Industrial Scheduling Problems Research Articles

Color-Coating Scheduling With a Multiobjective Evolutionary Algorithm Based on Decomposition and Dynamic Local Search

The color-coated steel coil is a high value-added product for steel enterprises, and its production process is affected by multiple factors. How to provide operators with appropriate scheduling schemes is the key to improve the economic benefits of enterprises. In this article, for the scheduling of a single color-coating turn, we establish a multiobjective optimization model that minimizes the number of insertions of transition coils, the thickness jump penalty of adjacent coils, and the switching times of the backup rollers. To address this problem, we propose a piecewise coding approach to ensure that each individual meets the production constraints. Besides, a multiobjective evolutionary algorithm (MOEA) based on decomposition and dynamic local search (D-DLS) strategy is proposed (MOEA/D-DLS). More specifically, the color-coating multiobjective scheduling problem is decomposed into a series of single-objective subproblems and optimized simultaneously. Furthermore, based on the speed of evolution of these subproblems, local search is performed on partial subproblems dynamically. The proposed algorithm is used to solve eight multiobjective scheduling problem instances of color-coating with different scales, and the experimental results demonstrate that the proposed algorithm is very effective compared with four state-of-the-art algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Practical production scheduling problems in iron & steel industry generally need to optimize conflicting objectives simultaneously, which is very hard for practitioners to make appropriate decisions with manual experience. The decomposition-based multiobjective evolutionary algorithm (MOEA) can help practitioners of color-coating scheduling to achieve a set of Pareto optimal decisions with good distribution and tradeoff among three objectives. Since the scheduling of the other production lines shares many similarities with our problem, the proposed model and algorithm can also be applicable to these problems.

Dataset for solving a hybrid flexibility strategy on personnel scheduling problem in the retail industry.

This data article describes datasets from a home improvement retail store located in Santiago, Chile. The datasets have been developed to simultaneously solve a staffing and tour scheduling problem that incorporates flexible contracts and multiskilled staff. This Data in Brief article is related to the published article “Hybrid flexibility strategy on personnel scheduling: Retail case study” [1]. The datasets contain real, processed, and simulated data. Regarding the real and processed datasets, they are presented for three different store sizes (4, 5 or 6 departments). Real datasets include information about the employment-contract characteristics, cost parameters, and a forecast of the number of employees required in each department for each day of the week and each time period into which the operating day is divided. As regards the data processed for the case study, they include the set of skill sets considering that the employees can be trained in a maximum of two store departments. Regarding the simulated datasets, they include information about the random parameter of staff demand in each store department. The simulated data are presented in 90 text files classified by: (i) Store size (4, 5 or 6 departments). (ii) Coefficient of variation (10, 20, 30%). (iii) Instance identification number (10 instances per scenario that resulted from combining the store sizes and coefficients of variation). Researchers can use the datasets for benchmarking the performance of different approaches with the one presented by Porto et al. [1], and in consequence, they can find solutions to the same (or similar) type of personnel scheduling problem. The dataset includes an Excel workbook that can be used to randomly generate staff demand instances according to a chosen coefficient of variation.

A Two-stage Simulated Annealing-based Scheduling Algorithm for a Make-and-Pack Production Plant

Abstract Industrial scheduling problems are characterized by their highly combinatorial nature due to the large number of alternative solutions. The presence of discrete and continuous decisions in scheduling models usually lead to discrete optimization problems. Although powerful solvers and algorithms exist for this class of problems, real-life applications often still require prohibitively long computation times, severely hindering their practical deployability. An alternative way to solve hard discrete optimization problems is the use of metaheuristic algorithms, which provide approximate solutions in short computation times. In this contribution, a scheduling algorithm based on simulated annealing is developed and applied to a make-and-pack process from the consumer goods industry. The algorithm consists of two stages, the first of which determines the allocation of the orders to the production lines, while the second refines the schedule by optimizing the sequence within each line. Two different layouts of the plant are examined, one where the make-and-pack stages are directly coupled and another where a finite intermediate buffer is used to decouple the two production stages. The generated production schedules are compared to nominal ones provided by the planners at the plant, underlining the potential of the proposed approach, both in terms of solution quality and necessary computation time.

