International Journal of Industrial Engineering & Production Research

---

One of the most important issues in urban planning is developing sustainable public transportation. The basic condition for this purpose is analyzing current condition especially based on data. Data mining is a set of new techniques that are beyond statistical data analyzing. Clustering techniques is a subset of it that one of it’s techniques used for analyzing passengers’ trip. The result of this research shows relations and similarities in different segments that its usage is from strategic to tactical and operational areas. The approach in transportation is completely novel in the part of trip patterns and a novel process is proposed that can be implemented in highway analysis. Also this method can be applied in traffic and vehicle treats that need automatic number plate recognition (ANPR) for data gathering. A real case study has been studied here by developed process.

---

In any supply chain, distribution planning of products is of great importance to managers. With effective and flexible distribution planning, mangers can increase the efficiency of time, place, and delivery utility of whole supply chain. In this paper, inventory routing problem (IRP) is applied to distribution planning of perishable products in a supply chain. The studied supply chain is composed of two levels a supplier and customers. Customers’ locations are geographically around the supplier location and their demands are uncertain and follow an independent probability distribution functions. The product has pre-determined fixed life and is to be distributed among customers via a fleet of homogenous vehicles. The supplier uses direct routes for delivering products to customers. The objective is to determine when to deliver to each customer, how much to deliver to them, and how to assign them to vehicle and routes. The mentioned problem is formulated and solved using a stochastic dynamic programming approach. Also, a numerical example is given to illustrate the applicability of proposed approach.

---

Achieving optimal production cycle time for improving manufacturing processes is one of the common problems in production planning. During recent years, different approaches have been developed for solving this problem, but most of them assume that mean quality characteristic is constant over production run length and sets it on customer’s target value. However, the process mean may drift from an in-control to an out-of-control at a random point in time. This study aims to select the production cycle time and the initial setting of mean quality characteristic, so that the expected total cost, consisting of quality loss and maintenance costs as well as ordering and holding costs, already considered in the classic models is minimized. To investigate the effect of mean process setting, a computational analysis on a real world example is performed. Results show the superiority of the proposed approach compared to the classical economic production quantity model.

---

The locomotive assignment and the freight train scheduling are important problems in railway transportation. Freight cars are coupled to form a freight rake. The freight rake becomes a train when a locomotive is coupled to it. The locomotive assignment problem assigns locomotives to a set of freight rakes in a way that, with minimum locomotive deadheading time, rake coupling delay and locomotive coupling delay all freight rakes are hauled to their destinations. Scheduling freight trains consists of sequencing and ordering freight trains during the non-usage time between passenger trains but with no interference and with minimum delay times. Solving these two problems simultaneously is of high importance and can be highly effective in decreasing costs for rail transportation. In this paper, we aim to minimize the operational costs for the locomotive assignment and the freight train scheduling by solving these two problems concurrently. To meet this objective, an efficient and effective algorithm based on the ant colony system is proposed. To evaluate the performance of the proposed solution method, twenty-five test problems, which are based on the conditions of Iran Railways, are solved and the computational results are reported.

---

Managing income is a considerable dimension in supply chain management in current economic atmosphere. Real world situation makes it inevitable not to design or redesign supply chain. Redesign will take place as costs increase or new services for customers’ new demands should be provided. Pricing is an important fragment of Supply chain due to two reasons: first, represents revenue based each product and second, based on supply-demand relations enables Supply chain to provide demands by making suitable changes in facilities and their capacities. In this study, Benders decomposition approach used to solve multi-product, multi-echelon and multi-period supply chain network redesign including price-sensitive customers.

---

Resource planning in large-scale construction projects has been a complicated management issue requiring mechanisms to facilitate decision making for managers. In the present study, a computer-aided simulation model is developed based on concurrent control of resources and revenue/expenditure. The proposed method responds to the demand of resource management and scheduling in shell material embankment activities regarding large-scale dam projects of Iran. The model develops a methodology for concurrent management of resources and revenue/expenditure estimation of dam's projects. This real-time control allows managers to simulate several scenarios and adopt the capability of complicated working policies. Results validation shows that the proposed model will assist project managers as a decision support tool in cost-efficient executive policymaking on resource configuration.
 

---

Surgical theater is one of the most expensive hospital sources that a high percentage of hospital admissions are related to it. Therefore, efficient planning and scheduling of the operating rooms (ORs) is necessary to improve the efficiency of any healthcare system. Therefore, in this paper, the weekly OR planning and scheduling problem is addressed to minimize the waiting time of elective patients, overutilization and underutilization costs of ORs and the total completion time of surgeries. We take into account the available hours of ORs and the surgeons, legal constraints and job qualification of surgeons, and priority of patients in the model. A real-life example is provided to demonstrate the effectiveness and applicability of the model and is solved using ε-constraint method in GAMS software. Then, data envelopment analysis (DEA) is employed to obtain the best solution among the Pareto solutions obtained by ε-constraint method. Finally, the best Pareto solution is compared to the schedule used in the hospitals. The results indicate the best Pareto solution outperforms the schedule offered by the OR director.

