A.A. Elimam

Tabu Search in Audit Scheduling

In this paper, we present a heuristic procedure based on tabu search (TS) and intelligent memory structures for solving this problem. The TS method starts with a feasible schedule generated by a greedy forward dispatching procedure, then through a series of intensification and diversification steps it improves on the initial schedule. The improvement in the value of the objective function is observed interactively. The search can be terminated as the improvement in the value of the objective function stabilizes. Application of the TS method to different audit scheduling problems has consistently improved the value of the cost minimization objective function.

Audit Scheduling with Overlapping Activities and Sequence Dependent Setup Costs

Abstract Audit firms are faced with the complex job of scheduling auditors to audit tasks. The scheduling becomes more complex as the firm needs to consider real life issues in determining an optimal schedule. Among these issues are the setup times and costs emanating from changing the assignments of the auditors and the lead and lag relationships between the audit tasks. Audit scheduling with overlapping activities and sequence-dependent setup cost has not been treated in literature. This paper presents a formulation and a solution approach for this audit scheduling problem. First, the problem is represented by an activity network with lead/lag relationships. Then the network is analyzed to determine the early and late finish times of activities. An integer linear program (ILP), which uses the early and late finish times of activities to reduce the number of decision variables, is formulated. A four-auditor two-engagement example is used to illustrate the ILP model and its solution. The results indicate that incorporating the setup cost and the overlapping of activities yields lower cost schedules leading to sizable savings in the cost of audits. The proposed treatment is of merit in providing realistic schedules that can be easily implemented

Two engineering applications of a constrained shortest-path model

This paper presents integer programming models for the determination of (a) optimal sequences of wastewater treatment processes, and (b) minimum cost, energy efficient composite wall and roof structures. The first model has been used to determine optimal sequences of wastewater treatment processes for a combined industrial and sanitary wastewater facility in Kuwait. The second model was used to assist design engineers in meeting government standards for the thermal resistance of building structures. Remarkably, the two models have essentially the same structure: a shortest path model with additional linear side constraints. The two practical applications are described and solved, and computational experience is discussed.

Incentives and yield management in improving productivity of manufacturing facilities

Abstract Financial incentives are used to improve productivity and quality in manufacturing and service facilities. This improvement in productivity would normally release part of the facilitys productive capacity. Without stimulating additional demand to consume this released capacity, the facility would Be unable to tap the full benefits of the improvements in productivity. Hence, yield management is introduced in an attempt to entice more demand and increase revenues. In this paper, we develop a Non Linear Programming (NLP) model to jointly determine the optimum financial incentives and price discount levels for each rate class. The model aims at maximizing net revenues. It includes nonlinear relationships representing the impact of incentives on productivity and quality improvements as well as the effect of price discounts on customer demand in each market segment. The generic nature of our NLP model makes it applicable to all multi-product manufacturing facilities covering sales, production and delivery. The model is applied to determine the optimum incentive and price discount levels for perishable products in a multi-product ready-mix concrete plant. It is demonstrated that the model is useful in maximizing net revenues through productivity improvements and an increase in customer demand.

Knapsack-Based Approaches to the Makespan Problem on Multiple Processors

Abstract In this paper we study the problem of scheduling n independent jobs available at time zero on m ≥ 2 parallel and identical processors with the objective of minimizing the makespanJWe propose two approaches, both are Knapsack-based. The first is analytical and depends on reducing the set of m occupancy constraints to a single Diophantine equation. We capitalize on the very special structure of the ILP to effect the reduction with small multipliers. The second approach is an iterative heuristic procedure that is based on the observation that a two-machine makespan problem is trivially reduced to a Knapsack problem. Computational experience indicates the superiority of this approach over other existing approaches. Realistic problems of up to 100 jobs on 10 machines are solved in a few seconds on the IBM 370/165.

Integration of equipment planning and project scheduling

Abstract It is common to schedule project activities first, then the utilization of equipment and its operating crew is planned based on such schedule. Real world experiences indicate that activity scheduling can be heavily impacted by the resources needed. In particular, if a project requires highly specialized and expensive equipment type, then one needs to take into account the schedule and cost of such resources in developing the project schedule. In this paper, we consider the impact of using expensive and specialized equipment on the project schedule and its cost. The integration of project scheduling with equipment planning introduces new trade-offs that would lead to more realistic and cost-effective project schedules. Such trade-offs include the costs of: activity crashing, equipment setup, transition, idle time, and operator’s overtime as well as the project worth; rewards for early completion and penalty for delayed completion. An efficient mixed integer linear program is developed to capture the real world considerations of the integrated problem. The solution of the program yields the optimal schedules for the activities, equipment, and operators. These schedules involve crashing of critical and non-critical activities. An efficient heuristic is also developed and used to schedule large size projects. Optimal schedules are analyzed and computational experiences are provided.

