A DECISION SUPPORT SYSTEM FOR THE ALLOCATION OF YARD CRANES AND BLOCKS IN CONTAINER TERMINALS

2011 ◽  
Vol 28 (06) ◽  
pp. 803-829 ◽  
Author(s):  
CANRONG ZHANG ◽  
ZHIHAI ZHANG ◽  
LI ZHENG ◽  
LIXIN MIAO
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Programming Model ◽  
Solution Procedure ◽  
Container Terminals ◽  
Mixed Integer ◽  
Scarce Resources ◽  
Iterative Decomposition ◽  
Gantry Cranes

This paper examines the allocation of yard cranes and blocks for yard activities in container terminals. In this paper, the yard cranes are confined to rail mounted gantry cranes (RMGC), which are characterized by the restricted traveling range on a pair of rails. Since RMGCs and yard blocks are tightly bound to each other, when allocating them, we should make sure that the RMGCs allocated for a yard activity are able to together cover the blocks allocated for the corresponding yard activity. In addition, considering that there are four basic activities occurring in the yard which compete with each other for the scarce resources and have different requirements and priorities in the allocation of blocks and yard cranes, we treat them in a single model rather than in multiple independent models as were generally done in literature. A mixed integer programming model is constructed, and an iterative decomposition solution procedure is proposed for the problem. Based on the solution procedure, a decision support system is developed and implemented for a terminal in Tianjin seaport. Using the actual data, the numerical experiments show the effectiveness and efficiency of the decision support system.

scholarly journals A Python-based multicriteria portfolio selection DSS

2020 ◽  
Author(s):  
Panos Xidonas ◽  
Haris Doukas ◽  
Elissaios Sarmas
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Portfolio Optimization ◽  
Support System ◽  
Portfolio Management ◽  
Programming Model ◽  
Mixed Integer ◽  
Management Decision ◽  
Integer Quadratic Programming ◽  
Goal Programming Model

Our purpose in this article is to develop an integrated portfolio management decision support system, which takes into account the inherent multidimensional nature of the problem, while allowing the decision maker, i.e. investor, to incorporate his/her preferences in the decision process. The proposed decision support system has been developed in Python programming language and consists of two components: The first component is associated with the security selection phase, while the second component is associated with the portfolio optimization phase. In the first phase, four discrete multicriteria methods are employed; the PROMETHEE II, the ELECTRE III, the MAUT and the TOPSIS. After the cumulative integration of the results, a series of mathematical programming models are applied in the sec- ond phase, that of multicriteria portfolio optimization; a mixed-integer quadratic programming model, a goal programming model, a genetic algorithm model, and a multiobjective PROMETHEE flow model. Finally, the proposed approach is tested through a large-scale illustrative application in several stock markets and various sectors, analyzing simultaneously a very large number of securities.

scholarly journals A DECISION SUPPORT SYSTEM FOR SURGERY SEQUENCING AT UZ LEUVEN'S DAY-CARE DEPARTMENT

2011 ◽  
Vol 10 (03) ◽  
pp. 435-450 ◽  
Author(s):  
BRECHT CARDOEN ◽  
ERIK DEMEULEMEESTER
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Objective Function ◽  
Support System ◽  
Day Care ◽  
Programming Model ◽  
Care Center ◽  
Mixed Integer ◽  
Day Care Center ◽  
Software Applications

In this paper, we test the applicability of a decision support system (DSS) that is developed to optimize the sequence of surgeries in the day-care center of the UZ Leuven Campus Gasthuisberg (Belgium). We introduce a multi-objective function in which children and prioritized patients are scheduled as early as possible on the day of surgery, recovery overtime is minimized, and recovery workload is leveled throughout the day. This combinatorial optimization problem is solved by applying a pre-processed mixed integer linear programming model. We report on a 10-day case study to illustrate the performance of the DSS. In particular, we compare the schedules provided by the hospital with those that are suggested by the DSS. The results indicate that the DSS leads to both an increased probability of obtaining feasible schedules and an improved quality of the schedules in terms of the objective function value. We further highlight some of the major advantages of the application, such as its visualization and algorithmic performance, and also report on the difficulties that were encountered during the study and the shortcomings that currently delay its implementation in practice, as this information may contribute to the success rate of future software applications in hospitals.