---

Project scheduling problem with resources constraint is a well-known problem in the field of project management. The applicable nature of this problem has caused the researchers’ tendency to it. In this study, project scheduling with resource constraints and the possibility of interruption of project activities as well as renewable resources constraint has been also applied along with a case study on construction projects. Construction projects involve complex levels of work. Making wrong decisions in selecting methods and how to allocate the necessary resources such as manpower and equipment can lead to the results such as increasing the predetermined cost and time. According to NP-Hard nature of the problem, it is very difficult or even impossible to obtain optimal solution using optimization software and traditional methods. In project scheduling using CPM method, critical path is widely used; however, in this method, the resource constraints is not considered. Project Scheduling seek proper sequence for doing the project activities. This study has been conducted using Bees meta-heuristic algorithm, with the aim of optimizing the project completion time. Finally, the results obtained from three algorithms and GAMS software shows that this algorithm has better performance than and the solution among the other algorithms and has achieved the accurate solutions.

---

Development of every society is incumbent upon energy sector’s technological and economic effectiveness. The electricity industry is a growing and needs to have a better performance to effectively cover the demand. The industry requires a balance between cost and efficiency through careful design and planning. In this paper, a two-stage stochastic programming model is presented for the design of electricity supply chain networks. The proposed network consists of power stations, transmission lines, substations, and demand points. While minimizing costs and maximizing effectiveness of the grid, this paper seeks to determine time and location of establishing new facilities as well as capacity planning for facilities. We use chance constraint method to satisfy the uncertain demand with high probability. The proposed model is validated by a case study on Southern Khorasan Province’s power grid network, the computational results show that the reliability rate is a crucial factor which greatly effects costs and demand coverage. 

---

Supply chain members coordinate with each other in order to obtain more profit. The major mechanisms for coordination among supply chain echelons are pricing, inventory management, and ordering decisions. This paper concerns these mechanisms in a multi-echelon supply chain consisting of multiple suppliers, one manufacturer, and multiple retailers in order to study the price and leadtime competition, where the make-to-order production mode is employed and consumers are sensitive to retail price and leadtime. In the current study, a novel inventory model is presented, where the manufacturer has an exclusive supplier for every required component of its final product. The interactions and decisions of the firms are observed in multiple time periods. Moreover, each supply chain member has equal power and make their decisions simultaneously. The proposed model considers the relationships among three echelon supply chain members based on a non-cooperative Nash game with pricing and inventory decisions. An iterative solution algorithm is proposed to find Nash equilibrium point of the game. Several numerical examples are presented to study the application of the model as well as the effectiveness of the algorithm. Finally, a comprehensive sensitivity analysis is performed and some important managerial insights are highlighted.
 

---

In this paper, an integrated mathematical model of the dynamic cell formation and production planning, considering the pricing and advertising decision is proposed. This paper puts emphasis on the effect of demand aspects (e.g., pricing and advertising decisions) along with the supply aspects (e.g., reconfiguration, inventory, backorder and outsourcing decisions) in developed model. Due to imprecise and fuzzy nature of input data such as unit costs, capacities and processing times in practice, a fuzzy multi-objective programming model is proposed to determine the optimal demand and supply variables simultaneously. For this purpose, a fuzzy goal programming method is used to solve the equivalent defuzzified multi-objective model. The objective functions are to maximize the total profit for firm and maximize the utilization rate of machine capacity. The proposed model and solution method is verified by a numerical example.

---

One of the basic assumptions of classical production-inventory models is that all products are of perfect quality. However, in real manufacturing situations, the production of defective items is inevitable, and a fraction of the items produced may be naturally imperfect. In fact, items may be damaged due to production and/or transportation conditions in the manufacturing process. On the other hand, some reworkable items exist among imperfect items that can be made perfect by additional processing. In addition, the classical production-inventory models assume that there is only one product in the system and that there is an unlimited amount of resources. However, in many practical situations, several products are produced and there are some constraints related to various factors such as machine capacity, storage space, available budget, number of allowable setups, etc. Therefore, we propose new constrained production-inventory models for multiple products where the manufacturing process is defective and produces a fraction of imperfect items. A percentage of defective items can be reworked, and these products go through the rework process to become perfect and return to the consumption cycle. The goal is to determine economic production quantities to minimize the total cost of the system. The analytical solutions are each derived separately by Lagrangian relaxation method, and a numerical example is presented to illustrate and discuss the procedure. A sensitivity analysis is performed to investigate how the variation in the inputs of the models affects the total cost of the inventory system. Finally, some research directions for future works are discussed.

