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Showing 3 results for Mixed Integer Nonlinear Programming
MULTI-OBJECTIVE LOCATION-ROUTING MODEL WITH MULTIPLE TRANSPORTATION MODES FOR DESIGNING RELIEF NETWORKS UNDER UNCERTAINTY
Shadan Sadighbehzadi, Zohreh Moghaddas, Amirreza Keyghobadi, Mohsen Vaez-Ghasemi,
Volume 29, Issue 4 (12-2018)
Abstract
Natural disasters and crisis are inevitable and each year impose destructive effects on human as injuries and damage to property. In natural  disasters and after the outbreak of the crisis, demand for logistical goods and services increase. Effective distribution of emergency aid could have a significant role in minimizing the damage and fatal accident. In this study, a three-level relief chain including a number of suppliers in fixed locations, candidate distribution centers and affected areas at certain points are considered. For this purpose a mixed integer nonlinear programming model is proposed for open transportation location routing problem by considering split delivery of demand. In order to solve a realistic problem, foregoing parameters are considered as fuzzy in our proposed mode. The objectives of the proposed model include total cost minimization, minimization of the maximum travel time of
vehicles and minimization of unmet demands. In order to solve the problem of the proposed model, fuzzy multi-objective planning is used. For efficiency and effectiveness of the proposed model and solution approach, several numerical examples are studied. Computational results show the effectiveness and efficiency of the model and the proposed approach.
Simulation based optimization model for logistic network in a multi-stage supply chain network with considering operational production planning "truck loading system and transportation network"
Mohsen Khezeli, Esmaeil Najafi, Mohammad Haji Molana, Masoud Seidi,
Volume 32, Issue 2 (6-2021)
Abstract
One of the most important fields of logistic network is transportation network design that has an important effect on strategic decisions in supply chain management. It has recently attracted the attention of many researchers. In this paper, a multi-stage and multi-product logistic network design is considered.
This paper presents a hybrid approach based on simulation and optimization (Simulation based optimization), the model is formulated and presented in three stages.  At first, the practical production capacity of each product is calculated using the Overall Equipment Effectiveness (OEE) index, in the second stage, the optimization of loading schedules is simulated. The layout of the loading equipment, the number of equipment per line, the time of each step of the loading process, the resources used by each equipment were simulated, and the output of the model determines the maximum number of loaded vehicles in each period. Finally, a multi-objective model is presented to optimize the transportation time and cost of products. A mixed integer nonlinear programming (MINLP) model is formulated in such a way as to minimize transportation costs and maximize the use of time on the planning horizon. We have used Arena simulation software to solve the second stage of the problem, the results of which will be explained. It is also used GAMS software to solve the final stage of the model and optimize the transporting cost and find the optimal solutions. Several test problems were generated and it showed that the proposed algorithm could find good solutions in reasonable time spans.
A Mathematical Model for Sustainable and Resilient Supply Chain by Considering Synchronization in the Production and Distribution Network
Mohsen Khezeli, Esmaeil Najafi, Mohammad Haji Molana, Masoud Seidi,
Volume 33, Issue 2 (6-2022)
Abstract
Nowadays, supply chain management (SCM) is an interesting problem that has attracted the attention of many researchers. Transportation network design is one of the most important fields of SCM. In this paper, a logistics network design is considered to optimize the total cost and increase the network stability and resiliency. First, a mixed integer nonlinear programming model (MINLP) is formulated to minimize the transportation time and transportation cost of products. The proposed model consists of two main stages.
One is a normal stage that minimizes the transportation and holding costs, all manufacturers are also assumed to be healthy and in service. In this stage, the quantity of customer demand met by each manufacturer is eventually determined.
The second is the resilience stage. A method is presented by creating an information network in this supply chain for achieving the resilient and sustainable production and distribution chain that, if some manufacturers break down or stop production, Using the Restarting and load sharing scenarios in the reactive approach to increase resilience with accepting the costs associated with it in the supply network and return to the original state in the shortest possible time, the consequences of accidental failure and shutdown of production units are managed.
Two capacities are also provided for each manufacturer
  • Normal capacity to meet the producer's own demand
  • Load sharing capacity, Determine the empty capacity and increase the capacity of alternative units to meet the out-of-service units demand
In order to solve the model, we used GAMS & Matlab software to find the optimal solutions. A hybrid priority-based Non-dominated Sorting Genetic Algorithms (NSGA-II) and Sub-population Genetic Algorithm (SPGA- II) is provided in two phases to find the optimal solutions. The solutions are represented with a priority matrix and an Allocated vector. To compare the efficiency of two algorithms several criteria are used such as NPS, CS and HV. Several Sample problems are generated and solved that show the Sub-population Genetic Algorithm (SPGA- II) can find good solutions in a reasonable time limit.
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