Research Trends and Outlooks in Assembly Line Balancing Problems

This paper presents the findings from the survey of articles published on the assembly line balancing problems (ALBPs) during 2014-2018. Before proceeding a comprehensive literature review, the ineffectiveness of the previous ALBP classification structures is discussed and a new classification scheme based on the layout configurations of assembly lines is subsequently proposed. The research trend in each layout of assembly lines is highlighted through the graphical presentations. The challenges in the ALBPs are also pinpointed as a technical guideline for future research works

Modeling and Solution Methodologies for Mixed-Model Sequencing in Automobile Industry

The global competitive environment leads companies to consider how to produce high-quality products at a lower cost. Mixed-model assembly lines are often designed such that average station work satisfies the time allocated to each station, but some models with work-intensive options require more than the allocated time. Sequencing varying models in a mixed-model assembly line, mixed-model sequencing (MMS), is a short-term decision problem that has the objective of preventing line stoppage resulting from a station work overload. Accordingly, a good allocation of models is necessary to avoid work overload. The car sequencing problem (CSP) is a specific version of the MMS that minimizes work overload by controlling the sequence of models. In order to do that, CSP restricts the number of work-intensive options by applying capacity rules. Consequently, the objective is to find the sequence with the minimum number of capacity rule violations. In this dissertation, we provide exact and heuristic solution approaches to solve different variants of MMS and CSP. First, we provide five improved lower bounds for benchmark CSP instances by solving problems optimally with a subset of options. We present four local search metaheuristics adapting efficient transformation operators to solve CSP. The computational experiments show that the Adaptive Local Search provides a significant advantage by not requiring tuning on the operator weights due to its adaptive control mechanism. Additionally, we propose a two-stage stochastic program for the mixed-model sequencing (MMS) problem with stochastic product failures, and provide improvements to the second-stage problem. To tackle the exponential number of scenarios, we employ the sample average approximation approach and two solution methodologies. On one hand, we develop an L-shaped decomposition-based algorithm, where the computational experiments show its superiority over solving the deterministic equivalent formulation with an off-the-shelf solver. We also provide a tabu search algorithm in addition to a greedy heuristic to tackle case study instances inspired by our car manufacturer partner. Numerical experiments show that the proposed solution methodologies generate high-quality solutions by utilizing a sample of scenarios. Particularly, a robust sequence that is generated by considering car failures can decrease the expected work overload by more than 20\% for both small- and large-sized instances. To the best of our knowledge, this is the first study that considers stochastic failures of products in MMS. Moreover, we propose a two-stage stochastic program and formulation improvements for a mixed-model sequencing problem with stochastic product failures and integrated reinsertion process. We present a bi-objective evolutionary optimization algorithm, a two-stage bi-objective local search algorithm, and a hybrid local search integrated evolutionary optimization algorithm to tackle the proposed problem. Numerical experiments over a case study show that while the hybrid algorithm provides a better exploration of the Pareto front representation and more reliable solutions in terms of waiting time of failed vehicles, the local search algorithm provides more reliable solutions in terms of work overload objective. Finally, dynamic reinsertion simulations are executed over industry-inspired instances to assess the quality of the solutions. The results show that integrating the reinsertion process in addition to considering vehicle failures can keep reducing the work overload by around 20\% while significantly decreasing the waiting time of the failed vehicles

Optimasi Penjadwalan Perawat dengan Algoritma Late Acceptance Hill Climbing Hyper-Heuristic Menggunakan Benchmark Dataset dari Rumah Sakit di Norwegia

