Tamer F. Abdelmaguid

In this paper, traditional process design rules for deep drawing of box shapes are used to develop constraints within a dynamic programming (DP) approach to generate a set of alternative optimized process plans that minimize the number of drawing stages and heat treatments. The DP approach is capable of scanning a wide range of different alternative values for process parameters which allows for generating more rational process plans compared to a traditional rule-based (RB) approach. The validation of the process parameters for the multi-stage deep drawing of box-shaped parts are investigated using the finite element method with full account of formability limits. This careful finite element analysis guides the selection of the appropriate optimized process plan leading to the least severity of deformation. Two case studies are presented which suggest that the combination of DP and finite element validation could be a valuable, reliable and a more rational computer aided process planning (CAPP) approach to this complicated problem.

Flexible job shops are characterized by the existence of alternative –not necessarily identical- machines, which permits parts to have different alternative routes in the shop. In a dynamic scheduling situation in which jobs continuously arrive over time, part routing rules are needed to deal with such routing flexibility as much as sequencing rules which define the order by which parts are admitted to machines. This paper considers a dynamic flexible job shop scheduling in which there are sequence-dependent setup times and machines are prone to failure. Three new routing rules are proposed and compared with another two rules from the literature through simulation experiments.

The process design of turning operations involves the determination of suitable values for the different process parameters such as cutting speed, feed rate and depth of cut, with the objective of optimizing the economics of the process. This paper provides a new mathematical model that takes into consideration costs of quality and tool wear compensation in the determination of the optimal cutting conditions for the turning operation of multiple similar parts. Based on that model, an optimization approach is proposed. An illustrative example is presented to demonstrate the characteristics of the optimal solutions.

Purpose: To present a concise literature review on the optimization techniques used for the single stage and multistage deep drawing process, and to identify directions for future research. A perspective on a comprehensive optimized computer aided process planning is provided for multistage deep drawing processes. This is an integrated rule base/dynamic programming/finite element approach that minimizes the total number of stages and heat treatment needed.
Design/methodology/approach: Relevant research is classified according to the major process parameters and the optimization techniques used. Main features and major outcome of the applications are presented.
Findings: There is a lack in the literature in providing a comprehensive approach for optimizing the multistage deep drawing process.
Research limitations/implications: Directions for future research towards integrative models for optimizing the multistage deep drawing process that take into consideration economic as well as operational objectives are identified.
Originality/value: This paper provides a guide for researchers in the field of deep drawing and identifies some directions for future research that can be pursued. It also gives some insights to practitioners in that field on how integrated models can improve the economics and the quality of the process planning decisions for multistage deep drawing.

Metal Working Fluids (MWFs) are known to improve machining performance, yet they have poor ecological and health side effects. Therefore, eliminating or reducing their quantity in machining operations is crucial. The Minimum Quantity Lubrication (MQL) is a new sustainable manufacturing technique that can achieve signifi cant reduction in the MWF used compared to traditional wet fl ooding, while maintaining high performance. This paper provides an experimental investigation to study the characteristics of the fl ow of the MWF in a turning process utilising the MQL technique and to analyse the effect of the WMF’s behaviour on cutting force, surface roughness and tool wear. Several experiments are conducted considering different workpiece materials and cutting parameters. Based on the experimental results, the Response Surface Methodology (RSM) is used to provide mathematical models that relate the main cutting parameters, the workpiece material properties and the MWF viscosity and fl ow rate with cutting force, surface roughness and tool wear. A non-linear, multi-objective optimisation problem is formulated for a case study with the objectives of minimising production time and maximising tool life. It is demonstrated that the second version of the Non-dominated Sorting Genetic Algorithm (NSGA-II) is an effi cient technique for generating a set of well-spread Pareto front solutions, which helps in determining the most appropriate values of MQL and cutting parameters.

In this paper, the problem of minimizing the number of drawing stages and heat treatments needed for the multi-stage deep drawing of cylindrical shells is addressed. A conventional rule-based computer-aided process planning (CAPP) is utilized within a dynamic programming approach to generate a set of alternative optimal process plans. A finite element analysis is carried out to guide the selection of the appropriate process parameters and to verify the applicability of the selected process plans. A case study is presented to demonstrate the developed approach, and the results suggest that the combination of rulebased CAPP, dynamic programming and finite element validation could be a valuable, reliable and quick computer aided process planning approach to this complicated problem.

Due to the NP-hardness of the job shop scheduling problem (JSP), many heuristic approaches have been proposed; among them is the genetic algorithm (GA). In the literature, there are eight different GA representations for the JSP; each one aims to provide subtle environment through which the GA’s reproduction and mutation operators would succeed in finding near optimal solutions in small computational time. This paper provides a computational study to compare the performance of the GA under six different representations.

This paper addresses the integrated production-inventory-distribution decisions in a three-echelon supply chain (SC) network. The studied SC network is composed of a raw material processor that supplies different types of processed raw materials to a set of manufacturing facilities which in turn deliver their end products to a set of retailers facing external demand from end customers. A mixed integer linear programming model is provided with the objective of minimizing the overall production, inventory and distribution costs. Balanced and synchronized ordering policies are applied to the studied problem and their impacts on the different cost elements are assessed.

