International Journal of Industrial Engineering & Production Research

---

Capacitated p-median problem (CPMP) is a well-known facility-location problem, in which p capacitated facility points are selected to satisfy n demand points in such a way that the total assigned demand to each facility does not exceed its capacity. Minimizing the total sum of distances between each demand point and its nearest facility point is the objective of the problem. Developing an efficient solution method for the problem has been a challenge during last decades in literature. In this paper, a hybrid met heuristic called GACO is developed to find high quality and fast solutions for the CPMP. The GACO combines elements of genetic algorithm and ant colony optimization met heuristics. Computational results on standard test problems show the robustness and efficiency of the algorithm and confirm that the proposed method is a good choice for solving the CPMP.

---

  This paper deals with minimization of tardiness in single machine scheduling problem when each job has two different due-dates i.e. ordinary due-date and drop dead date. The drop dead date is the date in which jobs’ weights rise sharply or the customer cancels the order. A linear programming formulation is developed for the problem and since the problem is known to be NP-hard, three heuristic algorithms are designed for the problem based on Tabu search mechanism. Extensive numerical experiments were conducted to observe and compare the behavior of the algorithms in solving the problem..

---

  In this paper, we consider a flow shop scheduling problem with bypass consideration for minimizing the sum of earliness and tardiness costs. We propose a new mathematical modeling to formulate this problem. There are several constraints which are involved in our modeling such as the due date of jobs, the job ready times, the earliness and the tardiness cost of jobs, and so on. We apply adapted genetic algorithm based on bypass consideration to solve the problem. The basic parameters of this meta-heuristic are briefly discussed in this paper. Also a computational experiment is conducted to evaluate the performance of the implemented methods. The implemented algorithm can be used to solve large scale flow shop scheduling problem with bypass effectively .

---

An interactive heuristic approach can offer a practical solution to the problem of linear integer programming (LIP) by combining an optimization technique with the Decision Maker’s (DM) judgment and technical supervision. This is made possible using the concept of bicriterion linear programming (BLP) problem in an integer environment. This model proposes two bicriterion linear programs for identifying a feasible solution point when an initial infeasible solution point is provided by the decision maker or when the searching process leaves the region of feasibility seeking for a better pattern to improve the objective function. Instructions regarding the structure of such BLP problems are broadly discussed. This added property offers a great degree of flexibility to the decision making problem solving process.

The heuristic engine is comprised of four algorithms: Improve, Feasible, Leave, and Backtrack. In each iteration, when a selected algorithm has been terminated, the DM is presented with the results and asked to reevaluate the solution process by choosing an appropriate algorithm to follow. It is shown that the method converges to the optimal solution for most of the time. A solution technique for solving such a problem is introduced with sufficient details.

---

The Network Design Problem (NDP) is one of the important problems in combinatorial optimization. Among the network design problems, the Multicommodity Capacitated Network Design (MCND) problem has numerous applications in transportation, logistics, telecommunication, and production systems. The MCND problems with splittable flow variables are NP-hard, which means they require exponential time to be solved in optimality. With binary flow variables or unsplittable MCND, the complexity of the problem is increased significantly. With growing complexity and scale of real world capacitated network design applications, metaheuristics must be developed to solve these problems. This paper presents a simulated annealing approach with innovative representation and neighborhood structure for unsplittable MCND problem. The parameters of the proposed algorithms are tuned using Design of Experiments (DOE) method and the Design-Expert statistical software. The performance of the proposed algorithm is evaluated by solving instances with different dimensions from OR-Library. The results of the proposed algorithm are compared with the solutions of CPLEX solver.  The results show that the proposed SA can find near optimal solution in much less time than exact algorithm.

---

In this paper, we propose an iterated greedy algorithm for solving the blocking flow shop scheduling problem with total flow time minimization objective. The steps of this algorithm are designed very efficient. For generating an initial solution, we develop an efficient constructive heuristic by modifying the best known NEH algorithm. Effectiveness of the proposed iterated greedy algorithm is tested on the Taillard's instances. Computational results show the high efficiency of this algorithm with comparison state-of-the-art algorithms. We report new best solutions for 88 instances of 120 Taillard's instances.

---

Response surface methodology is a common tool in optimizing processes. It mainly concerns situations when there is only one response of interest. However, many designed experiments often involve simultaneous optimization of several quality characteristics. This is called a Multiresponse Surface Optimization problem. A common approach in dealing with these problems is to apply desirability function approach combined with an optimization algorithm to determine the best settings of control variables. As the response surfaces are often nonlinear and complex a number of meta-heuristic search techniques have been widely for optimizing the objective function. Amongst these techniques genetic algorithm, simulated annealing, tabu search and hybridization of them have drawn a great deal of attention so far. This study presents the use of harmony search algorithm for Multiresponse surface optimization. It is one of the recently developed meta heuristic algorithms that has been successfully applied to several engineering problems. This music inspired heuristic is conceptualized from the musical process of searching for a perfect state of harmony. The performance of the algorithm is evaluated by an example from the literature. Results indicate the efficiency and outperformance of the method in comparison with some previously used methods.