Mixed integer linear programming approaches for solving the raw material allocation, routing and scheduling problems in the forest industry

Fil: Bordon, Maximiliano Ramon. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Santa Fe. Instituto de Desarrollo y Diseno. Universidad Tecnologica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseno; Argentina

An Optimization Model for Production Planning in the Synthetic Fertilizer Industry

Production planning plays a key role in manufacturing. Different industrial requirements impose different conditions, eventually leading to different and non-trivial production plans. In this work, a particular planning problem which arises in synthetic fertilizer manufacturing is considered, to which the industrial conditions in the process of making fertilizer are incorporated. As in standard industrial scheduling problems, we assume a situation where different types of synthetic fertilizer are produced by different mixers having different capacities, in order to fulfil the demand of each vehicle that receives fertilizer from the outlets of those mixers. Loading the fertilizer to vehicles that wait in a queue at the factory premises and cleaning costs when using some mixer to produce two fertilizer types makes our problem significantly different to existing scheduling problems in literature. We first survey related problems and attempt to find the venue of our problem among those standard planning problems. Then we attempt to generate a production plan, addressing the industry-specific conditions for fertilizer manufacturing by reformulating as a binary integer program aimed at minimizing the total waiting time of the vehicles to which the fertilizers are loaded and the machine cleaning cost, satisfying industrial constraints. Due to the non-linearity of the optimization model, we adopt optimization heuristics to generate solutions.

An improved particle swarm optimization algorithm to solve hybrid flowshop scheduling problems with the effect of human factors – A case study

This paper addresses the multi-stage hybrid flowshop scheduling problem with identical parallel machines at each stage by considering the effect of human factors. The various levels of labours and the effects of their learning and forgetting are studied. The minimization of the weighted sum of the makespan and total flow time is the objective function. Since the problem is NP-hard, an improved version of the particle swarm optimization (PSO) algorithm is presented to solve the problem. A dispatching rule and a constructive heuristic are incorporated to improve the initial solutions of the PSO algorithm. The variable neighbourhood search (VNS) algorithm is also hybridized with the PSO algorithm to attain the optimal solutions consuming less computational time. An industrial scheduling problem of an automobile manufacturing unit is discussed. Moreover, several instances of the random benchmark problem are used to validate the performance of the proposed algorithm. Computational experiments have been performed and the results prove the effectiveness of the proposed approach.

Google vs IBM: A Constraint Solving Challenge on the Job-Shop Scheduling Problem

The job-shop scheduling is one of the most studied optimization problems from the dawn of computer era to the present day. Its combinatorial nature makes it easily expressible as a constraint satisfaction problem. In this paper, we compare the performance of two constraint solvers on the job-shop scheduling problem. The solvers in question are: OR-Tools, an open-source solver developed by Google and winner of the last MiniZinc Challenge, and CP Optimizer, a proprietary IBM constraint solver targeted at industrial scheduling problems. The comparison is based on the goodness of the solutions found and the time required to solve the problem instances. First, we target the classic benchmarks from the literature, then we carry out the comparison on a benchmark that was created with known optimal solution, with size comparable to real-world industrial problems.

Hybrid bi-objective gray wolf optimization algorithm for a truck scheduling problem in the automotive industry

This paper investigates a bi-objective truck scheduling problem (BTSP) in the automotive industry, where a set of containers are transported in batches from a cross-dock to an assembly manufacturer. Each container is characterized by an arbitrary size, an unequal release time and a due date. The problem is to assign containers into batches and schedule these batches on identical trucks so as to minimize the makespan and the total weighted earliness and tardiness cost. A mixed integer linear programming (MILP) model is developed for solving BTSP to optimality via an epsilon-constraint method. Due to NP-hardness of the considered problem, a hybrid bi-objective gray wolf optimization (HBGWO) algorithm is proposed by combining the decomposition framework and the gray wolf optimizer (GWO) metaheuristic. A new solution representation scheme is developed to accommodate the problem characteristic. In addition, the Gaussian mutation (GM) and a tailored local search are introduced to enhance the algorithm’s convergence ability and exploitation performance, respectively. Computational results indicate that the proposed hybrid algorithm is effective and efficient in solving BTSP with different scales.

Optimal Production Scheduling in the Dairy Industries

This work presents the application of a Mixed-Integer Linear Programming (MILP) model that was previously developed in our group for the lot-sizing and production scheduling problem in a real-life industry. In particular, the case of yoghurt production, a representative food process, in a large-scale dairy facility from Greece is studied in detail. The model takes into account all constraints typically met in production scheduling (inventory limitations, material balances, equipment capacity, etc.) having as major objective the minimization of cost. New information regarding new orders, order modifications and/or cancellations and deviations of actual to planned production is considered by utilizing a rolling horizon algorithm. The suggested solution strategy has been successfully integrated with the industrial partner’s system to facilitate the decision-making process for the production scheduling of the facility.

A Genetic Algorithm for Scheduling a Semi-Continuous Process Industry: A Case Study

In today’s competitive industry, scheduling plays a significant role in improving the efficiency of manufacturing systems. Hence, many scholars and practitioners have been researching to enhance the quality of scheduling methods. In this research, the focus is on solving a real-world scheduling problem in a food industry which was previously dealt with a very time-consuming manual method without high-quality solutions. The problem is to find the best schedule for producing multiple products on multiple machines in a semi-continuous manufacturing system. Having a continuous section in the system makes scheduling too complicated and very different from typical scheduling problems found in literature. In this paper, metaheuristics based on genetic algorithm is proposed to solve the problem.

