Hamid Allaoui

Integrating simulation and optimization to schedule a hybrid flow shop with maintenance constraints

The scheduling problems have been discussed in the literature extensively under the assumption that machines are continuously available. However, in most real life industrial settings a machine can be unavailable for many reasons, such as unforeseen breakdowns (stochastic unavailability) or due to a scheduled preventive maintenance where the periods of unavailability are known in advance (deterministic unavailability). In this paper, we deal with the hybrid flow shop scheduling problem under maintenance constraints to optimize several objectives based on flow time and due date. In this model, we take also on consideration setup, cleaning and transportation times. This paper has three goals. The first is to show how we can integrate simulation and optimization to tackle this practical problem which is NP-hard on the strong sense. The second is to illustrate by an experimentation study that the performance of heuristics applied to this problem can be affected by the percentage of the breakdown times. The last is to show that this approach can perform better than NEH heuristics under certain conditions.

Scheduling two-stage hybrid flow shop with availability constraints

Most of the literature on scheduling assumes that machines are always available. However, in real life industry, machines may be subject to some unavailability periods due to maintenance activities such as breakdowns (stochastic case) and preventive maintenance (deterministic case). In this paper we investigate the two-stage hybrid flow shop scheduling problem with only one machine on the first stage and m machines on the second stage to minimize the makespan. We consider that each machine is subject to at most one unavailability period. The start time and the end time of each period are known in advance (deterministic case) and only the non-resumable case is studied. First we discuss the complexity of the problem. Afterwards, we give the Branch and Bound model for this problem. Last, we calculate the worst-case performances of three heuristics: LIST algorithm, LPT algorithm and H-heuristic.

Sustainability assessment of food chain logistics

Food chain logistics plays an important role in the sustainability performance of the food sector. Therefore, project SCALE (Step Change in Agri-food Logistics Ecosystems) started as a collaborative international project, aiming for tools and frameworks for the food sector to make a step change in operational practices. A sustainability framework is introduced to propose a structured and rational method for assessing sustainability. Next, we empirically apply the framework, based upon explorative web-based research and semi-structured interviews with the best practice players in the field in the Netherlands, the UK and France. Findings provide clear insights into the current state of the art regarding the use of sustainability performance indicators, companies’ sustainability strategies, supply chain redesign strategies currently applied in practice and experienced barriers to sustainability improvement. Overall, logistics service providers seem to have a wait-and-see attitude towards sustainability where food companies are more proactive following market demands for more sustainable products.

A multi-objective green supply chain network design

In this paper, we study a green supply chain network design problem with environmental concerns. We are interested in the environmental investments decisions in the design phase and propose a multi-objective optimization model that captures the a compromise between the total cost and the environment influence. This work addresses the optimization of the supply chain design considering economical and environmental issues. We explicitly consider two objective functions. The first one, measures the total cost: fixed setup cost, environmental protection investment, transportation cost, logistics cost, waste treatment cost and energy consumption cost. The second one, measures the total CO2 emission in all the supply chain. The strategic decisions considered in the model are warehouses and distribution centers location, building technology selection and processing/distribution planning.

Minimizing the weighted sum of maximum earliness and maximum tardiness costs on a single machine with periodic preventive maintenance

We consider the problem of scheduling a set of jobs on a single machine against a common and restrictive due date. In particular, we are interested in the problem of minimizing the weighted sum of maximum earliness and maximum tardiness costs. This kind of objective function is related to the just-in-time environment where penalties, such as storage cost and additional charges for late delivery, should be avoided. First we present a mixed integer linear model for the problem without availability constraints and we prove that this model can be reduced to a polynomial-time model. Secondly, we suppose that the machine undergoes a periodic preventive maintenance. We present then a second mixed integer linear model to solve the problem to optimality. Although the latter problem can be solved to optimality for small instances, we show that the problem reduces to the one-dimensional bin packing problem. Computational results show that the proposed algorithm best fit decreasing performs well.