Penjadwalan perawat yang terdapat dalam sebuah rumah sakit merupakan sebuah permasalahan yang hingga saat ini masih sering diselesaikan dengan cara manual. Penjadwalan perawat dengan cara manual akan memakan waktu yang cukup lama selaras dengan jumlah perawat yang dibutuhkan. Selain itu, untuk melakukan penjadwalan perawat harus mempertimbangkan banyak kepentingan yaitu misalnya kepentingan perawat, kepentingan pemilik rumah sakit, dan kepentingan pasien. Penjadwalan perawat dengan cara manual akan menimbulkan permasalahan. Dari segi perawat, penjadwalan manual seringkali mengakibatkan pembagian beban kerja yang tidak merata, sehingga perawat yang memiliki beban kerja lebih banyak daripada perawat yang lain akan merasa kecewa dengan jadwal tersebut. Dari segi pemilik rumah sakit, penjadwalan manual tidak mempertimbangkan efektifitas perawat. Hal ini menyebabkan perawat yang dibutuhkan semakin banyak dan akan mengakibatkan pada meningkatnya biaya yang dialokasikan untuk upah perawat. Dari segi pasien, penjadwalan manual terkadang mengalami kesusahan untuk mengalokasikan perawat yang memiliki kompetensi tertentu yang dibutuhkan oleh pasien. Oleh karena itu, diperlukan optimasi penjadwalan perawat untuk menghasilkan jadwal yang optimal dalam memenuhi kebutuhan semua kepentingan yang terlibat dan mempersingkat waktu yang dialokasikan untuk melakukan penjadwalan perawat. Dalam dunia komputasi, permasalahan penjadwalan perawat merupakan masalah yang termasuk dalam golongan NP-Hard Problem. Artinya, hingga saat ini belum ada algoritma eksak yang dapat menyelesaikannya karena banyaknya kemungkinan yang terjadi dan adanya batasan yang harus dipenuhi. Oleh karena itu, algoritma heuristic dibutuhkan untuk menyelesaikan permasalahan penjadwalan perawat. Begitu pula dengan penjadwalan perawat yang terdapat pada rumah sakit di Norwegia. Penjadwalan perawat pada rumah sakit di Norwegia juga memiliki batasan-batasan yang tidak boleh dilanggar sehingga sulit untuk diselesaikan dengan cara manual. Batasan yang terdapat pada rumah sakit di Norwegia ada dua yaitu, hard constraint dan soft constraint. Tujuan dari optimasi penjadwalan perawat pada rumah sakit di Norwegia ini yaitu untuk meminimalkan pelanggaran yang terjadi pada soft constraint. Dalam tugas akhir ini, optimasi benchmark dataset rumah sakit di Norwegia diselesaikan dengan menggunakan algoritma Late Acceptance Hill Climbing dengan dibandingkan dengan Hill Climbing. Hasil dari tugas akhir ini yaitu sebanyak 3 dari 7 instance berhasil feasible. Pada seluruh instance yang feasible, optimasi menggunakan algoritma Late Acceptance Hill Climbing mampu menghasilkan solusi yang lebih optimal daripada Hill Climbing dengan selisih penurunan penalti antara 2% hingga 7%. Hill Climbing hanya mampu menurunkan penalti sebesar 78% pada OpTur4, 74% pada OpTur5, dan 5% pada OpTur7. Sedangkan Late Acceptance Hill Climbing mampu menurunkan penalti sebesar 80% pada OpTur4, 81% pada OpTur5 dan 7% pada OpTur7 jika dibandingkan dengan solusi awal. ===================================================================================================== Nurse Rostering in a hospital is a problem that still often solved manually. Manually rostering takes a lot time that goes in line with the number of nurses needed. In addition, it must consider a lot of interests i.e. the interests of the nurse, the interests of the owner of the hospital, and the patient’s need. Manually rostering will cause many problem. From the nurse’s side, manual rostering does not consider the nurse effectiveness. This cause more nurses needed and will result in increased costs allocated for nurse’s wages. From the patient’s side, manual rostering sometimes have trouble to allocate the nurse who have the specific competencies needed by the patient. Therefore, optimization of nurse rostering is needed in order to create an optimal roster that meet the needs of all the interests involved, and to shorten the time allocated to rostering. In computing, nurse rostering problem is an issue that belongs to the NP-Hard Prolem. It means that until now there is not exact algorithm that can solve it, because of many possibilities and the constraints that must be met. Therefore, heuristic algorithms are needed to solve this problem. Nurse rostering at hospitals in Norway also has constraints that should not be violated, so it is difficult to resolve manually. The constraints are hard and soft constraint. The goal of optimizing of nurse rostering at hospitals in Norway is to minimize violations that may occur in soft constraint. In this final project, optimization of Norwegian hospital benchmark dataset is completed using the Late Acceptance Hill Climbing algorithm compared to Hill Climbing. The result of this final project are 3 out of 7 instace are feasible. For the all instances that are feasible, optimization using the Late Acceptance Hill Climbing algorithm is able to obtain more optimal solution than Hill Climbing with the difference in penalty reduction between 2% to 7%. Hill Climbing is only able to reduce penalties by 78% on OpTur4, 74% on OpTur5, and 5% on OpTur7. Whereas Late Acceptance Hill Climbing is able to reduce penalties by 80% in OpTur4, 81% in OpTur5 and 7% in OpTur7 when compared to the initial solution

Concurrent Probabilistic Simulation of High Temperature Composite Structural Response

