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Over the last few years, inventory-location network design models due to the incorporation of two main decisions in network design, meaning inventory control and facility location, has attracted great attention from both industries and researchers. Existing inventory-location models are mainly applicable for nonperishable inventories. However, since, perishable inventory comprise a large proportion of the inventory distributed through the distribution networks, and due to the important role of the network design on the final cost and quality of the products, development of inventory-location models suitable for perishable inventory is of great importance. Therefore, the objective of this research is development of integrated inventory-location models for distribution network design of fixed- and random-lifetime perishable products, that has been achieved through the following four phases. The first phase is to select the best structure for the distribution network between a centralized and decentralized configuration. Previous research works that admire a centralized structure consisting of suppliers, distribution centers and retailers, considered for simplicity that the products dispatched from the supplier are maintained at the distribution centers locations before being demanded by the customer. It is equivalent to this assumption that if a customer places an order to a retailer, and the retailer is out-of-stock the customer waits for the products to be available. However, in practice it is not true and a fraction of customers encountering stock-out switches to other markets. To this end, the first part of this study formulates the centralized and decentralized structures when inventory can be stored at both retailers’ and DCs’ location. Sensitivity analysis is conducted to determine the value of centralization. Results indicate that the value of centralization is dependent on the parameters of the problem, and that for the parameters of the problem considered in

Perishable products, which include medical and pharmaceutical items as well as food products, are quite common in commerce and industries. Developing efficient network designs for storage and distribution of perishable products plays a prominent role in the cost and quality of these products. This paper aims to investigate and analyze the impact of applying an integrated approach for network design of perishable products. For this purpose, the problem has been formulated as a mixed integer nonlinear mathematical model that integrates inventory control and facility location decisions. To solve the integrated model, a memetic algorithm (MA) is developed in this study. For verification of the proposed algorithm, its results are compared with the results of an adapted Lagrangian relaxation heuristic algorithm from the literature. Moreover, sensitivity analysis of the main parameters of the model is conducted to compare the results of the integrated approach with a decoupled method. The results show that as the products become more perishable, application of an integrated method becomes more reasonable in comparison with the decoupled one.

One significant decision in supply chain management is to determine the configuration of the supply chain network. However, studies that analyze different configurations are rare in the literature. This paper considers a supply chain network consisting of one supplier and a number of retailers. Two different configurations were proposed and were compared in terms of total cost for the network. In the first configuration, retailers directly order to the supplier and hold the inventory to meet the demand of customers. However, in the second configuration, a number of intermediate facilities were established to order products from the supplier and satisfy the demands of retailers. The inventory of retailers in the latter configuration is stored in the intermediate facilities. These two configurations were compared in terms of total cost. Numerical calculations indicate that the latter configuration was preferred in most cases.

Many industrial problems need to be optimized several responses simultaneously. These problems are named multiple response optimization (MRO) and they can have different objectives such as Target, Minimization or Maximization. Committee machine (CM) as a set of some experts such as some artificial neural networks (ANNs) in combination with genetic algorithm (GA) is applied for modeling and optimization of MRO problems. In addition, optimization usually is done on Global Desirability (GD) function. Current article is a development for recent authors' work to determine economic run number for application of CM and GA in MRO problem solving. This study includes a committee machine with four different ANNs. The CM weights are determined with GA which its fitness function is minimizing the RMSE. Then, another GA specifies the final solution with object maximizing the global desirability. This algorithm was implemented on five case studies and the results represent the algorithm can get higher global desirability by repeating the runs and economic run number (ERN) depends on the MRO problem objective. ERN is ten for objective “Target”. This number for objectives which are mixture of minimization and maximization ERN is five. The repetition are continued until these ERN values have considerable increased in maximum GD with respect to average value of GD. More repetition from these ERN to forty five numbers cause a slight raise in maximum GD.

Network design is by nature costly and optimization models play significant role in reducing the unnecessary cost components of a distribution network. This study proposes a genetic algorithm to solve a distribution network design model. The structure of the chromosome in the proposed algorithm is defined in a novel way that in addition to producing feasible solutions, it also reduces the computational complexity of the algorithm. Computational results are presented to show the algorithm performance.

Joint location inventory problems involve determination of two key decisions in a supply chain network: inventory control decisions and facility location decisions. This paper addresses a supply chain network consisting of a supplier and a set of retailers, in which a number of distribution centers (DCs) are to be located. The objectives of a joint location inventory problem are to identify the optimal number and location of the DCs, the assignment of the retailers to the DCs and determining the inventory control decision of the DCs so that the total cost is minimized. This paper extends the traditional joint location inventory model that considers the basic EOQ policy to replenish the inventory of retailers, by considering quantity discount as the inventory policy of the supply chain network. The new model can be applied to both EOQ and quantity discount policies. An efficient two-stage heuristic algorithm is developed to solve the model. Numerical experiments show the efficiency of the proposed algorithm.