Reprint of: Data-driven robust optimization under correlated uncertainty: A case study of production scheduling in ethylene plant

To hedge against the fluctuations generated from continuous production processes, practical solutions can be obtained through robust optimization induced by the classical uncertainty sets. However, uncertainties are sometimes correlated in industrial scheduling problems because of the connected process and various random factors. To capture and enrich the valid information of uncertainties, copulas are introduced to estimate the joint probability distribution and simulate mutual scenarios for uncertainties. Cutting planes are generated to remove unnecessary uncertain scenarios in the uncertainty sets, and then robust formulations induced by the cut set are proposed to reduce conservatism and improve the robustness of scheduling solutions. A real-world process of ethylene plant is introduced as the numerical case, and high-dimensional data-driven uncertainty sets are illustrated in detail. The proposed models are proved to control the fluctuation of consumed fuel gas below a lower level of conservatism.

Multi-items Batch Scheduling Model for a Batch Processor to Minimize Total Actual Flow Time of Parts through the Shop

This study is inspired by a batch scheduling problem in metal working industry which guarantees to satisfy a due date as a commitment to customers. Actual flowtime adopts the backward scheduling approach and considers the due date. Using the actual flowtime as the objective means that the solution is oriented to satisfy the due date, and simultaneosly to minimize the length of time of the parts spending in the shop. This research is to address a problem of scheduling batches consisting of multiple items of parts processed on a batch processor where the completed parts must be delivered several time at different due dates. We propose an algorithm to solve the problem.

A hybrid crow search algorithm to minimise the weighted sum of makespan and total flow time in a flow shop environment

In this paper, flow shop scheduling problems which were proved to be strongly NP-hard (non-deterministic polynomial time hard) are considered. The objective is to minimise the weighted sum of makespan and total flow time. For solving this problem, a recently developed meta-heuristics algorithm called as crow search algorithm is proposed. Moreover, the dispatching rules are hybridised with the crow search algorithm to improve the solution quality. An evaluation of the performance of the proposed algorithm is carried out by industrial scheduling problem and the results are compared with many dispatching rules and constructive heuristics. The results obtained by the proposed algorithm are much better than the dispatching rules and constructive heuristics. Random problem instances are also used to validate the performance of the proposed algorithm. The results are compared with many other meta-heuristics addressed in the literature and the results indicate the effectiveness of the proposed algorithm in terms of solution quality and computational time. To the best of our knowledge this is the first reported application of crow search algorithm to solve the scheduling problems.

Industrial scheduling with Monte Carlo tree search and machine learning

Although scheduling has been studied for decades, no general approach for solving industrial scheduling problems has yet been found. The variety of production systems often requires unique modifications of scheduling algorithms to meet industrial requirements. To address this challenge, an algorithm is proposed that combines Monte Carlo tree search and machine learning. It tries to eliminate the need for problem-specific knowledge while improving the production system’s performance. The algorithm has been investigated using a reentrant flow-shop problem. The results show that a combination of random-based search algorithms and machine learning is a promising way to handle complex industrial scheduling problems.

A hybrid crow search algorithm to minimise the weighted sum of makespan and total flow time in a flow shop environment

In this paper, flow shop scheduling problems which were proved to be strongly NP-hard (non-deterministic polynomial time hard) are considered. The objective is to minimise the weighted sum of makespan and total flow time. For solving this problem, a recently developed meta-heuristics algorithm called as crow search algorithm is proposed. Moreover, the dispatching rules are hybridised with the crow search algorithm to improve the solution quality. An evaluation of the performance of the proposed algorithm is carried out by industrial scheduling problem and the results are compared with many dispatching rules and constructive heuristics. The results obtained by the proposed algorithm are much better than the dispatching rules and constructive heuristics. Random problem instances are also used to validate the performance of the proposed algorithm. The results are compared with many other meta-heuristics addressed in the literature and the results indicate the effectiveness of the proposed algorithm in terms of solution quality and computational time. To the best of our knowledge this is the first reported application of crow search algorithm to solve the scheduling problems.