A computational structural/material analysis and design tool which would meet industry's future demand for expedience and reduced cost is presented. This unique software 'GENOA' is dedicated to parallel and high speed analysis to perform probabilistic evaluation of high temperature composite response of aerospace systems. The development is based on detailed integration and modification of diverse fields of specialized analysis techniques and mathematical models to combine their latest innovative capabilities into a commercially viable software package. The technique is specifically designed to exploit the availability of processors to perform computationally intense probabilistic analysis assessing uncertainties in structural reliability analysis and composite micromechanics. The primary objectives which were achieved in performing the development were: (1) Utilization of the power of parallel processing and static/dynamic load balancing optimization to make the complex simulation of structure, material and processing of high temperature composite affordable; (2) Computational integration and synchronization of probabilistic mathematics, structural/material mechanics and parallel computing; (3) Implementation of an innovative multi-level domain decomposition technique to identify the inherent parallelism, and increasing convergence rates through high- and low-level processor assignment; (4) Creating the framework for Portable Paralleled architecture for the machine independent Multi Instruction Multi Data, (MIMD), Single Instruction Multi Data (SIMD), hybrid and distributed workstation type of computers; and (5) Market evaluation. The results of Phase-2 effort provides a good basis for continuation and warrants Phase-3 government, and industry partnership

Scheduling in assembly type job-shops

Assembly type job-shop scheduling is a generalization of the job-shop scheduling problem to include assembly operations. In the assembly type job-shops scheduling problem, there are n jobs which are to be processed on in workstations and each job has a due date. Each job visits one or more workstations in a predetermined route. The primary difference between this new problem and the classical job-shop problem is that two or more jobs can merge to foul\u27 a new job at a specified workstation, that is job convergence is permitted. This feature cannot be modeled by existing job-shop techniques. In this dissertation, we develop scheduling procedures for the assembly type job-shop with the objective of minimizing total weighted tardiness. Three types of workstations are modeled: single machine, parallel machine, and batch machine. We label this new scheduling procedure as SB. The SB procedure is heuristic in nature and is derived from the shifting bottleneck concept. SB decomposes the assembly type job-shop scheduling problem into several workstation scheduling sub-problems. Various types of techniques are used in developing the scheduling heuristics for these sub-problems including the greedy method, beam search, critical path analysis, local search, and dynamic programming. The performance of SB is validated on a set of test problems and compared with priority rules that are normally used in practice. The results show that SB outperforms the priority rules by an average of 19% - 36% for the test problems. SB is extended to solve scheduling problems with other objectives including minimizing the maximum completion time, minimizing weighted flow time and minimizing maximum weighted lateness. Comparisons with the test problems, indicate that SB outperforms the priority rules for these objectives as well. The SB procedure and its accompanying logic is programmed into an object oriented scheduling system labeled as LEKIN. The LEKIN program includes a standard library of scheduling rules and hence can be used as a platform for the development of new scheduling heuristics. In industrial applications LEKIN allows schedulers to obtain effective machine schedules rapidly. The results from this research allow us to increase shop utilization, improve customer satisfaction, and lower work-in-process inventory without a major capital investment

Optimasi Penjadwalan Staf Rumah Sakit dengan Menggunakan Metode Late-Acceptance Hill-Climbing Hyperheuristic (Studi kasus: RSIA Kendangsari Merr Surabaya)

