the proposed stochastic programming model and the deterministic model.