---

Location-allocation of facilities in service systems is an essential factor of their performance. One of the considerable situations which less addressed in the relevant literature is to balance service among customers in addition to minimize location-allocation costs. This is an important issue, especially in the public sector. Reviewing the recent researches in this field shows that most of them allocated demand customer to the closest facility. While, using probability rules to predict customer behavior when they select the desired facility is more appropriate. In this research, equitable facility location problem based on the gravity rule was investigated. The objective function has been defined as a combination of balancing and cost minimization, keeping in mind some system constraints. To estimate demand volume among facilities, utility function(attraction function) added to model as one constraint. The research problem is modeled as one mixed integer linear programming. Due to the model complexity, two heuristic and genetic algorithms have been developed and compared by exact solutions of small dimension problems. The results of numerical examples show the heuristic approach effectiveness with good-quality solutions in reasonable run time.

---

periodic vehicle routing problem focuses on establishing a plan of visits to clients over a given time horizon so as to satisfy some service level while optimizing the routes used in each time period. This paper presents a new effective heuristic algorithm based on data mining tools for periodic vehicle routing problem (PVRP). The related results of proposed algorithm are compared with the results obtained by best Heuristics and meta-heuristics algorithms in the literature. Computational results indicate that the algorithm performs competitive in the accuracy and its small amount of solving time point of views.

---

The set covering problem (SCP) is a well-known combinatorial optimization problem. This paper investigates development of a local branching approach for the SCP. This solution strategy is exact in nature, though it is designed to improve the heuristic behavior of the mixed integer programming solver. The algorithm parameters are tuned by design of experiments approach. The proposed method is tested on the several standard instances. The results show that the algorithm outperforms the best heuristic approaches found in the literature.

---

This paper addresses a reliable facility location problem with considering facility capacity constraints. In reliable facility location problem some facilities may become unavailable from time to time. If a facility fails, its clients should refer to other facilities by paying the cost of retransfer to these facilities. Hence, the fail of facilities leads to disruptions in facility location decisions and this problem is an attempt to reducing the impact of these disruptions. In order to formulate the problem, a new mixed-integer nonlinear programming (MINLP) model with the objective of minimizing total investment and operational costs is presented. Due to complexity of MINLP model, two different heuristic procedures based on mathematical model are developed. Finally, the performance of the proposed heuristic methods is evaluated through executive numerical example. The numerical results show that the proposed heuristic methods are efficient and provide suitable solutions.

---

There are many numerous methods for solving large-scale problems in which some of them are very flexible and efficient in both linear and non-linear cases. League championship algorithm is such algorithm which may be used in the mentioned problems. In the current paper, a new play-off approach will be adapted on league championship algorithm for solving large-scale problems. The proposed algorithm will be used for solving large-scale solving support vector machine model which is a quadratic optimization problem and cannot be solved in a non polynomial time using exact algorithms or optimally using traditional heuristic ones in large scale sizes. The efficiency of the new algorithm will be compared to traditional one in terms of the optimality and time measures. The superiority of the algorithm can be compared versus older version.

---

In hybrid flow shop scheduling problem (HFS) with unrelated parallel machines, a set of n jobs are processed on k machines. A mixed integer linear programming (MILP) model for the HFS scheduling problems with unrelated parallel machines has been proposed to minimize the maximum completion time (makespan). Since the problem is shown to be NP-complete, it is necessary to use heuristic methods to tackle the moderate to large scale problems. This article presents a new bottleneck-based heuristic to solve the problem. To improve the performance of the heuristic method, a local search approach is embedded in the structure of the heuristic method. To evaluate the performance of the proposed heuristic method, a new lower bound is developed based on Kurz and Askin [1] lower bound. For evaluation purposes, two series of test problems, small and large size problems, are generated under different production scenarios.  The empirical results show that average difference between lower bound and optimal solution as well as lower bound and heuristic method are equal to 2.56% and 5.23%, respectively. For more investigation, the proposed heuristic method is compared by other well-known heuristics in the literature. The results verify the efficiency of the proposed heuristic method in term of number of best solution.

---

Public transportation has been one of the most important research fields in the two last decades. The purpose of this paper is to create a schedule for public transport fleets such as buses and metro trains with the goal of minimizing the total transfer waiting time. We extend previous research works in the field of transit schedule with considering headways of each route as decision variables. In this paper, we formulate the problem as a mixed integer linear programming model and solve it using ILOG CPLEX solver. For large-scale test instances, we develop a metaheuristic based on the scatter search algorithm to obtain good solutions in a reasonable CPU run times. Finally, in the computational section, the efficiency of the proposed model and developed algorithm are compared with the existing results in the literature on a real railway network.

