Tamer F. Abdelmaguid

This paper presents a novel, multi-objective scatter search algorithm (MOSS) for a bi-objective, dynamic, multiprocessor open-shop scheduling problem (Bi-DMOSP). The considered objectives are the minimization of the maximum completion time (makespan) and the minimization of the mean weighted flow time. Both are particularly important for improving machines’ utilization and customer satisfaction level in maintenance and healthcare diagnostic systems, in which the studied Bi-DMOSP is mostly encountered. Since the studied problem is NP-hard for both objectives, fast algorithms are needed to fulfill the requirements of real-life circumstances. Previous attempts have included the development of an exact algorithm and two metaheuristic approaches based on the non-dominated sorting genetic algorithm (NSGA-II) and the multi-objective gray wolf optimizer (MOGWO). The exact algorithm is limited to small-sized instances; meanwhile, NSGA-II was found to produce better results compared to MOGWO in both small- and large-sized test instances. The proposed MOSS in this paper attempts to provide more efficient non-dominated solutions for the studied Bi-DMOSP. This is achievable via its hybridization with a novel, bi-objective tabu search approach that utilizes a set of efficient neighborhood search functions. Parameter tuning experiments are conducted first using a subset of small-sized benchmark instances for which the optimal Pareto front solutions are known. Then, detailed computational experiments on small- and large-sized instances are conducted. Comparisons with the previously developed NSGA-II metaheuristic demonstrate the superiority of the proposed MOSS approach for small-sized instances. For large-sized instances, it proves its capability of producing competitive results for instances with low and medium density.

Smart grids that integrate household renewable energy sources and share information with households can help create and maintain a smarter data-driven environment. Within this environment, flexible home energy management policies that minimize household energy costs can be adopted. This paper considers a smart home with a renewable energy source that favors satisfying its energy needs at minimum cost. This is achievable by smartly scheduling the use of its domestic appliances to match a given energy grid tariff. Focusing on the case of Egypt in which an inclining block rate (IBR) tariff is imposed, this paper fills a gap in the literature regarding the load scheduling models aiming to minimize energy cost at the household level whenever such a tariff exists. A new mixed integer quadratic programming (MIQP) model is formulated for this scheduling problem, considering the adopted net metering system with installed domestic photovoltaic (PV) systems in Egypt. The model generates the optimal household load schedule and the optimal amounts of energy to exchange with the grid while considering all the system and consumer utility constraints. To assess the applicability of the proposed model, a survey is conducted to identify the diversity and characteristics of using the electrical appliances by the Egyptian households. Based on the collected survey results, the effectiveness of the proposed MIQP model is investigated. Results confirm the effectiveness of the proposed model to minimize energy cost for different categories of the Egyptian households.

Even though the practice of Industrial Engineering (IE) in Egypt is older than the great pyramid of Giza, many dedicated IE educational programs are relatively new in the Egyptian higher educational system when compared to other more established engineering disciplines. However, it has witnessed rapid growth in the last few decades to match the increasing demand by employers from both manufacturing and service sectors alike. This goes in parallel with the increase in scientific publications and graduate research theses nationwide. This chapter summarizes the history and development of the field of IE in Egypt. It covers the progress in the number of IE educational programs at both the undergraduate and graduate levels in Egyptian universities. In addition, the recent growth in relevant scientific output, as measured by the number of IE-relevant publications is presented and broken down in terms of both the area of IE research and contributing institution.

Hybrid flowshops are a special type of manufacturing systems, in which a stage may contain identical or unrelated parallel machines. This paper deals with a more practical approach for lot streaming hybrid flowshop in which the sublot sizes of jobs can vary from one stage to the next according to machines `speed. Two models of mixed-integer nonlinear programming are developed to minimise the make-span of two different hybrid flowshop systems. The first model deals with unrelated parallel machines with eligibility, independent setup time, and variable sublot sizes. The second model is a special case of the hybrid flowshop as it consists of multi-stages comprising one machine at the stages preceding the final stage, while the final stage includes unrelated parallel machines. The first model was studied and the data gathered were analyzed using ANOVA test to evaluate the factors’ effect on system. The factors are number of jobs, maximum number of batches, setup time, and machine’s configuration. The analysis revealed that all the factors were effective. The second model was compared to benchmarking published paper and it gets better results.