Rumah Sakit Ibu dan Anak (RSIA) Kendangsari merupakan rumah sakit swasta yang berlokasi di Surabaya. Terdapat banyak program layanan untuk ibu dan anak seperti Poli (Obgy, Anak, Bedah Penyakit Dalam), women’s health care, dan lain-lain. Disini peran staf menjadi salah satu hal yang penting untuk dapat melayani pasien sehingga dibutuhkan penjadwalan yang optimal untuk dapat melayani para pasien RSIA Kendangsari. Masalah yang terjadi di RSIA Kendangsari Surabaya saat ini adalah masih memakai cara manual untuk membuat jadwal staf di rumah sakit. Hal ini menyulitkan pengelola rumah sakit serta memakan waktu dan tenaga lebih banyak untuk membuat jadwal tersebut. Penjadwalan tersebut tidak memikirkan faktor pribadi staf sehingga staf merasa tidak senang dengan jadwal yang ada saat ini. Masalah ini dapat menyebabkan performa kinerja staf menurun akibat kelelahan dan stress karena jadwal yang tidak optimal dengan jumlah libur yang tidak seimbang. Sehingga perlu pembuatan jadwal yang adil untuk masing-masing staf. Dalam pembuatan jadwal yang optimal harus mempertimbangkan faktor dan regulasi dari rumah sakit dimana kendala yang ada dibagi menjadi 2 jenis kendala, yaitu hard constraint dan soft constraint. Maka dilakukan optimasi penjadwalan staf rumah sakit dengan menggunakan metode Hyper Heuristic dengan algoritma Late Acceptance Hill Climbing. Metode Hyperheuristic adalah metode penyelesaian masalah tingkat tinggi yang digunakan untuk memberikan solusi terhadap permasalahan dengan cara menghasilkan heuristic baru dengan menggunakan heuristic yang sudah ada. Pada penelitian ini juga digunakan algoritma late acceptance hill climbing yang merupakan perluasan dari algoritma hill climbing yaitu algoritma yang paling sederhana yang membandingkan nilai solusi akhir dengan satu kandidat solusi untuk mendapatkan nilai solusi yang optimal. Fungsi Tujuan yang digunakan adalah memaksimalkan nilai JFI (Jain Fairness Index) untuk mengukur keadilan jumlah libur pada masing-maisng staf rumah sakit, nilai JFI semakin optimal jika mendekati nilai 1. Penulis juga mengeksplorasi hasil optimasi dari late acceptance hill climbing dengan membandingkan dengan algoritma hill climbing. Hasil JFI pada penjadwalan unit instalasi Farmasi secara otomatis adalah 0.66 dan sesudah dioptimasi adalah 0.98, pada unit rawat jalan dan IGD secara otomatis adalah 0.81 dan sesudah dioptimasi adalah 0.98, pada unit ruang bayi dan nicu secara otomatis adalah 0.66 dan sesudah dioptimasi adalah 0.96, pada unit sim dan registrasi secara otomatis adalah 0.97 dan sesudah dioptimasi adalah 1, serta instalasi gizi dan café secara otomatis adalah 0.70 dan sesudah dioptimasi adalah 0.86. Sedangkan hasil yang didapat dari mengimplementasikan algoritma LAHC dan hill climbing adalah nilai JFI pada penjadwalan instalasi farmasi algoritma LAHC 0.963306 dan hill climbing 0.983543, unit rawat jalan dan IGD algoritma LAHC 0.983034 dan hill climbing adalah 0.973046, Ruang Bayi dan Nicu algoritma LAHC 0.969021 dan hill climbing adalah 0.963647, unit Sim dan Registrasi algoritma LAHC 1 dan hill climbing adalah 0.997922, unit Instalasi Gizi dan Cafe algoritma LAHC 0.864199 dan hill climbing adalah 0.862342. Ka ========================================================================================= Rumah Sakit Ibu dan Anak (RSIA) Kendangsari is a private hospital in Surabaya. There are many programs for mother and child services such as Poly (Obgy, Child, Internal Diseases Surgery), women's health care, and others. Here the role of nurses to be one of the important things to be able serving the patients who need optimal nurse scheduling to serve patients RSIA Kendangsari. The problem that happened at RSIA Kendangsari Surabaya at this time is still using manual way to make schedule of staff in hospital. This makes it difficult for hospital managers as well as more time and effort to make the schedule. The scheduling can not be thought of so that staff are not satisfied with the current schedule. This problem may lead to non-optimal performance with an unbalanced number of holidays. Requires setting up a fair schedule for each staff. In the optimal schedule making must remember the factors and regulation of the hospital where that is divided into 2 types of goals, namely hard constraints and soft constraints. So do the optimization of hospital staff scheduling using Hyper Heuristic method with Late Acceptance Hill Climbing algorithm. The Hyperheuristic Method is a problem-solving method that is being used to provide solutions to problems by generating new heuristics using existing heuristics. In this research, hill climbing delay acceptance algorithm is an extension of the hill climbing algorithm, which is the simplest algorithm that compares the optimal solution solution. Function The objective used is to maximize JFI (Jain Fairness Index) to measure the amount of amounts in each hospital staff, JFI is more optimal when looking at 1. The author also explores the optimization results of late acceptance hill climbing by comparing with the hill climbing algorithm.The JFI result in the Unit Instalasi Farmasi is 0.66 , rawat jalan IGD is 0.81 and after optimized is 0.98, Unit Ruang Bayi dan Nicu is 0.66 and after optimized is 0.96, Unit Sim dan Registrasi is 0.97 and after optimized is 1, Unit Gizi dan Café is 0.70 and after optimized is 0.86. The result of LAHC algorithm implementation and hill climbing is JFI value in unit instalasi farmasi LAHC 0.963306 and hill climbing 0.983543, unit rawat jalan dan IGD algorithm LAHC 0.983034 and hill climbing is 0.973046, unit ruang bayi dan nicu LAHC is 0.969021 and hill climbing algorithm is 0.963647, unit sim dan registrasi algorithm LAHC 1 and hill climbing is 0.997922, unit instalasi Gizi dan Café LAHC 0.864199 and hill climbing algorithm is 0.862342

