scholarly journals A MIXED INTEGER GOAL PROGRAMMING (MIGP) MODEL FOR DONATED BLOOD TRANSPORTATION PROBLEM – A PRELIMINARY STUDY

2021 ◽  
Vol 6 (2) ◽  
pp. 835
Author(s):  
Adibah Shuib ◽  
Puteh Maisarah Ibrahim
Keyword(s):  
Goal Programming ◽  
Blood Donation ◽  
Time Windows ◽  
Mixed Integer ◽  
Programming Approach ◽  
Blood Collection ◽  
Routing Problem ◽  
Total Travel Time ◽  
Preliminary Study ◽  
Donated Blood

Blood Supply Chain (BSC) concerns with flow of blood products from blood collection by donors to transfusion of blood components to patients. BSC comprises of collection, testing, processing, storage, distribution and transfusion activities, which are normally responsibility of Blood Centre and hospitals. In Malaysia, National Blood Centre (PDN) is responsible to organize blood donation, collection and processing. Current procedure practised by PDN is to have vehicles sending staffs and equipment while one vehicle is assigned to collect donated blood from donation sites and transport the blood to PDN within six hours. As consequence, vehicles shortages are encountered and resources optimization unachieved especially when many blood donation sites involved per day. This paper presents the results of a preliminary study which aims at proposing blood collection optimal routes for blood collecting vehicles that adhere to all pre-determined time windows for blood collection at blood donation sites. A Mixed Integer Goal Programming (MIGP) model based on Vehicle Routing Problem with Time Windows (VRPTW) has been formulated. The MIGP model pursues four goals, namely, to minimize total distance travelled, to minimize total travel time, to minimize total waiting time of vehicles and to minimize number of vehicles (routes). The model was solved using preemptive goal programming approach and existing heuristics for the VRPTW. Based on the results, it can be concluded that the donated blood can be collected and transported using reduced number of vehicles as proposed by the MIGP model’s optimal compared to the total number of vehicles used by current practice, Thus, the proposed VRPTW based MIGP model has promising significant impact for donated blood transportation in terms of resources optimization and costs savings. The model and approach could be easily extended to solve larger problem involving large number of donation sites with variants of time windows for the sites.

scholarly journals Implementation of the Model Capacited Vehicle Routing Problem with Time Windows with a Goal Programming Approach in Determining the Best Route for Goods Distribution

2020 ◽  
Vol 17 (2) ◽  
pp. 231-239
Author(s):  
Wahri Irawan ◽  
Muhammad Manaqib ◽  
Nina Fitriyati
Keyword(s):  
Vehicle Routing ◽  
Goal Programming ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Goal Function ◽  
Programming Approach ◽  
Routing Problem ◽  
Total Capacity ◽  
Vehicle Capacity ◽  
Number Of Customers

This research discusses determination of the best route for the goods distribution from one depot to customers in various locations using the Capacitated Vehicle Routing Problem with Time of Windows (CVRPTW) model with a goal programming approach. The goal function of this model are minimize costs, minimize distribution time, maximize vehicle capacity and maximize the number of customers served. We use case study in CV. Oke Jaya companies which has 25 customers and one freight vehicle with 2000 kg capacities to serve the customers in the Serang, Pandeglang, Rangkasbitung and Cikande. For simulation we use software LINGO. Based on this CVRPTW model with a goal programming approach, there are four routes to distribute goods on the CV. Oke Jaya, which considers the customer’s operating hours, with total cost is Rp 233.000,00, the total distribution time is 17 hours 57 minutes and the total capacity of goods distributed is 6150 kg.

scholarly journals Pengembangan Model Blood Mobile Collection Routing Problem (BMCRP) pada Proses Pengumpulan Darah

2018 ◽  
Vol 7 (2) ◽  
pp. 65
Author(s):  
Titi Iswari ◽  
Fran Setiawan ◽  
Carles Sitompul
Keyword(s):  
Time Windows ◽  
Mixed Integer ◽  
Blood Collection ◽  
Data Sets ◽  
Closing Time ◽  
Routing Problem ◽  
Total Distance ◽  
Hypothetical Data ◽  
Different Characteristics ◽  
The Mathematical Model

<p><em>This research develop a model of blood mobile collection using blood donor vehicle efficiently by determining the optimal route of blood collection to the points of blood collection. The model developed in the form of mixed integer nonlinear programming (MINLP) and this model is called Blood Mobile Collection Routing Problem (BMCRP). The purpose of this model is to minimize the total distance of the blood collection routing process in which each place of blood collection has the opening hours and the closing time (time windows) and the service time in each place. This study considers the blood age (spoilage time) for 6 hours to ensure blood quality. The mathematical model is then verified to determine whether the solution is in accordance with the characteristics of BMCRP. Verification is done by solving Blood Mobile Collection Routing small cases. The simulation of solving BMCRP is done by generating eight hypothetical data sets of small cases based on vehicle routing data problems with different characteristics. Verification of BMCRP uses LINGO software. From the simulation results, the BMCRP model can obtain optimal solutions with minimum total distance travelled and </em><em>does not violate any constraints on BMCRP.</em><em></em></p>

scholarly journals New general mixed-integer linear programming model for mobile workforce management

2021 ◽  
Author(s):  
András Éles ◽  
István Heckl ◽  
Heriberto Cabezas
Keyword(s):  
Programming Model ◽  
Time Windows ◽  
Mutual Exclusion ◽  
Parallel Execution ◽  
Mixed Integer ◽  
Management Problem ◽  
Routing Problem ◽  
Workforce Management ◽  
Computational Performance ◽  
Wide Range

