scholarly journals Robust sustainable multi-period hub location considering uncertain time-dependent demand

2021 ◽  
Author(s):  
amir khaleghi ◽  
Alireza Eydi
Keyword(s):  
Demand Function ◽  
Programming Model ◽  
Valid Inequalities ◽  
Interval Uncertainty ◽  
Time Dependent ◽  
Hub Location ◽  
Multi Objective ◽  
First Case ◽  
Proposed Model ◽  
Dependent Demand

This paper presents a mathematical programming model for designing a sustainable continuous-time multi-period hub network considering time-dependent demand. The present model can be used in situations where the distribution of parameters related to the demand function is unknown, and we only can determine the range of changes of these parameters. To model these conditions, we consider interval uncertainty for the demand function parameters. The proposed model is a nonlinear multi-objective model. The objectives of the model cover economic, environmental, and social aspects of sustainability. These objectives include minimizing total costs, minimizing emissions, and maximizing fixed and variable job opportunities. We linearize the model by using some linearization techniques, and then, with the help of Bertsimas and Sim’s method, we construct a robust counterpart of the model. We also present some valid inequalities to strengthen the formulation. To solve the proposed model, we use Torabi and Hassini method. From solving the proposed model, network design decisions and the best time to implement decisions during the planning horizon are determined. To validate the model, we solve a sample problem based on the Turkish dataset and compare the designed network in two cases: in the first case, the demand function parameters take nominal values, and in the second case, the value of these parameters can change up to 20% of their nominal values. The results show that in the second case, the total capacity selected for hubs and hub links is greater than the first case. To investigate changes in objective functions to parameters level of conservatism and probability of constraints violation, we perform sensitivity analysis on these parameters in both single-objective and multi-objective optimization cases and report the results.

A Bi-objective Multi-Product Multi-Constraint EPQ Model in a Stochastic Environment and Partial Shortage

2020 ◽  
Vol 19 (03) ◽  
pp. 567-587
Author(s):  
Seyedeh Sanaz Mirkhorsandi ◽  
Seyed Hamid Reza Pasandideh
Keyword(s):  
Goal Attainment ◽  
Programming Model ◽  
Multi Objective ◽  
Holding Costs ◽  
Inventory Costs ◽  
Economic Production ◽  
Proposed Model ◽  
Demand Rate ◽  
Nonlinear Programming Model ◽  
Epq Model

One of the classical models for inventory control is economic production quantity (EPQ), which is widely used in industry. In this paper, an EPQ model with partial shortage is developed by considering the real world conditions, and costs related to the backorder demand are taken as fixed and time-dependent. In the proposed model, determination of the inventory cycle length, the length of positive inventory cycle and backordered demand rate are considered in shortage period. The aim of the presented research is to minimize the total inventory costs and the space required for storage products so that the stochastic and classic constraints including holding costs, lost sales, backorder, budget, total number of productions and average shortage times should be satisfied while optimizing the multi-objective problem. Presented model is a bi-objective nonlinear programming model. Then, to solve the proposed model, three multi-objective decision-making methods including Lp-metric, goal programming and goal attainment are used. Besides, numerical examples are executed in small, medium and large scales by use of GAMS software, and the performance of the methods is compared in terms of objective functions and required CPU time. Finally, sensitivity analysis is done to determine the effect of change in the main parameters of the model on the objective function value.

scholarly journals A Location-Procurement-Distribution Model Combined with Data Envelopment Analysis Model under a Multi-period Environment and Gradual Budget Injection for Humanitarian Relief Supply Chains

2021 ◽  
Author(s):  
Leyla Fazli
Keyword(s):  
Data Envelopment Analysis ◽  
Programming Model ◽  
Mixed Integer ◽  
Distribution Model ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Humanitarian Relief ◽  
Analysis Model ◽  
Multi Objective ◽  
Proposed Model