scholarly journals A decision support system to assure high-performance maintenance service

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Giuseppe Aiello ◽  
Julio Benítez ◽  
Silvia Carpitella ◽  
Antonella Certa ◽  
Mario Enea ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Preventive Maintenance ◽  
Programming Model ◽  
Laboratory Method ◽  
Maintenance Management ◽  
Maintenance Policy ◽  
Content Type ◽  
Blockchain Technology

PurposeThis study aims to propose a decision support system (DSS) for maintenance management of a service system, namely, a street cleaning service vehicle. Referring to the information flow management, the blockchain technology is integrated in the proposed DSS to assure data transparency and security.Design/methodology/approachThe DSS is designed to efficiently handle the data acquired by the network of sensors installed on selected system components and to support the maintenance management. The DSS supports the decision makers to select a subset of indicators (KPIs) by means of the DEcision-MAaking Trial and Evaluation Laboratory method and to monitor the efficiency of performed preventive maintenance actions by using the mathematical model.FindingsThe proposed maintenance model allows real-time decisions on interventions on each component based on the number of alerts given by sensors and taking into account the annual cost budget constraint.Research limitations/implicationsThe present paper aims to highlight the implications of the blockchain technology in the maintenance field, in particular to manage maintenance actions’ data related to service systems.Practical implicationsThe proposed approach represents a support in planning, executing and monitoring interventions by assuring the security of the managed data through a blockchain database. The implications regard the monitoring of the efficiency of preventive maintenance actions on the analysed components.Originality/valueA combined approach based on a multi-criteria decision method and a novel mathematical programming model is herein proposed to provide a DSS supporting the management of predictive maintenance policy.

scholarly journals An integrated decision support system for ERP implementation in small and medium sized enterprises

2014 ◽  
Vol 27 (4) ◽  
pp. 358-384 ◽  
Author(s):  
Ying Xie ◽  
Colin James Allen ◽  
Mahmood Ali
Keyword(s):  
Decision Support ◽  
Nonlinear Programming ◽  
Decision Support System ◽  
Support System ◽  
Regression Models ◽  
Programming Model ◽  
Resource Allocations ◽  
Erp Implementation ◽  
Content Type ◽  
Nonlinear Programming Model

Purpose – Implementing enterprise resource planning (ERP) is a challenging task for small- and medium-sized enterprises (SMEs). The purpose of this paper is to develop an integrated decision support system (DSS) for ERP implementation (DSS_ERP) to facilitate resource allocations and risk analysis. Design/methodology/approach – Analytical regression models are developed using data collected through a survey conducted on 400 SMEs that have implemented ERP systems, and are validated by a simulation model. The validated analytical regression models are used to construct a nonlinear programming model that generates solutions for resource allocations, such as time and budget. Findings – ERP implementation cost increases along the time horizon, while performance level increases up to a point and remains unchanged. To maximise or achieve a certain level of performance within a budget limitation, CSFs are prioritised as: project management (highest), top management, information technology, users and vendor support (lowest). SMEs are recommended to concentrate effort and resources on CSFs that have a greater impact on achieving their desired goals while optimising utilisation of resources. Research limitations/implications – DSS_ERP proves to be beneficial to SMEs in identifying required resources and allocating resources, but could be further tested in case studies for its practical use and benefits. Practical implications – DSS_ERP serves as a useful tool for SMEs to predict required resources and allocate them prior to ERP implementation, which maximises the probability of achieving predetermined targets. It also enables SMEs to analyse risk caused by changes to resources during ERP implementation, and helps them to be better prepared for the risks. Originality/value – The research contributes to the scarce research on ERP implementation using scientific methods. A novel nonlinear programming model is constructed for ERP implementation under time and budget limitations, facilitating resource allocations in an ERP implementation, which has not been reported in any previous research. The research offers a theoretical basis for empirical studies of resource allocations in ERP implementation.