The total maintenance cost can be reduced by grouping maintenance actions of several components. This paper contributes to the existing literature by introducing an enhanced maintenance optimization approach that considers the effect of maintenance crew loading due to grouping on the maintenance decisions of multi-component systems. A modified mathematical model is firstly developed for evaluating the failure probability function of each component, the remaining useful life and the maintenance cost. Economic and structural dependencies are taken into consideration. A simulation is secondly implemented to provide estimates of the associated costs with changes in the decision variables. Using the simulation model, an optimization approach based on a genetic algorithm is thirdly developed to minimize the long-term mean maintenance cost per unit time. Computational results show that the proposed maintenance optimization approach provides considerable maintenance cost savings and emphasizes the importance of considering the effect of maintenance crew constraints in maintenance scheduling.

This paper addresses a bi-objective dynamic multiprocessor open shop scheduling problem in which the simultaneous objectives of minimizing both the mean weighted flow time and the makespan are considered. This problem is commonly encountered in maintenance and healthcare diagnostic systems. Since it is NP-hard for both objectives, efficient heuristics are needed to quickly generate a set of non-dominated solutions that a decision maker would choose from. For this sake, two metaheuristic approaches based on the non-dominated sorting genetic algorithm (NSGA-II) and the multi-objective grey wolf optimizer (MOGWO) are developed in this paper. Both metaheuristics are hybridized with simulated annealing (SA) local search. Parameter tuning computational experiments are conducted first on a set of 30 small instances from the literature for which Pareto optimal solutions are known. Then, computational experiments on large randomly generated instances are conducted. Computational results for small instances show that the NSGA-II is capable of generating non-dominated solutions that are very close to the optimal Pareto front. Results also reveal that the performance of the NSGA-II is better in most of the cases compared to the MOGWO under different settings of the studied problem for both small and large instances. However, for large instances with large number of workstations and jobs, low loading level and high percentage of busy machines at the beginning of the schedule, the difference in performance between both metaheuristics is minor.

Frost affects horticultural plants considerably and result in multi-dimensional harms: from economic losses to psychological problems for people involved in horticulture. As a result, prevention of frost in horticulture is of utter most importance for many countries. In this paper, first we propose a novel green energy-integrated solution, a hybrid renewable energy-based system involving active heaters, for this less studied, but very important problem. We then develop a multi-objective robust optimization-based formulation for optimization of the proposed system in order to (i) optimize the distribution of a given number of active heaters in a given large-scale orchard to optimally heat the orchard by the proposed frost prevention system and (ii) optimize the layout of the thermal energy distribution network to minimize the total pipe length (which is directly related to the installation cost and the cost of energy losses during energy transfer). Finally, the resulting optimization problem is approximated using a discretization scheme. A case study is provided to give an idea of the potential savings using the proposed optimization method compared to the result from a heuristic-based design, which showed a 24.13% reduction in the total pipe length and a 54.29% increase in optimal heating. Compared to current active frost prevention methods, the proposed hybrid green energy system for frost protection is a cleaner, environmentally friendly and potentially cost-effective solution.

An important element in the integration of the fourth industrial revolution is the development of efficient algorithms to deal with dynamic scheduling problems. In dynamic scheduling, jobs can be admitted during the execution of a given schedule, which necessitates appropriately planned rescheduling decisions for maintaining a high level of performance. In this paper, a dynamic case of the multiprocessor open shop scheduling problem is addressed. This problem appears in different contexts, particularly those involving diagnostic operations in maintenance and health care industries. Two objectives are considered simultaneously—the minimization of the makespan and the minimization of the mean weighted flow time. The former objective aims to sustain efficient utilization of the available resources, while the latter objective helps in maintaining a high customer satisfaction level. An exact algorithm is presented for generating optimal Pareto front solutions. Despite the fact that the studied problem is NP-hard for both objectives, the presented algorithm can be used to solve small instances. This is demonstrated through computational experiments on a testbed of 30 randomly generated instances. The presented algorithm can also be used to generate approximate Pareto front solutions in case computational time needed to find proven optimal solutions for generated sub-problems is found to be excessive. Furthermore, computational results are used to investigate the characteristics of the optimal Pareto front of the studied problem. Accordingly, some insights for future metaheuristic developments are drawn.

