Mohamed-Aly Louly

Optimal supply planning in MRP environments for assembly systems with random component procurement times

This paper examines supply planning in an MRP environment for assembly systems under lead time uncertainties. Indeed, inventory control in a supply chain is crucial for companies who wish to satisfy their customer demands on time as well as controlling costs. A common approach is to use the MRP techniques. However, these techniques are based on the supposition that lead times are known. In an actual supply chain the lead times are often random variables. Therefore, we develop an efficient exact model to aid in MRP parameterization under lead time uncertainties, more precisely to calculate planned lead times when the component procurement times are random. The aim is to find the values of planned lead times which minimize the sum of the average component holding cost and the average backlogging cost. The developed approach is based on a mathematical model of this problem with discrete decision variables and a branch and bound algorithm.

Calculating safety stocks for assembly systems with random component procurement lead times: A branch and bound algorithm

In this paper, a discrete single-level multi-component inventory control model for assembly systems with random component procurement lead times is considered. The economic order quantity (EOQ) policy is used for a type of finished product. The requirements of the components are constant and cyclic (periodic), and their values per period are deduced from the EOQ for the finished product. The paper focuses on the components safety stock calculation. The objective is to minimise the average holding cost of the components while keeping the desired service level for the finished product. For this, an upper bound, two lower bounds, two dominance properties and an efficient branch and bound algorithm are suggested. Several tests are executed and conclusions are drawn. The proposed model provides a substantial saving for assembly systems with a large number and unreliable delivery of components as in semi-conductor and automotive industries.

Optimal MRP offsetting for assembly systems with stochastic lead times: POQ policy and service level constraint

This study deals with component supply planning in assembly systems, i.e. where several types of components are needed to produce one finished product. The actual component lead times have random deviations, so they can be considered as random variables. MRP approach with Periodic Order Quantity policy is considered. The aim is to find the optimal MRP offsetting. The proposed model and algorithms minimize the sum of the setup and average holding costs for the components, while satisfying a desired service level.

A Goal Programming Model for Staff Scheduling at a Telecommunications Center

In this paper, a mathematical model is developed to solve a staff scheduling problem for a telecommunications center. Currently, weekly schedules are manually produced. The manual nature of the process and the large number of constraints and goals lead to a situation where the used schedules are both inefficient and unfair. A zero-one linear goal programming model is suggested to find an optimized cyclical schedule. The center objectives as well as the engineers’ preferences are taken into account. The developed model had to produce balanced schedules that provide the required coverage while satisfying fairness considerations, in terms of weekends off, working night shifts, isolated days on, and isolated days off. A staffing analysis and mathematical properties have been developed to find the optimal parameters of the staff scheduling model. A 6-week scheduling period has been suggested instead of the current weekly period. Work patterns have been suggested to improve schedules quality. These work patterns have been mathematically formulated as a set of soft constraints. The suggested mathematical model has been implemented using Lingo software. The optimal cyclical schedule has been found. It significantly increases both efficiency and staff satisfaction. The suggested approach can be used for any similar staff scheduling problem.

Supply Planning and Inventory Control under Lead Time Uncertainty: A Review

Abstract In literature, most papers examine several stochastic demand processes where order lead times are constant. In reality, lead time is rarely constant; unpredictable events in the supply system cause unpredictable delays. It is true that in certain special cases, lead time uncertainty has essentially no effect and can be ignored. Nevertheless more often, lead time fluctuations strongly degrade performance, just as demand uncertainty does. Seemingly, lead time uncertainty has been neglected for a long time in favor of studying demand uncertainties. Industry agrees that it is overdue and there is a need to rectify this oversight. Nowadays, this gap in research activity begins to be filled in order to respond to companies having non-deterministic lead-times constraints. This paper reviews some of existing literature of supply planning under uncertainty of lead times. The extensive literature review that we compiled consists of citations from 1970 to 2012. A classification scheme for supply planning under uncertainty is defined.

Efficient Bounding Schemes for the Two-Center Hybrid Flow Shop Scheduling Problem with Removal Times

We focus on the two-center hybrid flow shop scheduling problem with identical parallel machines and removal times. The job removal time is the required duration to remove it from a machine after its processing. The objective is to minimize the maximum completion time (makespan). A heuristic and a lower bound are proposed for this NP-Hard problem. These procedures are based on the optimal solution of the parallel machine scheduling problem with release dates and delivery times. The heuristic is composed of two phases. The first one is a constructive phase in which an initial feasible solution is provided, while the second phase is an improvement one. Intensive computational experiments have been conducted to confirm the good performance of the proposed procedures.

The Nonpermutation Flowshop Scheduling Problem: Adjustment and Bounding Procedures

We consider the makespan minimization in a flowshop environment where the job sequence does not have to be the same for all the machines. Contrarily to the classical permutation flowshop scheduling problem, this strongly NP-hard problem received very scant attention in the literature. In this paper, some improved single-machine-based adjustment procedures are proposed, and a new two-machine-based one is introduced. Based on these adjustments, new lower and upper bounding schemes are derived. Our experimental analysis shows that the proposed procedures provide promising results.

Physician scheduling using goal programming-an application to a large hospital in Saudi Arabia

This paper proposes a goal programming model that provides Physician schedules with improved efficiency and staff satisfaction at an Emergency Department. Monthly schedules are currently manually produced by one physician using an Excel sheet. He is spending one entire week each month to build a schedule for physicians. The proposed model takes into account numerous constraints and goals. A comparison between the hospital and the proposed schedules shows major outperformance of the proposed one.

MRP Offsetting for Assembly Systems with Random Component Delivery Times: A Particular Case

This paper considers component supply planning for assembly systems where several types of components are needed to produce one finished product. The actual component lead times have random deviations. The aim of this study is to find the optimal MRP offsetting when the Periodic Order Quantity (POQ) policy is used. The proposed model and algorithms minimize the sum of the setup and average holding costs for the components, while satisfying a desired service level for finished product.

MRP parameterization under lead times uncertainties: Case of multilevel serial production systems

A supply planning for multilevel serial production systems under lead time uncertainties is considered. The techniques used in industry are often based on the assumption that the lead times are known. However, in a supply chain the lead times are often random variables. Therefore, it is necessary to find the best values of the planned lead times minimising the total cost. It is supposed that the demand for the finished product and its due date are known. It is assumed also that the component lead time at each level is a random discrete variable. No restrictive hypothesis is made on such random variables; only that one supposes that the distribution probabilities are known. Therefore, we develop an efficient model to aid in MRP parameterization under lead time uncertainties, more precisely to calculate planned lead times when the component procurement times are random. The aim is to find the values of planned lead times which minimize the total cost and satisfy customers. We consider the criterion to minimize the average holding and tardiness costs at each level while respect the customer service level. The proposed approach is based on a mathematical model of this problem with discrete decision variables. Several properties of the objective function are proven.