Lot Streaming and Preventive Maintenance in a Multiple Product Permutation Flow Shop with Intermingling

2014 ◽  
Vol 564 ◽  
pp. 689-693 ◽  
Author(s):  
Navid Mortezaei ◽  
Zulkifli Norzima ◽  
S.H. Tang ◽  
Mohd Yusuff Rosnah
Keyword(s):  
Mathematical Model ◽  
Preventive Maintenance ◽  
Flow Shop ◽  
Mixed Integer ◽  
Programming Formulation ◽  
Lot Streaming ◽  
Proposed Model ◽  
Permutation Flow Shop ◽  
Integer Linear Model ◽  
Multiple Product

A mathematical model forlot streaming problem with preventive maintenance was proposed. A mixed-integer linear model for multiple-product lot streaming problems was also developed. Mixed-integer programming formulation was presented which will enable the user to identify optimal sublot sizes and sequences simultaneously. Two situations were considered:1) all machines were available, and 2) all machines needed preventive maintenance tasks. For both situations a new mixed-integer formulation was developed. To demonstrate the practicality of the proposed model, numerical example was used. It showed that the percentage of make span reduction due to lot streaming in permutation flow shop is 54% when compared to consistent sublots with intermingling case.

scholarly journals Integration of Lot Sizing and Flow Shop Scheduling with Lot Streaming

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Navid Mortezaei ◽  
Norzima Zulkifli
Keyword(s):  
Flow Shop ◽  
Lot Sizing ◽  
Flow Shop Scheduling ◽  
Mixed Integer ◽  
Lot Streaming ◽  
Optimal Sequence ◽  
Shop Scheduling ◽  
Proposed Model ◽  
Integer Linear Model ◽  
No Wait

We will develop a mathematical model for the integration of lot sizing and flow shop scheduling with lot streaming. We will develop a mixed-integer linear model for multiple products lot sizing and lot streaming problems. Mixed-integer programming formulation is presented which will enable the user to find optimal production quantities, optimal inventory levels, optimal sublot sizes, and optimal sequence simultaneously. We will use numerical example to show practicality of the proposed model. We test eight different lot streaming problems: (1) consistent sublots with intermingling, (2) consistent sublots and no intermingling between sublots of the products (without intermingling), (3) equal sublots with intermingling, (4) equal sublots without intermingling, (5) no-wait consistent sublots with intermingling, (6) no-wait equal sublots with intermingling, (7) no-wait consistent sublots without intermingling, and (8) no-wait equal sublots without intermingling. We showed that the best makespan can be achieved through the consistent sublots with intermingling case.

Production Scheduling in a Flexible Hybrid Flow Shop in the Food Industry Based on the Theory of Constraints

2021 ◽  
Vol 52 ◽  
pp. 124-136
Author(s):  
Adel Mendoza-Mendoza ◽  
Wilfrido Ospino-Castro ◽  
Daniela Romero-Martínez
Keyword(s):  
Mathematical Model ◽  
Production Scheduling ◽  
Flow Shop ◽  
Flow Line ◽  
Theory Of Constraints ◽  
Mixed Integer ◽  
Hybrid Flow Shop ◽  
Timely Response ◽  
Integer Linear Model ◽  
The Mathematical Model

This paper proposes a mathematical model for production scheduling, whose objective is to maximize the profits or Throughput of a company in the food sector through a Flexible Hybrid Flow, based on the theory of constraints. Considering the company's production configuration, which is a two-stage hybrid flow line, a mixed integer linear model programming (MILP) was formulated and programmed to adequately represent the real situation. The mathematical model developed in this study that is an easy and effective tool that helps to control the production process, by optimizing the quantities of each product to be produced, as well as establishing the sequence in which they must be carried out, which becomes an advantage against its competitors and also obtain a timely response to the needs of demand and compliance with the commitments made to its customers. The results obtained with the MILP, with reasonable computational times, allow for maximizing profits, considering the constraints of the problem.

scholarly journals An Optimized Mathematical Model for Items Supplies Planning of a Logistic System

2016 ◽  
Vol 10 (10) ◽  
pp. 133
Author(s):  
Mohammad Ali Nasiri Khalili ◽  
Mostafa Kafaei Razavi ◽  
Morteza Kafaee Razavi
Keyword(s):  
Mathematical Model ◽  
Mixed Integer ◽  
Logistics System ◽  
Proposed Model ◽  
Multiple Objective Decision Making ◽  
System Requirements ◽  
Purchasing Logistics ◽  
The Cost ◽  
The Mathematical Model ◽  
First Time

Items supplies planning of a logistic system is one of the major issue in operations research. In this article the aim is to determine how much of each item per month from each supplier logistics system requirements must be provided. To do this, a novel multi objective mixed integer programming mathematical model is offered for the first time. Since in logistics system, delivery on time is very important, the first objective is minimization of time in delivery on time costs (including lack and maintenance costs) and the cost of purchasing logistics system. The second objective function is minimization of the transportation supplier costs. Solving the mathematical model shows how to use the Multiple Objective Decision Making (MODM) can provide the ensuring policy and transportation logistics needed items. This model is solved with CPLEX and computational results show the effectiveness of the proposed model.

scholarly journals On the Selective Vehicle Routing Problem

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 771 ◽  
Author(s):  
Cosmin Sabo ◽  
Petrică C. Pop ◽  
Andrei Horvat-Marc
Keyword(s):  
Mathematical Model ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Mixed Integer ◽  
The Novel ◽  
Routing Problem ◽  
Proposed Model ◽  
Benchmark Instances ◽  
Number Of Customers ◽  
Optimal Set

