This paper presents a scheduling problem for unrelated parallel machines with sequence-dependent setup times, using simulated annealing (SA). The problem accounts for allotting work parts of L jobs into M parallel unrelated machines, where a job refers to a lot composed of N items. Some jobs may have different items while every item within each job has an identical processing time with a common due date. Each machine has its own processing times according to the characteristics of the machine as well as job types. Setup times are machine independent but job sequence dependent. SA, a meta-heuristic, is employed in this study to determine a scheduling policy so as to minimize total tardiness. The suggested SA method utilizes six job or item rearranging techniques to generate neighborhood solutions. The experimental analysis shows that the proposed SA method significantly outperforms a neighborhood search method in terms of total tardiness.

Unrelated parallel machine scheduling with setup times using simulated annealing

In this paper, we propose a procedure, based on statistical design of experiments and gradient descent, that finds effective settings for parameters found in heuristics. We develop our procedure using four experiments. We use our procedure and a small subset of problems to find parameter settings for two new vehicle routing heuristics. We then set the parameters of each heuristic and solve 19 capacity-constrained and 15 capacity-constrained and route-length-constrained vehicle routing problems ranging in size from 50 to 483 customers. We conclude that our procedure is an effective method that deserves serious consideration by both researchers and operations research practitioners.

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Using Experimental Design to Find Effective Parameter Settings for Heuristics

This paper reviews the literature related to the class of scheduling problems that involve sequence-dependent setup times (costs), an important consideration in many practical applications. It focuses on papers published within the last decade, addressing a variety of machine configurations including single machine, parallel machine, flow shop, and job shop systems and reviews the optimization and heuristic solution methods used for each category. Since lot sizing is so intimately related to scheduling, this paper reviews work that integrates these issues in relationship to each configuration. This paper provides a perspective of this line of research, gives conclusions, and discusses fertile research opportunities posed by this class of scheduling problems.

Scheduling and lot sizing with sequence-dependent setup: A literature review

Preface. Acknowledgements. 1. Introduction. 1.1 Planning and decision support. 1.2 Forest management planning. 1.3 History of forest planning.- Discrete problems. 2. Unidimensional problems. 2.1 Decisions under risk and uncertainty. 2.2 Measuring utility and value. 2.2.1 Estimating a utility function. 2.2.2 Estimating a value function.- 3. Multi-criteria decision problems. 3.1 Theoretical aspects. 3.2 Multi-attribute utility functions. 3.2.1 Function forms. 3.2.2 Basis for estimating the weights. 3.2.3 SMART. 3.3 Even Swaps. 3.4 Analytic hierarchy process. 3.4.1 Decision problem. 3.4.2 Phases of AHP. 3.4.3 Uncertainty in AHP. 3.4.4 ANP. 3.5 A'WOT.- 4. Uncertainty in multi-criteria decision making. 4.1 Nature of uncertainty. 4.2 Fuzzy set theory. 4.2.1 Membership functions and fuzzy numbers. 4.2.2 Fuzzy goals in decision making. 4.2.3 Fuzzy additive weighting. 4.3 Possibility theory in decision making. 4.4 Evidence theory. 4.5 Outranking methods. 4.5.1 Outline. 4.5.2 PROMETHEE method. 4.5.3 ELECTRE method. 4.5.4 Other outranking methods. 4.6 Probabilistic uncertainty in decision analysis. 4.6.1 Stochastic multicriteria acceptability analysis (SMAA). 4.6.2 SMAA-O. 4.6.3 Pairwise probabilities.- Continuous problems. 5. Optimization. 5.1 Linear programming. 5.1.1 Primal problem. 5.1.2 Dual problem. 5.1.3 Forest planning problem with several stands. 5.1.4 JLP software. 5.2 Goal programming. 5.3 Integer programming. 5.4 Uncertainty in optimization. 5.5 Robust portfolio modelling. 5.5.1 Principles of the method. 5.5.2 Use of RPM in forest planning.- 6. Heuristic optimization. 6.1 Principles. 6.2 Objective function forms. 6.3 HERO. 6.4 Simulated annealing and threshold accepting. 6.5 Tabu search. 6.6 Genetic algorithms. 6.7 Improving the heuristic search. 6.7.1 Parameters of heuristic optimisation techniques. 6.7.2 Expanding the neighbourhood. 6.7.3 Combining optimisation techniques.- Cases with several decision makers. 7. Group decision making and participatory planning. 7.1 Decision makers and stakeholders. 7.2 Public participation process. 7.2.1 Types of participation process. 7.2.2 Success of the participation process. 7.2.3 Defining the appropriate process. 7.3 Tools for eliciting the public preferences. 7.3.1 Surveys. 7.3.2 Public hearings. 7.4 Problem structuring methods. 7.4.1 Background. 7.4.2 Strategic options development and analysis (SODA). 7.4.3 Soft systems methodology (SSM). 7.5 Decision support for group decision making.- 8. Voting methods. 8.1 Social choice theory. 8.1.1 Outline. 8.1.2 Evaluation criteria for voting systems. 8.2 Positional voting schemes. 8.2.1 Plurality voting. 8.2.2 Approval voting. 8.2.3 Borda count. 8.3 Pairwise voting. 8.4 Fuzzy voting. 8.5 Probability voting. 8.6 Multicriteria approval. 8.6.1 Original method. 8.6.2 Fuzzy MA. 8.6.3 Multicriteria approval voting.- Application viewpoints. 9. Behavioural aspects. 9.1 Criticism towards decision theory. 9.1.1 Outline. 9.1.2 Satisficing or maximizing?. 9.1.3 Rules or rational behaviour?. 9.2 Image theory. 9.3 Prospect theory.- 10. Practical examples of using MCDS methods. 10.1 Landscape ecological planning. 10.2 Participatory planning. 10.3 Spatial objectives and heuristic optimisation in practical forest planning.- 11. Final remarks.-

