Asghar Moeini

A robust posterior preference decision-making approach to multiple response process design

Setting of process variables to meet required specification of quality characteristics is one of the important problems in quality control processes. In general, most industrial and production systems are dealing with several different responses and the problem is to simultaneously optimise these responses. To obtain the most satisfactory solution, a decision-makers (DM) preference on the trade-offs among the quality characteristics should be incorporated into the optimisation procedure. This study suggests a robust posterior preference articulation approach based on a non-dominated sorting genetic algorithm (NSGA-II) to optimise multiple responses. In order to minimise the variation in deviation of responses from targets, maximum and sum of deviations are taken into consideration. To investigate the performance of the suggested approach, a computational analysis on a real world chemical engineering example is performed. Results show the superiority of the proposed approach compared to the existing techniques.

Dependency-aware software release planning

The existing software release planning models aim to find a subset of software requirements with the highest value on the assumption that the value of a selected subset of requirements equals to the Accumulated Value (AV) of that subset. This assumption however, does not hold due to the Value-related Dependencies among software requirements. To address this, we have formulated an integer programming model for software release planning that finds a subset of software requirements with the highest Overall Value (OV) where overall value of a selected subset of requirements captures the impacts of value-related dependencies on the value of that subset. We have demonstrated through several simulations that maximizing the accumulated value of a selected subset of requirements may conflict with maximizing the overall value of that subset.

An Integer Linear Programming Model for Binary Knapsack Problem with Dependent Item Values

Binary Knapsack Problem (BKP) is to select a subset of items with the highest value while keeping the size within the capacity of the knapsack. This paper presents an Integer Linear Programming (ILP) model for a variation of BKP where the value of an item may depend on presence or absence of other items in the knapsack. Strengths of such Value-Related Dependencies are assumed to be imprecise and hard to specify. To capture this imprecision, we have proposed modeling value-related dependencies using fuzzy graphs and their algebraic structure. We have demonstrated through simulations that our proposed ILP model is scalable to large number of items.

i-DEMATEL method for integrated manufacturing technology selection

Purpose – Computer integrated manufacturing (CIM) refers the manufacturing concept based on the use of computers to control and exchange information for the entire production process. As a result, manufacturing can be faster and less error-prone. However, managing and implementing technologies in the CIM environment are challenging processes for managements and manufacturing organizations. These processes become complex and tedious when one is dealing with many decision parameters. The paper aims to discuss these issues. Design/methodology/approach – This study reports an Interval Decision Making Trial and Evaluation Laboratory (i-DEMATEL) method for evaluating and selecting the CIM technologies that takes into account management objectives. This method can relieve the limitation of the relationship matrix about the assumption of the symmetrical relationship. As a result, it can solve complicated relationship structure problems. Findings – Based on a survey on the current technologies in manufacturing ins...

A framework for stochastic scheduling of two-machine robotic rework cells with in-process inspection system

Scheduling of two-machine robotic rework cells with in-process inspection scenario.Converting multiple-sensor systems into single-sensor systems.Deriving cycle times of two different cycles based on a geometric distribution.A mechanism of finding the maximum expected throughput for in-process inspection.Focusing on the domain of the problem for three various kinds of pickup scenarios. This study is focused on the domain of a two-machine robotic cell scheduling problem for three various kinds of pickup scenarios: free, interval, and no-wait pickup scenarios. Particularly, we propose the first analytical method for minimizing the partial cycle time of such a cell with a PC-based automatic inspection system to make the problem more realistic. It is assumed that parts must be inspected in one of the production machines, and this may result in a rework process. The stochastic nature of the rework process prevents us from applying existing deterministic solution methods for the scheduling problem. This study aims to develop a framework for an in-line inspection of identical parts using multiple contact/non-contact sensors. Initially, we convert a multiple-sensor inspection system into a single-sensor inspection system. Then, the expected sequence times of two different cycles are derived based on a geometric distribution, and finally the maximum expected throughput is pursued for each individual case with free pickup scenario. Results are also extended for the interval and no-wait pick up scenarios as two well-solved classes of the scheduling problem. The waiting time of the part at each machine after finishing its operation is bounded within a fixed time interval in cells with interval pickup scenario, whereas the part is processed from the input conveyor to the output conveyor without any interruption on machines in cells with no-wait pickup scenario. We show a simple approach for solving these two scenarios of the problem which are common in practice.