This paper extends the traditional job shop scheduling problem (JSP) by incorporating the routing and scheduling decisions of the material handling equipment. It provides a generic definition and a mixed integer linear programming model for the problem considering the case of heterogeneous multiple-load material handling equipment. A constructive heuristic is developed for solving the problem. This heuristic is based on the wellknown Giffler and Thompson’s algorithm for the JSP with modifications that account for the routing decisions of the material handling equipment and their effect on the start times of the manufacturing operations. Different dispatching rules are integrated into the heuristic, and experiments are conducted to study their effect on the makespan along with the determination of the computational time requirements of the developed heuristic.

We study an inventory-routing problem in which multiperiod inventory holding, backlogging, and vehicle routing decisions are to be taken for a set of customers who receive units of a single item from a depot with infinite supply. We consider a case in which the demand at each customer is deterministic and relatively small compared to the vehicle capacity, and the customers are located closely such that a consolidated shipping strategy is appropriate. We develop constructive and improvement heuristics to obtain an approximate solution for this NP-hard problem and demonstrate their effectiveness through computational experiments.

In the literature of the combinatorial optimization problems, it is a commonplace to find more than one mathematical model for the same problem. The significance of a model may be measured in terms of the efficiency of the solution algorithms that can be built upon it. The purpose of this article is to present a new network model for the well known combinatorial optimization problem – the job shop scheduling problem. The new network model has similar structure as the disjunctive graph model except that it uses permutations of jobs as decision variables instead of the binary decision variables associated with the disjunctive arcs. To assess the significance of the new model, the performances of exact branchand- bound algorithmic implementations that are based on both the new model and the disjunctive graph model are compared.

We study the integrated inventory distribution problem which is concerned with multiperiod inventory holding, backlogging, and vehicle routing decisions for a set of customers who receive units of a single item from a depot with infinite supply. We consider an environment in which the demand at each customer is deterministic and relatively small compared to the vehicle capacity, and the customers are located closely such that a consolidated shipping strategy is appropriate. We develop a constructive heuristic to obtain an approximate solution for this NP-hard problem and demonstrate its effectiveness through computational experiments.

Material handling tasks in the job shops have to be coordinated with manufacturing operations in order to efficiently utilize the production resources and to reduce the accumulation of work-in-process inventory. This paper extends the traditional job shop scheduling problem by incorporating the scheduling of the material handling tasks. It provides a generic definition and a network model for the integrated scheduling problem, which can be customized to both automated systems, such as the flexible manufacturing systems, and manually operated ones. Based on the network model, an exact branch-and-bound algorithm is developed for the objective of minimizing the maximum completion time of all manufacturing and material handling tasks.

Deep Drawing is an important sheet metal forming process that appears in many industries. Like other sheet metal forming processes, deep drawing is characterized by very complicated deformation affected by the process parameters which include die and punch shapes, blank shape, the blank holding force, material properties and lubrication. Due to the complex functions that describe the process output as related to these parameters, the optimization in the deep drawing process is a challenging task. Recently, there has been a growing interest in the research community to apply optimization techniques to this process. In this paper we present a comprehensive review on the optimization techniques used and identify directions for future research.

The job shop scheduling problem (JSP) arises in low-volume production systems in which products are made to order. In such production systems, jobs usually differ considerably in their processing sequences and times. Solving the JSP is particularly important for the efficient utilization of production resources and for satisfying due dates. However, the JSP is fairly complex and known to be NP-hard. This complexity demands continuous efforts for developing efficient solution approaches. This paper suggests a new network model for the JSP which can be used in future exact and approximate algorithmic developments. The new network model is experimentally compared with the well known disjunctive graph model through an exact branch-and-bound algorithmic implementation. The experimental results indicate that there are some merits in the new model that may guide a search algorithm to quickly reach an optimal solution.

We introduce a new genetic algorithm (GA) approach for the integrated inventory distribution problem (IIDP). We present the developed genetic representation and use a randomized version of a previously developed construction heuristic to generate the initial random population. We design suitable crossover and mutation operators for the GA improvement phase. The comparison of results shows the significance of the designed GA over the construction heuristic and demonstrates the capability of reaching solutions within 20% of the optimum on sets of randomly generated test problems.

In this paper, the problem of simultaneous scheduling of machines and identical automated guided vehicles (AGVs) in flexible manufacturing systems is addressed with the objective of minimizing the makespan. This problem is composed of two interrelated decision problems: the scheduling of machines, and the scheduling of AGVs. Both problems are known to be NP-complete, resulting in a more complicated NP-complete problem when they are considered simultaneously. A new hybrid Genetic-algorithm/heuristic coding scheme is developed for the studied problem. The developed coding scheme is combined with a set of genetic algorithm (GA) operators selected from the literature of the applications of GAs to the scheduling problems. The algorithm is applied to a set of 82 test problems, which was constructed by other researchers, and the comparison of the results indicates the superior performance of the developed coding.

Over the past 10 years, operating expenses for demand responsive transit (DRT) have more than doubled as demand for this mandated service has expanded. The DRT systems that we studied consist of dial-a-ride programs that transit agencies use for point-to-point pick-up and delivery of the elderly and handicapped. Many advanced technologies and management practices have been proposed and implemented to improve the efficiency of the service; but, evidence for the effectiveness of these actions has been based upon projections or small pilot studies. We present the results of a nationwide study involving 62 transit agencies. Our analysis indicates that the use of paratransit computer aided dispatching (CAD) system and agency service delivery provide a productivity benefit while the use of financial incentives has a detrimental impact on productivity. Also, the use of advanced communication technology has a beneficial impact on operating cost while the use of financial incentives has a detrimental impact.