An investigation of multi-objective hyper-heuristics for multi-objective optimisation

In this thesis, we investigate and develop a number of online learning selection choice function based hyper-heuristic methodologies that attempt to solve multi-objective unconstrained optimisation problems. For the first time, we introduce an online learning selection choice function based hyperheuristic framework for multi-objective optimisation. Our multi-objective hyper-heuristic controls and combines the strengths of three well-known multi-objective evolutionary algorithms (NSGAII, SPEA2, and MOGA), which are utilised as the low level heuristics. A choice function selection heuristic acts as a high level strategy which adaptively ranks the performance of those low-level heuristics according to feedback received during the search process, deciding which one to call at each decision point. Four performance measurements are integrated into a ranking scheme which acts as a feedback learning mechanism to provide knowledge of the problem domain to the high level strategy. To the best of our knowledge, for the first time, this thesis investigates the influence of the move acceptance component of selection hyper-heuristics for multi-objective optimisation. Three multi-objective choice function based hyper-heuristics, combined with different move acceptance strategies including All-Moves as a deterministic move acceptance and the Great Deluge Algorithm (GDA) and Late Acceptance (LA) as a nondeterministic move acceptance function. GDA and LA require a change in the value of a single objective at each step and so a well-known hypervolume metric, referred to as D metric, is proposed for their applicability to the multi-objective optimisation problems. D metric is used as a way of comparing two non-dominated sets with respect to the objective space. The performance of the proposed multi-objective selection choice function based hyper-heuristics is evaluated on the Walking Fish Group (WFG) test suite which is a common benchmark for multi-objective optimisation. Additionally, the proposed approaches are applied to the vehicle crashworthiness design problem, in order to test its effectiveness on a realworld multi-objective problem. The results of both benchmark test problems demonstrate the capability and potential of the multi-objective hyper-heuristic approaches in solving continuous multi-objective optimisation problems. The multi-objective choice function Great Deluge Hyper-Heuristic (HHMO_CF_GDA) turns out to be the best choice for solving these types of problems

An investigation of multi-objective hyper-heuristics for multi-objective optimisation

In this thesis, we investigate and develop a number of online learning selection choice function based hyper-heuristic methodologies that attempt to solve multi-objective unconstrained optimisation problems. For the first time, we introduce an online learning selection choice function based hyperheuristic framework for multi-objective optimisation. Our multi-objective hyper-heuristic controls and combines the strengths of three well-known multi-objective evolutionary algorithms (NSGAII, SPEA2, and MOGA), which are utilised as the low level heuristics. A choice function selection heuristic acts as a high level strategy which adaptively ranks the performance of those low-level heuristics according to feedback received during the search process, deciding which one to call at each decision point. Four performance measurements are integrated into a ranking scheme which acts as a feedback learning mechanism to provide knowledge of the problem domain to the high level strategy. To the best of our knowledge, for the first time, this thesis investigates the influence of the move acceptance component of selection hyper-heuristics for multi-objective optimisation. Three multi-objective choice function based hyper-heuristics, combined with different move acceptance strategies including All-Moves as a deterministic move acceptance and the Great Deluge Algorithm (GDA) and Late Acceptance (LA) as a nondeterministic move acceptance function. GDA and LA require a change in the value of a single objective at each step and so a well-known hypervolume metric, referred to as D metric, is proposed for their applicability to the multi-objective optimisation problems. D metric is used as a way of comparing two non-dominated sets with respect to the objective space. The performance of the proposed multi-objective selection choice function based hyper-heuristics is evaluated on the Walking Fish Group (WFG) test suite which is a common benchmark for multi-objective optimisation. Additionally, the proposed approaches are applied to the vehicle crashworthiness design problem, in order to test its effectiveness on a realworld multi-objective problem. The results of both benchmark test problems demonstrate the capability and potential of the multi-objective hyper-heuristic approaches in solving continuous multi-objective optimisation problems. The multi-objective choice function Great Deluge Hyper-Heuristic (HHMO_CF_GDA) turns out to be the best choice for solving these types of problems