On the reduction method for integer linear programs, II☆

Abstract The problem of the sequential reduction of linear equations in non-negative discrete variables has been treated by several authors since its introduction by Elmaghraby and Wig in 1969. This paper provides tighter conditions on the multipliers, especially for the case of bivalent (0, 1) variables in homogeneous equations (zero right side). An earlier version of this paper appeared in 1980 under the same title (OR Report No. 125, N.C. State University). The current paper extends the previous results, and presents a comparative analysis with other approaches. In particular, we give conditions under which our multipliers are ‘better’ than others, where ‘better’ is precisely defined.

Operational strategies for Kuwait's 100 kWe/0.7MWth solar power plant

Abstract In March 1981 a 0.7 MW th solar thermal power plant was commissioned at Kuwaits Sulaibiya Solar Complex. The cogeneration of solar power plant was designed to be the main energy supplier for an agricultural desert settlement 35 km south-west of Kuwait City. The power plant produces both the electric and thermal energy needs for its own internal uses and those of the food/water/power complex. The electric users, outside the power plants own needs, include water pumping from a 110 metre deep water well, an outdoor irrigation network, four desert greenhouses, a walk-in cooler, air conditioning, a reverse osmosis (R.O.) desalination plant, as well as the electric power needs for a multistage flash (M.S.F.) desalination plant, offices, workshop, data acquisition and lighting. The reject thermal energy from the power plant is utilized to power an M.S.F. desalination plant, and the domestic hot water needs. The power plant operational strategies are aimed at satisfying the energy needs for this food/water/power complex under prevailing solar radiation conditions while minimizing the inconvenience to the user (the complex) and maximizing the percent of the total energy derived from the sun (solar fraction). Surplus energy is stored as electric energy, thermal energy, or used to desalinate additional volumes of brackish water which can be stored in strategic water reservoirs. During periods of low solar radiation the power plant may be operated at partial load to supply the essential electric energy needs, charge the thermal storage, or provide the thermal energy needs for the MSF desalination system. An energy cost accounting system was developed to encourage the user to minimize his electric consumption during periods of low solar radiation. A mathematical energy model for the power plant was utilized to predict its output and suggest the optimum operational strategy according to the users priorities, and predict surpluses or shortages that have to be accommodated by the emergency secondary energy source.

A Decision Support System (DSS) for agricultural pesticide production planning

Abstract The paper presents a Decision Support System (DSS) for the production planning of Agricultural pesticide. The unique features of pesticide production are identified and the resulting planning trade-offs are explained. The DSS consists of six loosely coupled modules. A mathematical programming model forms the basis of the DSS, whose objective is to minimize the total cost of production. The model is also designed to capture seasonal demand fluctuations, variations in labor cost, source, cost and availability of raw material and plant operating mode. The DSS is illustrated by planning the manufacturing of nine products on two production lines to satisfy demand over eighteen months. The system has been in use for pesticide production planning in a major US plant for more than five years. Such use helped in rationalizing production planning decisions and led to tangible savings in production and inventory cost, over the duration of its use.

OPTIMUM SELECTION AND OPERATION OF SLUDGE DEWATERING PROCESSES

The paper presents a life cycle costing model and a mathematical programming model to identify the least cost sludge dewatering process and the optimum polymer dose to be used for sludge dewatering, respectively. The life cycle costing model is based on an infinite horizon and it allows for incremental expansion in the capacities of the sludge dewatering process as demand rises. A new nonlinear formulation for the minimization of the combined costs of polymer dose and transportation is presented. These models were used for the selection of the least cost sludge dewatering process and the determination of the optimum polymer dose for two wastewater treatment plants in Kuwait. These formulations are generic and hence can be used for different types of sludge dewatering processes or polymers. Relationships have also been developed to assess the impact of distances to disposal or reuse site(s) from the wastewater treatment plant location on the optimum polymer dose.