Maintenance and health care diagnostic systems are generally composed of different workstations pertaining to technologically different processes. A workstation is composed of one or more parallel machines. In such systems, the multiprocessor open shop scheduling problem is commonly encountered. It is concerned with assigning processing intervals for each job on machines that need to be selected in each requested workstation. Meanwhile, jobs do not require a specific order for visiting workstations. This paper considers a static, deterministic version of the problem in which jobs do not have to visit all workstations, the workstations do not necessarily have identical machines, and the processing times depend on both the job and the machine. The objective is to minimize the maximum completion time (makespan) which is commensurate with maximizing the utilization of the available machines. To the best of our knowledge, this problem structure has not been considered in the literature before despite its existence in real-life applications. Since it is NP-hard problem, efficient heuristics are needed to generate near optimal solutions in practically acceptable computational times. In this paper, two neighborhood search functions and two solution combination functions are developed and used within a scatter search with path relinking metaheuristic, along with a new distance definition between solutions. Computational experiments are conducted first to select the best levels of the metaheuristic parameters. Then, computational experiments are conducted on specially designed instances that take into consideration different settings of the studied problem. This is followed by computational experiments on a set of benchmark instances of the proportionate multiprocessor open shop scheduling problem which is a special case of the studied problem for which other metaheuristics have been developed in the literature. Results show that the developed metaheuristic is capable of generating optimal or near-optimal solutions for different configurations of the studied problem. In addition, it generates competitive results for the proportionate case compared to the available metaheuristics with 18 new upper bounds; among them seven are optimal.

The declining demand for fluorescent lamps, along with a recent currency floatation, forced a major glass tube manufacturer in Egypt to adopt a mixed Make-to-Stock (MTS) - Make-to-Order (MTO) strategy. In this research, a production control policy is proposed to effectively guide the involved product-mix decisions towards reducing the total cost. A simulation model is developed which is divided into three interconnected modules, namely decision, production, and order fulfillment. Along with the simulation model, a randomized search algorithm is applied to find appropriate values for the control variables of the proposed policy. Results provide evidence for the effectiveness of the proposed production control policy in reducing the total cost. Sensitivity analyses are conducted to investigate the effects of raw material and energy prices on the production parameters and the control variables of the proposed policy. It is found that the increase in raw material prices influences the production parameters; however, it does not affect the control variables of the proposed policy. On the other hand, the increase in energy prices influences both.

This paper considers a decision making problem encountered by a natural gas pipeline construction company having a set of ongoing projects and facing unpredictable risks that can result in large deviations from planned schedules. This situation forces the company to consider the decision of halting one or more projects to avoid future losses and to allow for possible reallocation of some of their resources to other ongoing projects. This decision making problem involves different factors and criteria that need to be combined in an organized structure that exploits assessments of experts managing such projects. The analytic hierarchy process (AHP) is found to be suitable for guiding decisions in this problem. A case study for a major natural gas pipeline construction company in Egypt is presented, where three ongoing projects are considered. The proposed AHP structure, along with collected pairwise comparison scores and calculated priorities, suggests halting one project. Sensitivity analysis is conducted to investigate the effect of changes in the pairwise comparison scores assigned to the main criteria on the final decision. The results and analysis provide some insights regarding the application of the AHP and the relative importance of the factors affecting decisions.

A mixed integer linear programming (MILP) model for production planning in garment industry is developed. The model considers capacity and financial planning decisions for mixed make-to-order (MTO)/make-to-stock (MTS) environment when demand exhibits predictable fluctuations. In the literature, existing models present little focus for capacity distribution between MTO and MTS products along with the effect of the cash availability on the production decisions. The developed model is applied to a real-life case study in Egypt, and the sensitivity of the results are analyzed. The model was very sensitive to the increases in the fabric prices and subcontracting costs while the overall net profits were not significantly affected by the changes in the inventory holding costs. The amount of MTS production increases with cash availability; while partitioning the capacity to 60% and 40% for MTO and MTS products respectively proved to be the best option and found to have a significant contribution on the revenues and in maintaining financial stability.

Finding a minimum spanning tree in a given network is a famous combinatorial optimization problem that appears in different engineering applications. Even though this problem is solvable in polynomial time, having efficient mathematical programming models is important as they can provide insights for formulating larger models that integrate other decisions in more complex applications. In the literature, there are ten different integer and mixed integer linear programming (MILP) models for this problem. They are variants of set packing, cuts, network flow and node level formulations. In addition, this paper introduces an efficient node level MILP model. Comparisons for the eleven models are provided. First, the models are compared in terms of the number of decision variables and the number of constraints. Then, computational comparisons using a commercial MILP solver on sets of randomly generated instances of different sizes are conducted. Results provide evidence that the proposed MILP model is competitive in terms of the computational time needed for proving optimality of generated solutions for instances with up to 50 nodes. Meanwhile, the LP relaxation of a multi-commodity flow MILP model which has integer polyhedron provides stable computational times despite its larger size.

