Review on bio-based plastic for future applications

This paper reviews the future applications of bio-based plastics. Most plastics are made through petrochemical processes. In other words, they start out as the chemical byproducts of oil refining, which are turned into a variety of plastics through chemical processes that form long molecular chains known as polymers. These polymers give plastics their structure. Bioplastics are biodegradable materials that come from renewable sources and can be used to reduce the problem of plastic waste that is suffocating the planet and contaminating the environment. The advantages of using bioplastics are bioplastics won’t leach chemicals into food, non- toxic and offer a zero waste end life options. Bioplastics can be recycled with conventional plastics to produce a great material for food packaging. It also has a socio�economic benefit that often have a positive impact on the consumers who are increasingly becoming aware of environmental issues. As conclusion, it is proven that bioplastics give promising future to cleaner and safer world

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

A statistical learning based approach for parameter fine-tuning of metaheuristics

Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selection of appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem.Peer ReviewedPostprint (published version

Solving job shop scheduling problem using genetic algorithm with penalty function

This paper presents a genetic algorithm with a penalty function for the job shop scheduling problem. In the context of proposed algorithm, a clonal selection based hyper mutation and a life span extended strategy is designed. During the search process, an adaptive penalty function is designed so that the algorithm can search in both feasible and infeasible regions of the solution space. Simulated experiments were conducted on 23 benchmark instances taken from the OR-library. The results show the effectiveness of the proposed algorithm

Longterm schedule optimization of an underground mine under geotechnical and ventilation constraints using SOT

Long-term mine scheduling is complex as well time and labour intensive. Yet in the mainstream of the mining industry, there is no computing program for schedule optimization and, in consequence, schedules are still created manually. The objective of this study was to compare a base case schedule generated with the Enhanced Production Scheduler (EPS®) and an optimized schedule generated with the Schedule Optimization Tool (SOT). The intent of having an optimized schedule is to improve the project value for underground mines. This study shows that SOT generates mine schedules that improve the Net Present Value (NPV) associated with orebody extraction. It does so by means of systematically and automatically exploring the options to vary the sequence and timing of mine activities, subject to constraints. First, a conventional scheduling method (EPS®) was adopted to identify a schedule of mining activities that satisfied basic sets of constraints, including physical adjacencies of mining activities and operational resource capacity. Additional constraint scenarios explored were geotechnical and ventilation, which negatively effect development rates. Next, the automated SOT procedure was applied to determine whether the schedules could be improved upon. It was demonstrated that SOT permitted the rapid re-assessment of project value when new constraint scenarios were applied. This study showed that the automated schedule optimization added value to the project every time it was applied. In addition, the reoptimizing and re-evaluating was quickly achieved. Therefore, the tool used in this research produced more optimized schedules than those produced using conventional scheduling methods.Master of Applied Science (MASc) in Natural Resources Engineerin

MULTIOBJECTIVE OPTIMIZATION: TIME-COST APPLICATION IN CONSTRUCTION

INTRODUCTION AND BACKGROUND In today´s market-driven economy, the ability to minimize the time and/or cost of a project could determine the profitability and even the survival of a construction company. The increasing acceptance of alternative tenders and different project delivery systems, such as design and build, management contracting, build-operate-transfer, partnering, etc., allows greater flexibility in construction duration. This also means that both construction time and cost should be considered concomitantly at the estimation and planning and stages Several approaches to solve the time-cost optimization (TCO) problem have been proposed in the last years: mathematical, heuristic and search methods

Time-cost Trade-off Analysis for Highway Construction projects

The Construction industry, which can be in the form of residential building, commercial, public and utility buildings, or civil engineering building, has a huge influence on any nation\u27s economy. Its influence can be either manifested in its contribution to the economy or the service it provides to the community. In order to build any infrastructure project with a balanced cost, time, and quality, project managers search for alternatives that can satisfy these contradicting attributes. The traditional time-cost trade-off was enhanced with the three-dimensional time-cost- quality optimization in the last two decades. The optimization is aimed to minimize the time and cost as much as possible while increasing the quality of the infrastructure to be built. The issue of financing in developing countries has been a bottle neck of success in constructing infrastructure like highway. Many researchers have concluded in their studies the causes of time and cost overrun in high-way construction were, contractors\u27 financial problems, Inflation, progress payments delay by owner, political issues, variations, lack of managemental skills, cost fluctuation of materials during construction, environmental issues, Shortage in equipment, Inadequate contractor experience etc. The number of studies in the literature that deals with financial optimization and cash-flow analysis to address the problem of financing and inflation are getting more attention. The cash-flow analysis and maximum overdraft to be paid give a good indication to the main participants about the trends toward cost and time overrun. They can also help in making a proper decision right at the beginning. The purpose of this study is to deal with the optimization of time and profit of highway constructions taking in to consideration the amount of available credit and future value of the cost of each activity and cash-flow analysis in a comprehensive model. This type of analysis gives the contractor how its profit will be influenced with his allowable credits and the time associated with it. Besides, the model also generates a line of balance scheduling for the project as highways are among the repetitive projects. The cash-flow analysis gives extra information on the overdraft so that it can be optimized to find good combination of execution of the activities which will minimize the overdraft, interest paid to banks and most importantly maximize the profit to be gained by the project using GA approach. This type of analysis also gives alternatives for contractors how much profit would they like to gain by providing different amount of credits. At first the profit and time are optimized individually to get the maximum profit and minimum time for completing the project. Then the multi-objective optimization using goal programing takes place which tries to minimize the deviation from the optimum individual values by assigning importance weight to the individual objectives to find the near optimal solution. The model is tested for different allowable credits and its sensitivity analysis outcomes are plotted to see the relationship between the allowable credits and the profit. To validate the efficiency of the developed model, it is applied to a project from the literature that addresses scheduling and cost optimization of repetitive projects. It is found that the outcome of the model that maximizes the profit and minimizing the time outlooks the results of the literature with 4.65% and 0.38% improvement in duration and cost of the project respectively

A statistical learning based approach for parameter fine-tuning of metaheuristics

Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selectionof appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem.Peer Reviewe

Job Shop Optimization Through Multiple Independent Particle Swarms

This study examines the optimization of the Job Shop Problem (JSP) by a search space division scheme and use of the meta-heuristic method of Particle Swarm Optimization (PSO) to solve it. The JSP is a well known combinatorial problem from the field of Deterministic Scheduling. It is considered the one of the hardest in the class of NP-Hard problems. The PSO algorithm is a meta-heuristic optimization method modeled after the behavior of a flock of birds. "Particles" are initialized in the search space of a problem by assigning them a position, which represents a solution to the objective function, and a velocity. They "fly" through the search space with out direct control, but are given both a personal component and a social component of the best positions found. The proposed method uses this meta-heuristic to solve the JSP by assigning each machine in a JSP an independent swarm of particles.School of Electrical & Computer Engineerin