Abstract Humanmade or natural catastrophes such as droughts, floods, earthquakes, storms, coups, economic and political crises, wars, and so forth impact various areas of the world annually. Furthermore, the lack of adequate preparations and proper coping against them causes nations to suffer heavy losses and casualties, which are sometimes irrecoverable. Consequently, as an essential activity in crisis management, humanitarian relief logistics has been of particular importance and has taken a good deal of notice at the international level during recent years. Aid facilities location and the storage of necessary commodities before a disaster and the proper distribution of relief commodities among demand points following a disaster are critical logistical strategies to improve performance and reduce latency when responding to a given disaster. In this regard, this study presents a stochastic multi-objective mixed-integer non-linear programming model in a two-level network that includes warehouses and affected areas. The model aims at minimizing total social costs, which include the expense of founding warehouses, the expense of procuring commodities, and deprivation cost, as well as maximizing fulfilled demands and warehouses utility. In this study, several pre-disaster periods, a limited budget for establishing warehouses and procuring relief commodities with their gradual injection into the system, the time value of money, various criteria for evaluating warehouses, the risk of disruption in warehouses and transportation networks, and heterogeneous warehouses are considered. The maximization of warehouses utility is done according to a data envelopment analysis model. Moreover, a multi-objective fuzzy programming model called the weighted max-min model is applied to solve the proposed model. Ultimately, the outcomes of the evaluation and validation of the proposed model show its appropriate and efficient performance.

scholarly journals An Optimization to Schedule Train Operations with Phase-Regular Framework for Intercity Rail Lines

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Huimin Niu ◽  
Minghui Zhang
Keyword(s):  
Programming Model ◽  
Hybrid Genetic Algorithm ◽  
Minimum Cost ◽  
Proposed Model ◽  
Rail Line ◽  
Service Oriented ◽  
Scheduling Method ◽  
Train Length ◽  
Crossover And Mutation ◽  
Dependent Demand

The most important operating problem for intercity rail lines, which are characterized with the train operations at rapid speed and high frequency, is to design a service-oriented schedule with the minimum cost. This paper proposes a phase-regular scheduling method which divides a day equally into several time blocks and applies a regular train-departing interval and the same train length for each period under the period-dependent demand conditions. A nonlinear mixed zero-one programming model, which could accurately calculate the passenger waiting time and the in-train crowded cost, is developed in this study. A hybrid genetic algorithm associated with the layered crossover and mutation operation is carefully designed to solve the proposed model. Finally, the effectiveness of the proposed model and algorithm is illustrated through the application to Hefei-Wuhan intercity rail line in China.

scholarly journals Multi objective fuzzy inventory model with deterioration, price and time dependent demand and time dependent holding cost

2020 ◽  
Vol 11 (3) ◽  
pp. 928
Author(s):  
Satya Kumar Das ◽  
Sahidul Islam
Keyword(s):  
Graphical Representation ◽  
Fuzzy Programming ◽  
Inventory Model ◽  
Time Dependent ◽  
Selling Price ◽  
Multi Objective ◽  
Holding Cost ◽  
Fuzzy Inventory ◽  
Cost Parameters ◽  
Dependent Demand

In this paper, we have formulated an inventory model with time dependent holding cost, selling price as well as time dependent demand. Multi-item inventory model has been considered under limitation on storage space. Due to uncertainty all the require cost parameters are taken as generalized trapezoidal fuzzy number. Our proposed multi-objective inventory model has been solved by using fuzzy programming techniques which are FNLP, FAGP, WFNLP and WFAGP methods. A numerical example is provided to demonstrate the application of the model. Finally to illustrate the model and sensitivity analysis and graphical representation have been shown. 

A Hybrid Fuzzy Multiple Objective Approach to Lotsizing, Pricing, and Marketing Planning Model

2012 ◽  
pp. 271-289 ◽  
Author(s):  
R. Ghasemy Yaghin ◽  
S.M.T. Fatemi Ghomi
Keyword(s):  
Programming Model ◽  
Performance Criterion ◽  
Industrial Case Study ◽  
Multi Objective ◽  
Fuzzy Environment ◽  
Multi Objective Programming ◽  
Inventory Investment ◽  
Marketing Planning ◽  
Proposed Model ◽  
General Demand

Given high variability of demands, a manufacturer has to decide about the products’ prices and lotsizing from a supplier. Due to imprecise and fuzzy nature of parameters such as unit costs and marketing function, a hybrid fuzzy multi-objective programming model including both quantitative and qualitative objectives is proposed to determine the optimal price, marketing expenditure, and lotsize. Considering pricing, marketing, and lotsizing decisions simultaneously, the model maximizes the profit, return on inventory investment (ROII) (as a financial performance criterion), and total customer satisfaction under general demand function with a time-varying pattern in fuzzy environment. After applying appropriate strategies to defuzzify the original model, the equivalent multi-objective crisp model is then transformed by a fuzzy goal programming method. A soft computing, particle swarm optimization (PSO) is applied to solve the final crisp problem. An industrial case study is provided to show the applicability and usefulness of the proposed model and solution method. Finally, concluding remarks are reported.