The assignment of stores in shopping centers is a challenging task due to conflicting factors related to the accessibility of store locations and the power of attraction of the competing brands. In a previous work, the Authors proposed an evenhanded approach of assigning stores to empty locations in shopping centers, aiming to balance the distribution of flow across all shopping center areas (blocks). A mixed integer linear programming (MILP) model was devised targeting the minimization of the differences of flows between blocks. Because of the complexity and the relatively large size of the problem in real life, a solution algorithm based on tabu search (TS) is proposed in this sequel paper to provide efficient solutions. TS features such as tabu list, tabu tenure, aspiration criteria, short and long-term memory, and diversification are developed to improve the search process. The proposed TS algorithm is tested on a number of generated instances in a numerical study. Results prove the efficiency of the algorithm in solving large size instances for which exact methods cannot obtain feasible solutions in reasonable time.

This work is concerned with the process design of multistage deep drawing, where an integrated artificial intelligence (AI) approach is presented with a special focus on box shaped parts. This approach combines three AI tools, namely part shape recognition, expert system for process design governing rules, and search and optimization via dynamic programming. Validation and final selection of optimized process plans are done using finite element analysis with full account of the formability limits of the material used. The main advantage of the proposed integrated approach is its capability of generating valid, optimized process plans in a relatively short time compared to traditional approaches. Two case studies are presented for demonstrating its effectiveness.

Overcrowding in emergency departments (EDs) is a serious problem that can harm patients and lead to negative operational and financial performances for hospitals. This paper integrates the Six Sigma methodology with discrete event simulation (DES) to guide improvement decisions, in which we target the reduction of overcrowding in EDs with a special attention on the medical equipment utilization and the influence of changing the medical equipment technology on patients’ waiting time and consequently their satisfaction. The Six Sigma methodology, based on the “Define, Measure, Analyze, Improve, and Control (DMAIC)” format, is used to analyze the EDs overcrowding problem, diagnose its causes, and control the performance improvement plans. The DES is used within the “Improve” phase in order to provide a prognosis for the expected performance under the proposed improvement scenarios and to evaluate their effects on the ED performance measures. This research investigates the benefits of the application of the quality improvement methods in the Egyptian healthcare system which has different characteristics compared to developed countries. A case study in a private tertiary hospital is used in the current investigation. We propose process-based modifications that can help reduce the overcrowding problem, increase patient throughput, and reduce patient length of stay. The case study demonstrates the effectiveness of using the integrated Six Sigma-DES approach on reducing the ED crowdedness. The use of DES in the “Improve” phase provides inexpensive assessments of the improvement alternatives and eliminates the troubles associated with real system modifications.

The process planning of V-bending involves the determination of a feasible sequence of bending tasks to achieve the final desired product shape. The feasibility of such a sequence is materialized by the absence of collision between the sheet metal and the tool set or any part of the press brake. Meanwhile, efficient process planning targets the minimization of the number of bending setup and handling tasks. This paper presents an enhanced automated feature recognition system for effectively determining part shape features that are suitable for feasible and efficient process planning of the V-bending process. The developed system automatically recognizes and reasons information of bend lines, and relations between them form STEP AP-203 format. It provides additional information regarding the relationships between bend lines based on a new classification that can facilitate efficient selection of tools and bend sequences. It also provides an easier approach for the estimation of some bend parameters compared to previous methods in the literature. An example is provided to demonstrate the benefit of applying the developed system in generating more efficient process plans.

Abstract This paper addresses the makespan minimization problem in scheduling flexible job shops whenever there exist separable sequence-dependent setup times. An extension to the neighborhood search functions of Mastrolilli and Gambradella, developed for the flexible job shop scheduling problem (FJSP), is provided. It is shown that under certain conditions such an extension is viable. Accordingly, a randomized neighborhood search function is introduced, and its best search parameters are determined experimentally using modified FJSP benchmark instances. A tabu search approach utilizing the proposed neighborhood search function is then developed, and experimentations are conducted using the modified instances to benchmark it against a lower bound. Experimental results show that on average, the tabu search approach is capable of achieving optimality gaps of below 10% for instances with low average setup time to processing time ratios.

In this paper, a hybrid particle swarm optimization (PSO)-tabu search (TS) approach is proposed for solving the proportionate multiprocessor open shop scheduling problem (PMOSP) with the objective of minimizing the makespan. The PSO part of the proposed approach is used for randomly searching the machine selection decisions, while the TS part conducts local improvements for the routing and sequencing subproblems. Experimentations are conducted on 100 benchmark problems which are divided into four equal sets with 2, 4, 8 and 16 processing centers. The analysis shows that the proposed hybrid approach produces competitive results compared to previously developed TS and genetic algorithm approaches, especially for intermediate size problems of 4 and 8 processing centers. The average optimality gap of the proposed approach is found to be below 5.6% from the lower bound for the four sets, and ten new upper bounds are found, among them two are provably optimal.

