Search results

The purpose of this paper is to develop efficient heuristics for determining the route design and inventory management of inbound parts which are delivered for manufacturing, assembly, or distribution operations and for which there is limited storage space. The shipment frequencies and quantities are coordinated with the available storage space and the vehicle capacities.

Two heuristics that generate near optimal solutions are proposed. The first heuristic has an iterative routing phase that maximizes the savings realized by grouping suppliers together into routes without considering the storage constraint and then calculates the pickup frequencies in the second phase to accommodate the storage constraint. The second heuristic iteratively executes a routing and a pickup frequency phase that both account for the storage constraint. A lower bound is also developed as a benchmark for the heuristic solutions.

Near optimal solutions can be obtained in a reasonable amount of time by utilizing information about the amount of storage space in the route design process.

The traditional emphasis on high vehicle utilization in transportation management can lead to inefficient logistics operations by carrying excess inventory or by using longer, less efficient routes. Route formation and pickup quantities at the suppliers are simultaneously considered, as both are important from a logistics standpoint and are interrelated decisions.

The two proposed heuristics dynamically define seed sets such that the solutions to the capacitated concentrator location problem (CCLP) are accurately estimated. This increased accuracy helps in generating near‐optimal solutions in a practical amount of computing time.

The purpose of this study was to create an air movement operations planning model to rapidly generate air mission request (AMR) assignment and routing courses of action (COA) in order to minimize unsupported AMRs, aircraft utilization and routing cost.

In this paper, the US Army Aviation air movement operations planning problem is modeled as a mixed integer linear program (MILP) as an extension of the dial-a-ride problem (DARP). The paper also introduces a heuristic as an extension of a single-vehicle DARP demand insertion algorithm to generate feasible solutions in a tactically useful time period.

The MILP model generates optimal solutions for small problems (low numbers of AMRs and small helicopter fleets). The heuristic generates near-optimal feasible solutions for problems of various sizes (up to 100 AMRs and 10 helicopter team fleet size) in near real time.

Due to the inability of the MILP to produce optimal solutions for mid- and large-sized problems, this research is limited in commenting on the heuristic solution quality beyond the numerical experimentation. Additionally, the authors make several simplifying assumptions to generalize the average performance and capabilities of aircraft throughout a flight.

This research is the first to solve the US Army Aviation air movement operations planning problem via a single formulation that incorporates multiple refuel nodes, minimization of unsupported demand by priority level, demand time windows, aircraft team utilization penalties, aircraft team time windows and maximum duration and passenger ride time limits.

Inventory control models deal with production planning in order to minimize inventory and shortage cost, while cellular manufacturing analysis mainly addresses how machines should be grouped and parts be produced. A mathematical programming model is developed using an integrated approach for production and inventory planning in a cellular manufacturing environment. The mathematical programming model minimizes inter‐cell material handling cost, finished‐good inventory cost and system set‐up cost. The non‐linear mixed integer programming model cannot be directly solved for real size practical problems due to its NP‐complexity. A decomposition‐based heuristic algorithm was then developed to efficiently solve the integrated planning and control problem. Numerical examples are provided to test and illustrate the model and the solution method presented in this paper.

The purpose of this paper is to develop an effective and efficient approach to exploit meta‐heuristic in particle swarm optimization (PSO) for the job shop scheduling problem (JSP), a class of NP‐hard optimization problems. The approach is to be built on a PSO with multiple independent swarms. PSO was inspired by bird flocking and animal social behaviors. The particles operate collectively like a swarm that flies through the hyperdimensional space to search for possible optimal solutions. The behavior of the particles is influenced by their tendency to learn from their personal past experience and from the success of their peers to adjust their flying speed and direction. Research in fusing the multiple‐swarm concept into PSO is well‐established in solving single objective optimization problems and multimodal problems.

