Scope for industrial applications of production scheduling models and solution methods

Integrating Continuous and Batch Processes with Shared Resources in Closed-Loop Scheduling: A Case Study on Tuna Cannery

Scheduling tasks in production facilities are usually hybrid optimization problems of a large combinatorial nature. They involve solving, in near-real time, the integration of the operation of several batch units of continuous dynamics with the discrete manufacture of items in processing lines. Moreover, one has to deal with uncertainty (process delays, unexpected stops) and the management of shared resources (energy, water, etc.) including decisions made by plant operators: still, some tasks in the scheduling layers are done manually. Manufacturing Execution Systems (MESs) are intended to support plant personnel at this level. However, there is still much work to do in terms of performing automatic scheduling, computed in real time, that guides managers to achieve an optimal operation of such complex cyber-physical systems. This work proposes a closed-loop approach to handle the uncertainty arising when facing the online scheduling of supply lines and parallel batch units. These units often share some resources, so effects due to concurrent resource consumption on the system dynamics are explicitly considered in the presented formulation. The proposed decision support system is tested onsite in a tuna cannery, to handle short-term online scheduling of sterilization processes that deal with limited steam, carts, and operators as shared resources.

Double Formable Integral Transform for Solving Heat Equations

Chemistry, physics, and many other applied fields depend heavily on partial differential equations. As a result, the literature contains a variety of techniques that all have a symmetry goal for solving partial differential equations. This study introduces a new double transform known as the double formable transform. New results on partial derivatives and the double convolution theorem are also presented, together with the definition and fundamental characteristics of the proposed double transform. Moreover, we use a new approach to solve a number of symmetric applications with different characteristics on the heat equation to demonstrate the usefulness of the provided transform in solving partial differential equations.

Design and Operation of Multipurpose Production Facilities Using Solar Energy Sources for Heat Integration Sustainable Strategies

Industrial production facilities have been facing the requirement to optimise resource efficiency, while considering sustainable goals. This paper addresses the introduction of renewable energies in production by exploring the combined design and scheduling of a multipurpose batch facility, with innovative consideration of direct/indirect heat integration using a solar energy source for thermal energy storage. A mixed-integer linear programming model is formulated to support decisions on scheduling and design selection of storage and processing units, heat exchange components, collector systems, and energy storage units. The results show the minimisation of utilities consumption, with an increase in the operational profit using combined heat integration strategies for the production schedule. A set of illustrative case-study examples highlight the advantages of the solar-based heat storage integration, assessing optimal decision support in the strategic and operational management of these facilities.

A Simulation Model of Power Demand Management by Manufacturing Enterprises under the Conditions of Energy Sector Transformation

This paper addresses electricity consumption management in manufacturing enterprises. The research aims to provide manufacturing enterprises with an effective tool to control electricity costs. Recently, some factors have been observed to affect the rapid changes in the operating conditions of enterprises. These include the transformation of the power sector toward renewable energy, the disruption of supply chains resulting from a coronavirus pandemic, political crises, and process automation. A method for the analysis and management of electricity consumption in enterprises based on simulation modeling is proposed. The simulation model contains predefined objects representing physical system elements and the data processing algorithm. The production order execution time, energy consumption, employee overtime, and machine load are included in the model. The results show that it is possible to determine the level of power available for the process completion and its influence on the production volume and realization time. In the studied case, when the available power was reduced by half, there was an increase in order execution time of nearly 25 percent and an increase in energy consumption of nearly 15 percent. The method can be used in the operational activities of enterprises as well as extended to different types of production processes.

Innovative System for Scheduling Production Using a Combination of Parametric Simulation Models

The article deals with the design of an innovative system for scheduling piece and small series discrete production using a combination of parametric simulation models and selected optimization methods. An innovative system for solving production scheduling problems is created based on data from a real production system at the workshop level. The methodology of the innovative system using simulation and optimization methods deals with the sequential scheduling problem due to its versatility, which includes several production systems and due to the fact that in practice, several modifications to production scheduling problems are encountered. Proposals of individual modules of the innovative system with the proposed communication channels have been presented, which connect the individual elements of the created library of objects for solving problems of sequential production scheduling. With the help of created communication channels, it is possible to apply individual parameters of a real production system directly to the assembled simulation model. In this system, an initial set of optimization methods is deployed, which can be applied to solve the sequential problem of production scheduling. The benefit of the solution is an innovative system that defines the content of the necessary data for working with the innovative system and the design of output reports that the proposed system provides for production planning for the production shopfloor level. The DPSS system works with several optimization methods (CR—Critical Ratio, S/RO—Slack/Remaining Operations, FDD—Flow Due Date, MWKR—Most Work Remaining, WSL—Waiting Slack, OPFSLK/PK—Operational Flow Slack per Processing Time) and the simulation experiments prove that the most suitable solution for the FT10 problem is the critical ratio method in which the replaceability of the equipment was not considered. The total length of finding all solutions by the DPSS system was 1.68 min. The main benefit of the DPSS system is the combination of two effectively used techniques not only in practice, but also in research; the mentioned techniques are production scheduling and discrete computer simulation. By combining techniques, it is possible to generate a dynamically and interactively changing simulated production program. Subsequently, it is possible to decide in the emerging conditions of certainty, uncertainty, but also risk. To determine the conditions, models of production systems are used, which represent physical production systems with their complex internal processes. Another benefit of combining techniques is the ability to evaluate a production system with a number of emerging problem modifications.

Unified Genetic Algorithm Approach for Solving Flexible Job-Shop Scheduling Problem

This paper proposes a novel genetic algorithm (GA) approach that utilizes a multichromosome to solve the flexible job-shop scheduling problem (FJSP), which involves two kinds of decisions: machine selection and operation sequencing. Typically, the former is represented by a string of categorical values, whereas the latter forms a sequence of operations. Consequently, the chromosome of conventional GAs for solving FJSP consists of a categorical part and a sequential part. Since these two parts are different from each other, different kinds of genetic operators are required to solve the FJSP using conventional GAs. In contrast, this paper proposes a unified GA approach that enables the application of an identical crossover strategy in both the categorical and sequential parts. In order to implement the unified approach, the sequential part is evolved by applying a candidate order-based GA (COGA), which can use traditional crossover strategies such as one-point or two-point crossovers. Such crossover strategies can also be used to evolve the categorical part. Thus, we can handle the categorical and sequential parts in an identical manner if identical crossover points are used for both. In this study, the unified approach was used to extend the existing COGA to a unified COGA (u-COGA), which can be used to solve FJSPs. Numerical experiments reveal that the u-COGA is useful for solving FJSPs with complex structures.