Traditionally, the process design of the turning operation focused on the objective of optimising the economics of the process; while quality issues are dealt with separately. Recently, some research utilised Taguchi's loss function (TLF) to represent deviations from target quality levels by equivalent costs that can be integrated into the process design economic models. One shortcoming of that approach is the difficulty of estimating accurate quality loss coefficients for TLF. This paper introduces a multiobjective optimisation approach for the process design of a single-pass, multi-part turning process. Two objectives are considered simultaneously; the first is related to the economics of the process, specifically maximising the throughput. The second objective aims at minimising the deviations of the dimensions of the produced parts from the target value. The non-dominated sorting genetic algorithm (NSGA-II) is used to provide efficient solutions. The effectiveness of the proposed multiobjective approach is demonstrated through an illustrative example.

The efficient design of the supply chain network is crucial for good performance and robust functionality. In this paper, the facility location-allocation problem in the strategic stage of the supply chain planning is addressed. The facility location decision is studied for a 4-layer supply chain, with location decision in 2 layers (plants and distribution centers). The study incorporates tactical decisions along with the facility location decision. These include planning inventory levels, flow of products, and position of the customer order decoupling points. The decisions are studied on a multi-period (dynamic) planning horizon, and the problem is formulated as a mixed-integer linear programming model with profit maximization objective. The model is tested on a number of generated instances for the problem, and the optimum solutions are obtained and discussed.

In this paper, a study of the problem of shopping center layout design is presented. Assignment of shops to locations in a shopping center should be performed in a way that ensures balance in the distribution of flow across all shopping center areas. This can lead to the success of the shopping center, and consequently raise its rent return. This study proposes a facility layout assignment model for shopping centers with the objective of maximizing flow capturing in each location and balancing it across all shopping center areas. Flow capturing by a given location is controlled by the power of attraction (weight) of the shop assigned to it, and is affected by the flow at nearby locations more than flow at more distant locations. The model also calculates the flow in each area in the center as a combination of the flows at locations that belong to that area. The model is tested on a number of randomly generated problems, and the optimum layout is found for each generated example.

In the multiprocessor open shop scheduling problem, jobs are to be processed on a set of processing centers—each having one or more parallel identical machines, while jobs do not have a pre-specified obligatory route. A special case is the proportionate multiprocessor open shop scheduling problem (PMOSP) in which the processing time on a given center is not job-dependent. Applications of the PMOSP are evident in health care systems, maintenance and repair shops, and quality auditing and final inspection operations in industry. In this paper, a tabu search (TS) approach is presented for solving the PMOSP with the objective of minimizing the makespan. The TS approach utilizes a neighborhood search function that is defined over a network representation of feasible solutions. A set of 100 benchmark problems from the literature is used to evaluate the performance of the developed approach. Experimentations show that the developed approach outperforms a previously developed genetic algorithm as it produces solutions with an average of less than 5 % deviation from a lower bound, and 40 % of its solutions are provably optimal.

Deep drawing is an important sheet metal forming process that appears in many industrial fields. It involves pressing a blank sheet against a hollow cavity that takes the form of the desired product. Due to limitations related to the properties of the blank sheet material, several drawing stages may be needed before the required shape and dimensions of the final product can be obtained. Heat treatment may also be needed during the process in order to restore the formability of the material so that failure is avoided. In this paper, the problem of minimizing the number of drawing stages and heat treatments needed for the multistage deep drawing of cylindrical shells is addressed. This problem is directly related to minimizing manufacturing costs and lead time. It is required to determine the post-drawing shell diameters along with whether heat treatment is to be conducted after each drawing stage such that the aforementioned objectives are achieved and failure is avoided. Conventional computer-aided process planning (CAPP) rules are used to define the search space for a dynamic programming (DP) approach in which both the post-drawing shell diameter and material condition are used to define the states in the problem. By discretizing the range of feasible shell diameters starting from the initial blank diameter down to the final shell diameter, the feasible transitions from state to another is represented by a directed graph, based upon which the DP functional equation is easily defined. The DP generates a set of feasible optimized process plans that are then verified by carrying out finite element analysis in which the deformation severity and the resulting strains and thickness variations are investigated. Two case studies are presented to demonstrate the effectiveness of the developed approach. The results suggest that the proposed approach is a valuable, reliable and quick computer aided process planning approach to this complicated problem.