In this paper, we develop an integrated vendor-buyer production-inventory model for items with imperfect quality and inspection errors. The production process is imperfect and produces a certain number of defective items with a known probability density function. We consider the policy in which the delivery quantity to the buyer is identical at each shipment. Once the buyer receives the lot, a 100% screening process of the lot is conducted, and the screening process and demand proceed simultaneously. The screening process is not perfect. The inspector may incorrectly classify a non-defective item as defective, or incorrectly classify a defective item as non-defective. The expected integrated total annual cost of the vendor and the buyer is derived and a solution procedure is provided to find the optimal solution. Numerical examples show that the integrated model gives an impressive cost reduction in comparison to an independent decision by the buyer.

A joint vendor-buyer strategy is analyzed which is beneficial to both the players in the supply chain. The demand is assumed to be trapezoidal. It is established numerically that the joint venture decreases the total cost of the supply chain when compared with the independent decision of the buyer. To entice the buyer to order more units, a permissible credit period is offered by the vendor to the buyer. A negotiation factor is incorporated to share the cost savings.

The human error has been reported as a major root cause in road accidents in today’s world. The human as a driver in road vehicles composed of human, mechanical and electrical components is constantly exposed to changing surroundings (e.g., road conditions, environment)which deteriorate the driver’s capacities leading to a potential accident. The auto industries and transportation authorities have realized that similar to other complex and safety sensitive transportation systems, the road vehicles need to rely on both advanced technologies (i.e., Advanced Driver Assistance Systems (ADAS)) and Passive Safety Systems (PSS) (e.g.,, seatbelts, airbags) in order to mitigate the risk of accidents and casualties. In this study, the advantages and disadvantages of ADAS as active safety systems as well as passive safety systems in road vehicles have been discussed. Also, this study proposes models that analyze the interactions between human as a driver and ADAS Warning and Crash Avoidance Systems and PSS in the design of vehicles. Thereafter, the mathematical models have been developed to make reliability prediction at any given time on the road transportation for vehicles equipped with ADAS and PSS. Finally, the implications of this study in the improvement of vehicle designs and prevention of casualties are discussed.

This paper addresses the problem of lot sizing and scheduling problem for n-products and m-machines in flow shop environment where setups among machines are sequence-dependent and can be carried over. Many products must be produced under capacity constraints and allowing backorders. Since lot sizing and scheduling problems are well-known strongly NP-hard, much attention has been given to heuristics and metaheuristics methods. This paper presents two metaheuristics algorithms namely, Genetic Algorithm (GA) and Imperialist Competitive Algorithm (ICA). Moreover, Taguchi robust design methodology is employed to calibrate the parameters of the algorithms for different size problems. In addition, the parameter-tuned algorithms are compared against a presented lower bound on randomly generated problems. At the end, comprehensive numerical examples are presented to demonstrate the effectiveness of the proposed algorithms. The results showed that the performance of both GA and ICA are very promising and ICA outperforms GA statistically.

The capacitated arc routing problem is one of the most important routing problems with many applications in real world situations such as snow removing, winter gritting, refuse collection, etc. Since this problem is NP-hard, many of researchers have been developed numerous heuristics and metaheuristics to solve it. In this paper, we propose a new constructive and improvement heuristic in which forming a vehicle’s tour is based on choosing an unserved edge randomly as current partial tour and then extending this partial tour from its both of end nodes base on four effective proposed criteria. When the vehicle load is near its capacity, it should come back to the depot immediately. Finally, the constructed tours are merged into more efficient and cheaper tours. The quality of this new approach was tested on three standard benchmark instances and the results were compared with some known existing heuristics and metaheuristics in the literature. The computational results show an excellent performance of our new method.

Cross docking play an important role in management of supply chains where items delivered to a warehouse by inbound trucks are directly sorted out, reorganized based on customer demands, routed and loaded into outbound trucks for delivery to customers without virtually keeping them at the warehouse. If any item is held in storage, it is usually for a short time, which is normally less than 24 hours. The proposed model of this paper considers a special case of cross docking where there is temporary storage and uses GRASP technique to solve the resulted problem for some realistic test problems. In our method, we first use some heuristics as initial solutions and then improve the final solution using GRASP method. The preliminary test results indicate that the GRASP method performs better than alternative solution strategies.

In this paper, an integrated layout model has been considered to incorporate intra and inter-department layout. In the proposed model, the arrangement of facilities within the departments is obtained through the QAP and from the other side the continuous layout problem is implemented to find the position and orientation of rectangular shape departments on the planar area. First, a modified version of QAP with fewer binary variables is presented. Afterward the integrated model is formulated based on the developed QAP. In order to evaluate material handling cost precisely, the actual position of machines within the departments (instead of center of departments) is considered. Moreover, other design factors such as aisle distance, single or multi row intra-department layout and orientation of departments have been considered. The mathematical model is formulated as mixed-integer programming (MIP) to minimize total material handling cost. Also due to the complexity of integrated model a heuristic method has been developed to solve large scale problems in a reasonable computational time. Finally, several illustrative numerical examples are selected from the literature to test the model and evaluate the heuristic.

In this paper, a new optimization technique known as Teaching–Learning-Based Optimization (TLBO) is implemented for solving high dimensional function optimization problems. Even though there are several other approaches to address this issue but the cost of computations are more in handling high dimensional problems. In this work we simulate TLBO for high dimensional benchmark function optimizations and compare its results with very widely used alternate techniques like Differential Evolution (DE) and Particle Swarm Optimization (PSO). Results clearly reveal that TLBO is able to address the computational cost issue for all simulated functions to a large dimensions compared to other two techniques.

Cost estimation of new products has always been difficult as only few design, manufacturing and operational features will be known. In these situations, parametric or non-parametric methods are commonly used to estimate the cost of a product given the corresponding cost drivers. The parametric models use priori determined cost function where the parameters of the function are evaluated from historical data. Non-parametric methods, on the other hand, attempt to fit curves to the historic data without predetermined function. In both methods, it is assumed that the historic data used in the analysis is a true representation of the relation between the cost drivers and the corresponding costs. However, because of efficiency variations of the manufacturers and suppliers, changes in supplier selections, market fluctuations, and several other reasons, certain costs in the historic data may be too high whereas other costs may represent better deals for their corresponding cost drivers. Thus, it may be important to rank the historic data and identify benchmarks and estimate the target costs of the product based on these benchmarks. In this paper, a novel adaptation of cost drivers and cost data is introduced in order to use data envelopment analysis for the purpose of ranking cost data and identify benchmarks, and then estimate the target costs of a new product based on these benchmarks. An illustrative case study has been presented for the cost estimation of landing gears of an aircraft manufactured by an aerospace company located in Montreal, CANADA.

In many real-world applications, the quality of a process or a particular product can be characterized by a functional relationship called profile. A profile builds the relationships between a response quality characteristic and one or more explanatory variables. Monitoring the quality of a profile is implemented to understand and to verify the stability of this functional relationship over time. In some real applications, a fuzzy linear regression model can represent the profile adequately where the response quality characteristic is fuzzy. The purpose of this paper is to develop an approach for monitoring process/product profiles in fuzzy environment. A model in fuzzy linear regression is developed to construct the quality profiles by using linear programming and then fuzzy individuals and moving-range (I-MR) control charts are developed to monitor both intercept and slope of fuzzy profiles to achieve an in-control process. A case study in customer satisfaction is presented to show the application of our approach and to express the sensitivity analysis of parameters for building a fuzzy profile.