AbstractA mathematical model is introduced to solve a mobile workforce management problem. In such a problem there are a number of tasks to be executed at different locations by various teams. For example, when an electricity utility company has to deal with planned system upgrades and damages caused by storms. The aim is to determine the schedule of the teams in such a way that the overall cost is minimal. The mobile workforce management problem involves scheduling. The following questions should be answered: when to perform a task, how to route vehicles—the vehicle routing problem—and the order the sites should be visited and by which teams. These problems are already complex in themselves. This paper proposes an integrated mathematical programming model formulation, which, by the assignment of its binary variables, can be easily included in heuristic algorithmic frameworks. In the problem specification, a wide range of parameters can be set. This includes absolute and expected time windows for tasks, packing and unpacking in case of team movement, resource utilization, relations between tasks such as precedence, mutual exclusion or parallel execution, and team-dependent travelling and execution times and costs. To make the model able to solve larger problems, an algorithmic framework is also implemented which can be used to find heuristic solutions in acceptable time. This latter solution method can be used as an alternative. Computational performance is examined through a series of test cases in which the most important factors are scaled.

scholarly journals Location Routing Problem with Consideration of CO2 Emissions Cost: A Case Study

2020 ◽  
Vol 21 (2) ◽  
pp. 225-234
Author(s):  
Ananda Noor Sholichah ◽  
Y Yuniaristanto ◽  
I Wayan Suletra
Keyword(s):  
Co2 Emissions ◽  
Time Windows ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Distribution Centers ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing ◽  
Study Results

Location and routing are the main critical problems investigated in a logistic. Location-Routing Problem (LRP) involves determining the location of facilities and vehicle routes to supply customer's demands. Determination of depots as distribution centers is one of the problems in LRP.  In LRP, carbon emissions need to be considered because these problems cause global warming and climate change. In this paper, a new mathematical model for LRP considering CO2 emissions minimization is proposed. This study developed a new  Mixed Integer Linear Programming (MILP)  model for LRP with time windows and considered the environmental impacts.  Finally, a case study was conducted in the province of Central Java, Indonesia. In this case study, there are three depot candidates. The study results indicated that using this method in existing conditions and constraints provides a more optimal solution than the company's actual route. A sensitivity analysis was also carried out in this case study.

scholarly journals Matheuristics and Column Generation for a Basic Technician Routing Problem

Algorithms ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 313
Author(s):  
Nicolas Dupin ◽  
Rémi Parize ◽  
El-Ghazali Talbi
Keyword(s):  
Machine Learning ◽  
Column Generation ◽  
Time Window ◽  
Time Windows ◽  
Machine Learning Techniques ◽  
Mixed Integer ◽  
Routing Problems ◽  
Routing Problem ◽  
Feasible Solutions ◽  
Time Window Constraints

This paper considers a variant of the Vehicle Routing Problem with Time Windows, with site dependencies, multiple depots and outsourcing costs. This problem is the basis for many technician routing problems. Having both site-dependency and time window constraints lresults in difficulties in finding feasible solutions and induces highly constrained instances. Matheuristics based on Mixed Integer Linear Programming compact formulations are firstly designed. Column Generation matheuristics are then described by using previous matheuristics and machine learning techniques to stabilize and speed up the convergence of the Column Generation algorithm. The computational experiments are analyzed on public instances with graduated difficulties in order to analyze the accuracy of algorithms for ensuring feasibility and the quality of solutions for weakly to highly constrained instances. The results emphasize the interest of the multiple types of hybridization between mathematical programming, machine learning and heuristics inside the Column Generation framework. This work offers perspectives for many extensions of technician routing problems.

scholarly journals Optimal vehicle route schedules in picking up and delivering cargo containers considering time windows in logistics distribution networks: A case study

2020 ◽  
Vol 26 (4) ◽  
pp. 174-184
Author(s):  
Thi Diem Chau Le ◽  
Duy Duc Nguyen ◽  
Judit Oláh ◽  
Miklós Pakurár
Keyword(s):  
Mathematical Model ◽  
Simulated Annealing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Distribution Networks ◽  
Mixed Integer ◽  
Vehicle Group ◽  
Real Problem ◽  
Routing Problem

AbstractThis study describes a pickup and delivery vehicle routing problem, considering time windows in reality. The problem of tractor truck routes is formulated by a mixed integer programming model. Besides this, three algorithms - a guided local search, a tabu search, and simulated annealing - are proposed as solutions. The aims of our study are to optimize the number of internal tractor trucks used, and create optimal routes in order to minimize total logistics costs, including the fixed and variable costs of an internal vehicle group and the renting cost of external vehicles. Besides, our study also evaluates both the quality of solutions and the time to find optimal solutions to select the best suitable algorithm for the real problem mentioned above. A novel mathematical model is formulated by OR tools for Python. Compared to the current solution, our results reduced total costs by 18%, increased the proportion of orders completed by internal vehicles (84%), and the proportion of orders delivered on time (100%). Our study provides a mathematical model with time constraints and large job volumes for a complex distribution network in reality. The proposed mathematical model provides effective solutions for making decisions at logistics companies. Furthermore, our study emphasizes that simulated annealing is a more suitable algorithm than the two others for this vehicle routing problem.

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