integrated production-distribution planning (pdp) is one of the most important approaches in supply chain networks. we consider a supply chain network (scn) to consist of multi suppliers, plants, distribution centers (dcs), and retailers. a bi-objective mixed integer linear programming model for integrating production-distribution designed here aim to simultaneously minimize total net costs in ...

optimization of cutting conditions is a non-linear optimization with constraint and it is very important to the increase of productivity and the reduction of costs. in recent years, several evolutionary and meta-heuristic optimization algorithms were introduced. the cuckoo optimization algorithm (coa) is one of several recent and powerful meta-heuristics which is inspired by the cuckoos and the...

hub covering problem (hlp) is a very popular areas of research for its wide ranges of applications in different service or manufacturing industries. this paper considers a bi-objective hub covering location problem with congestion. the objectives minimize the total transportation cost and the total waiting time for all hobs, respectively. the resulted multi-objective decision-making problem is ...

In today’s dynamic market, organizations must be adaptive to market fluctuations. In addition, studies show that material-handling cost makes up between 20 and 50 percent of the total operating cost. Therefore, this paper considers the problem of arranging and rearranging, when there are changes in product mix and demand, manufacturing facilities such that the sum of material handling and rearr...

Two well known stochastic optimization algorithms, simulated annealing and genetic algorithm are compared when using a sample to minimize an objective function which is the expectation of a random variable. Since they lead to minimum depending on the sample, a weighted version of simulated annealing is proposed in order to reduce this kind of overt bias. The algorithms are implemented on an opt...

Simulated Annealing is one the most well known local search methods. In practice, it is often used to solve discrete optimization problems; especially very tough problems, [54, 37, 34, 3, 2, 41]. Global optimization is computationally extremely challenging and for large instances, exact methods reach their limitations quickly. Hence, in practice, often local optimization methods are used. Simul...

This paper presents a new mathematical model, in which the location of hubs is fixed and their capacity is determined based on facilities and factories allocated to it. In order to feed the client's nodes, different types of vehicles of different capacities are considered, in which the clients are allocated to hubs, and types and numbers of vehicles are allocated to the factory's facilities. To...

The Robust Coloring Problem (RCP) is a generalization of the well-known Graph Coloring Problem where we seek for a solution that remains valid when extra edges are added. The RCP is used in scheduling of events with possible last-minute changes and study frequency assignments of the electromagnetic spectrum. This problem has been proved as NP-hard and in instances larger than 30 vertices, meta-...

In this paper, we give a new approach of hybrid direct search methods with metaheuristics of simulated annealing for finding a global minimum of a nonlinear function with continuous variables. First, we suggest a Simple Direct Search (SDS) method, which comes from some ideas of other well known direct search methods. Since our goal is to find global minima and the SDS method is still a local se...

The Economic Lot Scheduling Problem (ELSP) has been well-researched for more than 40 years. As the ELSP has been generally seen as NP-hard, researchers have focused on the development of efficient heuristic approaches. In this paper, we consider the time-varying lot size approach to solve the ELSP. A computational study of the existing solution algorithms, Dobson’s heuristic, Hybrid Genetic alg...