Ammar Al-Bazi

Developing Crew Allocation System for the Precast Industry Using Genetic Algorithms

: The Precast Concrete Industry (PCI) is one of the major contributors to the national economy and can be categorized as labor-intensive industry. It is currently experiencing shortcomings in terms of delivery products at a competitive cost and time. This is mainly due to the inefficiencies associate with planning and scheduling of skilled operators within crew configurations. This article presents a new strategy for efficient allocation of crews of workers in the precast concrete industry using Genetic Algorithms-based simulation modeling. The aim of this study is to develop a crew allocation system that can efficiently allocate possible crews of workers to precast concrete labor-intensive repetitive processes. Genetic algorithms (GAs) have been developed to solve this type of problem. Process mapping methodologies were used to identify and document the processes involved in producing precast components. Then process simulation was used to model and simulate all these processes and GAs were tailored to be embedded with the simulation model for a better search of promising solutions. GA operators were designed to suit this type of allocation problem. “Class Interval” selection strategy was developed to give a greater opportunity for the promising chromosomes to be chosen for further investigation. Dynamic crossover and mutation operators were developed to add more randomness to the search mechanism. The results showed that adopting different combinations of crews of workers had a substantial impact on reducing the process throughput time, minimizing resources cost, and achieving the required operatives utilization.

Simulation-based genetic algorithms for construction supply chain management: Off-site precast concrete production as a case study

The increased use of precast components in building and heavy civil engineering projects has led to the introduction of innovative management and scheduling systems to meet the demand for increased reliability, efficiency and cost reduction. The aim of this study is to develop an innovative crew allocation system that can efficiently allocate crews of workers to labour-intensive repetitive processes. The objective is to improve off-site precast production operations using multi-layered genetic algorithms (GAs). The multi-layered concept emerged in response to the requirement of modelling different sets of labour inputs. As part of the techniques used in developing a crew allocation ‘SIM_Crew’ system, a process mapping methodology is used to model processes of precast concrete operations and to provide the framework and input required for simulation. Process simulation is then used to model and imitate all production processes and GAs are embedded within the simulation model to provide a rapid and intelligent search. A multi-layered chromosome is used to store different sets of inputs such as crews working on different shifts and process priorities. The results illustrate that adopting different combinations of crews of workers has a substantial impact on the labour allocation cost and this should lead to increased efficiency and lower production cost. In addition, the results of the simulation show that reduced throughput and process-waiting times and improved resource utilisation profiles can be achieved when compared with a real-life case study.

Simulation-based optimisation using simulated annealing for crew allocation in the precast industry

ABSTRACT Numerous different combinations of crew alternatives can be deployed within a labour-intensive manufacturing industry. This can therefore often generate a large number of possible crew allocation plans. However, inappropriate selection of these allocation plans tends to lead to inefficient manufacturing processes and ultimately higher labour allocation costs. Thus, in order to reduce such costs, more allocation systems are required. The main aim of this study is to develop a simulation-based multi-layered simulated annealing system to solve crew allocation problems encountered in labour-intensive parallel repetitive manufacturing processes. The ‘multi-layered’ concept is introduced in response to the problem-solving requirements. As part of the methodology used, a process simulation model is developed to mimic a parallel repetitive processes layout. A simulated annealing module is proposed and embedded into the developed simulation model for a better search for solutions. Also, a multi-layered dynamic mutation operator is developed to add more randomness to the searching mechanism. A real industrial case study of a precast concrete manufacturing system is used to demonstrate the applicability and practicability of the developed system. The proposed system has the potential to produce more cost-effective allocation plans, through reducing process-waiting times as compared with real industrial-based plans.

Using genetic algorithms to improve crew allocation process in labour-intensive industries

The high cost of skilled workers in labour-intensive production industries has motivated senior production managers to identify the best allocation strategy of crews of workers to appropriate processes. The aim of this paper is to develop a crew allocation system using Genetic Algorithms-based simulation modeling. The objective is to optimally allocate crews of workers to labour-intensive production industries to minimise labour costs. In this paper, a simulation-based Genetic Algorithm (GA) system dubbed “SIM_Crew” is developed to simulate the physical processes of a labour-driven facility. The GA is tailored to be embedded with the developed simulation model for improved solution searching. A chromosome structure is designed to apply such problems and a probabilistic selection of promising chromosomes is applied as a selection strategy, n-points crossover and mutation strategies are designed to add more randomness to the searching process. A case study in the precast industry is presented to demonstrate and validate the model.

Anomaly analysis on an open DNS dataset

9 The increasing availability of open data and the demand to understand better the nature of anomalies and the causes underlying them in modern systems is encouraging researchers to analyse open datasets in various ways. These include both quantitative and qualitative methods. We show here how quantitative methods, such as timeline, local averages and exponentially weighted moving average analyses, led in this work to the discovery of three anomalies in a large open DNS dataset published by the Los Alamos National Laboratory. 10

A Constrained Fuzzy Knowledge-Based System for the Management of Container Yard Operations

The management of container yard operations is considered by yard operators to be a very challenging task due to the many uncertainties inherent in such operations. The storage of the containers is one of those operations that require proper management for the efficient utilisation of the yard, requiring rapid retrieval time and a minimum number of re-handlings. The main challenge is when containers of a different size, type, or weight need to be stored in a yard that holds a number of pre-existing containers. This challenge becomes even more complex when the date and time for the departure of the containers are unknown, as is the case when the container is collected by a third-party logistics company without any prior notice being given. The aim of this study is to develop a new system for the management of container yard operations that takes into consideration a number of factors and constraints that occur in a real-life situation. One of these factors is the duration of stay for the topmost containers of each stack, when the containers are stored. Because the duration of stay for containers in a yard varies dynamically over time, an ‘ON/OFF’ strategy is proposed to activate/deactivate the duration of stay factor constraint if the length of stay for these containers varies significantly over time. A number of tools and techniques are utilised for developing the proposed system including: discrete event simulation for the modelling of container storage and retrieval operations, a fuzzy knowledge-based model for the stack allocation of containers, and a heuristic algorithm called ‘neighbourhood’ for the container retrieval operation. Results show that by adopting the proposed ‘ON/OFF’ strategy, 5% of the number of re-handlings, 2.5% of the total retrieval time, 6.6% of the total re-handling time and 42% of the average waiting time per truck are reduced.

Agent based model for complex flow shop manufacturing systems with customer-related production disruptions

There have been number of frameworks and approaches proposed for the study of complex flow shop manufacturing systems. However, due to the continuous effects of customer disruptions such as cancellation, change in sequence and due time on flow shop production, there arise need to develop an adaptive system to respond to the effect of such disruptions. In this ongoing PhD work, we develop a Framework and Agent-Based Model to simulate flow shop production line and investigate the disruptions consequences and recovery strategy. An experimental case study using an Original Equipment Manufacturer (OEM) factory for automotive parts is adopted to verify and validate the proposed system. The new understanding presented in this work offers informed decision-making policies for manufacturing production activities.