Journal of Optimization in Industrial Engineering
Volume:7 Issue: 15, Summer and Autumn 2014

In today’s competitive transportation systems, passengers search to find traveling agencies that are able to serve them efficiently considering both traveling time and transportation costs. In this paper, we present a new model for the traveling salesman problem with multiple transporters (TSPMT). In the proposed model, which is more applicable than the traditional versions, each city has different transporting vehicles and the cost of travel through each city is dependent on the transporting vehicles type. The aim is to determine an optimal sequence of visited cities with minimum traveling times by available transporting vehicles within a limited budget. First, the mathematical model of TSPMT is presented. Next, since the problem is NP-hard, a new hybrid parallel simulated annealing algorithm with a new coding scheme is proposed. To analyze the performance of the proposed algorithm, 50 numerical examples with different budget types are examined and solved using the algorithm. The computational results of these comparisons show that the algorithm is an excellent approach in speed and solution quality.

In this paper, a novel mathematical model for a preemption multi-mode multi-objective resource-constrained project scheduling problem with distinct due dates and positive and negative cash flows is presented. Although optimization of bi-objective problems with due dates is an essential feature of real projects, little effort has been made in studying the P-MMRCPSP while due dates are included in the activities. This paper tries to bridge this gap by studying tardiness MMRCPSP, in which the objective is to minimize total weighted tardiness and to maximize the net present value (NPV). In order to solve the given problem, we introduced a Non-dominated Ranking Genetic Algorithm (NRGA) and Non-Dominated Sort Genetic Algorithm (NSGA-II). Since the effectiveness of most meta-heuristic algorithms significantly depends on choosing the proper parameters. A Taguchi experimental design method was applied to set and estimate the proper values of GAs parameters for improving their performances. To prove the efficiency of our proposed meta-heuristic algorithms, a number of test problems taken from the project scheduling problem library (PSPLIB) were solved. The computational results show that the proposed NSGA-II outperforms the NRGA.

The objective of this study was to determine the effects of grasp conditions (types of grasp, grasp width, glove and types of coupling) on maximal pushing force (MPF) and required pinch force (RPF) during snap fit assembly. The results indicated that the type of grasp, the type of coupling and wearing gloves have significant (p<0.05) effects on both MPF and RPF. Regarding the pair wise comparison, there was no significant difference in the effect between the lateral pinch and chuck pinch. MPF and RPF were also not affected significantly by the width of the grasp. Furthermore, there was an interactional effect between the type of coupling and the wearing or rather not wearing a glove. This, of course, only affected the MPF.

Vendor managed inventory is a continuous replenishment program that is designed to provide major cost saving benefits for both vendors and retailers. Previous research on this area mainly included single objective optimization models where the objective is to minimize the total supply chain costs or to maximize the total supply chain benefits. This paper presents a bi-objective mathematical model for single-manufacture multi-retailer with multi-product in order to maximize their benefits. It is assumed that demand is a decreasing and convex function of the retail price. In this paper, common replenishment cycle is considered for the manufacturer and its retailers. Then, the proposed model converts to the single-objective optimization problem using a weighted sum method. A genetic algorithm (GA) is applied to solve it and response surface methodology is employed to tune the GA parameters. Finally, several numerical examples are investigated to demonstrate the applicability of the proposed model and solution approach.

This paper presented a mixed integer programming for integrated scheduling and process planning. The presented process plan included some orders with precedence relations similar to Multiple Traveling Salesman Problem (MTSP), which was categorized as an NP-hard problem. These types of problems are also called advanced planning because of simultaneously determining the appropriate sequence and minimizing makespan in the process of scheduling. There are alternative machines for each operation and different sequences for each order, which create a flexible environment for production planning. In process planning ansd integrated scheduling, most mathematical models have two sets of ordered pairs with precedence or non-precedence relations between operations; therefore, these models cannot be solved using optimization software. Therefore, in this paper, this problem was modeled by a new approach and solved by GAMS software. The model was validated by the existing data in the literature.

Coordination is very important in supply chain management and it is one of the main factors in supply chain profitability. Bullwhip effect is one of the basic obstacles to achieve coordination in supply chains and reduction of this phenomenon has an important role in supply chain harmony. The other side, costs of supply chain can be mitigated and customer service level can be increased by reducing of bullwhip effect. Because measurement of bullwhip effect is very important in analysing and controlling of it, providing equations to investigate bullwhip effect behaviour based on real world supply chain conditions is necessary. The previous studies mostly concentrate on single product supply chain and few studies have been done on supply chains with more than one product. Here we quantify and investigate the bullwhip effect in a two-echelon supply chain with two products using control theory approach. Due to the relationship between demands of two products in our proposed supply chain, first order vector auto regressive model is used as demand process of the products. We also apply moving average method for lead-time demand forecasting within the «order up to» replenishment policy. We derive a closed form bullwhip measure and then bullwhip effect in a two-product supply chain is discussed and illustrated through a numerical example.

A facility layout problem is concerned with determining the best position of departments, cells, or machines on the plant. An efficient layout contributes to the overall efficiency of operations. It’s been proved that, when system characteristics change, it can cause a significant increase in material handling cost. Consequently, the efficiency of the current layout decreases or is lost and it does necessitate rearrangement. On the other hand, the rearrangement of the workstations may burden a lot of expenses on the system. The problem that considers balance between material handling cost and the rearrangement cost is known as the Dynamic Facility Layout Problem (DFLP). The objective of a DFLP is to find the best layout for the company facilities in each period of planning horizon considering the rearrangement costs. Due to the complex structure of the problem, there are few researches in the literature which tried to find near optimum solutions for DFLP with budget constraint. In this paper, a new heuristic approach has been developed by combining Genetic Algorithm (GA) and Parallel Simulated Annealing Algorithm (PSAA) which is the main contribution of the current study. The results of applying the proposed algorithm were tested over a wide range of test problems taken from the literature. The results show efficiency of the hybrid algorithm GA- to solve the Dynamic Facility Layout Problem with Budget Constraint (DFLPBC).

In this paper, a heuristic algorithm is proposed in order to solve a nonlinear lexicography goal programming (NLGP) by using an efficient initial point. Some numerical experiments showed that the search quality by the proposed heuristic in a multiple objectives problem depends on the initial point features, so in the proposed approach the initial point is retrieved by Data Envelopment Analysis to be selected as an efficient solution. There are some weaknesses in classic NLGP algorithm that lead to trapping into the local optimum, so a simulated annealing concept is implemented during the searching stage to increase the diversity of search in the solution space. Some numerical examples with different sizes were generated and comparison of results confirms that the proposed solution heuristic is more efficient than the classic approach. Moreover the proposed approach was extended for cases with ordinal weights of inputs or outputs. The computational experiments for 5 numerical instances and the statistical analysis indicate that the proposed heuristic algorithm is a robust procedure to find better preferred solution comparing to the classic NLGP.