A cross-entropy method for optimising robotic automated storage and retrieval systems

In this paper, we consider a robotic automated storage and retrieval system (AS/RS) where a Cartesian robot picks and palletises items onto a mixed pallet for any order. This robotic AS/RS not only retrieves orders in an optimal sequence, but also creates an optimal store ready pallet of any order. Adapting the Travelling Salesman Problem to warehousing, the decision to be made includes finding the optimal sequence of orders, and optimal sequence of items inside each order, that jointly minimise total travel times. In the first phase, as a control problem, we develop an avoidance strategy for the robot (or automatic stacker crane) movement sequence. This approach detects the collision occurrence causing unsafe handling of hazardous items and prevents the occurrence of it by a collision-free robot movement sequence. Due to the complexity of the problem, the second phase is attacked by a Cross-Entropy (CE) method. To evaluate the performance of the CE method, a computational analysis is performed over various test problems. The results obtained from the CE method are compared to those of the optimal solutions obtained using CPLEX. The results indicate high performance of the solution procedure to solve the sequencing problem of robotic AS/RSs.

An economical acceptance---rejection algorithm for uniform random variate generation over constrained simplexes

This paper develops an algorithm for uniform random generation over a constrained simplex, which is the intersection of a standard simplex and a given set. Uniform sampling from constrained simplexes has numerous applications in different fields, such as portfolio optimization, stochastic multi-criteria decision analysis, experimental design with mixtures and decision problems involving discrete joint distributions with imprecise probabilities. The proposed algorithm is developed by combining the acceptance–rejection and conditional methods along with the use of optimization tools. The acceptance rate of the algorithm is analytically compared to that of a crude acceptance–rejection algorithm, which generates points over the simplex and then rejects any points falling outside the intersecting set. Finally, using convex optimization, the setup phase of the algorithm is detailed for the special cases where the intersecting set is a general convex set, a convex set defined by a finite number of convex constraints or a polyhedron.

Identification of unidentified equality constraints for integer programming problems

Characterising the smallest dimension polytope containing all integer solutions of an integer programming problem can be a very challenging task. Frequently, this task is facilitated by identifying linear equality constraints that all integer solutions must satisfy. Typically, some of these constraints are readily available but others need to be discovered by more technical means. This paper develops a method to assist modellers to obtain such equality constraints. Note that the set of new equality constraints is not unique, and the proposed method generates a set of these new equality constraints for a sufficiently large dimension of the underlying problem. These generated constraints may be of a form that is easily extended for general case of the underlying problem, or they may be in a more complicated form where a generalisable pattern is difficult to identify. For the latter case, a new mixed-integer program is developed to detect a pattern-recognisable constraints. Furthermore, this mixed-integer program allows modellers to check if there is a new constraint satisfying specific criteria, such as only permitting coefficients to be 1, 0, and −1, or placing a limit on the number of non-zero coefficients. In order to illustrate the proposed method, a set of new equality constraints to supplement a previously published “Base polytope” are derived. Subsequently, exploiting these results, some techniques are proposed to tighten integer programming problems. Finally, relaxations of widely used TSP formulations are compared against one another and strengthened with help of the newly discovered equality constraints.

Utilisation of pruned Pareto-optimal solutions in the multi objective optimisation: an application to system redundancy allocation problems

Multi-objective optimisation problems normally have not one but a set of solutions, which are called Pareto-optimal solutions or non-dominated solutions. Once a Pareto-optimal set has been obtained, the decision-maker faces the challenge of analysing a potentially large set of solutions. Selecting one solution over others can be quite a challenging task because the Pareto set can contain an unmanageable number of solutions. This process is called post-Pareto optimality analysis. To deal with this difficulty, this study proposes the approach that promisingly prunes the Pareto optimal set. In this study, the newly developed approach uses Monte-Carlo simulation taking into account the decision maker’s prioritisation to prune the Pareto optimal set. Then, the central weight vector, the optimal frequently appearance index and upper and lower bands of weights are enclosed to each solution to facilitate selecting a final solution. The well-known redundancy allocation problem is used to show the performance of the proposed method.