---

Considering the high costs of the implementation and maintenance of gas distribution networks in urban areas, optimal design of such networks is vital. Today, urban gas networks are implemented within a tree structure. These networks receive gas from City Gate Stations (CGS) and deliver it to the consumers. This study presents a comprehensive model based on Mixed Integer Nonlinear Programming (MINLP) for the design of urban gas networks taking into account topological limitations, gas pressure and velocity limitations and environmental limitations. An Ant Colony Optimization (ACO) algorithm is presented for solving the problem and the results obtained by an implementation of ACO algorithm are compared with the ones obtained through an iterative method to demonstrate the efficiency of ACO algorithm. A case study of a real situation (gas distribution in Kelardasht, Iran) affirms the efficacy of the proposed approach.
 

---

This paper deals the stochastic cell formation problem (SCFP). The paper presents a new nonlinear integer programming model for the SCFP in which the effect of buffer size on the grouping efficacy of cells has been investigated. The objective function is the maximization of the grouping efficacy of cells. A chance constraint is applied to explore the effect of buffer on the SCFP. Processing time and arrival time of the part for each cell are considered stochastic and are following exponential probability distribution. To find out the optimal solution in a reasonable time, a heuristic approach is used to linearize the proposed nonlinear model. This problem has been known as an NP-hard problem. Therefore, two metaheuristic methods, namely; genetic algorithm and particle swarm optimization are employed to solve examples. The parameters of the algorithms are calibrated using Taguchi and full factorial methods, and the performances of the algorithms on the examples of various sizes are analyzed against global solutions obtained from Lingo software’s branch and bound (B&B) in terms of quality of solutions and computational time.

---

The fundamental function of a cellular manufacturing system (CMS) is based on definition and recognition of a type of similarity among parts that should be produced in a planning period. Cell formation (CF) and cell layout design are two important steps in implementation of the CMS. This paper represents a new nonlinear mathematical programming model for dynamic cell formation that employs the rectilinear distance notion to determine the layout in the continuous space. In the proposed model, machines are considered unreliable with a stochastic time between failures. The objective function calculates the costs of inter and intra-cell movements of parts and the cost due to the existence of exceptional elements (EEs), cell reconfigurations and machine breakdowns. Due to the problem complexity, the presented mathematical model is categorized in NP-hardness; thus, a genetic algorithm (GA) is used for solving this problem. Several crossover and mutation strategies are adjusted for GA and parameters are calibrated based on Taguchi experimental design method. The great efficiency of the proposed GA is then demonstrated via comparing with particle swarm optimization (PSO) and the optimum solution via GAMS considering several small/medium and large-sized problems. 

---

There is still a great deal of attention in cellular manufacturing systems and proposing capable metaheuristics to better solve these complicated optimization models. In this study, machines are considered unreliable that life span of them follows a Weibull distribution. The intra and inter-cell movements for both parts and machines are determined using batch sizes for transferring parts are related to the distance traveled through a rectilinear distance. The objectives minimize the total cost of parts relocations and maximize the processing routes reliability due to alternative process routing. To solve the proposed problem, Genetic Algorithm (GA) and two recent nature-inspired algorithms including Keshtel Algorithm (KA) and Red Deer Algorithm (RDA) are employed. In addition, the main innovation of this paper is to propose a novel hybrid metaheuristic algorithm based on the benefits of aforementioned algorithms. Some numerical instances are defined and solved by the proposed algorithms and also validated by the outputs of exact solver. A real case study is also utilized to validate the proposed solution and modeling algorithms. The results indicate that the proposed hybrid algorithm is more appropriate than the exact solver and outperforms the performance of individual ones.

---

In this paper, an inventory-routing problem for a network of appliance repair service is discussed including several repair depots and customers. The customer in this network makes a demand to have his/her faulty appliance repaired. Then, the repairman is assigned to the demand based on the skill needed for repairing of appliance differing for each one. The assigned repairman picks up the faulty appliance from the customer place using the vehicle for transferring faulty appliances to repair depot. The vehicle for picking up and delivering the appliances has a maximum capacity. Additionally, the repair depot needs spare parts to repair the faulty appliances that is supplied either by the supplier or lateral transshipment from the other depots. The capacitated vehicle inventory-routing problem with simultaneous pickup and delivery is NP-hard which needs special optimization procedure. Regarding the skill of repairman, it becomes more complex. Many solution approaches have been provided so far which have their pros and cons to deal with. In this study, an augmented angle-based sweep method is developed to cluster nodes for solving the problem. Finally, the heuristic is used in the main body of genetic algorithm with special representation.

---

In recent years, it has been proven that integrating statistical process control, maintenance policy, and production can bring more benefits for the entire production systems. In the literature of triple-concept integrated models, it has generally been assumed that the observations are independent. However, the existence of correlated structures in some practical applications put the traditional control charts in trouble. The mixed EWMA-CUSUM (MEC) control chart and the ARMA control chart are effective tools to monitor the mean of autocorrelated processes. This paper proposes an integrated model subject to some constraints for determining the decision variables of triple concepts in the presence of autocorrelated data. Three types of autocorrelated processes are investigated to study their effects on the results. Moreover, the results of the MEC and ARMA charts are compared. Due to the complexity of the model, a particle swarm optimization (PSO) algorithm is applied to select optimal decision variables. An industrial example and extensive comparisons are provided