A decision support system for operational production scheduling

Machines

Small and medium-sized engineering production companies face challenges that are related to unpredicted rapid changes of availability of the work force, materials and equipment. Those challenges are especially difficult to solve for companies focusing on unit or batch production and when they are collaborating with customers who require short lead times. A four-month observation was carried out in a metal processing company in Lithuania to understand the most common rising problems and developing solution for computerised decision support systems. It was discovered that the company needs a computerised “employee centred” system for the improvement of the allocation of tasks to employees. Such a need proved to be the most urgent one, especially during pandemics. An algorithm for the analysis and automated allocation of the employees’ tasks has been developed and tested. The proposed algorithm is universal and may be applied in different SMEs for engineering production.

Harmony through Working Together: OZCHI 94 …, 1994

This paper discusses the form of graphical objects for a "hybrid" human-computer scheduling system. The visual features of these objects represent the job attributes that a human scheduler may use in deciding how to allocate jobs to machines and to arrange the order of processing.

2008

The primary objective of this research is to solve the job-shop scheduling problems by minimizing the makespan. In this paper, we first developed a genetic algorithm (GA) for solving JSSPs, and then improved the algorithm by integrating it with three priority rules. The performance of the developed algorithm was tested by solving 40 benchmark problems and comparing their results with that of a number of well-known algorithms. For convenience of implementation, we developed a decision support system (DSS). In the DSS, we built a graphical user interface (GUI) for user friendly data inputs, model choices, and output generation. An overview of the DSS and the analysis of experimental results are provided.

International Journal of Operations Research and Information Systems, 2021

The manufacturing environment for apparel is subject to a variety of constraints, stochasticity, and unforeseen events. In order to create an accurate scheduling-system for this environment, these complexities must be considered. This article presents the development and the application of a scheduling and rescheduling decision support system for an apparel manufacturer. Furthermore, the results of applying the proposed system are presented and discussed. The scheduling and rescheduling decision support system presented in this article takes advantage of a variable neighborhood search and Monte Carlo simulation in order to minimize tardiness in the presence of different release times, sequence-based setup times, blocking, and resource constraints. The results show that the quality of the schedules generated by the proposed scheduling and rescheduling decision support system is superior to the current firm’s scheduling practice, which is based on an earliest due date heuristic. Moreo...

Computer Integrated Manufacturing Systems, 1993

A fundamental issue in scheduling tools for order production systems is the trade-off between the feasibility of a solution with respect to a set of dynamic local constraints and the evaluation of a schedule with respect to different global optimality criteria. CIM managers clamour for more flexible and easy to handle tools than those provided by classical scheduling theory. Decision Support Systems (DSS) have been proved to be valuable tools for manipulating schedules and for managing information on the production process. This paper surveys previous work on scheduling in production systems, and presents a methodology for facing production scheduling problems. New design criteria and interactive models for effectively supporting the methodology in flexible and dynamic production environments are discussed.

Computers & Industrial Engineering, 1987

This paper presents a conceptual framework for the application of expert systems for the selection and development of scheduling rules for a job shop. A portion of the framework is illustrated using an expert system developed using MI TM.

1998

Mathematical programming has been applied extensively to job shop scheduling problems. Problems have been formulated using integer programming , mixed-integer programming , and dynamic programming . Until recently, the use of these approaches has been limited because scheduling problems belong to the class of NP-complete problems. To overcome these deficiencies, a group of researchers began to decompose the scheduling problem into a number of subproblems, proposing a number of techniques to solve them. In addition, new solution techniques, more powerful heuristics, and the computational power of modern computers have enabled these approaches to be used on larger problems. Still, difficulties in the formulation of material flow constraints as mathematical inequalities and the development of generalized software solutions have limited the use of these approaches. proposed a methodology based on the decomposition of mathematical programming problems that used both Benders-type and Dantzig/Wolfe-type decompositions. The methodology was part of closed-loop, real-time, two-level hierarchical shop floor control system. The top-level scheduler (i.e., the supremal) specified the earliest start time and the latest finish time for each job. The lower level scheduling modules (i.e., the infimals) would refine these limit times for each job by detailed sequencing of all operations. A multicriteria objective function was specified that included tardiness, throughput, and process utilization costs. The decomposition was achieved by first reordering the constraints of the original problem to generate a block angular form, then transforming that block angular form into a hierarchical tree structure. In general, N subproblems would result plus a constraint set that contained partial members of each of the subproblems. The latter was termed the "coupling " constraints, and included precedence relations and material handling. The supremal unit explicitly considered the coupling constraints, while the infimal units considered their individual decoupled constraint sets. The authors pointed out that the inherent stochastic nature of job shops and the presence of multiple, but often conflicting, objectives made it difficult to express the coupling constraints using exact mathematical relationships. This made it almost impossible to develop a general solution methodology. To overcome this, a new real-time simulation methodology was proposed in to solve the supremal and infimal problems.

Contemporary Engineering Sciences

Today, all organizations are continually seeking to produce high quality, reliable, and timely goods and services that meet the needs of their customers. Therefore, production scheduling is one of the most complex activities in production management in organizations. In the present work, the production projection is developed in a Job Shop system using a mathematical model of linear integer programming whose objective is to minimize the total lateness. The developed model is validated by means of an application case with three jobs and four machines, obtaining the optimum programming according to the requirements. The results obtained have been compared with a scheduling software to evaluate the performance of the proposed model.

National Conference on Artificial Intelligence, 1982

IFAC Proceedings Volumes, 1995

In this paper, a decision support system for operation scheduling with the name Integrated Scheduling System (lSS) is presented. The ISS is a tandem system which consists of two main subsystems: RAPO and ESPRA The RAPO subsystem is responsible for schedule generation and is based on the deterministic discrete-event simulation. A newly developed algorithm which speeded up schedule preparation has been built into RAPO. The ESPRA subsystem is an expert system that selects the most promising scheduling strategies for further use in RAPO. The inference engine of ESPRA is based on a method which optimises the solution process for any rule-based backward-chaining diagnostic system.