scholarly journals Multi-phase and Integrated Multi-objective Cyclic Operating Room Scheduling Based on an Improved NSGA-ⅡApproach

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 599 ◽  
Author(s):  
Qian Lu ◽  
Xiaomin Zhu ◽  
Dong Wei ◽  
Kaiyuan Bai ◽  
Jinsheng Gao ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Operating Room ◽  
Programming Model ◽  
Second Phase ◽  
Nsga Ii ◽  
Surgical Specialty ◽  
Allocation Model ◽  
Multi Objective ◽  
Proposed Model ◽  
Multi Phase

The operating room (OR) is an important department in a hospital, and the scheduling of surgeries in ORs is a challenging combinatorial optimization problem. In this paper, we address the problem of multiple resource allocation of ORs and propose a surgery scheduling scheme for OR units. To solve this problem, a multi-phase and integrated multi-objective linear programming model is proposed. The first phase of the proposed model is a resource allocation model, which mainly focuses on the allocation of ORs for each surgical specialty (SS). Based on the results of the first phase, the second phase is the cyclic Master Surgical Schedule model, which aims to schedule the surgeries in each SS. The proposed models are solved by the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which was improved. Finally, two numerical experiments based on practical data are provided to verify the effectiveness of the proposed models as well as to evaluate the performance of the improved NSGA-II. Our final results illustrate that our proposed model can provide hospital managers with a series of “optimal” solutions to effectively allocate relevant resources and ORs for surgeries, and they show that the improved NSGA-II has high computational efficiency and is more suitable in solving larger-scale problems.

Optimal Selling Price and Order Size for Non-Instantaneous Deteriorating Items With Generalized Price and Time-Dependent Demand and Partial Backlogging

2018 ◽  
pp. 58-73
Author(s):  
Hardik N. Soni ◽  
Ashaba Devrajsinh Chauhan
Keyword(s):  
Waiting Time ◽  
General Framework ◽  
Lot Sizing ◽  
Time Dependent ◽  
Selling Price ◽  
Partial Backlogging ◽  
Lot Size ◽  
Proposed Model ◽  
Lot Sizing Problem ◽  
Dependent Demand

The problem of determining the optimal selling price and lot size for an inventory system with non-instantaneous deteriorating item is considered in this chapter. In order to provide general framework, the pricing and lot sizing problem is modeled assuming a general price and time dependent demand function. The model allows for backlogging of demand which is characterized by decreasing function of waiting time. As the problem involves revenue and costs, a natural objective function for the model is profit per period. First, the sub problem in which price is fixed is solved to determine the optimal inventory policy. To broaden the problem, a procedure is developed for obtaining the optimal selling price and order size. To investigate the characteristics of the proposed model, numerical illustrations are presented.

An Interactive Possibilistic Programming Approach for Blood Bank Locations with Emergency Referral

2016 ◽  
Vol 5 (3) ◽  
Author(s):  
Amirhossein Mottaghi ◽  
Mahdi Alinaghian
Keyword(s):  
Programming Model ◽  
Careful Analysis ◽  
Blood Bank ◽  
Mixed Integer ◽  
Programming Approach ◽  
Multi Objective ◽  
Proposed Model ◽  
Possibilistic Programming ◽  
Key Factor ◽  
Basic Parameters

Regionalisation of Local Blood Banks (LBBs) is so vital for blood supply to facilitate the clinics in the obligated regions to go along with the requests in typical conditions and the moments of crisis. Determination of location of LBBs is a key factor in the selection of alternatives in the blood store networks. In this paper, we first develop a new multi-objective possibility mixed integer linear programming model (MOPMILP) for blood bank location problem considering distinctive contradictive goals at the same time and additionally the loose way of some basic parameters, for example, requirements of hospitals and number of emergency referrals for clinics or hospitals. At that point, in the wake of applying legitimate procedures for changing over this possibilistic model into a precise novel multi-objective linear model (MOLP), we propose a novel intelligent fuzzy method to understand this MOLP and find a proper trade off arrangement. Eventually, we propose a careful analysis and will see the performance of the proposed model.

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