Iranian Journal Of Operations Research
Volume:8 Issue: 1, Winter and Spring 2017

This special issue is a collection of refereed articles selected from the 13th International Industrial Engineering Conference (IIEC 2017). The initial selection was made by Dr. Hamed Fazlollahtabar who also wrote the following description. The accepted articles were reviewed going through the usual reviewing process of IJOR.

Nezam Mahdavi-Amiri
Editor-in-Chief


     The 13th International Industrial Engineering Conference (IIEC 2017) hosted by Mazandaran University of Science and Technology, Babol, Iran, was held on 22nd and 23rd of February 2017 at Mizban Complex, Babolsar, Mazandaran, Iran. The total number of papers received was 805, among which 378 papers were accepted in two categories of oral presentations (185 papers) and poster presentations (193 papers). The scientific committee of the conference selected a number of papers to be extended and considered for a special issue of the Iranian Journal of Operations Research (IJOR). For this, 25 selected papers were considered and 17 papers were chosen for the second round of review. Having strict review criteria, 11 papers were then selected and refereed for a final decision. After the review process, 6 papers were finally accepted for the special issue. The contents of the accepted papers follow here. Vehicle routing and scheduling in an environmentally friendly manner attracted researchers to develop both mathematical programming models and heuristics as solution approaches. Green supply network design under uncertainty was considered for a multi-mode production system. Facility planning and hub location problem in competitive conditions and multiple allocations were investigated. Robust optimization philosophy as an uncertainty treatment approach was studied using event-driven and attribute-driven concepts. An interesting application of operations research in forestry was studied developing stochastic dynamic programming with Markov chains.

     My special gratitude goes to Professor Nezam Mahdavi-Amiri, Editor-in-chief, and the editorial board members of IJOR for their cooperation and support during the past 10 months of preparing this special issue.

Hamed Fazlollahtabar
Coordinator for Special Issues IIEC2017
Department of Industrial Engineering
School of Engineering, Damghan University
Damghan, Iran

We present a bi-objective model for a green truck scheduling and routing problem at a cross-docking system. This model determines three key decisions at the cross dock: (1) defining a sequence and schedule of inbound trucks at the receiving door, (2) specifying a sequence and a schedule of outbound trucks at the shipping door, and (3) determining the routes of the outbound truck while serving customers. The first objective function is related to responsiveness of the network that minimizes time window violations and the second objective function minimizes total fuel consumption of trucks in order to consider the environmental factor of the network. Also, a learning effect is considered in loading and unloading process times. To solve the bi-objective model, an archived multi-objective simulated annealing (AMOSA) is used and modified. Finally, a number of test problems are solved and the efficiency of the proposed AMOSA is compared with the e-constraint method.

A genetic algorithm is proposed to optimize a tree-structured power distribution network considering optimal cable sizing. For minimizing the total cost of the network, a mixed-integer programming model is presented determining the optimal sizes of cables with minimized location-allocation cost. For designing the distribution lines in a power network, the primary factors must be considered as maximum allowable electrical flow in cables, permitted length of cables, maximum permitted voltage drops, and balance of load. The relationship between rates of electric current and cable sizes with consideration of constraints such as voltage drops and length are our essential data. To create a network with a minimum number of arcs and no closed loop such that all the nodes are covered, a minimum spanning tree technique is utilized. Here, we solve the problem using a genetic optimization algorithm and apply the offered approach to a real problem. By comparing the two extracted results from the proposed approach and an exact method, effectiveness of the genetic algorithm for optimization of power distribution network is shown. To demonstrate the validity of the offered model, a case study in Tehran power distribution company in Iran is made.

A multi-periodic, multi-echelon green supply chain network consisting of manufacturing plants, potential distribution centers, and customers is developed. The manufacturing plants can provide the products in three modes including production in regular time, production in over time, or by subcontracting. The problem has three objectives including minimization of the total costs of the green supply chain network, maximization of the average safe inventory levels of the manufacturing plants and the distribution centers and minimization of the environmental impacts of the manufacturing plants in producing, holding and dispatching the products and also the environmental impacts of the distribution centers in holding and dispatching the products. The problem is first formulated as a mixed-integer mathematical model. Then, in order to solve the model, the augmented weighted Tchebycheff method is employed and its performance in producing the Pareto optimal solutions is compared with the goal attainment method.

The hub location problems (HLP) constitute an important class of facility location problems that have been addressed by numerous operations researchers in recent years. HLP is a strategic problem frequently encountered in designing logistics and transportation networks. Here, we address the competitive multiple allocation HLP in a duopoly market. It is assumed that an incumbent firm (the leader) is operating an existing hub network in a market and an entrant firm (the follower) tries to enter the market by locating its own hubs aiming at capturing as much flow as possible from the leader. The customers choose one firm based on the service level (cost, time, distance, etc.) provided by the firm. We formulate the problem from the entrant firm’s point of view and propose an efficient tabu search based solution algorithm to solve it. Computational experiments show the capability of the proposed solution algorithm to obtain the optimal solutions in short computing times.

Over five decades have passed since the first wave of robust optimization studies conducted by Soyster and Falk. It is outstanding that real-life applications of robust optimization are still swept aside; there is much more potential for investigating the exact nature of uncertainties to obtain intelligent robust models. For this purpose, in this study, we investigate a more refined description of the uncertain events including (1) event-driven and (2) attribute-driven. Classical methods transform convex programming classes of uncertainty sets. The structural properties of uncertain events are analyzed to obtain a more refined description of the uncertainty polytopes. Hence, tractable robust models with a decent degree of conservatism are introduced to avoid the over-protection induced by classical uncertainty sets.

We present a stochastic dynamic programming approach with Markov chains for optimal control of the forest sector. The forest is managed via continuous cover forestry and the complete system is sustainable. Forest industry production, logistic solutions and harvest levels are optimized based on the sequentially revealed states of the markets. Adaptive full system optimization is necessary for consistent results. The stochastic dynamic programming problem of the complete forest industry sector is solved. The raw material stock levels and the product prices are state variables. In each state and at each stage, a quadratic programming profit maximization problem is solved, as a subproblem within the STDP algorithm.