Decision Support for Forest Management

In this communication, we strive to apply a novel simulated annealing to consider scheduling hybrid flowshop problems to minimize both total completion time and total tardiness. To narrow the gap between the theory and the practice of the hybrid flowshop scheduling, we integrate two realistic and practical assumptions which are sequence-dependent setup and transportation times into our problem. We apply a metaheuristic based on simulated annealing (SA) which strikes a compromise between intensification and diversification mechanisms to augment the competitive performance of our proposed SA. A comprehensive calibration of different parameters and operators are done. We employ Taguchi method to select the optimum parameters with the least possible number of experiments. For the purpose of performance evaluation of our proposed algorithm, we generate a benchmark against which the adaptations of high performing algorithms in the literature are brought into comparison. Moreover, we investigate the impacts of increase of number of jobs on the performance of our algorithm. The efficiency and effectiveness of our hybrid simulated annealing are inferred from all the computational results obtained in various situations.

An improved simulated annealing for hybrid flowshops with sequence-dependent setup and transportation times to minimize total completion time and total tardiness

A systematic procedure for setting parameters in simulated annealing algorithms

Search heuristics for a flowshop scheduling problem in a printed circuit board assembly process

Search heuristics for a parallel machine scheduling problem with ready times and due dates

We consider setup problems in flexible assembly systems (FASs) with an objective involving job priorities and station workloads. Because of the difficulty in solving the FAS setup problem, it is partitioned into two subproblems: the part type selection problem and the loading problem. We suggest a solution procedure in which branch-and-bound methods and heuristic methods are used for solving the subproblems. Part types are tentatively selected first without explicitly considering the system's restrictions, such as precedence relationships among tasks. As a result, the alternative for part type selection may not always be feasible for the loading problem. For a feasibility check and for a use in assigning tasks of selected part types, we develop an algorithm for finding a feasible sequence of the tasks. In the loading problem, tasks are assigned to stations using LPT-type rules with improvements tried through interchanging tasks. Results of computational tests on randomly generated problems are reported.

A heuristic for setting up a flexible assembly system

In rechargeable battery production—a component of mass customization—high quality, low cost, efficient delivery, and flexibility must be ensured. Efficient production operation can be achieved by solving the performance degradation problem, which is a limitation of mass customization. This degradation can be prevented through resilience, which can be achieved by satisfying four core functional requirements, which are as follows: (1) selecting robust actions; (2) measuring performance indicators; (3) notifying impermissible fluctuations; and (4) extracting the adjusted reactions. A digital twin (DT) is an advanced virtual asset that represents configuration, reflects functional units, and synchronizes information objects. This article describes DT application to satisfy the four core functional requirements and reflect the operational characteristics of three heterogeneous stations in rechargeable battery production. Analyses of the measures taken to achieve the resilience and operational characteristics of stations in rechargeable battery production are provided to present an appropriate design of the description. The asset description is designed with P4R classes based on this analysis. The designed asset description is applied to stations in rechargeable battery production, and the proposed method is verified with the implemented DT application. The proposed asset description presents an efficient method of satisfying the core activities and technical functionalities corresponding to the DT. The proposed method is an early case of DT usage in rechargeable battery production and can be considered as a reference for smart manufacturing technologies in the manufacturing domain in the future. 

Moon-Won Park

Papers

A systematic procedure for setting parameters in simulated annealing algorithms

Search heuristics for a flowshop scheduling problem in a printed circuit board assembly process

Search heuristics for a parallel machine scheduling problem with ready times and due dates

A heuristic for setting up a flexible assembly system

Asset Description of Digital Twin for Resilient Production Control in Rechargeable Battery Production