فهرست مطالب

  • Volume:28 Issue: 4, 2017
  • تاریخ انتشار: 1396/07/26
  • تعداد عناوین: 8
|
  • Arash Khosravi, Seyed Reza Hejazi*, Shahab Sadri Pages 351-365
    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.
    Keywords: Supply Chain Network Redesign, Capacity Planning, Multi, Period Pricing, Benders Decomposition method
  • Rassoul Noorossana Dr*, Mahnam Najafi Mrs Pages 367-376
    Change point estimation is as an effective method for identifying the time of a change in production and service processes. In most of the statistical quality control literature, it is usually assumed that the quality characteristic of interest is independently and identically distributed over time. It is obvious that this assumption could be easily violated in practice. In this paper, we use maximum likelihood estimation method to estimate when a step change has occurred in a high yield process by allowing a serial correlation between observations. Monte Carlo simulation is used as a vehicle to evaluate performance of the proposed method. Results indicate satisfactory performance for the proposed method.
    Keywords: Change point estimation, High yield process, Maximum likelihood estimation, Correlation, Statistical process control
  • Mosata Setak *, Shabnam Izadi, Hamid Tikani Pages 377-387
    Logistics planning in disaster response phase involves dispatching commodities such as medical materials, personnel, food, etc. to affected areas as soon as possible to accelerate the relief operations. Since transportation vehicles in disaster situations can be considered as scarce resources, thus, the efficient usage of them is substantially important. In this study, we provide a dynamic vehicle routing model for emergency logistics operations in the occurrence of natural disasters. The aim of the model is to find optimal routes for a fleet of vehicles to give emergency commodities to a set of affected areas by considering the existence of more than one arc between each two nodes in the network (multi-graph network). Proposed model considers FIFO property and focused on minimization of waiting time and total number of vehicles. Various problem instances have been provided to indicate the efficiency of the model. Finally, a brief sensitivity analysis is presented to investigate the impact of different parameters on the obtained solutions.
    Keywords: Time, dependent vehicle routing problem, Multi, graph, FIFO property, Disaster relief, Service time
  • Amin Saghaeeian, Reza Ramezanian Dr* Pages 389-401
    This study considers pricing, production and transportation decisions in a Stackelberg game between three-stage, multi-product, multi-source and single-period supply chains called leader and follower. These chains consist of; manufacturers, distribution centers (DCs) and retailers. Competition type is horizontal and SC vs. SC. The retailers in two chains try to maximize their profit through pricing of products in different markets and regarding the transportation and production costs. A bi-level nonlinear programming model is formulated in order to represent the Stackelberg game. Pricing decisions are based on discrimination pricing rules, where we can put different prices in different markets. After that the model is reduced to single-level nonlinear programming model by replacing Karush-Kuhn-Tucker conditions for the lower level (follower) problem. Finally, a numerical example is solved in order to analyze the sensitivity of effective parameters on price and profit.
    Keywords: Competitive supply chain, multi, product, pricing, Stackelberg game, three, stage supply chain, elastic demand.
  • Keyvan Roshan Mr., Mehdi Seifbarghy Prof., Davar Pishva Prof* Pages 403-427
    Preventive healthcare aims at reducing the likelihood and severity of potentially life-threatening illnesses by protection and early detection. In this paper, a bi-objective mathematical model is proposed to design a network of preventive healthcare facilities so as to minimize total travel and waiting time as well as establishment and staffing cost. Moreover, each facility acts as M/M/1 queuing system. The number of facilities to be established, the location of each facility, and the level of technology for each facility to be chosen are provided as the main determinants of a healthcare facility network. Since the developed model of the problem is of an NP-hard type, tri-meta-heuristic algorithms are proposed to solve the problem. Initially, Pareto-based meta-heuristic algorithm called multi-objective simulated annealing (MOSA) is proposed in order to solve the problem. To validate the results obtained, two popular algorithms namely, non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are utilized. Since the solution-quality of all meta-heuristic algorithms severely depends on their parameters, Taguchi method has been utilized to fine tune the parameters of all algorithms. The computational results, obtained by implementing the algorithms on several problems of different sizes, demonstrate the reliable performances of the proposed methodology.
    Keywords: Multi, objective Preventive healthcare problems (MOPHPs), Queuing system, Multi, objective simulated annealing (MOSA), NSGA, II, NRGA, Taguchi method
  • Parham Azimi*, Naeim Azouji Pages 429-439
    In this paper a novel modelling and solving method has been developed to address the so-called resource constrained project scheduling problem (RCPSP) where project tasks have multiple modes and also the preemption of activities are allowed. To solve this NP-hard problem, a new general optimization via simulation (OvS) approach has been developed which is the main contribution of the current research. In this approach, the mathematical model of the main problem is relaxed and solved then the optimum solutions were used in the corresponding simulation model to produce several random feasible solutions for the main problem. Finally, the most promising solutions were selected as the initial population of a genetic Algorithm (GA). To test the efficiency of the problem, several test problems were solved by the proposed approach and according to the results, the proposed concept has a very good performance to solve such a complex combinatoral problem. Also, the concept could be easily applied for other similar combinatorics.
    Keywords: Optimization via Simulation, Multi, mode Resource Constraint Project Scheduling Problem, Genetic Algorithm
  • Mojtaba Torkinejad, Iraj Mahdavi Prof*, Nezam Mahdavi-Amiri Prof, Mirmehdi Seyed Esfahani Prof Pages 441-460
    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.
    Keywords: Designing urban networks, optimization, tree structure, ant colony algorithm, pressure, velocity, metaheuristic algorithms
  • Babak Shirazi Dr* Pages 461-481
    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.
    Keywords: Resource planning, revenue, expenditures estimation, shell material embankment, concurrent control.