The Generalized Vehicle Routing Problem (GVRP) is an extension of the classical Vehicle Routing Problem (VRP), in which we are looking for an optimal set of delivery or collection routes from a given depot to a number of customers divided into predefined, mutually exclusive, and exhaustive clusters, visiting exactly one customer from each cluster and fulfilling the capacity restrictions. This paper deals with a more generic version of the GVRP, introduced recently and called Selective Vehicle Routing Problem (SVRP). This problem generalizes the GVRP in the sense that the customers are divided into clusters, but they may belong to one or more clusters. The aim of this work is to describe a novel mixed integer programming based mathematical model of the SVRP. To validate the consistency of the novel mathematical model, a comparison between the proposed model and the existing models from literature is performed, on the existing benchmark instances for SVRP and on a set of additional benchmark instances used in the case of GVRP and adapted for SVRP. The proposed model showed better results against the existing models.

scholarly journals Solving Permutation Flow Shop Scheduling Problem with Sequence-Independent Setup Time

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jabrane Belabid ◽  
Said Aqil ◽  
Karam Allali
Keyword(s):  
Greedy Algorithm ◽  
Flow Shop ◽  
Search Algorithm ◽  
Setup Time ◽  
Flow Shop Scheduling ◽  
Mixed Integer ◽  
Test Problems ◽  
Permutation Flow Shop ◽  
Iterated Greedy Algorithm ◽  
Iterated Greedy

In this paper, we study the resolution of a permutation flow shop problem with sequence-independent setup time. The objective is to minimize the maximum of job completion time, also called the makespan. In this contribution, we propose three methods of resolution, a mixed-integer linear programming (MILP) model; two heuristics, the first based on Johnson’s rule and the second based on the NEH algorithm; and finally two metaheuristics, the iterative local search algorithm and the iterated greedy algorithm. A set of test problems is simulated numerically to validate the effectiveness of our resolution approaches. For relatively small-size problems, it has been revealed that the adapted NEH heuristic has the best performance than that of the Johnson-based heuristic. For the relatively medium and large problems, the comparative study between the two metaheuristics based on the exploration of the neighborhood shows that the iterated greedy algorithm records the best performances.

A NEW MALMQUIST PRODUCTIVITY INDEX BASED ON SEMI-DISCRETIONARY VARIABLES WITH AN APPLICATION TO COMMERCIAL BANKS OF CHINA

2011 ◽  
Vol 10 (04) ◽  
pp. 713-730 ◽  
Author(s):  
GONG-BING BI ◽  
JING-JING DING ◽  
YAN LUO ◽  
LIANG LIANG
Keyword(s):  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Mixed Integer ◽  
Productivity Change ◽  
Data Envelopment ◽  
Bank Of China ◽  
Proposed Model ◽  
Efficiency Increase ◽  
Integer Linear Model ◽  
Changes Over Time

The Malmquist productivity index studies productivity change, that is, the technical progress or regress together with the efficiency changes over time. In a nonparametric framework, the index can be estimated by data envelopment analysis (DEA). In this paper, first all inputs are divided into three groups, namely, discretionary variables, nondiscretionary variables, and semi-discretionary variables, and then a mixed integer linear model is proposed to deal with semi-discretionary variables. The proposed models consider not only the properties of semi-discretionary inputs, but also the relationship between them and other inputs. By introducing such a relationship and the preferences of decision-makers (DMs), the models aid DMs in generating efficiency scores and finding proper benchmarking points. Finally, the Malmquist productivity index combining the proposed model is computed and illustrated by an empirical application to the evaluation of 17 branches of Bank of China in Anhui Province. The results show a slight decrease in productivity during the year 2007/2008, and the productivity change positively during 2008/2009 due largely to efficiency increase.

scholarly journals Lot streaming Permutation Flow shop with energy awareness

2021 ◽  
Vol Volume 12 (Issue 1) ◽  
pp. 25-36
Author(s):  
Florencia D’Amico ◽  
Daniel Alejandro Rossit ◽  
Mariano Frutos
Keyword(s):  
Flow Shop ◽  
Energy Awareness ◽  
Lot Streaming ◽  
Permutation Flow Shop

scholarly journals A Crew Scheduling Model to Incrementally Optimize Workforce Assignments for Offshore Wind Farm Constructions

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6963
Author(s):  
Daniel Rippel ◽  
Fatemeh Abasian Abasian Foroushani ◽  
Michael Lütjen ◽  
Michael Freitag
Keyword(s):  
Wind Farm ◽  
Crew Scheduling ◽  
Offshore Wind ◽  
Mixed Integer ◽  
Offshore Wind Farm ◽  
Offshore Area ◽  
Offshore Installations ◽  
Proposed Model ◽  
Control Scheme ◽  
Integer Linear Model

In the literature, different authors attribute between 15% to 30% of a wind farm’s costs to logistics during the installation, e.g., for vessels or personnel. Currently, there exist only a few approaches for crew scheduling in the offshore area. However, current approaches only satisfy subsets of the offshore construction area’s specific terms and conditions. This article first presents a literature review to identify different constraints imposed on crew scheduling for offshore installations. Afterward, it presents a new Mixed-Integer Linear Model that satisfies these crew scheduling constraints and couples it with a scheduling approach using a Model Predictive Control scheme to include weather dynamics. The evaluation of this model shows reliable scheduling of persons/teams given weather-dependent operations. Compared to a conventionally assumed full staffing of vessels and the port, the model decreases the required crews by approximately 50%. Moreover, the proposed model shows good runtime behavior, obtaining optimal solutions for realistic scenarios in under an hour.

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