This study examines the optimization of the JSP via a search space division scheme and use of the meta‐heuristic method of PSO by assigning each machine in a JSP an independent swarm of particles. The use of multiple swarms in PSO is motivated by the idea of “divide and conquer” to reduce the computational complexity incurred through solving a NP‐hard combinatorial optimization problem. The resulted design, JSP/PSO algorithm, fully exploits the computing power presented by the multiple‐swarm PSO.

Simulation experiments show that the proposed JSP/PSO algorithm can effectively solve the JSP problems from small to median size. If certain mechanism of information sharing between swarms can be incorporated, it is believed that the new design could offer even more computing power to tackle the large‐sized problems.

The proposed JSP/PSO algorithm is effective in solving JSPs. The proposed algorithm shows considerable promise when searching the space of non‐delay schedules. It demands relatively lower number of function evaluations compared to other state‐of‐the‐art. The drawback to the JSP/PSO is that the GT scheduling adopted is too computationally expensive. Future works will address this concern.

Rabbit chase (RC) is used as one of the most effective techniques in manufacturing systems, as such systems have high level of adaptability and increased productivity in addition to providing uniform workload balancing and skill improving environment. In assembly systems, RC inspires the development of walking worker assembly line (WWAL). On the other hand, U-type assembly lines (UALs) may provide higher worker utilization, lower space requirement and more convenient internal logistics when compared to straight assembly lines. In this context, this study aims to improve assembly line performance by generating RC cycles on WWAL with respect to task assignment characteristics of UAL within reasonable walking distance and space requirement. Therefore, a novel line configuration, namely, segmented rabbit chase-oriented U-type assembly line (SRCUAL), emerges.

The mathematical programming approach treats SRCUAL balancing problem in a hierarchical manner to decrease computational burden. Firstly, segments are generated via the first linear programming model in the solution approach for balancing SRCUALs to minimize total number of workers. Then, stations are determined within each segment for forward and backward sections separately using two different pre-emptive goal programming models. Moreover, three heuristics are developed to provide solution quality with computational efficiency.

The proposed mathematical programming approach is applied to the light-emitting diode (LED) luminaire assembly section of a manufacturing company. The adaptation of SRCUAL decreased the number of workers by 15.4% and the space requirement by 17.7% for LED luminaire assembly system when compared to UAL. Moreover, satisfactory results for the proposed heuristics were obtained in terms of deviation from lower bound, especially for SRCUAL heuristics I and II. Moreover, the results indicate that the integration of RC not only decreased the number of workers in 40.28% (29 instances) of test problems in U-lines, but also yielded less number of buffer points (48.48%) with lower workload deviation (75%) among workers in terms of coefficient of variation.

This study provides convenience for capacity management (assessing capacity and adjusting capacity by changing the number of workers) for industrial SRCUAL applications. Meanwhile, SRCUAL applications give the opportunity to increase the capacity for a product or transfer the saved capacity to the assembly of other products. As it is possible to provide one-piece flow with equal workloads via walking workers, SRCUAL has the potential for quick realization of defects and better lead time performance.

To the best of the authors’ knowledge, forward–backward task assignments in U-type lines have not been adapted to WWALs. Moreover, as workers travel overall the line in WWALs, walking time increases drastically. Addressing this research gap and limitation, the main innovative aspect of this study can be considered as the proposal of a new line design (i.e. SRCUAL) which is sourced from the hybridization of UALs and WWAL as well as the segmentation of the line with RC cycles. The superiority of SRCUAL over WWAL and UAL was also discussed. Moreover, operating systematic for SRCUAL was devised. As for methodical aspect, this study is the first attempt to solve the balancing problem for SRCUAL design.

The purpose of this paper is to evaluate two different asset selection methodologies and further examine these by forming optimal portfolios.

This paper deals with the problem of portfolio formation, broadly in two steps: asset selection and asset allocation by using the two different approaches for the first step and then well‐known mean variance portfolio optimization. In addition, the resulting portfolios are compared using Sharpe ratio.

The empirical observations prove the applicability of the methodology adopted in the research design, ordered weighted averaging (OWA)‐heuristic algorithm gives us a better portfolio from the sample observations. Also the asset selection procedures adopted in the research proves to be of help when an investor has to narrow down the number of assets to invest in.