Nowadays, many distribution networks deal with the distribution and storage of perishable products. However, distribution network design models are largely based on assumptions that do not consider time limitations for the storage of products within the network. This study develops a model for the design of a distribution network that considers the short lifetime of perishable products. The model simultaneously determines the network configuration and inventory control decisions of the network. Moreover, as the lifetime is strictly dependent on the storage conditions, the model develops a trade-off between enhancing storage conditions (higher inventory cost) to obtain a longer lifetime and selecting those storage conditions that lead to shorter lifetimes (less inventory cost). To solve the model, an efficient Lagrangian relaxation heuristic algorithm is developed. The model and algorithm are validated by sensitivity analysis on some key parameters. Results show that the algorithm finds optimal or near optimal solutions even for large-size cases.

In a Cellular Manufacturing System (CMS), parts based on their similarities are grouped into part families, and are assigned to one or more machine cells to be manufactured. In the design of a CMS, facility layout problem aims to arrange machine cells in the shop floor and facilities in the machine cells in order to have an efficient layout. In this study, a previously developed cellular layout model will be illustrated and validated for a well-known case from the literature. Lingo optimization software version 12.0 and an enumeration method will be used to solve, and validate the model. The results of this study show the validity of the proposed model.

Facility layout which is the arrangement of facilities in the shop-floor has a great impact on the performance of manufacturing systems. An effective layout decreases material handling cost, throughput time, lead-time and results in increasing productivity and efficiency of manufacturing systems. Although layout problems have significant roles on the efficacy of manufacturing systems, scant attention has been paid to the layout design in hybrid cellular manufacturing systems. In this paper, a mathematical model for layout problems in a hybrid cellular manufacturing system is proposed that minimizes the total material handling cost (both inter-cell and intra-cell material handling cost). To solve the model, a variant of a simulated annealing algorithm is developed. The results show that the developed algorithm outperforms the algorithm that was benchmarked from the literature in terms of solution quality and computation time.

Changes in demand, as one of the issues of volatile manufacturing systems, decline the performance of manufacturing systems over the time; especially, it degrades the effectiveness of layout in manufacturing systems. Although the layout of the arrangement of facilities on the shop floor play a significant role in the effectiveness of manufacturing systems, it has not absorbed the attention of researchers in hybrid manufacturing systems. In this paper, a new mathematical model for facility layout in a hybrid cellular manufacturing system has been proposed, which considers demand varying over the planning horizon. The model minimises the material handling cost. To solve the model, a simulated annealing algorithm from literature has been improved. The comparison of results between two algorithms shows the superiority of the improved algorithm in both the quality of solutions and computational time.

Facility layout design problems are a group of problems that involve the partitioning of a region into work centers or departments to minimize the costs associated with interactions between the work centers and departments. In cellular manufacturing systems, facility layout problem aims to find the most efficient arrangement of facilities within the machine cells and the layout of machine-cells within the workshop. In this paper, an algorithm for solving layout design has been proposed that arranges the machines within machine cells, and cells in the shop-floor in such a way that minimizes the total material handling cost. To validate the developed algorithm, the results obtained by the algorithm are compared to the results of an exhaustive search. Comparison of results shows the validity of the proposed algorithm.

One of the recent areas in distribution network design is integrated inventory location problems, which jointly determines the inventory control decisions and facility location decisions of a distribution network. A typical distribution network consists of suppliers, several retailers, and several distribution centers. Distribution centers order products from suppliers to fulfil demands of retailers. To achieve risk pooling benefits, inventory of several retailers is aggregated into one distribution center. This situation has made distribution centers more likely to take the advantage of quantity discount from suppliers. However, most previous models have investigated the problem under the basic economic order quantity EOQ with (Q, r) inventory policy and yet quantity discount has not been considered. This paper shows that considering quantity discount instead of EOQ policy can reduce the total cost and change the optimal configuration of the networks.

Distribution network design is one of the main issues in supply chain management. Two key decisions that must be determined to design a distribution network are facility allocation decisions and inventory control decisions. Despite the interaction exists between these two decisions, traditional methods ignored simultaneously optimizing them in a supply chain. Therefore, the obtained results are expected to be suboptimal in many situations. To deal with this problem, in recent years, joint location-inventory models are developed to simultaneously determine the inventory control decisions and facility locations decision of a distribution network. Majority of the previous joint location-inventory models considered the simplest inventory model that is economic order quantity (EOQ) to replenish the demands of retailers. However, a very common policy offered by suppliers is quantity discount that provides the buyers the opportunity of purchasing in larger quantities and reducing their total cost. When quantity discount is available the buyers need to trade-off between reduction in purchasing and ordering cost and increase in the inventory cost in order to find the optimal order quantity. However, EOQ (Q, r) model is not capable of doing such comparison and the buyers need to apply quantity discount models to find the optimal order quantity. Nevertheless, so far quantity discount has not been considered as inventory policy in joint location-inventory models. Thus, this research has formulated and solved a joint location-inventory problem, while all-unit quantity discount is considered as the inventory policy of distribution network. In order to solve the model, two heuristics and one enumeration algorithm are developed. It is shown that considering quantity discount instead of EOQ policy saves the total cost up to 4.5%. In addition, the network configurations are hown to be different under two policies. To investigate the performance of the developed heuristic algorithms