Abstract
This study examined the relationship between Environmental Performance (EP) and Financial Performance (FP) in the European food industry. The food industry is essential for population sustenance, but the rising population and the consequent increase in food production demand have implications for climate change. The aim of this study was to determine if businesses that consume water more efficiently and have lower CO2 emission intensities might experience improved financial performance. Financial and environmental data were sourced from external databases and company reports, and both quantile regression and correlation analyses were conducted. The results reveal that various sectors within the food industry exhibit different linkages between Environmental Performance and Financial Performance. Furthermore, our findings indicate that water use efficiency can significantly influence financial performance, either positively or negatively, while CO2 emission intensity did not exhibit a definitive impact on Financial Performance.

Abstract
This paper presents and evaluates an order release decision rule in a job-shop, based on the input-output control concept. The order release mechanism is part of a global decision-making scheme that includes four main decisions: accept/reject orders, define the order's due date, release the accepted jobs and dispatch it on the shop floor. This paper also presents an evaluation of two acceptance rules, four release mechanisms, and two dispatching rules, using four levels of due date tightness. Extensive simulation experiments were performed to compare the different decision rules, using several criteria: mean tardiness, percent tardy, mean absolute deviation, mean queue time in the shop floor and in the system, and machine usage. We conclude that considering the four decisions simultaneously improve the job-shop performance, and planning both the input and the output when deciding to release a job, leads to an improvement of the operational performance measures. Copyright © 2012 Inderscience Enterprises Ltd.

Abstract
There has been extensive research on workload and input-output control with the objective of improving manufacturing operations in job-shops. In this paper, a multiple decision-making scheme is proposed to plan and control operations in a general job-shop, and to improve delivery and workload related performance measures. The job-shop characteristics reinforce the need for designing a global system that controls both the jobs entering (order acceptance, due date setting and job release) and the work-in-process (dispatching), leading to an improvement of operational measures. Previous research has concentrated on scheduling a set of orders through the shop floor, according to some decision mechanism, in order to optimise some measure of performance (usually total lead time). This means that, since only a part of the decision-making system is being optimised, the resulting decision may be sub-optimal. In this paper it is shown that the performance of the different decision rules changes when they are considered simultaneously. Hence, a higher level approach, where the four decisions (order acceptance, due date setting, job release and dispatching) are considered at the same time, should be adopted to improve job-shop operational performance.

Abstract
Work flows in a job-shop are determined not only by the release load but also by the number of accepted orders. In this paper the common assumption of accepting all incoming orders regardless of shop condition is relaxed. Instead of placing the orders in a 'pre-shop pool' queue, as in previous research, orders that arrive at the shop, when it is highly congested, may be immediately rejected or their due dates may be negotiated. This paper explores the idea of controlling the workload since the acceptance/rejection stage. A new acceptance/rejection rule is proposed, and tests are conducted to study the sensitivity of job-shop performance to different order acceptance parameters, like the tolerance of the workload limit and the due date extension acceptance. The effect of the negotiation phase on the job-shop performance is evaluated using a simulation model of a generic random job-shop that allow us to conclude that having a negotiation phase prior to rejection improves almost all workload performance measures. Different tolerances of the workload limit slightly affect the performance of the job-shop.

Abstract
Work flows in a job-shop are determined not only by the release load but also by the number of accepted orders. In this paper the common assumption of accepting all incoming orders regardless of shop condition is relaxed. Instead of placing the orders in a 'pre-shop pool' queue, as in previous research, orders that arrive at the shop, when it is highly congested, may be immediately rejected or their due dates may be negotiated. This paper explores the idea of controlling the workload since the acceptance/rejection stage. A new acceptance/rejection rule is proposed, and tests are conducted to study the sensitivity of job-shop performance to different order acceptance parameters, like the tolerance of the workload limit and the due date extension acceptance. The effect of the negotiation phase on the job-shop performance is evaluated using a simulation model of a generic random job-shop that allow us to conclude that having a negotiation phase prior to rejection improves almost all workload performance measures. Different tolerances of the workload limit slightly affect the performance of the job-shop.

Abstract
Make-to-order companies, such as job shops, have been extensively studied. Some of those studies emphasise the importance of the workload control in order to improve manufacturing operations. In this paper a multiple decision-making scheme, with the purpose of planning and controlling operations and getting better delivery and workload related performance measures, as well as one order release decision rule are proposed. The decision-making scheme includes four main decisions: (i) accept or reject an in-coming order; (ii) define the order's due date; (iii) release the accepted jobs; and (iv) dispatch the jobs at the station level. Extensive simulation experiments were performed to compare the proposed rule with the benchmark mechanisms, as well as with rules presented in previous studies. They led to the conclusion that considering the four decisions simultaneously can improve the job shop measures of performance, and that the proposed release rule is the best in almost all instances.