---

In this paper, a fuzzy multi-objective optimization model in the logistics of relief chain for response phase planning is addressed. The objectives of the model are: minimizing the costs, minimizing unresponsive demand, and maximizing the level of distribution and fair relief. A multi-objective integer programming model is developed to formulate the problem in fuzzy conditions and transformed to the deterministic model using Jime'nez approach. To solve the exact multi-objective model, the ε-constraint method is used. The resolved results for this method have shown that this method is only able to find the solution for problems with very small sizes. Therefore, in order to solve the problems with medium and large sizes, multi-objective cuckoo search optimization algorithm (MOCSOA) is implemented and its results are compared with the NSGA-II. The results showed that MOCSOA in all cases has the higher ability to produce higher quality and higher-dispersion solutions than NSGA-II.
 

---

Companies and firms, nowadays, due to mounting competition and product diversity, seek to apply virtual cellular manufacturing systems to reduce production costs and improve quality of the products. In addition, as a result of rapid advancement of technology and the reduction of product life cycle, production systems have turned towards dynamic production environments. Dynamic cellular manufacturing environments examine multi-period planning horizon, with changing demands for the periods. A dynamic virtual cellular manufacturing system is a new production approach to help manufacturers for decision making. Here, due to variability of demand rates in different periods, which turns to flow variability, a mathematical model is presented for dynamic production planning. In this model, we consider virtual cell production conditions and worker flexibility, so that a proper relationship between capital and production parameters (part-machine-worker) is determined by the minimum lost sales of products to customers, a minimal inventory cost, along with a minimal material handling cost. The problems based on the proposed model are solved using LINGO, as well as an epsilon constraint algorithm.

---

Integrated treatment planning for cancer patients has high importance in intensity modulated radiation therapy (IMRT). Direct aperture optimization (DAO) is one of the prominent approaches used in recent years to attain this goal. Considering a set of beam directions, DAO is an integrated approach to optimize the intensity and leaf position of apertures in each direction. In this paper, first, a mixed integer-nonlinear mathematical formulation for the DAO problem in IMRT treatment planning is presented. Regarding the complexity of the problem, two well-known metaheuristic algorithms, particle swarm optimization (PSO) and differential evolution (DE), are utilized to solve the model. The parameters of both algorithms are calibrated using the Taguchi method. The performance of two proposed algorithms is evaluated by 10 real patients with liver cancer disease. The statistical analysis of results using paired samples t-test demonstrates the outperformance of the PSO algorithm compared to differential evolution, in terms of both the treatment plan quality and the computational time. Finally, a sensitivity analysis is performed to provide more insights about the performance of algorithms and the results revealed that increasing the number of beam angles and allowable apertures improve the treatment quality with a computational cost.
 

---

Modeling supply chain planning problems is considered one of the most critical planning issues in Supply Chain Management (SCM). Nowadays, decisions making must be sufficiently sustainable to operate appropriately in a complex and uncertain environment of the market for many years to beyond the next decade. Therefore, making these decisions in the presence of uncertainty is a critical issue,as highlighted in a large number of relevant publications over the past two decades.The purpose of this investigation is to model a multilevel supply chain problem and determine the constraints that prevent the flow from performing properly, subject to various sources and types of uncertainty that characterize the flow. Therefore, it attempts to establish a generic model that relies on the stochastic approach.  Several studies have been conducted on uncertainty in order to propose an optimal solution to this type of problem. Thus, in this study, we will use the method of "Mixed integer optimization program" which is the basis of the algorithm that will be employed. This inaccuracy of the supply chain is handled by the fuzzy sets. In this paper, we intend to provide a new model for determining optimal planning of tactical and strategical decision-making levels, by building a conceptual model. Therefore, it enables us to model the mathematical programming problem. We investigate in this attempt, attention to solving the mathematical model. So in the resolution we are going through the algorithm in machine learning, therefore providing as in the end an optimal solution for the planning of production.

---

Several industrial products such as moulds, dies, engine block, automotive parts, etc., require machining of a large number of holes. Similarly, applications like boiler plates, food-business processing separator's, printed circuit boards, drum and trammel screens, etc., consist of a matrix of a large number of holes. Many machining operations, such as drilling, enlargement, tapping, or reaming, are needed to achieve the final sizes of individual holes, resulting in a variety of possible sequences to complete the hole-making operations. The major issue involved in hole-making operations is the tool travel time. It is often vital to determine the optimal sequence of operations so that the overall processing cost of hole-making operations can be minimized. In this work, thus an attempt is made to minimize the total tool travel of hole-making operations by using a relatively new optimization algorithm known as modified shuffled frog leaping for the determination of the optimal sequence of operations. Modification is made in the present shuffled frog-leaping algorithm by using three parameters with their positive values in order to widen the search capability of the existing algorithm. This paper considers three case studies of a rectangular matrix of holes to explain the proposed procedure. The outcomes of optimization with a modified shuffled frog-leaping algorithm are compared to those obtained with the genetic algorithm and the ant colony algorithm. Additionally, the higher dimensional problem of 20 x 20 rectangular matrix of holes is considered in this work. 

---

 

---

 

---