scholarly journals A MODEL-DRIVEN DECISION SUPPORT SYSTEM FOR REALLOCATION OF SUPPLY TO ORDERS UNDER UNCERTAINTY IN CERAMIC COMPANIES

2015 ◽  
Vol 21 (4) ◽  
pp. 596-625 ◽  
Author(s):  
M. M. E. ALEMANY ◽  
A. A. ◽  
Andrés BOZA ◽  
Vicente S. FUERTES-MIQUEL
Keyword(s):  
Decision Support ◽  
Mathematical Programming ◽  
Decision Support System ◽  
Decision Maker ◽  
Support System ◽  
Programming Model ◽  
Decision Makers ◽  
Mathematical Programming Model ◽  
Finished Good ◽  
Model Driven

In ceramic companies, uncertainty in the tone and gage obtained in first quality units of the same finished good (FG) entails frequent discrepancies between planned homogeneous quantities and real ones. This fact can lead to a shortage situation in which certain previously committed customer orders cannot be served because there are not enough homogeneous units of a specific FG (i.e., with the same tone and gage). In this paper, a Model-Driven Decision Support System (DSS) is proposed to reassign the actual homogeneous stock and the planned homogeneous sublots to already committed orders under uncertainty by means of a mathematical programming model (SP-Model). The DSS functionalities enable ceramic decision makers to generate different solutions by changing model options. Uncertainty in the planned homogeneous quantities, and any other type of uncertainty, is managed via scenarios. The robustness of each solution is tested in planned and real situations with another DSS functionality based on another mathematical programming model (ASP-Model). With these DSS features, the ceramic decision maker can choose in a friendly fashion the orders to be served with the current homogeneous stock and the future uncertainty homogeneous supply to better achieve a balance between the maximisation of multiple objectives and robustness.

Designing a scheduling decision support system for the skin pass line: A case study of the steel finishing line

2020 ◽  
Vol 234 (13) ◽  
pp. 1640-1655 ◽  
Author(s):  
Parinaz Vaez ◽  
Armin Jabbarzadeh ◽  
Nader Azad
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Heuristic Algorithm ◽  
Production Scheduling ◽  
Mixed Integer ◽  
Total Production ◽  
Steel Company ◽  
Manual Method ◽  
Skin Pass

In this paper, we investigate the scheduling policies in the iron and steel industry, and in particular, we formulate and propose a solution to a complicated problem called skin pass production scheduling in this industry. The solution is to generate multiple production turns for the skin pass coils and, at the same time, determine the sequence of these turns so that productivity and product quality are maximized, while the total production scheduling cost, including the costs of tardiness, flow of material, and the changeover cost between adjacent and non-adjacent coils, is minimized. This study has been prompted by a practical problem in an international steel company in Iran. In this study, we present a new mixed integer programming model and develop a heuristic algorithm, as the commercial solvers would have difficulty in solving the problem. In our heuristic algorithm, initial solutions are obtained by a greedy constraint satisfaction algorithm, and then a local search method is developed to improve the initial solution. The experimental results tested on the data collected from the steel company show the efficiency of the proposed heuristic algorithm by solving a large-sized instance in a reasonable computation time. The average deviation between the manual method and the heuristic algorithm is 30%. Also, in all the components of the objective function, the algorithm performs better compared to the manual method. The improved values are greater than 15. In addition, we develop a commercial decision support system for the implementation of the proposed algorithm in the steel company.

A FUZZY MULTI-OBJECTIVE BILEVEL DECISION SUPPORT SYSTEM

2009 ◽  
Vol 08 (01) ◽  
pp. 93-108 ◽  
Author(s):  
YA GAO ◽  
GUANGQUAN ZHANG ◽  
JIE LU
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Decision Problem ◽  
Programming Model ◽  
Multiple Objectives ◽  
Objective Functions ◽  
Multi Objective ◽  
Solution Methods

In a bilevel decision problem, both the leader and the follower may have multiple objectives, and the coefficients involved in these objective functions or constraints may be described by some uncertain values. To express such a situation, a fuzzy multi-objective bilevel (FMOBL) programming model and related solution methods are introduced. This research develops a FMOBL decision support system through implementing the proposed FMOBL methods.

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