The analysis provides two different methodologies for portfolio formation – though the asset allocation is based on the mean variance portfolio optimization, the asset selection methods adopted provide a systematic approach to select the efficient securities.

This paper shows that OWA can be used to decide the order of inputs for the heuristic algorithm. Also an attempt is made to use data envelopment analysis to find a solution to the problem of portfolio formation.

The purpose of this paper is to realize the integrated optimization of process planning and scheduling in printed circuit board assembly (PCBA).

Logical and numerical contour matrix is used to describe the constituent of component and machine for different PCBA processes on the basis of polychromatic sets (PS) theory, and a PS model of PCBA is built. A hybrid genetic algorithm (GA) is developed to optimize the component allocation, PCB assignment and assembly sequence simultaneously.

Integration of PCBA process planning and scheduling (PCBAPPS) can bridge the gap between design and manufacturing to guarantee the assembly quality and improve the production efficiency. However, PCBAPPS have to search for the optimal result in their own vast solution space. They are complex combinatorial optimization problems. The optimization of PCBAPPS constructs a unified solution space which includes two sub‐solution space stated above. In this paper, dynamic optimization of PCBAPPS is implemented and the solution efficiency is improved.

PS model holds unified standard form on the basis of logical contour and numerical matrix. It is adopted to describe the static structure and dynamic characteristic of PCBA system and combine with GA to solve the integrated optimization problem of PCBAPPS effectively and dynamically.

The aim of this work is to show how evolutionary computation can improve the quality of 3D‐FE mesh that is a crucial task for field evaluations using 3‐D FEM analysis.

The evolutionary approach used for optimizing 3D mesh generation is based on the bacterial chemotaxis algorithm (BCA). The objective function corresponds to the virtual bacterium best habitat, and the motion rules followed by each virtual bacterium are inspired to the natural behaviour of bacteria in real habitat.

The obtained results show that the present approach returns good accuracy performances with low‐computational costs.

The procedure is robust and converges for all the practical cases examined for validation.

The adoption of a correct optimization algorithm is fundamental to obtain good performances in terms of robustness of the results and the low‐computational costs. In this sense, the BCA is a valid instrument for improving the quality of 3D‐FE mesh.

The success of any organization in a knowledge-based economy depends on effective knowledge transferring and then proper use of the transferred knowledge. As is known, optimizing the knowledge transferring costs in a product portfolio plays an important role in improving productivity, competitive advantage and profitability of any organization. Therefore, this paper aims to determine an optimal product portfolio by minimizing the konlwedge transferring costs.

Here, a fuzzy binary linear programming model is used to select an optimal product portfolio. The model is capable of considering the knowledge transferring costs while taking into account the human-hours constraints for each product by a fuzzy approach. Using fuzzy ranking functions, a reasonable solution of the model can be achieved by classical or metaheuristic algorithms.

Numerical experiments indicate that the proposed fuzzy model is practically effective.

The contributions of this work essentially consist of considering knowledge transferring costs in selecting an optimal product portfolio and using the fuzzy data which make the model more realistic.

This study aims to present a novel optimization technique to solve the combined economic emission dispatch (CEED) problem considering transmission losses, valve-point loading effects, ramp rate limits and prohibited operating zones. This is one of the most complex optimization problems concerning power systems.

The proposed algorithm has been called advanced particle swarm optimization (APSO) and was created by applying several innovative modifications to the classic PSO algorithm. APSO performance was tested on four test systems having 14, 40, 54 and 120 generators.

The suggested modifications have improved the accuracy, convergence rate, robustness and effectiveness of the algorithm, which has produced high-quality solutions for the CEED problem.

The results obtained by APSO were compared with those of several other techniques, and the effectiveness and superiority of the proposed algorithm was demonstrated. Also, because of its superlative characteristics, APSO can be applied to many other engineering optimization problems. Moreover, the suggested modifications can be easily used in other population-based optimization algorithms to improve their performance.