Sustainable agro-food supply chain design using two-stage hybrid multi-objective decision-making approach

Sustainability of agro-food supply chains has recently become the subject of greater interest from consumers, firms, governmental organizations and academia as the environment continues to deteriorate. One of the most critical factors influencing the sustainability of an agro-food supply chain is its network design. A particularly challenging aspect in this context is the broad range of influencing indicators associated with the Triple Bottom Line (TBL) of sustainability that need to be considered. However, many of these indicators could not be fully integrated or measured by single-step optimization problems. This paper presents a critical literature review of operational research methods for the design of sustainable supply chains. A novel two-stage hybrid solution methodology is proposed. In the first stage, a partner selection is performed using a hybrid multi criteria decision making based on Analytic Hierarchy Process (AHP) method and the Ordered Weighted Averaging (OWA) aggregation method. The result obtained in the first stage is used in the second stage to develop a multi-objective mathematical model to optimize the design of the supply chain network. This approach allows the simultaneous consideration of all three dimensions of sustainability including carbon footprint, water footprint, number of jobs created and the total cost of the supply chain design. The proposed approach generates a Pareto frontier to aid users in making decisions. Numerical experiments are completed utilizing data from an agro-food company to demonstrate the efficiency and effectiveness of the proposed solution methodology. The analyzes of the numerical results provide important organizational, practical and policy insights on (1) the impact of financial and environmental sustainability on supply chain network design (2) the tradeoff analysis between environmental emission, water footprint, societal implications and associated cost for making informed decision on supply chain investment.

Reducing transportation greenhouse gas emissions with collaborative distribution

Purpose - – The purpose of this paper is to study the potential for improving sustainability performance in food supply networks by implementing collaborative distribution. Food supply chains generate a significant portion of CO Design/methodology/approach - – Such collaboration could be in the form of sharing trucks by retailers, to increase the fill rate of the vehicles and to reduce their empty running. A case study of the logistics network of a British company, distributing fruits and vegetables, is carried out. The company sends the products from a distribution centre to 27 retailers’ warehouses of 3 different companies in the UK. A simulation study is carried out to measure the reduced traveled distance for delivery of the products and the reduced amount of CO Findings - – With this approach, the total CO Originality/value - – The theoretical contribution of the paper is important both for showing the role of simulation and collaborative distribution for developing the green supply chain solutions management and their indicating to the applications to logistics and product delivery.

Stochastic single machine scheduling with random common due date

This paper studies the single machine scheduling problem for minimising the expected total weighted deviations of completion times from random common due date. Jobs have exponentially distributed processing times and the common due date is a generalised Erlang distribution. The optimal schedules are shown to be ∧-shaped. Moreover, we give the optimal schedules when the machine is subject to stochastic breakdowns with independent and exponentially distributed uptimes and downtimes.

Simulation‐based approach to joint production and preventive maintenance scheduling on a failure‐prone machine

Purpose – The purpose of this study is to propose and model an integrated production‐maintenance strategy for a failure‐prone machine in a just‐in‐time context.Design/methodology/approach – The proposed integrated policy is defined and a simulation model is developed to investigate it.Findings – The paper focuses on finding simultaneously two decision variables: the period (T) at which preventive maintenance actions have to be performed; and the sequence of jobs (S). These values minimize the maintenance costs (MC) and the expected total earliness and tardiness costs (ETC) away from a common due‐date D.Practical implications – The paper attempts to integrate in a single model the two main aspects of any manufacturing and production systems: production and maintenance. It focuses on a stochastic scheduling problem in which n immediately available jobs are to be scheduled jointly with the preventive maintenance. The effect of the period (T) and the sequence of job (S) on the expected total cost are shown th...

A robustness framework for a stochastic hybrild flow shop to minimize the makespan

The robustness of a solution or an algorithm is one of the most new challenges in the scheduling area during the last ten years. It can be studied when the uncertainty should be taken into account. This uncertainty can be involved by some unknown factors such as: machine breakdown, early or tardy arrival times and changes of processing times. The main effort should not only be devoted to find a good solution but also a robust solution which can resist to changes of input data. Therefore we propose, in this paper, a robustness framework to address the problem of scheduling hybrid flow shop under uncertainty. After introducing some concepts related to the robustness, a brief literature review taking into account the robustness is given. Then we introduce a robustness framework for the stochastic hybrid flow shop problem to minimize makespan. Last we focus on validating our models by an experimental study