New Multi-Commodity Flow Formulations for the Generalized Pooling Problem

The generalized pooling problem is involved in many planning and scheduling problems in the petrochemical industry. Compared to the standard pooling problem where the blenders (or pools) are not allowed to be connected to one another, the generalized pooling problem has a more complex network structure and allows more types of problem formulations. The state-of-the-art generalized pooling formulations adopt a multi-commodity flow (MCF) strategy that was first proposed by Alfaki and Haugland (2013a) and proved to be stronger than the classical p-formulation. This paper proposes two new MCF formulations for the generalized pooling problem, using mixing and split fractions of blenders rather than the commodity flow fractions. The case study results show that, for some cases, the proposed formulations perform better than the existing MCF formulations, but none of the formulations dominates others for all cases. The results also show that formulations which have similar sizes and similarly tight linear programming relaxations may have dramatically different performance.

Data-driven robust optimization under correlated uncertainty: A case study of production scheduling in ethylene plant

To hedge against the fluctuations generated from continuous production processes, practical solutions can be obtained through robust optimization induced by the classical uncertainty sets. However, uncertainties are sometimes correlated in industrial scheduling problems because of the connected process and various random factors. To capture and enrich the valid information of uncertainties, copulas are introduced to estimate the joint probability distribution and simulate mutual scenarios for uncertainties. Cutting planes are generated to remove unnecessary uncertain scenarios in the uncertainty sets, and then robust formulations induced by the cut set are proposed to reduce conservatism and improve the robustness of scheduling solutions. A real-world process of ethylene plant is introduced as the numerical case, and high-dimensional data-driven uncertainty sets are illustrated in detail. The proposed models are proved to control the fluctuation of consumed fuel gas below a lower level of conservatism.

Modified Cuckoo Optimization Algorithm (MCOA) to solve Precedence Constrained Sequencing Problem (PCSP)

In recent years, new meta-heuristic algorithms have been developed to solve optimization problems. Recently-introduced Cuckoo Optimization Algorithm (COA) has proven its excellent performance to solve different optimization problems. Precedence Constrained Sequencing Problem (PCSP) is related to locating the optimal sequence with the shortest traveling time among all feasible sequences. The problem is motivated by applications in networks, scheduling, project management, logistics, assembly flow and routing. Regarding numerous practical applications of PCSP, it can be asserted that PCSP is a useful tool for a variety of industrial planning and scheduling problems. However it can also be seen that the most approaches may not solve various types of PCSPs and in related papers considering definite conditions, a model is determined and solved. In this paper a new approach is presented for solving various types of PCSPs based on COA. Since COA at first was introduced to solve continuous optimization problems, in order to demonstrate the application of COA to find the optimal sequence of the PCSP, some proposed schemes have been applied in this paper with modifications in operators of the basic COA. In fact due to the discrete nature and characteristics of the PCSP, the basic COA should be modified to solve PSCPs. To evaluate the performance of the proposed algorithm, at first, an applied single machine scheduling problem from the literature that can be formulated as a PCSP and has optimal solution is described and solved. Then, several PCSP instances with different sizes from the literature that do not have optimal solutions are solved and results are compared to the algorithms of the literature. Computational results show that the proposed algorithm has better performance compared to presented well-known meta-heuristic algorithms presented to solve various types of PCSPs so far.

Robust Order Scheduling in the Discrete Manufacturing Industry: A Multiobjective Optimization Approach

Order scheduling is of vital importance in discrete manufacturing industries. This paper takes fashion industry as an example and discusses the robust order scheduling problem in the fashion industry. In the fashion industry, order scheduling focuses on the assignment of production orders to appropriate production lines. In reality, before a new order can be put into production, a series of activities known as preproduction events need to be completed. In addition, in real production process, owing to various uncertainties, the daily production quantity of each order is not always as expected. In this paper, by considering the preproduction events and the uncertainties in the daily production quantity, robust order scheduling problems in the fashion industry are investigated with the aid of a multiobjective evolutionary algorithm called nondominated sorting adaptive differential evolution (NSJADE). The experimental results illustrate that it is of paramount importance to consider preproduction events in order scheduling problems in the fashion industry. We also unveil that the existence of the uncertainties in the daily production quantity heavily affects the order scheduling.

A MIP formulation and a heuristic solution approach for the bottling scheduling problem in the wine industry

In this work, we address the bottling scheduling problem that arises in the wine industry when the packing requests from clients need to be allocated to the production lines. This problem also appears in a large variety of industries, but especially in packaged food companies. Based on the operations of a large Chilean winery we worked with, we developed a MIP model that exhibits industry-specific features such as different types of wine resources and oenological process constraints. This model can be reduced to an n job, m parallel machine scheduling problem, which is known to be NP-hard, so we developed a greedy heuristic algorithm in order to find a feasible bottling schedule in a reduced computing time. We show that the proposed solution approach is a very promising alternative to efficient MIP solvers like CPLEX. Particularly, the greedy heuristic is able to schedule all the jobs in 98% of the test instances and the computational times are very reasonable even for large industrial cases.